103189 by UNIGIS América Latina

Master Thesis ǀ Tesis de Maestría submitted within the UNIGIS MSc programme presentada para el Programa UNIGIS MSc at/en

Interfaculty Department of Geoinformatics- Z_GIS Departamento de Geomática – Z_GIS University of Salzburg ǀ Universidad de Salzburg

Support Decision System for Biodiversity conservation in Ecuador using the GAP analysis data Sistema de toma de decisiones para conservación de Biodiversidad usando los datos del análisis de vacíos de conservación para Ecuador Continental by/por

Maestrante: Linda Marybel Grijalva Buitrón 01224159 A thesis submitted in partial fulfilment of the requirements of the degree of Master of Science– MSc Anton Eitzinger PhD

Quito - Ecuador, 18 de Mayo 2020

Compromiso de Ciencia Por medio del presente documento, incluyendo mi firma personal certifico y aseguro que mi tesis es completamente el resultado de mi propio trabajo. He citado todas las fuentes que he usado en mi tesis y en todos los casos he indicado su origen.

Quito, 13 de Mayo de 2020

(Lugar, Fecha)

(Firma)

Agradecimiento: A mi esposo por todo su apoyo y amor incondicional.

Resumen

En Ecuador, el análisis de vacíos de conservación fue ejecutado en el año 2013. Fue una evaluación de la medida en que los Sistemas Ecuatorianos de Zonas Protegidas cumplen con los objetivos de protección establecidos por una nación o región para representar su diversidad biológica. La identificación de áreas de conservación prioritarias para la conservación y el análisis de vacíos son complementarios. Esta investigación es un esfuerzo de estructurar un sistema de toma de decisiones para complementar el análisis de vacíos de conservación de la biodiversidad usando sus resultados con el propósito de publicarlos con sus variables en una interface Web. La revisión bibliográfica de este estudio distingue los componentes de un SDSS. Presenta los fundamentos para el uso específico de esta clase de sistemas en problemas complejos y su aplicación explicita en la Biología de la Conservación y el análisis de vacíos de conservación en Ecuador. Se presenta los métodos comunes de implementación de esta categoría de aplicación considerando la incertidumbre y la interacción con otros procesos. Se desarrolló un prototipo implementado en arquitectura cliente-servidor y software de código abierto. Sus salidas de datos son gráficos estáticos con tablas de reportes y mapas web dinámicos. Representan a los orígenes de presión antrópica y ubicaciones relevantes para la biodiversidad a nivel de provincia. De este trabajo se extraen la necesidad de una completa documentación y sistemático levantamiento de procesos y clasificación de datos acerca del análisis de vacíos de conservación en Ecuador. Incorporar un módulo de mejoramiento de este proceso requiere una retroalimentación de expertos y usuarios finales para automatizarlo con criterios explícitos. Además, un marco conceptual y el prototipo propuesto facilita otros estudios sobre el análisis de vacíos y los métodos acerca de un SDSS.

Palabras Claves: Conservación, Biodiversidad, GAP, SDSS, WEB

Abstract The Ecuadorian Biodiversity GAP analysis was executed in 2013. It was an assessment of the extent to which the Ecuadorian Protected Areas Systems meet protection goals set by a nation or region to represent its biological diversity. The identification of high-priority areas and the conservation Gap Analysis are complementary. This research was an effort of structuring a Support Decision System for complementing the Biodiversity Gap Analysis using its outputs with the purpose of publishing them and context variables on a Web-based interface. The literature review of this study distinguishes the components of an SDSS. It presents the foundation for using this specific class of system on complex problems and its explicit application at Conservation Biology and Ecuadorian Biodiversity Gap Analysis. It presents the common methods of implementing this category of application considering the uncertainty and interaction with other processes. It deployed a prototype implemented on a client-server architecture and open source software. Its outputs are static graphics with table reports and dynamic web maps. They present anthropic sources of pressure and relevant biodiversity location at province level. Conclusion drawn from this work includes the need for a complete documentation and systematic process survey and data classification about Ecuadorian Gap Analysis itself. Incorporating an SDSS module and improving this process requires expertsâ&#x20AC;&#x2122; and end usersâ&#x20AC;&#x2122; feedback for identifying and automatizing the explicit criteria. Furthermore, a conceptual framework and its prototype is proposed to facilitate further studies of GAP analysis and SDSS methods.

Keywords: Conservation, Biodiversity, GAP, SDSS, WEB

Contents Glossary and Acronyms ...................................................................................................................... 5 List of Figures: .................................................................................................................................... 8 List of Tables:...................................................................................................................................... 8 1. Introduction.................................................................................................................................... 9 1.1 Background................................................................................................................................... 9 1.2 Objectives and Research Questions ........................................................................................... 11 1.3 Hypothesis .................................................................................................................................. 12 1.4 Justification ................................................................................................................................ 12 1.5 Scope .......................................................................................................................................... 14 2. Literature review .......................................................................................................................... 15 2.1 Geography .................................................................................................................................. 15 2.1.1 DSS................................................................................................................................... 16 2.1.2 GIS ................................................................................................................................... 18 2.1.3 Development methods of Spatial decision support systems. ......................................... 19 2.2 Conservation Biology.................................................................................................................. 22 2.2.1 Conservation Planning..................................................................................................... 22 2.2.2 GAP Analysis .................................................................................................................... 24 2.3 Relevant Previous Researches.................................................................................................... 27 3. Methodology ................................................................................................................................ 30 3.1 Study Area .................................................................................................................................. 30 3.2 SDSS Prototype: Global Concept ................................................................................................ 31 3.3 Methodology .............................................................................................................................. 33 3.3.1 Data Collection ................................................................................................................ 34 3.3.2 Design .............................................................................................................................. 35 3.3.3 Development ................................................................................................................... 45 3.3.4 Testing and validation ..................................................................................................... 57 4. Results .......................................................................................................................................... 58 5. Discussion ..................................................................................................................................... 66 6. Conclusion .................................................................................................................................... 70 7. Bibliography ................................................................................................................................. 71 Annex 1: Information Sources And Gap Analysis Variables ............................................................. 78 Table A: Ecuadorian Institutions and their provided information ........................................... 78

Table B: International research institutions ............................................................................. 79 Table C: Total of spatial variables and geographic information............................................... 80 Annex 2: Layer Publishing Process Geoserver ................................................................................. 81 Annex 3: Structured and Tested Queries At Postgres ...................................................................... 82 Mining Parameters per Provinces ............................................................................................ 82 GAP Solutions ........................................................................................................................... 82 Oil Activities.............................................................................................................................. 85 Population ................................................................................................................................ 86 Annex 4: Html, Php and Js Code....................................................................................................... 87 Web Pages and Query Pages .................................................................................................... 87 Upper Menu: Home ................................................................................................................. 87 Side Menu: About..................................................................................................................... 88 Side Menu: Contact .................................................................................................................. 89 Upper Menu: GAP Analysis Results .......................................................................................... 91 Side Menu: Hexagons / province Best Solution ....................................................................... 92 Side Menu: Hexagons / province Best Solution including PANE area...................................... 94 Side Menu: Hexagons / region Best Solution ........................................................................... 97 Side Menu: Hexagons / region Best Solution including PANE area ......................................... 99 Side Menu: Hexagons / province Solutions Intersection ....................................................... 101 Side Menu: Hexagons / province Protected Areas ................................................................ 103 Side Menu: Hectares /province Remaining vegetation ......................................................... 105 Upper Menu: Source of Pressure ........................................................................................... 107 Side menu Mining Concessions .............................................................................................. 109 Side menu Oil Activities .......................................................................................................... 111 Side menu Population ............................................................................................................ 113 WEB HTML and GIS GEOEXT JS code ...................................................................................... 115 Annex 5: Geoext ............................................................................................................................. 123 Annex 6: Validation Process ........................................................................................................... 129 Check List: Elements, Events and Functionality ..................................................................... 129 Tested User Interface ............................................................................................................. 131 WEBGIS SDSS .......................................................................................................................... 136 Combo Box: Change Theme and Drag and drop options (Check box layers)......................... 139 Displayed Layers: Theme Neptune......................................................................................... 140

Displayed Layers: Theme Classic ............................................................................................ 140 Displayed Layers: Theme Gray ............................................................................................... 140 Displayed Layers: Theme Accessibility ................................................................................... 140 Annex 7: Installation Processes .................................................................................................. 141 DATA BASE: POSTGRESQL 9.4 and POSTGIS 2.1.5.................................................................. 141 WEB PUBLISHER: Installation process of GEOSERVER ........................................................... 141 WAMP Simulator: BITNAMI and Console integration with POSTGRES .................................. 141

Glossary and Acronyms Biodiversity conservation: A conservation work or any set of action assumed by a project, people or organization to reach goals and objectives for conserving biological entities: species, communities, or ecosystems Biological diversity: It is the variety of life and its processes; and it includes the variety of living organisms, the genetic differences among them, and the communities and ecosystems in which they occur. CBD: Convention on Biological Diversity CONDESAN: Consorcio para el Desarrollo Sostenible de la Ecorregión Andina. A LatinAmerican NGO focusing on environmental studies. Conservation planning: It can be described by steps that help planners to identify and sequence their tasks and decisions. CSS: Cascading Style Sheet DBMS: Database Management System. It is a computer program designed to manage a database, numerous structured data, and operations on the data. DGMS: Dialog Generation and Management System. The DMGS is also known as User Interface. DSS: Decision support systems. The computer science defines as an interactive computerbased system that aid users in judgment and choice activities. It doesn’t make the decision but only support human decision. EMAP: US Environmental Monitoring /Assessment Program EPA: US Environmental Protection Agency ER Model: Entity-Relationship model ERD: Entity-Relationship Diagrams. It is a relational method for diagraming databases. GAD: Gobierno Autónomo Descentralizado. Article 238 of the Constitution of the Republic of Ecuador, establishes that rural parish councils, municipal councils, metropolitan councils, provincial councils and regional councils constitute the Decentralized Autonomous Governments (GAD), and they enjoy political, administrative autonomy and financial, and are governed by the principles of solidarity, subsidiarity, inter-territorial equity, integration and citizen participation. That is, they are the institutions that make up the territorial organization of the Ecuadorian State. GAP analysis: Biodiversity conservation defines this analysis as a process of overlaying species distribution maps, land management data and biodiversity threats in an effort of identifying unprotected habitat areas. GIS: Geographic Information System. It collects geospatial data that can be stored to analyze maps, weather data, social, commercial, historical, satellite images, etc.

HTML: Hyper Text Markup Language. The hypertext language is used to structure and present the content of WWW (World Wide Web). Hotspots: Hotspots of biodiversity are defined as the areas featuring high concentrations of species such as areas of high-value is an important resource for conservation planning IAR: Individual Area Requirement. IVPC: Identificación de Vacíos y Prioridades De Conservación en el Ecuador Continental 2013. IT: Information Technology. MARXAN: It is the most widely used decision support software for conservation planning. globally. MAXENT: Maximum Entropy. MBMS: Model-base management system. It stores, manipulates, and retrieves models in a manner that is analogous to the management of data within a database management system. MIS: Management Information Systems. It is a computer system consisting of hardware and software that serves as the backbone of an organization’s operations. An MIS gathers data from multiple online systems, analyzes the information, and reports data to aid in management decision-making. MVP: Minimum Viable Population NGO: Non-Government Organization. It is a non-profit organization which works towards the betterment of society. It's a broad term. NGO's are organized on a local, national and international level to serve specific local and political issues. NGO's rely on various sources for funding projects, and salaries. OGC: Open Geospatial Consortium PANE: Patrimonio de Áreas Naturales del Estado Ecuatoriano RDBMS: Relational Database Management. A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database. REACH: Minimum area of habitat needed to support a pair of individuals. ROMC: Stands for (representations (R), operations (O), memory aids (M) and control (C)) analysis). It is a methodology for developing a DSS. SDL: Scene Description Language SDLC: Systems Development Life Cycle. It is a conceptual model used in project management that describes the stages involved in an information system development project, from an initial feasibility study through maintenance of the completed application. SDLC can apply to technical and non-technical systems.

SDM: Species Distribution Modeling. It is also known under other names including climate envelope-modeling, habitat modeling, and environmental or ecological niche-modeling. The aim of SDM is to estimate the similarity of the conditions at any site to the conditions at the locations of known occurrence or non-occurrence of a phenomenon SDSS: Spatial Decision Support System. It is defined as a system which comprises a decision support system (DSS), a geographic information system (GIS), and a model base management system. SLD: Styled Layer Descriptor SNAP: Sistema Nacional de Ă reas Protegidas SQL: Structured Query Language SSD: Star Schema Diagrams. It is a schema of dimensional model called. The star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. It consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. TCP/IP: Transport Control Protocol / Internet Protocol UI: User Interface. Also known as Dialog Generation and Management System (DGMS). URI: Uniform Resource Identifier WAMP: Windows, Apache, MySQL, and PHP. It is a software that provides a complete, fully-integrated WAMP development environment. WCS: Web Coverage Service Web GIS: It is a part of the front end of the UI (User Interface) subsystem is that has capabilities to integrate several data-bases for manipulation, analysis for visualization, and display information online. It provides a mechanism for bridging the gap between the general public and experts. WFS: Web Feature Service WMS: Web Map Server WWW: World Wide Web

List of Figures: Figure 1: Overall SDSS development process ................................................................................................... 21 Figure 2: Regions and Hexagons Continental Ecuador .................................................................................... 26 Figure 3: Study Area SDSS Continental Ecuador .............................................................................................. 30 Figure 4: Conceptual Diagram of Decision Support System for GAP Analysis and Biodiversity Conservation . 32 Figure 5: General Steps DSS GAP Analysis 2013 .............................................................................................. 34 Figure 6: Existing information and sources collected on the Final Version of geo-database project IVPC-2013 ......................................................................................................................................................................... 35 Figure 7: Database, Server Site and Client Site ................................................................................................ 37 Figure 8: Data Base Structure .......................................................................................................................... 44 Figure 9: Hexagon GAP Analysis / Source of Pressure SDSS: Model Entity Relationship ................................. 44 Figure 10: Site Structure definition of information architecture and user design ........................................... 50 Figure 11: Layer publishing process ................................................................................................................. 52 Figure 12: SQL POSTGRES query and its native OUTPUT ................................................................................. 54 Figure 13: Embedded Query using PHP code ................................................................................................... 55 Figure 14: GEOEXT interaction with software and files. .................................................................................. 55 Figure 15: GEOEXT Open Source Customized application. .............................................................................. 56 Figure 16: Starting SDSS Prototype Web Page ................................................................................................ 60 Figure 17: Upper Menu, Gap Analysis Results Side Menu ............................................................................... 61 Figure 18: Side Menu: Hexagons / province Best Solution .............................................................................. 62 Figure 19: Upper Menu: Source of Pressure .................................................................................................... 62 Figure 20: Side Menu Mining Concessions ...................................................................................................... 63 Figure 21: Upper Menu SDSS ........................................................................................................................... 63 Figure 22: Published layer Best Solution of Gap Analysis 2013 ....................................................................... 63 Figure 23: SDSS Best Solution vs. Ecuadorian provinces .................................................................................. 64

List of Tables: Table 1: Regions, remnant vegetation and hexagons. .................................................................................... 26 Table 2: Selected Indicators ............................................................................................................................. 41 Table 3: Selected Vector Layers ....................................................................................................................... 42 Table 4: Identified Queries ............................................................................................................................... 43 Table 5: Description of Model Entity Relationship........................................................................................... 47 Table 6: Description of Model Entity Relationship........................................................................................... 48 Table 7: Installed versions of software ............................................................................................................ 49

1. Introduction 1.1 Background In the last two decades, public interest in environmental issues owed to human activities in forest and agricultural ecosystems has increased significantly. Therefore, these topics became part of public policy. Structuring a public policy requires reliable information therefore the implementation of information systems created in the resource management context had contributed to generating useful data in the decision support process. For policy decision making, information for decision support is essential and derives from research activities (Bareth, 2009). The research activities that generate information for supporting the process of decision making are articulated on systems called DSS, the Decision Support Systems, also defined as knowledge based system (Druzdzel & Flynn, 1999). The DSS systems are capable of creating the knowledge exchange cycle which has five stages. The five stages are: Data collection, production of information (Processing the data), reporting of information, verification of information, and synthesis of verified information (Bareth, 2009). A very simple definition of decision-making process is focusing on a problem and giving several alternatives for its solution. The process of decomposing and formalizing a problem is often called modeling (Druzdzel & Flynn, 1999).The environment and its biological biodiversity are very complex. Modeling is required trying of understanding any of its structure. The shortfall of natural resources and the increasing need of structured public policy have derived on the creation of the systems and software application of science to biodiversity conservation problems has increased (Bareth, 2009). In the last decade, the intention of protecting the biodiversity has augmented especially in countries defined as megadiverse. There are seventeen mega-diverse countries. They harbor the majority of the Earth's species according to Conservation International a nonprofit environmental organization (Williams, 2001). Ecuador is one mega diverse country (Jacobs, 2016). It has generated numerous processes and activities towards conserving existing species in land and territorial waters

of the country. The biodiversity conservation studies in Ecuador have their origins in a group of activities pointed at the compliance of the Convention on Biological Diversity (CBD) Program of Work on Protected Areas which was adopted in February 2004. “This country, as a signatory of the CBD, is committed to apply more than 90 activities of the Ecuador ’s work program, designed to strengthen its National Protected Areas System (SNAP, from the Spanish Sistema Nacional de Áreas Protegidas)” (Terán et al., 2006 , p.4). As result of this activities, Ecuador has included a considerable area of its territory on its National Protected Areas System. However, these areas were created without the process of identifying the patterns of biodiversity distribution in the country. In order of improving the process of creating a reserve, a biodiversity conservation gap analysis was done for Ecuador. An extended concept of the biodiversity conservation gap analysis can be defined as a geographic study that identifies conservation priorities based on the current status of biodiversity, its representativeness inside of the SNAP, and the relevant socioeconomic information that affects its persistency in the future. It includes several processes like: data collection process about biodiversity patterns distribution, sampling and collecting information on field, mapping the spatial distribution of biodiversity and using software for creating protecting areas that guaranties the accurate representation of the biodiversity in a minimal possible area (Cuesta-Camacho, Peralvo, Luna, & Campos, 2007). Conceptually, the GAP analysis for Biodiversity Conservation is an explicit exercise analyzing land use and land cover change. Since Ecuador is extremely biodiverse, the GAP analysis is especially complex (Cuesta-Camacho, Peralvo, Muriel, Bustamante, & Torres, 2013). The first biodiversity conservation gap analysis was done and written in 2006. It provides recommendations to identify the gap and priority areas for conservation of terrestrial and marine biodiversity in continental and marine platform using several software tools. According to this report, conservation efforts in Ecuador had included a significant extension of land on the SNAP. However, the original process of including places showed important inaccuracies from the biodiversity conservation viewpoint.

The authors (Cuesta-Camacho et al., 2007) of the report detected three main problems related to the biodiversity distribution patterns in the continental Ecuador: Several zones that have a large number of important species had not been included in the SNAP; some protected areas are not big enough for maintaining representative statistical samplings of ecosystems, communities and species that should be protected. Landscape species like the spectacled bear should not be protected on reserves that have less than 100.000 ha; and an imbalanced distribution of SNAP areas exists at the country level. This causes that numerous endemic biodiversity isnâ&#x20AC;&#x2122;t included in protected territory defined by the SNAP. The second biodiversity conservation gap analysis report updated in 2013 used part of the existing methodology and new version of the software tools. Furthermore, it incorporates various sources of biodiversity information and reveals important improvements in the collected data. One of the biodiversity data resources is the most recent ecosystems map for Ecuador (MAE, 2013) which is the result of a collaborative effort between the Ministry of the Environment and specialists from different research institutions. Also, the information about species distributions is more complete and built upon recent advances in collections of field data from different sources (Cuesta-Camacho et al., 2007). This report was elaborated at national scale. However, the existing data should be analyzed at different levels of political units to be reviewed in detail. Analyzing scenarios in different regions and political units can help to streamline biodiversity conservation (CuestaCamacho et al., 2013). 1.2 Objectives and Research Questions The main purpose of this research is to create a Spatial Decision Support System for Biodiversity conservation in Ecuador based on the selected and relevant data taken from the 2013 GAP analysis information. The specific objectives of this system are two. The first goal is to generate spatial and thematic queries that allow analyzing the existing geo-database at different political scales and areas of interest. The second is to structure an accurate user interface for this DSS that allows the interpretation of different results of the spatial data and criteria of biodiversity variables for this particular procedure.

The research questions for this investigation are: What output information of the 2013 GAP Analysis is relevant and can be selected for creating a Spatial Support System for Biodiversity? What elements are considered to structure a web user interface capable of including spatial and thematic information permitting the interpretation of the results of the biodiversity conservation gap analysis? 1.3 Hypothesis A spatial decision-making system can be created using the data generated by the biodiversity conservation gap analysis process of Ecuadorian territory. 1.4 Justification The Biodiversity conservation gap Analysis for Ecuador had been run at national scale twice in 2006 (Cuesta-Camacho et al., 2007) and 2013 (Cuesta-Camacho et al., 2013). Between the generations of these two reports, the technology and software tools used for processing data have progressed. The performance and capacity of new computers allow massive processing of data, which was a limitation for running some processes in 2006. Nowadays, heavy data process can be done using computers with better capacity in its resources of memory, processor and disk. Approximately since 2015, the development of interfaces using web software is more accessible than it was on 2006. The biodiversity conservation gap analysis as a component of biodiversity conservation planning must deal with two types of changes. First, biodiversity is not static in time or space but generated and maintained by natural processes. Second, humans are altering the planet in diverse ways at ever faster rates (Pressey, Cabeza, Watts, Cowling, & Wilson, 2007). Based on this statement, this analysis has to be run periodically. Consequently, the groups of maps and variables used as input for creating this analysis and report update periodically too. Furthermore, the GIS databases and information stored as a set of criterion maps represents a snapshot of a particular decision scenario at every update. None of the two-biodiversity conservation gap analysis reports for Ecuadorian territory 2006 or 2013 had created or used a DSS to explore, present, or granular the existing information. Since, the volume, management, and inquiry of the data at different political units are complicate to process. Creating a DSS using the information generated by this analysis process will stress some of the important outputs for the actors. For example: It

could help to locate and focus the attention on the conflicting sites that have more biodiversity threads and conflicts. On the other hand, in the 1980s the development of spatial decision support for environmental resource management started in several fields for example forest and agro-ecosystem management, biodiversity conservation, and hydrological planning (Bareth, 2009). Since 1990, several protocols exist for systematic conservation planning. They emerged from the efforts to use detailed biogeographic distribution information for the design of conservation area networks and the need of including socioeconomic criteria in these designs. These new approaches are algorithmic and rely heavily on computers because they need to process large quantities of data rapidly (Sarkar et al., 2006). The implementation of complex SDSS (Spatial Decision Support System) started on late 1990s because at that moment, it was possible to combine several availabilities of spatial data and communication, computing, positioning, geographic information system (GIS) and remote sensing technologies (Sugumaran & Sugumaran, 2007). The Decision Support System for Biodiversity conservation in Ecuador using data from the GAP analysis 2013 will generate interfaces with relevant information for decision support and research activities. The creation of this DSS is important because it will create and possibly expand the study of the results of the GAP analysis implementing queries that allow focus on specific indicators that will identify important biodiversity areas. It will permit presenting scenarios or locating the major conflicting sites in which biodiversity conservation and socioeconomic are confronted using several existing indicators collected on the geodatabases. Also, the DSS will show the protected areas and ecosystems threatened by several human activities. In conclusion, Decision Support System for Biodiversity conservation in Ecuador using data from the biodiversity conservation gap analysis 2013 will be used for a deeper examination of the existing data at different levels and the possible definition of area of interest for biodiversity studies. This system has a framework of a DSS decision support system provides a tool to combine and transform spatial and non-spatial data into a resultant decision (Ozan, Kauffmann, & Sireli, 2003).

1.5 Scope This study will collect the geographic data generated by the Biodiversity Gap Analysis process done at 2013 and some of the main criteria of the experts in order of understand the process itself and systematize some of information that could be used for deeper examination or in the decision-making process. Ecuadorian Biodiversity Gap Analysis has its published results from 2006 and 2013 expressed at national scale. The geographic analysis unit was defined by its methodology as a hexagon of 50 hectares which is the smaller scale that can be investigated with current data. This study will examine the current existing data and its possibilities of inclusion on a SDSS. This investigation will try to identify and integrate different parameters that facilitate the decision- making process at a finer political scale than the region level. This information could support land use planning processes at national and sub-national levels. Also, could be used by actors such as NGOs (Non-Government Organizations), GADs (Gobiernos autรณnomos desentralizados), Ministerio del Medio Ambiente (Ecuadorian Environment Ministry), and Universities, that may need to identify specific areas for future biodiversity studies and conservation plans. The structure of the proposed SDSS for this study will have a WEB interface that could be displayed on Internet or a local web page.

2. Literature review There are two main branch of science that contributes with the concepts of this study: Geography and Conservation Biology which are described in two subchapters. It also includes a theorical context of SDSS and web applications in a third subchapter that summaries some relevant studies that support this research. 2.1 Geography Geography science provides the theoretical framework with the concepts of SDSS (A spatial decision support system). There are several definitions from different authors. Based on the main components, a SDSS is defined as a system which comprises a DSS, a GIS, and a model base management system (MBMS, Bareth, 2009). On the other hand, there are several concepts related to SDSS that include an important scenario which is when the decision makers face a complex spatial problem, they often have multiple, conflicting objectives for its solution. One of these kinds of concept defines that SDSS are explicitly designed to support a research decision process for complex spatial problems. SDSS provides a framework for integrating database management systems with analytical models, graphical display, tabular reporting, and expert knowledge of decision makers (Densham, 1991). Another concept defines an “SDSS as an interactive, computer-based systems developed to support decision makers in achieving a higher effectiveness while solving a semistructured decision problem involving territorial aspects” (Murgante, Borruso, & Lapucci, 2009, p. 2). All the definitions above are complementary. They join in the basic concept of the decision that has three main components: decision alternatives, preferences, and uncertainty (Druzdzel & Flynn, 1999). The decision alternatives involve a group of geographic choices given a set of outputs. The preferences refer to the group of criteria incorporated at the design of the system by the decision makers (Densham, 1991). Finally, the uncertainty factor is everything that is unknown. A focus definition of SDSS based on its origin tells: “Spatial decision support systems are a subgroup of DSS and are generally employed in decision problems, in which spatial dimensions play a significant role” (Ozan et al., 2003 , ¶ 1). A SDSS has characteristics that

facilitate a decision research process that can be described as interactive, integrative and participative. It provides additional capabilities like: the input of spatial data, the representation of the complex spatial relations and structures that are common in spatial data, the inclusion of analytical techniques and geographical analysis and the output of spatial forms including maps (Densham, 1991).The main components of a SDSS are two: the DSS and a GIS. 2.1.1 DSS

The first component of a SDSS is a DSS. The computer science defines DSSs as an interactive computer-based system that aid users in judgment and choice activities (Druzdzel & Flynn, 1999). Its development requires an interdisciplinary approach and involves disciplines such as computer science, decision theory, statistics, psychology, information and knowledge engineering, and organizational science. A DSS consists of two major subsystems: human decision-makers and computer systems. Therefore, a DSS doesn’t make the decision but only support human decision (Maina, Amin, & Yazid, 2014, p. 284). “Unlike expert systems, which mimic human decision makers in making repetitive decisions in a narrow domain, DSS do not replace decision makers but rather support them in solving different decision problems, which are often not well-structured” (Ozan et al., 2003, ¶ 4). Consequently, the processed data using a DSS will result on information that facilitate the decision-making process. There are three fundamental elements of a DSS system: Database management system (DBMS), MBMS, and Dialog generation and management system (DGMS; Druzdzel & Flynn, 1999). The first element of a DSS is a database management system. A DBMS is a computer program designed to manage a database, numerous structured data, and operations on the data. It serves as a data bank for the DSS. The aim is storing large quantities of data that are relevant to the class of problems for which the DSS has been designed and provides logical data structures that represent the abstract construction of processes (Druzdzel & Flynn, 1999).

The structures on a database are building using standard methods either a relational or a dimensional model. Relational methods like Entity-Relationship Diagrams known as ERDs or dimensional model called star schema diagrams or SSDs (Corral, Schuff, & St. Louis, 2006). An entity-relationship model (ERM) is a theoretical and conceptual technique of showing data relationships in software development. ERM is a database modeling technique that generates an abstract diagram or visual representation of a system’s data that can be helpful in designing a relational database. These diagrams are known as entity-relationship diagrams, ER diagrams or ERDs (TECNOPEDIA, 2020a ¶ 1). On the other hand: the SSD is a schema is a data warehousing architecture model where one fact table references multiple dimension tables, which, when viewed as a diagram, looks like a star with the fact table in the center and the dimension tables radiating from it. It is the simplest among the data warehousing schemas and is currently in wide use (TECNOPEDIA, 2020b ¶ 1). The DBMS on a SDSS must be able to store and manipulate locational, topological and thematic data types. It will support cartographic display, spatial query and analytical modeling (Densham, 1991).The second element of a DSS is a MBMS. A model representation is a computer reader formalization of a user’s problem. Consequently, models have different representations according to their use (Ramirez, Ching, & St Louis, 1993). The purpose of an MBMS is to transform data from the DBMS into information that is useful in decision making. Because many problems that the user of a DSS will manage with may be unstructured, the MBMS should also be capable of assisting the user in model building. “The main function of a MBMS is containing the library of different models necessary for decision making processes and the routines to maintain and manage them” (Murgante et al., 2009, p. 3). A MBMS stores, manipulates, and retrieves models in a manner that is analogous to the management of data within a database

management system (Bennett, 1997). MBMS is compose of a set of computer programs embedded within DSS generator that allows users to create models, restructure, update models, and delete a model (Maina et al., 2014). In SDSS there are tree approaches that can be used to embed modeling techniques. The first is to use DBMSâ&#x20AC;&#x2122;s macro or script programming language. The second is to develop libraries of analytical routines. And the third approach is a full development of a MBMS that stores elements of models (Densham, 1991). The third element of a DSS is the DGMS. This is the only part of a DSS that allows the userâ&#x20AC;&#x2122;s access to the data subsystem (database and database management) and model subsystem (model base and model base management software (Maina et al., 2014). The user communicates with the DSS through the DGMS. Its primary responsibility is to provide intuitive interfaces. The DMGS is also known as UI (User Interface; Druzdzel & Flynn, 1999). The DGMS is the result of a systematic and conceptual process. The DSS components show their output for the user with a UI subsystem. The functions of the UI subsystems are to provide interaction between decision-makers and DSS. 2.1.2 GIS

The second component of a SDSS is a GIS. A GIS is implicit designed to support spatial decision making. The core functions of capturing, storing, manipulating, analyzing and displaying spatial data are part of a GIS. As a geoprocessing system, it supports the decisions research process and enables decision makers to use their chosen decisionmaking processes based on spatial information (Densham, 1991). GIS collects geospatial data that can be stored to analyze maps, weather data, social, commercial, historical, satellite images, etc. (Karnatak, Saran, Bhatia, & Roy, 2009). An effective database design and GIS analysis techniques are very important. The biodiversity conservation prioritization is one of the complex issues for the conservation authorities. Various ecological and socio-economic drivers govern the spatial distribution of biologically rich communities. These drivers are important inputs to the modeling process with different rank criteria and probabilistic weight in order to arrive at a decision-making process (Karnatak et al., 2009, p.4).

All these components have to be articulated on one database for better planning and decision making. Consequently, an effective geospatial analysis tool needs a well-organized, well managed and tuned database system for a quick and accurate analysis in multi-user environment. The non-geographic data follows the standardization process. The developer needs to design a normalized or optimization structure of the database. The main objective of normalization is to reduce the redundancy, remove atomicity and make data accessing faster (Karnatak et al., 2009, p. 4). In general, the strategy of decision analysis is to divide the original decision problem into small parts. However, it is necessary to follow a methodology to develop the SDSS that can be summarized on four broad steps. First, the two kinds of non-spatial data and spatial data must be organized. Second, the databases have to be designed. Third, the databases have to be built on a software database. Fourth, the geospatial data and the tabular data will be represented and analysis in WEB GIS page that will show the results and allows the experts analyzing the data (Karnatak et al., 2009). 2.1.3 Development methods of Spatial decision support systems.

Historically, the discussion of development methods has been focused on the Decision Support Systems. They were born in late 1960â&#x20AC;&#x2122;s because the products produced by Management Information Systems (MIS) were not meeting many of the decision-making needs of management in deploying software. In 1970â&#x20AC;&#x2122;s, the concept of a DSS was defined as an interactive computer-based systems which help decision makers utilize data and models to solve unstructured problems (Gorry & Scott Morton, 1971). This concept introduces the term unstructured problem and establishes the characteristics of structured, unstructured and semi-structured problems (Veronica, 2006). A structured problem is repetitive and routine. They require little judgment, evaluation, or insight. Most of them can be solved applying known formulas in which the values of the key variables are also known. In structured problems the decision-making process can be easily automated. It can be solved specifying algorithms, designing decision rules, finding the problem, designing an alternative solution, and selecting the best solution. In contrast, the unstructured problems are unusual and non-routine. They require

significant efforts of judgment, evaluation, and human creativity. They cannot be solved with formulas and values of some key variables are unknown, consequently, these problems are very difficult to systematize. On the other hand, semi-structured problems contain the elements of structured and unstructured problems because they can be solved using both human judgment and programming (Veronica, 2006). Veronica (2006) defines, from the business viewpoint, a DSS as an important class of information systems that use data, models and knowledge to help managers solve semi-structured and unstructured problems. Most of researchers agree that this type of systems cannot be developed using a traditional system development approach because of its complexity. There are many types of DSS development methodologies driven by: decision, process, data or system. The decision driven approach focuses on the comprehensive analysis of decision-making process and on supporting and improving this process using decision support systems. In the second methodology, the process driven methodology, the central component of DSS development is the representation of the system capacities using processes. The third approach, the data driven method, concentrates on DSS database definition, design, construct and management. Finally, the system driven approach focuses on the analysis of the internal component of DSS. It is made based on the system theory. Almost all methodologies, except system-driven, specify the sequence of phases that must be followed in order to develop DSS. The main phases of decision support system development process are: requirements definition, analysis, design; prototype design and test; implementation; maintenance and evolution. Veronica (2006) listed some relevant existing methodologies for developing a DSS were: the phased methodology, the ROMC (representations (R), operations (O), memory aids (M) and control (C) analysis), the evolutionary method, prototyping and enduser development. On the other hand, Power (2002) classified another group of methodologies. These were: systems development life cycle (SDLC), rapid prototyping, and end user development. After a detailed analysis and comparison of the existing systems development methodologies, they suggested that the most suitable

development method for solving semi-structured and unstructured problems developing a DSS is the prototyping method (Veronica, 2007; Power, 2002). Sugumaran and Degroote (2010), compared and analyzed the existing DSS software development methods presented by Veronica (2007) and Power(2002). They conclude the development of DSS as well as SDSS software addresses complex problems and suggested the use of a prototyping process is the most appropriate method for developing these systems. The software development of SDSS software requires a carefully planned and iterative process in order to build a successful product.

Figure 1: Overall SDSS development process Source: Sugumaran and Degroote (2010, p. 271)

Figure 1 describes the process of developing an SDSS detailed by Sugumaran and Degroote in 2010. It starts with the definition of the problem and the identification of its participants in this case the Stakeholders, followed by, an iterative process of definition of the requirements, system design, development of prototypes, and testing. According to the authors, the described methodology and the progress of the iterations is a responsibility of modelers, scientists, Information Technology (IT) /GIS specialists, and programmers. These participants of the process have to use information and input obtained from the design and testing phases in the iterative process to guide the development of prototypes and finally the finished implementation. The testing stage should include end users who can determine if the system is meeting the necessary requirements (Sugumaran & Degroote, 2010).

Sugumaran and Degroote in 2010 also described the most common techniques of developing an SDSS. The common method is joining or using two or more separate pieces of software together into one system. Joining these systems have several strategies for doing it. The first approach is no coupling the existing software. It consists on using the separate software independently with no digital communication between them. The second method could be a full integration in single software. The third strategy could be a free connection approaches, in which custom software is developed to allow data file sharing between two or more software components. This last method is the most common for developing a SDSS. This approach requires a minimum of development time utilizing the functionality of the individual coupled programs. Customizing existing software has the advantage of the fact that users are already familiar with using the software. Consequently, the cost of development can be low as existing functionality of the customized software can be utilized. The customization of existing software for could be commercial software or open-source software (Sugumaran & Degroote, 2010). 2.1.3.1 WEB GIS

The web platform enabled GIS to display its information facilitating decisionmaking at the strategic, tactical, and operational levels; support for the performance of administrative operations; and serve as a gateway for decision makers and general users to access the system conveniently and effectively (Karnatak et al., 2009, p. 16).

Web GIS normally is a part of the front end of the UI subsystem. It has capabilities to integrate several data-bases for manipulation, analysis for visualization, and display information online. It provides a mechanism for bridging the gap between the general public and experts. Thus, it offers solutions that are accessible to non-experts (Boroushaki & Malczewski, 2010). Web GIS became a low cost and easy way of disseminating geospatial data and processing tools (Maina et al., 2014). 2.2 Conservation Biology 2.2.1 Conservation Planning

The definition of Conservation biology focuses on â&#x20AC;&#x153;the application of science to conservation problems, addresses the biology of species, communities and ecosystems that are perturbed either directly or indirectly by human activities or other agents. Its

goal is to provide principles and tools for preserving biological diversity”(Soulé, 1985, p. 727). It differs from most other biological sciences because Conservation Biology may have to make decisions or recommendations about design and management (Soulé, 1985). According to the Final Consensus Report of the Keystone Policy Dialogue on Biological Diversity on Federal Lands on 1991, biological diversity is the variety of life and its processes; and it includes the variety of living organisms, the genetic differences among them, and the communities and ecosystems in which they occur (KeystoneCenter, 1991). “Biodiversity Conservation could be defined as a conservation work or any set of action assumed by a project, people or organization to reach goals and objectives for conserving biological entities: species, communities, or ecosystems” (Salafsky et al., 2008, p.828). In 1992 the International Convention on Biological Diversity was signed by national governments with a view to halting the decline of global biodiversity. Historically, Biodiversity Conservation is a discipline that has developed from amateur pursuits by wildlife enthusiasts in the 1960s to today’s complex community of multi-national NGOs, government agencies and research institutions. Nowadays, the conservation sector is largely funded by government grants, private donations and sponsorship (Black, Meredith, & Groombridge, 2011, p. 1165). In the context of Conservation Biology, the Conservation Planning can be described by steps that help planners to identify and sequence their tasks and decisions (Pressey et al., 2007). Conservation planning is inherently spatial. Biodiversity Conservation is based on both spatial and biodiversity conservation planning criterions. The central goals of a conservation plan are representation and persistence. Representation requires that all relevant features of biodiversity are adequately accounted for in a plan. On the other hand, persistence refers to the need for planning to go beyond the representation of biodiversity patterns (Sarkar et al., 2006). Conservation planning is an activity in which social, economic and political imperatives. It is also a dynamic process in which tools are supposed to aid decision makers in identifying good policy options. The purpose of conservation planning tools is helping the users to make decisions and not to exclude them from decision-making. In this context,

conservation planning tools are decision support systems. Specifically, a conservation planning tool is defined as software that mainly has two characteristics. First, it can be used to guide decisions about conservation action for biodiversity, although it may also be used for planning other natural resources. Second, it can either identify a set of complementary sites needed to achieve quantitative targets for biodiversity features or recognize the complementary contribution that individual sites make to biodiversity conservation within a region (Sarkar et al., 2006). One of the major challenges for conservation biology is to stop the ongoing and accelerating decline of biodiversity. The identification of “hotspots” of biodiversity defined as the areas featuring high concentrations of species such as areas of high-value is an important resource for conservation planning (Vignoli, Bombi, Bologna, Capizzi, & Salvi, 2013, p. 586). In the available literature, it is shown that the different landscape models are developed to characterize biodiversity having various degrees of uncertainty on biodiversity where computer-based models can directly interact with the biodiversity experts to generate a knowledgebase for the biodiversity conservation prioritization by implementing analytical hierarchy processing technique to identify biologically rich sites (Karnatak et al., 2009, p. 5). 2.2.2 GAP Analysis

The identification of high-priority areas and the conservation gap analysis are complementary. “A gap analysis is a method to identify biodiversity for example: species, ecosystems and ecological processes not adequately conserved within a protected area systemor through other effective and long-term conservation measures”(Dudley, 2008, p. 44). On the other hand, a “GAP analysis is an assessment of the extent to which a protected area system meets protection goals set by a nation or region to represent its biological diversity”(Kashta, 2010, p. 16). “Gap analyses can vary from simple exercises based on a spatial comparison of biodiversity with existing protected areas to complex studies that need detailed data gathering and analysis, mapping and use of software decision packages”(Parrish & Dudley, 2011¶ 2).

One of the important components of a GAP analyses is the modeling of geographic distributions of species. The Species Distribution Modeling or SDM is also known under other names including climate envelope-modeling, habitat modeling, and environmental or ecological niche-modeling. The aim of SDM is to estimate the similarity of the conditions at any site to the conditions at the locations of known occurrence or non-occurrence of a phenomenon (Hijmans & Elith, 2014, p.1). “Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation” (Phillips & Dudík, 2008, p. 161). One of the most used method and software is MAXENT (Maximum Entropy). It is a software for modeling species geographic distributions. “MAXENT is also a modeling technique, achieving high predictive accuracy” (Phillips & Dudík, 2008, p. 161). The generated data with SDM was generated using R and MAXENT software in GAP analysis 2013. R is a free software programming language and software environment for statistical computing and graphics is usually used with MAXENT in some of the software routines (Hijmans & Elith, 2014). On the other hand, MARXAN is the software that was used with GAP Analysis 2013 for providing decision support for conservation planning goals such as the design of new reserve systems, reporting on the performance of existing reserve systems and developing multiple-use zoning plans for natural resource management (Watts & Possingham, 2013). “The process of overlaying species distribution maps, land management data and biodiversity threats in an effort of identifying unprotected habitat areas is defined by biodiversity conservation as GAP Analysis” (Larson & Sengupta, 2004, p. 1). The GAP Analysis of Ecuador had been done using the hexagonal grid system twice in 2006 and 2013. The design feature is a hexagonal cell size of 500ha. The size of the hexagon was defined as an analysis unit for maintaining the concordance of patterns of the restricted species and the software processing limitations at that time (Cuesta-Camacho et al., 2007).

The hexagonal grid system was developed by the US Environmental Protection Agency (EPA) for the EMAP Environmental Monitoring /Assessment Program. It uses the hexagonal design of 635 KmÂ˛.The hexagonal subdivision of equal area, low perimeter to area ratio and minimal scale distortion over large areas. It has been used on several GAP Analyses at the United States (Joly & Myers, 2001).

Figure 2: Regions and Hexagons Continental Ecuador

Figure 2 shows Ecuadorian territory using a hexagonal grid. The Ecuadorian area has been divided on its tree regions: Amazonia, Cost and Highlands. The continental Ecuador has 25.341.500 that has been divided on 50683 hexagons of 50 ha (Cuesta-Camacho et al., 2013). Table 1 illustrates the three regions and its hexagonal units used for Ecuadorian Biodiversity GAP Analysis. Table 1: Regions, remnant vegetation and hexagons. Source: Cuesta-Camacho et al. (2013)

Region

Total Area (ha)

Percentage of Total Area

Number of Hexagon

Remnant Vegetation Area

Percentage of Remnant Vegetation Area

Number of Hexagon Remnant Vegetation

Coast Andean Highlands Amazonia Total

7.896.000 6.684.000 10.761.500 25.341.500

31% 26% 42% 100%

15.792 13.368 21.523 50.683

2.023.679 6.409.254 6.895.613 15.328.545

13% 42% 45% 100%

4.047 12.819 13.791 30.657

Table 1 summarizes the regions, remnant vegetation and hexagons of continental Ecuador and its total areas measured in hectares. It includes the number of hexagons per regions ant the remnant vegetation on them. This analysis was done using levels of

biological aggregation: species and ecosystems. The selected indicators at species level were 753 species that belongs to four groups of organisms (tree vertebrates and one of vascular plants). The groups were: amphibian, reptiles, birds and vascular plants. About this last group 180 are angiosperms and 55 species of ferns. The selected species were selected according expert criteria of representativeness on the three main regions of continental Ecuador. The occurrence of species data was compiled of several scientific sources. Pontificia Catholic University contributed with the Herbarium data, zoologic information, reptiles and amphibians’ collections. CONDESAN (Consorcio para el Desarrollo Sostenible de la Ecorregión Andina) shared its private data collection of vascular plants; complemented with a private collection of bird database of the Biologist Juan Fernando Freile (Cuesta-Camacho et al., 2013). 2.3 Relevant Previous Researches The published works about SDSS focusing on GIS, WEB, Conservation Ecology and Biodiversity Gap Analysis are scarce. The reviewed papers are not precisely of previous existing works in the exactly same field of knowledge or the application that is the main purpose of this research. However, they contribute with elements, concepts or structures for building a customized methodology for this thesis. The reviewed literature with similar approaches to the present work are: Geospatial database organization and spatial decision analysis for biodiversity databases in Web GIS environment (Karnatak et al., 2009), A SDSS-based Ecological-economic Modeling Approach for Integrated River Basin Management on Different Scale Levels – The Project FLUMAGIS (Volk et al., 2007), A spatial decision support system to identify species-specific critical habitats based on size and accessibility using US GAP data (Larson & Sengupta, 2004), Patterns of mammalian species richness and habitat association in Pennsylvania (Joly & Myers, 2001) and Multiple Scale Integrated Range Maps for Modeling Predicted Distributions of Vertebrate Species in the U.S. Virgin Islands (Gould & Solórzano, 2009b). These five studies were divided in two groups: the first group addresses the concepts and structure of a SDSS and the second group are three documents fetch technical aspects of the GAP analysis. The first two studies address the concepts and structure of a SDSS. They show the importance of well-organized database, the complexity of data management for

a DSS on environmental issues and the necessity of including social variables. The second group have three documents related to technical aspects of the GAP analysis. One of the documents has a SDSS applied on a specific GAP analysis executed with punctual variables and concepts, used as an extra tool for locating unprotected areas derived from the GAP analysis. The second pair of documents contains key concepts of the GAP analysis, ecology and landscape ecology. A good example of the development of this type of SDSS tool is the paper written by Karnatak et al. (2009). This work took place in India and focus on the problem of the organization of databases for biodiversity conservation, the construction of the SDSS and its results using the WEB-GIS visualization. It considers variables relevant for a biodiversity SDSS for example species richness and ecosystems classifications. Furthermore, it highlights the importance of using RDBMS (relational database management system) and ERD on developing databases for organizing biological and GIS data. This research shows the integration of geographic data and plain tables implementing a Biodiversity SDSS. Its front end has a user interface that allows interaction with the GIS data and options for end users, experts and decision-makers. FLUMAGIS (Volk et al., 2007) is a SDSS constructed with environment data, socioeconomic variables and spatial information. Its structure is applied to planning process on river basins. It integrates methods for ecological and socio-economic assessment, scalespecific modeling, knowledge processing and techniques for visualization (Geneletti, 2004). Its main research focus on the complexity of having different disciplines, geography scales, highlights the importance of creating a DSS with environmental planning purposes and shows the importance of visualization of maps at different administrative government units for the socioeconomic variables. It uses socioeconomic variables, political units, and factors of potential conflicts. Larson and Sengupta (2004) created a DSS using a GAP Analysis, remote sensing, existing vegetative cover and species-specific information. This SDSS tries to identify unprotected areas using three GAP analysis concepts. The utilized parameters were: IAR (Individual Area Requirement), MVP (Minimum Viable Population), and REACH (Minimum area of habitat needed to support a pair of individuals). The focus of this research was analyzing

the influence of specific species parameters providing a level of understanding of the interaction between the species and its specific habitat distribution. It also contains an example on the Arkansas state as study area to test the algorithms. This study is relevant for this thesis because it includes the context of decreasing of biodiversity caused by anthropogenic causes and several key concepts of GAP analysis. It gives a sharp definition of the GAP analysis as a tool of identifying unprotected areas in an existing conservation system. The methodology of this document illustrates the GAP process and necessary maps used for obtaining a result. It evaluates the known occurrences of the species through the use of hexagon grids EMAP. This SDSS was created for finding suitable habitats for some specific species in a specific area of interest. ‘Patterns of mammalian species richness and habitat association in Pennsylvania’ (Joly & Myers, 2001) characterizes the complexity composition of an hexagon. It includes the landscape ecology variables choosing spatial data related to habitat heterogeneity, total forested area, extending of human influence, habitat fragmentation, roads and amount of water. These variables are important portraying the hexagonal units of 635 km². It concludes that the road density, topographic variation and amount of forest are the best predictors for species richness within a hexagon. Also, it brings the key concept of hotspot that in this context is defined as a hexagon or group of hexagons that represent local maxima of species richness. This research presents a framework and structure of a hexagon as the basic unit of GAP Analysis. Multiple Scale Integrated Range Maps for Modeling Predicted Distributions of Vertebrate Species in the U.S. Virgin Islands (Gould & Solórzano, 2009b) is a particular experience of a GAP Analysis on the US Virgin Islands emphasis the requirement of decreasing the size of the standard hexagons mapping unit of 635 Km² with the purpose of increasing the accuracy of vertebrate species occurrence mapping. “Some species occupy very small regions that may not be adequately mapped with the original hexagon size”(Gould & Solórzano, 2009a, p. 13). This experience gives the perspective of the need of adjusting the size of the hexagons depending of the occurrence mapping or the size of the study area. This published study explains the variation of the hexagon size for the GAP analysis on small countries for example Ecuador.

3. Methodology 3.1 Study Area The study area used for this SDSS is the continental Ecuador. Ecuador has its boundaries with Colombia and Peru. The area of interest does not consider the Pacific Ocean neither Galapagos islands.

Figure 3: Study Area SDSS Continental Ecuador

Figure 3 shows that the continental area of Ecuador has three main regions: coast, highlands, and Amazonian. The coast limits with the Pacific Ocean at west. The highlands in the center are part of the Andean range and the Amazonian at the east. These continental Ecuadorian physiographic regions have the following approximated distribution and areas. The coastal region has around 150 Km width between the Andes and the Pacific shore. Its area is around of 26% of the country surface (Cuesta-Camacho et al., 2007). The Andean Highlands region has around 43% of the continental area. It has three branches of mountains and volcanos (Cuesta-Camacho et al., 2007). The amazon region represents the 30% of the continental area. It includes the bottom of the Andes and a wide plain area (Cuesta-Camacho et al., 2007).

The administrative political units at province level on each region have its own distribution. The coastal region consists of seven provinces: Esmeraldas, Santo Domingo de los Tsáchilas, Manabí, Guayas, Santa Elena, Los Ríos and El Oro. The Andean Highlands Region has ten provinces: Carchi, Imbabura, Pichincha, Cotopaxi, Tungurahua, Bolívar, Chimborazo, Cañar, Azuay and Loja. This region has the Andes Mountains that occupies the center of the country. The amazon region has seven provinces Orellana, Pastaza, Napo, Sucumbíos, Morona Santiago, and Zamora Chinchipe (INEC, 2016). The continental Ecuador has a tropical clime. The temperature varies with the altitude and regions. The coastal region is mostly flatland and it is near to the Pacific Ocean. This region has warm temperature. It has two seasons: winter and summer. The winter is warmer than summer and it rains (TERRA, 2019). The costal summer is dry and the temperature is lower than winter. The annual average temperature on the north pacific coast varies 25° - 31° centigrade. The climate on Andean highlands varies with the altitude. Its elevation starts at 500m to 6000m. It has the same seasons as the coastal region (TERRA, 2019). The dry season starts at June and ends at September. The Amazonian region is warm and humid all year. The rain is abundant and present all the time. The drier months start at December and end on February (Lonly Planet, 2019). This study area was chosen since Ecuador is a mega diverse country. The database of GAP Analysis has relevant information of biodiversity and threats. The three regions of continental Ecuador have a great biological diversity. They have different levels of endemism and species richness. The coast and highlands have more endemic species than the amazon. However, the amazon region has greater number of species (CuestaCamacho et al., 2007). 3.2 SDSS Prototype: Global Concept Biodiversity conservation prioritization is one of the complex issues for the conservation authorities. Various ecological and socio-economic drivers govern the spatial distribution of biologically rich communities. These drivers are important inputs to the modeling process with different rank criteria and probabilistic weight in order to arrive at a decision-making process (Karnatak et al., 2009, p. 413).

The proposed SDSS prototype identifies and uses the main results of the GAP Analysis focusing and granulating some of the variables based on political administrative units and spatial layers available for Ecuador. This study presents a prototype that groups and displays selected context variables and the collected solutions of the Ecuadorian GAP analysis 2013. These parameters are represented at administrative government units on available scales. It uses open source tools GIS and WEB platform to display the context variables in a graphical mode. It may locate or visualize specific sites identifying major conflicts between biodiversity and several sources of pressure. In the global context of the theoretical Ecuadorian GAP Analysis, the SDSS is an independent software module that has some predefined criteria and generates reports and displays maps. It can be seen on the following conceptual schema:

Figure 4: Conceptual Diagram of Decision Support System for GAP Analysis and Biodiversity Conservation Source: Cuesta-Camacho et al. (2013, p.32)

Figure 4 shows a simplified schema of the Ecuadorian GAP Analysis 2013. It shows the input data processed using the selection of priority biodiversity areas. This process generates secondary information that feeds the activities of the applied methodology and criteria for prioritization. The result is a prioritization scenario. In the graphic, the

management information system has the input of the of the initial data, secondary information, and prioritization scenarios. Its output sends information to the DSS. The DSS module generates mapping and cartographic views and reports. The doted arrow comes back to the methodology and criteria for creating more prioritization scenarios (Cuesta-Camacho et al., 2013). 3.3 Methodology Following the methodology proposed by Sugumaran and Degroote (2010), the main requirement of the CONDESAN scientists that are in this case the stakeholders of this system is collecting the data, classifying the information and presenting graphic results that can be presented in a web interfaces to be published and the consoling the information in a database that allows to facilitate the creation of complex cross tables and queries. Sugumaran and Degroote methodologyâ&#x20AC;&#x2122;s is coupled to this implementation. It just incorporates the element of data recollection, this because since CONDESAN is an NGO most of its work are resulting of consultants and are scattered on different directories and sources of digital storages. The proposed SDSS identifies and uses the main results of the Ecuadorian GAP Analysis 2013th focusing on some of the variables based on political administrative units. The identified main steps are: data collection, design, development and system validation. SDSS for GAP Analysis 2013. It starts with the data collection that is the input for design and development. Once the development is done the system has to be validated. Figure 5 describes the broad process showing each procedure and its input to design the

Figure 5: General Steps DSS GAP Analysis 2013

3.3.1 Data Collection

The data collection process consisted on searching the sources of information and results of GAP Analysis 2013 stored in CONDESAN servers. The first step was reviewing generated documentation about sources of information, concepts, biodiversity and gap analysis itself. Once this activity was finished, the structure of the database was revised as well as the existing compiled layers were visualized one by one using ARCGIS for exploring spatial information. The existing database documented on the final geo-database report of GAP analysis or IVPC for its initial letters on Spanish and the given name of the project Identificación de Vacíos y Prioridades De Conservación en el Ecuador Continental 2013 (Baquero, 2013) are summarized on figure 6.

Figure 6: Existing information and sources collected on the Final Version of geo-database project IVPC-2013 Adapted from: Baquero (2013)

In the schema on figure 6, the database has two main sources of information: local Ecuadorian information and external information. The local Ecuadorian public information was collected by CONDESAN using the framework of the project IVPC. Its result was the Ecuadorian biodiversity GAP analysis 2013.The input data comes from published online information generated by Ecuadorian government institutions and agreements between the private and public entities with CONDESAN. The total of local institutions that contribute with information for the Ecuadorian GAP analysis were ten. The external information came from scientific and global organization were four in total. The complete list of local and external sources of information can be seen in Annex 1 (table A and B). 3.3.2 Design The design process has two components, the design of the database, and the selection of the application structure for the interaction between components. Designing the database has tree steps: Identifying criteria, selecting geographic layers and defining the queries. On the other hand, the selection of the application structure for the interaction between components has three activities: model definition, choosing the software and required software for implementation.

3.3.2.1 The selection of the application structure for the interaction between components

The selected application structure for the interaction between components is the clientserver architecture. This model describes the relationship of cooperating programs in an application. This architecture is a distributed application. It divides tasks or workloads between service providers (servers) and service requesters, called clients. Often clients and servers operate over a computer network on separate hardware. A server is a high-performance host that shares its resources with clients. A client does not share any of its resources, but requests a server's content or service function (Farnaghi, Mansourian, & Toomanian, 2009, Âś 6). The resulting maps present the visual results. With this purpose and the suggested infrastructure is client-server architecture. Based on client - server architecture, the software infrastructure can be chosen for building the databases based on the characteristics of the data. The database has to be built on a software product. There are several products in the market for building a database. The main two groups of products are commercial and open source products. This study chose open source software over its commercial equivalent. The open source software are applications which its source code is available with a license and also provides the rights of using and changing it with any purpose (OPENSOURCE, 2015). On the spectrum of open source software for databases, the most used are MYSQL, SQLITE and POSTGRESQL. The most popular products like MYSQL (MYSQL, 2015) and SQLITE (SQLITE, 2015) have some geographic function but its performance is low. The best performance on development and spatial management is POSTGRESQL (POSTGRESQL, 2019). This product permits the creation of the Structured Query Language (SQL) tables and the management of geographic data with web interaction. POSTGRESQL is a relational database management system. It provides full compatibility with the open source products that permits publishing and displaying maps, in this case GEOSERVER and OPEN LAYERS. It has advanced features for processing, managing and obtaining the data called store procedures. The tabular database had been built on POSTGRESQL and the geographic database uses the spatial database engine POSTGIS.

The implementation of the client-server model requires the interaction of software for each component. In this case the components are: creating the database, publishing the database and developing the web page. Creating the database requires the database software on the server site. The database was created using POSTGRESQL at server site. Its primary function is to store data, and retrieve it later, as requested by other software applications. The client software can be located on the same or it can be run on another computer across a network including the Internet. POSTGIS manage as a backend the spatial database for geographic information systems (POSTGRESQL, 2015). Publishing a map requires several open source tools. The main reason is the security concerns about the database. Therefore, this type of connection uses a middleware.

Figure 7: Database, Server Site and Client Site

Figure 7 details the software server and client site in the process applied to publishing a web-map using open source software. This connection works using GEOSERVER (http://geoserver.org/) serving the data from POSTGIS to OPENLAYERS HTML WEBPAGE and an open source user interface GIS like GEOEXT (https://geoext.github.io/geoext2/). This structure allows POSTGIS processing big amounts of data and its operations. Developing the web page at server site requires an HTTP Server and a PHP server. The open source software implemented as web server was Apache HTTP (APACHE, 2015). PHP server is a scripting language designed for web development. PHP code is used as part of Hypertext Markup Language (HTML) code at client site. It is often used in combination with various engines and web frameworks (The PHP Group, 2015).

Developing the web page uses HTTP and PHP Server for testing purposes. These services have to be available locally on the host until this process is complete. This is the reason for using simulation software. The selected open source software was BITNAMI WAMP (WAMP stands for Windows, Apache, MySQL, and PHP). It provides a complete, fullyintegrated WAMP development environment. On the client side, the interaction between the database and server components is done through the UI. The UI was created using DREAMWEAVER a software capable of managing client-site PHP, JAVASCRIPT and HTML5. These are programming codes that allows the communication between the database and the end user. PHP client side is programming code embedded in hypertext HTML code and allows the bidirectional interaction with the database through the PHP server site. JAVASCRIPT is used as part of web browsers, whose implementations allow client-side scripts to interact with the user (JavaScript, 2015). The hypertext language (HTML5) is used to structure and present the content of WWW (World Wide Web). Dreamweaver version CS6 works with HTML, XHTML, Cascading Style Sheet (CSS), JavaScript, and PHP. It incorporates HTML 5 and CSS3 to formatting controls of the web page. An output web page is the result of the interaction between the browsers, pageâ&#x20AC;&#x2122;s HTML, CSS and in some cases JAVASCRIPT and PHP (server side code) that adds interactivity to the webpage code (McFarland, 2012). Some additional software is used for completing punctual activities like the communication between computers and the design of the layout on the published maps. The communication between the components is done using the TCP/IP (Transport Control Protocol / Internet Protocol). This communication software allows the intranet and internet connection as well as the implementation of the architecture client-server model. The published layouts updates and modifies its content using QGIS. This software is also capable of generating the type of files that could be upload by GEOSERVER. 3.3.2.2 Designing the database

Designing a database is a procedure that has its own activities: identifying criteria, selecting geographic layers, defining queries and modeling the database.

Identifying criteria The criteria for creating this specific database for structuring this prototype of SDSS was defined by the available information. First the scale of this study for the queries is strictly Ecuadorian provinces. It excludes the rest of administrative unitsâ&#x20AC;&#x2122; parishes and cantons and the analysis using hexagons is also excluded. Second, the indicators most be sources of pressure and biodiversity parameters existing in final scenarios and results of the Ecuadorian GAP Analysis 2013 and some of its context variables on a webpage oriented to emphasizing the source of pressure on natural resources per province. Third, it may include context variables for clarifying the location of some indicators. These additional parameters facilitate the visualization of the smaller areas of interest may reveal some overlooked realities given by the actual region scale. Fourth, the collected variables of this study are mainly vector and contain the information required using the specified indicators. The raster data has been excluded for two reasons. First, most of this information is related to temperature, precipitations, water vapor and bioclimatic variables. These data were the input for creating the resulting scenarios of the biodiversity conservation gap analysis. Second, the visualization of raster data using the webserver is very slow. Selecting the data The data selection had two components: selecting variables for building the queries and choosing the vector layers for publishing. Selecting variables for building the queries was a part of reviewing the existing documentation and collecting the generated layers. The original 2013 GAP Analysis geo-database collects 264 hundred variables (Annex 1, table C) for its process and more than 50 layers of generated data and collected information. The processed geographic data have several groups of indicators. The selected data for this SDSS are the variables that highlight the main sources of pressure over the natural resources. The table 2 shows the selected parameters for building the queries. Choosing the vector layers for publishing, is the second part of selecting the data. The selected vector layers had been set with the geographic parameters: datum WGS84 and the cartographic projection UTM Zone 17S. These vector layers had to be converted from geo-database to shapefile format for importing and publishing on POSTGRES database

and POSTGIS through GEOSERVER. The chosen variables allow the visualization of the existing sources of pressure over the biodiversity represented on the GAP Solutions, the system of protected areas and the remaining areas of vegetation at continental Ecuador. The table 3 lists the vector context variables and their description that can be visualized through the WEBGIS: Defining queries The selected variables for building the queries have the purpose of giving a context of the main environmental source of pressures over the biodiversity per provinces and presenting the selected places as the results of the GAP analysis. The structure of the queries for the environmental source of pressures summaries these indicators and presents a report per province. This information is useful to locate the future areas of research interest or focusing conservation efforts based on the existing environmental pressure. According to Larrea (2013), Ecuador ranks among the most biodiverse countries in the World and it has been an oil exporter since 1972. The oil and mining regions are also severely affected by environmental degradation in this country. On the other hand, Ecuador experienced rapid economic growth, but the sectorial performance was uneven, with the fastest growth rates in manufacturing and construction, while agriculture barely kept the pace of population growth (Larrea, 2013). In the context of biodiversity conservation gap analysis, the most relevant existing variables that cause environmental pressures in Ecuador are: Mining, Oil Activities, and population.

Table 2: Selected Indicators Adapted from: Cuesta et al. (2017) Information Source

Field

Description

Mining concessions Listed areas per hexagon Mining concessions Min_Oto Granted Areas per hexagon Mining concessions Min_Tra Pending Areas per hexagon Mining concessions Min_Tot Area Total per Hexagon Mining concessionsMin_NuC Number of concessions per hexagon Best Solution (best Solution_1 solution) x hexagon (1/0). 20-30 % of goals. Best Solution with lock Solution_2 in PANE (best solution) x hexagon (1/0) CONDESAN Intersection of 2013 Solutions (best) 174 y SolMar_Pri and 175 outside of PANE Protected Areas Pane Hexagons that are inside the PANE areas Remaining areas of Veg_rem_ha vegetation Oil activities - Spills per Pras_Der hexagon Oil activities - Wells Pras_Poz per hexagon Min_Ins

Area_Urb DPob_Pri VDPob_Pri Phog_Pri

Unit

Type

Source of pressure

Number

Source of pressure

Hex

GAP Solutions

Hex

GAP Solutions

Binary

GAP Solutions

Points Points

Urban Area x Hexagon

Population density 2010 Variation of Population density 2001-2010 Average people per household

People /km2 People /km2 People

Pressure sources Source of pressure Source of pressure Source of pressure Source of pressure Source of pressure

Table 3: Selected Vector Layers

Adapted from: Cuesta et al. (2017) Represented Variable

Description

Type

Name of the file

Mining

Source pressure from mining activities

Vector

mining_UTM.shp

Ecuadorian Provinces

Administrative Division of Ecuador

Vector

nxprovincias.shp

PANE

Ecuadorian Protected Areas, SNAP System

Vector

PANE.shp

Veg_rem_ha

Remaining areas of vegetation

Vector

Veg_rem.shp

Solution_174

Best solution for GAP Analysis

Vector

Solution_174_Hex1.shp

Solution_175

Best solution for GAP Analysis with lock in PANE

Vector

Soluition_175_Hex2.shp

Spills

Oil Spills

Vector

Spills.shp

Oil_wells

Oil Wells

Vector

Oil_wells.shp

Locations

Population locations

Vector

Locations.shp

On the other hand, the biodiversity is presented using a polygon layer of the existing Ecuadorian protected areas (PANE) variable, the two best solutions of the GAP analysis and the remnant vegetation. The PANE variable gives the context of location of the protected areas established on continental Ecuador. The main solutions of 2013 th biodiversity GAP analysis shows the areas that has to be protected. The selected solutions identified the priority areas of conservation based on the current state of biodiversity, representativeness inside of the protected system areas. The solutions incorporate the two methodological stages of 2013 GAP analysis. The first one that focus on selection sites of major importance for conservation planning based on biodiversity and representativeness goals. The second moment prioritize selected areas that include social and environment pressure criteria over biodiversity (Cuesta-Camacho et al., 2013). The solution 174 shows prioritized the areas outside of the PANE and the solution 175 displays the prioritized areas inside the PANE areas. These variables use the administrative units of the Ecuadorian provinces for emphasizing the local perspective of the conflicts. This SDSS has only predefined queries totalizing the

most relevant parameters. The table 4 contains the pre-establish set of queries defined by the experts: Modeling the database

This process had two phases. The first stage is for the tabular data using the entityâ&#x20AC;&#x201C; relationship model (ER model). The second stage is the creation of the spatial database. The spatial database is a relational geographic database standardized with the boundary and standard projection system of Ecuador. The framework of the IVPC Project allowed the organization of the information in several groups of thematic geo-databases with vector data. The compiled information was standardized with the geographic projection UTM, DATUM WGS84 and 17 SOUTH ZONE (Baquero, 2013). Based on these stages the sources of information for the designed databases of the SDSS database are the following:

Table 4: Identified Queries Variable

noMining

GAP Solutions

MAE 2012

Oil Activities

Population

Field

Query

Unit

Min_Ins

Total Mining concessions - Listed areas per hexagon per province

Min_Oto

Total Mining concessions - Granted Areas per hexagon per province

Min_Tra

Total Mining concessions - Pending Areas per hexagon per province

Min_Tot

Total Mining concessions - Area Total per Hexagon per province

Min_NuC

Total Mining concessions- Number of concessions per hexagon per province

Number

Solution_174

Best Solution (best solution) x hexagon (1/0). 20-30 % de goals per province

Hex

Solution_175

Best Solution with lock in PANE(best solution) x hexagon (1/0) per province

Hex

Solution_174

Best Solution (best solution) x hexagon (1/0). 20-30 % de goals per region

Hex

Solution_175

Best Solution with lock in PANE(best solution) x hexagon (1/0) per region

Hex

SolMar_Pri

Intersection of Solutions (best) 174 and 175 outside of PANE per province

Pane

Total Protected Areas - Hexagons that are inside the PANE areas

Veg_rem_ha

Remaining areas of vegetation

Pras_Der

Total Spills per hexagon per province

Points

Pras_Poz

Total Wells per hexagon per province

Points

Binary Ha Ha

Area_Urb

Total Urban Area x Hexagon per province

DPob_Pri

Population density 2010 per province

People /km2

VDPob_Pri

Variation of Population density 2001-2010 per province

People /km2

Phog_Pri

Average people per household per province

People

Figure 8: Data Base Structure

Using the information described in Figure 8. The basic database structure using the ER model is the following:

Figure 9: Hexagon GAP Analysis / Source of Pressure SDSS: Model Entity Relationship

Figure 9 has the entities represented on the diagram contain the required information for processing the queries and its visualization. The objects in the diagram are tables stored at POSGRES database. The database interacts with PHP for querying and displaying the information with GEOSERVER and WEBGIS. The entities can be grouped by three classes: Hexagon GAP Variables and administrative units of continental Ecuador, sources of pressure over biodiversity and GAP analysis results and, context variables. The first group of objects in the diagram are Hexagon GAP variables and administrative units of continental Ecuador. These objects have the purpose of producing the queries of the hexagonal variables. The second group of objects represent the sources of pressure over biodiversity. They were included on the SDSS database for visualizing and contextualizing the existing pressure over the natural sources. The third group of the tables represents the visualization of the GAP analysis results and the necessary variables of context to visually understand the results. Table 5 summarizes the variables and its purpose developing the SDSS. Description of Relationship The main purpose of the entity NXPROVINCIA is the visualization of the administrative units. Table 6 shows a relationship with HEXAGON GAP VARIBLES had been established for a future development of more granular spatial queries. The relationship was established one to many and it is required at least one field between the two entities.

3.3.3 Development

The development of this specific SDSS software addresses complex problems and suggest the use of a prototyping process (Sugumaran & Degroote, 2010). The development of SDSS module for Biodiversity GAP analysis uses the most common technique of SDSS developing that is joining or using two or more separate pieces of software together into one system. One of the approaches is no coupling the existing software. It consists on using the discrete software separately with no digital communication between them (Sugumaran & Degroote, 2010). This process structures the system itself creating a separate structure that uses the outputs of GAP Analysis. It creates specific predefined queries and web display of selected data layers. These outputs present some relevant

data for general public and decision-makers. The development of a SDSS combines four general processes: installing the client-server infrastructure, creating the web page, structuring the database/geo-database and queries and publishing the results on Webbased GIS user interfaces.

Table

Table 5: Description of Model Entity Relationship Individual Description

Purpose

Hexagon GAP Variables

The Hexagon GAP Variables are indicators that had been processed through the GAP analysis and they are converted at its unit hexagon. This entity contains the hexagonal variables from which the queries are obtained.

Query

NXPROVINCIA

It is an entity that defines a province as the administrative unit that used to focus the results visually.

Query Display

Mining_UTM

This entity contains the imported vector information necessary to display all the mining concession regardless of its type with the visual forms of points.

Display

Oil_Wells

This object contains the imported vector information to display the existing oil wells. Its purpose is visualizing the oil wells in the context of Ecuadorian administrative divisions.

Display

Oil_Spills

This object contains the imported vector information to display the existing oil spills. Its purpose is visualizing the oil wells in the context of Ecuadorian administrative divisions.

Display

Locations

It contains the essential data for locating population centers as source of pressure for existing vegetation and biodiversity.

Display

PANE

It allows contextualizing geographically the Ecuadorian Protected areas for visually interpreting the presented solutions of GAP analysis.

Display

Veg_Rem

It expresses the areas of existing vegetation areas in three categories high, medium and low, for visually locate the areas where the vegetation is still present or not. This solution shows the geographic areas where exists the gap of conservation. It is the main result of the GAP analysis.

Display

This solution shows the geographic areas where exists the gap of conservation lock it in the protected areas of the PANE. It is the second main result of the GAP analysis.

Display

Sol_174

Sol_175

Display

Table 6: Description of Model Entity Relationship Entity

Primary Key FID

Attributes

Foreign

MIN_INS

DPA_PROVIN

MIN_OTO

DPA_DESPRO

MIN_TRA MIN_TOT MIN_NUC SOLUTION_1 SOLUTION_2 SOLMAR_PRI PANE VEG_REM_HA

HEXAGON GAP ANALYSIS

PRAS_DER PRASPOZ AREA_URB DPOB_PRIV DPOB_PRI PHOG_PR DPA_PROVIN DPA_DESPRO COD_PROVIN GEOM FID

DPA_PROVIN

GEOM

DPA_DESPRO DPA_VALOR NXPROVINCIAS

DPA_ANIO REI_CODIGO REN_CODIGO PEE_CODIGO GEOM

Installation of the client-server infrastructure Installing client- server infrastructure is a group of activities that provides the essential software for developing the system. The database server site was installed using POSTGRESQL and POSTGIS (Annex 7). GEOSERVER was installed for publishing the geodata (Annex 7). The webserver environment was installed using the simulator BITNAMI WAMP server. It includes the windows emulation of APACHE, PHP and an additional component that connects POSTGRES (Annex 7). On the client site, the installation for

developing the webpage was done using DREAMWEAVER, OPEN LAYERS, GEOEXT 2, NOTEPAD++ and QGIS. This client-server environment is installed on a desktop computer with WINDOWS 8 operating system that contains both parts of the architecture for this development. The software installed versions classified on its function on client-server architecture are summarized at table 7. Table 7: Installed versions of software Service

Software

Server

POSTGRESQL

Version

Server

POSGIS

2.1.5

Server

BITNAMI WAMP (APACHE)

5.4.39

Server

GEOSERVER

2.7.1.1

Client

DREAMWEAVER HTML5, CSS6

Client

OPEN LAYERS

Client

GEOEXT

Client

NOTEPAD++

6.8.3

Client

QGIS

2.8.3

9.4

CS6 2.13.1 2

Creating the web page Creating the web page focuses on the process of developing elements that allows the user to choose the available options of existing queries and gives the necessary instructions called input for executing the internal process. Additionally, the visual presentation of the web page has to give a context and the necessary information for the system. According to the process described for the website of the University of Texas at Austin (UT, 2014) and the book Web Style Guide 3rd edition (Lynch & Horton, 2009), the phases generally described for this procedure are: planning and project definition, site structure definition of information architecture and user design. Planning and project definition consist on preparing the web project focus on text and the visual output of the theme. In this case, the data produced by the queries and the vector data that should be display in order of making the SDSS understandable for the end user using the data of GAP Analysis 2013. Defining the site structure of information architecture and user design. This process involves the creation of the main structure of the web page with its levels and menus. Figure 10 shows the site structure definition of information architecture and user design.

Figure 10: Site Structure definition of information architecture and user design

The visual designing of the web page was done using: DREAM WEAVER and BITNAMI WAPP STACK 2. These tools DREAM WEAVER and BITNAMI WAPP STACK worked together on this particular activity because the developing process of a web page has a coding and end user view. They had been chosen because DREAM WEAVER permits the client- side coding interaction using the elements HTML, CSS and PHP languages for developing buttons, hyperlinks, text and graphics for clarifying the context and organization of the web pages. BITNAMI WAMP STACKS provides the emulation of the HTPP and PHP server for displaying the end user view on any commercial browser MICROSOFT INTERNET EXPLORER, MOZILLA, CHROME, etc. BITNAMI WAPP Stack also emulates PostgreSQL and web server Apache. The development of this website had three process embedded. They were: structuring the database, publishing the stored layers on GEOSERVER, and developing the Queries and Web-GIS user interfaces. Structuring the database and geo-database. The database tables were created using the web diagram showed in Figure 10 and MER model as a reference Figure 9. They were imported from shapefile layers using the SQL tools provided by POSTGRES DATABASE.

Building predefined queries was done searching the layer of HEXAGON GAP Analysis and identifying the specific fields to be query and represented. This layer was modified adding the data of national administrative unit per province. These fields were transformed as a hexagonal variable through the process of spatial join option using ARCGIS with the option of intersect (ESRI, 2013). This process was done to expedite the execution of the queries. It optimized the performance and time responses instead of performing a geographic query each time this procedure is called through the web interface. It reduces the data redundancy. Most of the selected vector layers with the purpose of visualizing were not modified. However, specific layers like remaining vegetation required a reclassification of the data in order of presenting the data in more compressible way. A new database was created on POSTGRES. Uploading the layers on POSTGRES DATABASE requires a process in order to be compatible with the selected database. The IPVC is a GEODATABASE native format of ARCGIS that contains the data. Instead, POSTGRES uses the shapefile format. Consequently, the data of interest must be exported on this format and then imported to the database. The shapefiles were imported to POSTGRES DATABASE using the graphic tool of SHP2PGSQL located at the menu PLUGINS\ POSTGIS SHAPE TO DBF LOADER and the encoding option must be UTF-8 to LATIN1 for avoiding errors related to the special characters. The complete structure of each query and its development using SQL language could be reviewed in Annex 3. The rest of the tables were used with its content and spatial information for publishing maps stored in the SDSS POSTGRES database on GEOSERVER. The process of publishing maps using an OPEN SOURSE platform on a Web-based GIS user interfaces permits the visualization of spatial data, context variables and GAP results. This activity involves the use of software and concepts. However, there is no way to join a web page directly to a database because of security concerns; normally a middleware is used to join them together. The open source middleware software used as a connection between the web page and POSTGIS are GEOSERVER and OPENLAYERS. GEOSERVER is the software at server site. It manage data in a standard way, OGC Web Map Server (WMS), Web Feature Service (WFS) or it acts as a geographic server to serve data from POSTGIS database to OPENLAYERS HTML web page (GIS STACKEXCHANGE, 2015). The process of publishing

starts with the interaction between GEOSERVER and POSTGRES. It is complemented using styles for polishing the end user understandability about the presented information (Monde Geospatial, 2015a). The following graphic summaries the process of publishing a layer on GEOSERVER:

Figure 11: Layer publishing process

Figure 11 contains the publishing process. Publishing a layer on GEOSERVER starts creating a WORKSPACE. This workspace conceptually is defined by a name and a Namespace URI (Uniform Resource Identifier). A URI is similar to a URL, except URI do not need to point to an actual location on the web, and only need to be a unique identifier (Open Source Geospatial Foundation GEOSERVER, 2015b). In this case, the URI was set on http://localhost:8088/. This configuration works for a local machine on port 8088 invoking the GEOSERVER service. The screenshot of the creation of the workspace and its technical details can be seen in Annex 2. The next step is creating a connection between POSTGRES and GEOSERVER. GEOSERVER calls this process STORE. The connection is established with the database with the purpose of defining the connection parameters once. It is necessary to register a store before configuring datasets within it. A GEOSERVER STORE is a connection between data source POSTGRES (SDSS data base) and GEOSERVER. It could contain raster or vector data. A data source can be a file or group of files, a table in a database, a single raster file, or a directory (Open Source Geospatial Foundation GEOSERVER, 2015a). This process and its specific parameters for the SDSS POSTGRES database and the workspace TESIS is summarized at Annex 2. Publishing a layer was done from GEOSERVER using the option LAYERS\ ADD A NEW RESOURCE\NEW LAYER and select the workspace and its store in this case SDSS. This process was repeated

each time for each layer. The added layers on SDSS database are published using the option PUBLISH. Each added layer must be edited for customizing the two important parameters: the coordinate reference system and the bounding boxes that should be defined at the option EDIT LAYER. The coordinate system code was 32717, the UTM zone was 17 south and the coordinate system WGS84. Once the coordinate system is defined, the BOUNDING BOXES were calculated using the options COMPUTE FROM DATA and COMPUTE FROM NATIVE BOUNDS. Each layer can be seen using the option LAYER PREVIEW using the option OPEN LAYERS. GEOSERVER shows layers with a default style. Most of the time, it is necessary to customize styles in order to give a better presentation of the published layers. One of the methods for customizing styles is the creation of the SDL (Scene Description Language) files. Stylizing the published layers was done using the QGIS software. In order of creating the SLD file a new POSTGIS connection was created using the left menu and the icon of POSTGRES and the ADDITION symbol. This connection permits adding POSTGIS TABLES that are listed after connecting to SDSS database. At this screen, a layer must be selected. The selected layer asks for defining the coordinate system. The layer must be drag and drop from the BROWSER PANEL to the LAYERS PANEL. On LAYER PANEL, the DEFAULT STYLE has to be changed to your own STYLE. After adjusting the colors and symbols the style was exported for each layer using the option SAVE STYLE as SLD FILE. This procedure was applied for the polygon and point vector layers at QGIS for stylizing the published data. Once the SLD FILES were generated Open GEOSERVER \LOGIN. On the left menu STYLES \NEW STYLE\STYLE FILE the option SLD FILE was chosen. The WORKSPACE and the defined name of the STYLE was input. After that, the option VALIDATE and VERIFY were used checking that the UPPER SCREEN had NO ERRORS on validating. Finally, on the left menu LAYERS each LAYER was selected and the STYLE was applied using the menu PUBLISHING TAB and changing the option Default Style for the custom style. Each layer was tested for its preview presentation pressing the button PREVIEW LEGEND. These changes were saved. This process was repeated for each layer that is store at SDSS database. Developing the Queries and Web-GIS user interfaces

The criteria used for developing the queries were totalize the main variables per province in the case of the sources of pression and in the case of the results of the gap analysis was

counting the number of hexagons per province of the relevant variables. Retrieving this information has two specific processes: creating an SQL query at POSTGRES DATABASE and embedding the query for publishing it using HTML language. The process of creating a query through the SQL native language was done at POSTGRES using the SQL tool. They had been tested for its correct functioning at the POSTGRES interface. The total of queries at the native language could be seen at Annex 3. Figure 12 shows the query that totalizes the mining variables using SQL native language and its native output:

Figure 12: SQL POSTGRES query and its native OUTPUT

The second process was embedding the query for publishing it using HTML language. This process took the original queries and embedding the code using PHP languages that allowed requesting the data from POSTGRES and formatting it on HTML format. The next screenshot shows the PHP coding developed for this application. Figure 13 contains PHP code that has three main components the connection with the database, the SQL query, the HTML language for presenting and formatting the results using DREAM WEAVER for its edition. Developing of a Web-GIS user interface using OPEN SOURCE software was done using a GEOEXT application (Monde Geospatial, 2015b). GEOEXT enables building GIS applications through the web. It is a JavaScript framework that combines the GIS functionality of OPENLAYERS with the user interface of the EXTJS library. The version used for this development was the version 2. It uses the base libraries

of OPENLAYERS 2.13.1 and EXTJS 4.2.1 (GEOEXT2, 2012a). The function of this software is shown in the following graphic proportionated by MONDE GEOSPATIAL on its tutorial of WEBGIS using POSTGIS, GEOSERVER and GEOEXT (Monde Geospatial, 2015c).

Figure 13: Embedded Query using PHP code

Figure 14: GEOEXT interaction with software and files. Source: Monde Geospatial (2015c)

Figure 14 provided by MONDE GEOSPATIAL shows the interaction of the standards on publication maps WCS (Web Coverage Service), WFS (Web Feature Service) and WMS (Web Map Server) using the middleware software GEOSERVER or MAPSERVER and the

stored layers as individual SHAPEFILES, vector data stored at POSTGIS or raster data presented as GEOTIFF. The services WMS, WFS and WCS are OGC (Open Geospatial Consortium) standards that provide several maps services interacting with GEOEXT. The service WMS stands for Web Map Service is a specification that defines an HTTP interface for requesting georeferenced map images from a server. It provides a standard interface for requesting a geospatial map image. In the case GEOSERVER also supports the standard control of the map output Styled Layer Descriptor (SLD; GEOSERVER, 2015c). The WFS service stands for Web Feature Service. This specification is used for exchanging vector data over the Internet. It also enables query both the data structure and the source data supporting the operation of locking and edit (GEOSERVER, 2015a). The WCS service stands for Web Coverage Service. This is a standard that refers to the receiving of geospatial information as coverages. It provides an interface for how to request the raster source of a geospatial image (GEOSERVER, 2015b). This process of developing consisted of reusing the source code of the examples of GEOEXT and OPEN LAYERS libraries. The SDSS user interface was created customizing code and the WMS service. The service WMS was chosen since the published maps are vector and allows the use of SDL standard for improving the end user visualization of the maps. The method for deploying the application was taken from MONDE GEOSPATIAL audiovisual material and the guidelines for this implementation is shortened using the following graphic:

Figure 15: GEOEXT Open Source Customized application. Adapted from: Geospatial (2015c)

Figure 15 summaries the process of customizing the web front end using Legend Three template. A detailed description of the files could be seen in Annex 4. The complete set of customized files for the SDSS should be reviewed at Annex 5. 3.3.4 Testing and validation Testing

Testing this SDSS was done in two phases during developing and at production environment. During the development the queries and the displayed layers were tested. The queries were tested first with the native tool POSTGRES SQL for verifying the fields, operators and language. Tested queries were incorporated to the PHP files and its HTML end user interface. On the other hand, the uploaded layers and the spatial database were tested displaying the layers and its style using the LAYER PREVIEW option at GEOSERVER. The production environment is the final destination for a web application or a website (Microsoft, 2012). The SDSS had been deployed, tested and ready for its final use. The production environment for this application is the same development environment. However, this application with its components could be placed on a cloud infrastructure or inside CONDESANâ&#x20AC;&#x2122;s infrastructure. Validation

This process tested and debugged the built SDSS about the front-end Web GIS user interfaces options, queries and its visual outputs. The front-end Web GIS user interface validation is the proof testing of those elements of a website that the customers sees and interacts with directly. The validations of the elements were done using each options of the created system and analyzing the results. It has two key elements: check list of elements- events and tested user interface (HTML pages and PHP queries, WEBGIS SDSS and changing web themes). The complete checklist and tested interfaces of the system should be seen at Annex 6.

4. Results Several consultancies within the framework of the IVPC project generated documentation and geographic data about Ecuadorian Biodiversity GAP Analysis stored at CONDESAN. This study started collecting this information, organizing these data and researching about this Conservation Biology process. The results of this study about the SDSS as part of a Biodiversity Gap Analysis should be addressed about its theoretical structure, implementation and the actual as well as future uses. This SDSS has the theoretical elements of a DSS and GIS technology. Conceptually, this DSS selected variables about existing conflicts between the anthropic use of natural resources and biodiversity. It faces a complex spatial problem, and it has multiple and conflicting objectives for its solution. It is a semi - structured system that not replace the knowledge of an expert only provides elements that could be used for an analysis. This prototype is a system that collected, selected and summarized the relevant variables that rises the pressure over the natural resources using the main criteria presented on GAP analysis 2013 results. It created static graphics and dynamic maps that display spatial information and tabular reports in a web platform with the purpose of presenting this information to a wider public. For example, local or international actors such as GADS, Ministerio del Medio Ambiente (Ecuadorian Ministry of Environment), NGOs and Universities. This software implementation has its client-server architecture developed and documented using exclusively OPEN SOURCE software. Each software product and version has been classified according of its role on this architecture. The type of software permits the reutilization of functions and developed applications without buying a commercial license for every component of the developed system. However, the understanding and personalization of the application is a time-consuming process. Its use is accurate for small organizations and NGO. On the other hand, the use of commercial products facilitates development since they have a product support structure with updated documentation and direct access with experts. Its database design was built using entity-relationship diagrams known as ERDs for identifying the existing objects and the relationships between them (Figure 10). This

modeling method could be completed at any time of development using expertsâ&#x20AC;&#x2122; feedback. At this moment, the model has only one entity that has one relationship created between HEXAGON GAP ANALYSIS (complete database of GAP Analysis) and NXPROVINCIA (administrative unit province) this relationship produces spatial queries according to the diagram. This entity has in its design foreign keys like DPA_PROVIN (provincial ID) and DPA_DESPRO (Provincial name). Using this schema, the database structure can be updated replacing them with layers new shapefiles of provinces or a new Hexagon Gap Analysis. Also, this relationship could be reproduced using other administrative units following the diagram. In the design, there are several objects that has not been connected with any database relationship. These objects are original vector layers that are used for representing visually the variables of source of anthropic pressure. For example: Location points of mining, oil wells and towns. On the other hand, the variables that are relevant for biodiversity were PANE (National Protected Areas), VEG_REM (Remnant Vegetation), and Biodiversity GAP Analysis solutions SOL_174 and SOL_175. The NXPROVINCIA is repeated in the diagram on purpose illustrating that this information is used also as a context variable on the visualization schema. The implemented SDSS has a robust POSTGRES DBMS capable of managing the database, the numerous structured data, and its operations. It is able to store and manipulates spatial and thematic data types. The predefined queries are only representing the variables totalized and grouped by thematic source of pressure or biodiversity variable. These queries can be customized according statistics analysis or an explicit expert criterion. However, a spatial join of the provinces was added to the gap analysis table instead of using frequent spatial queries for reducing the times of response on the generation of the queries and consequently the generation of reports. These reports are available in two levels regions and provinces the original data contained the regions and the provinces where added through the construction of the database. The customized database enables the future option of getting the data using SQL language instead of multiple crosstabs for generation of reports for sending to local actors, scientific publication or donors.

Its Web GIS interface gives a visual spatial location of the high impact human activities and the necessities of conservation at glance and presents these scientific results in the most didactic way. The implementation of published relevant indicators using queries using HTML language permits an improved communication through a webpage with its audience. The SDSS prototype has two subsystems united in an initial home page: the buttons GAP Analysis Results and Sources of Pressure trigger the subsystem of the global indicators of administrative units composed by predefined queries and the button SDSS executes the second subsystem related to the dynamic map.

Figure 16: Starting SDSS Prototype Web Page

Figure 16 shows the starting SDSS Prototype web page. It has four buttons. The home button presents general information. The GAP Analysis results button triggers the web pages with the specific thematic queries that totalize the number of hexagons per provinces related to several biodiversity variables. The Sources of Pressure button activates the web pages with the static queries related to mining, oil and population. Finally, the SDSS button opens the dynamic map and the geographic layers.

Figure 17: Upper Menu, Gap Analysis Results Side Menu

Figure 17 shows the web page of Gap Analysis Results in which the side menu changes listing the available choices of static queries related to the totalized hexagons per variable: best solution per province, best solution including PANE area per province, best solution per region, best solution including PANE area per region, best solution intersection per province, protected areas per province, and remaining vegetation per province. Each option button presents a static graphic and their totals. Figure 18 presents the web page trigger by the button Hexagons /province. It has a static graphic of the biodiversity gap analysis best solution, a short description of the query, and a summary table of the number of hexagons per province of this scenario. This short report could help the actors to select a province with high number of hexagons as a study area. Annex 6 (Validation process at section HTML pages and PHP queries) presents the complete list of the output screens of every button at the side menu of the Gap Analysis results web page. Figure 19 presents the starting web page of Source of Pressure button. This web page has a static graphic related to the oil activities and its side menu shows three options: mining concessions, oil activities and population.

Upper Side: Static Graphic

Down Side: Description and Query Data

Figure 18: Side Menu: Hexagons / province Best Solution

Figure 19: Upper Menu: Source of Pressure

Upper Side: Static Graphic

Down Side: Description and Query Data

Figure 20: Side Menu Mining Concessions

Figure 21: Upper Menu SDSS

Figure 22: Published layer Best Solution of Gap Analysis 2013

Figure 22 presents the published and displayed best solution gap analysis geographic layer. It presents the relevant group of hexagons distributed at the Ecuadorian Territory. The rest of the layers could be overlay and combined for creating specific analysis depending on the actorâ&#x20AC;&#x2122;s needs. At this point, the system requires an expert or a decision-makers criterion to create a specific analysis. However, the prototype has two systems. They were created to combine the static queries and its reference graphics with the dynamic map. The global idea of this screen structure for the end user is specify a place adding geographic layers that could

complete an idea or interest. The prototype creates scenarios for example: A scientist could be interest in a province with the mayor number of hexagons of the best solution. This actor could use the system choosing the menu GAP Analysis Results and the option Hexagons / province Best Solution (Figure 18). Using this output query and graphic the user could choose at glance the area of interest. The analysis could be the Pastaza Province that has the major number of hexagons of the best solution for the GAP analysis. This result could be analyzed with major detail using the zoom in tool by the expert using the SDSS and selecting the variables best solution and provinces.

Figure 23: SDSS Best Solution vs. Ecuadorian provinces

Figure 23 shows a scenario of combination of two layers and the related static query for its expert analysis related to locations, biodiversity highlights, or another scenario. This prototype requires refinement. Figure 1 described the development process, it shows the testing process comes back to development cycle and system design of the system using the feedback of the stakeholders, developers, IT/GIS specialists for improving the queries, reports and the system itself. The future software implementation of a SDSS for Ecuadorian biodiversity gap analysis should include end users to the testing and validation stages for determining if the system is meeting the requirements. It also has to expand the levels to the administrative units to parishes, cantons and hexagon to increase the awareness of the local actors about the existing sources of pressure over the environment using more granular information.

Its Web interface should have some interactive options for the end-user to enable choosing additional variables and personalized queries. Since the application has its Web structure it should be published and placed on a cloud hosting with its front end and its database. There is commercial hosting with open source structures for example ACUGIS hosting.

5. Discussion A Biodiversity Gap Analysis is a SDSS per se. By definition any SDSS and a Biodiversity Gap Analysis are complex geographic exercises. Both of them confront important scenarios where the decision makers face a complex spatial problem, they often have multiple, conflicting objectives for its solution. Ecuadorian biodiversity gap Analysis executed at 2013 is complex collection of scientific data and geographic layers. Systematizing the complete process takes the collaboration of scientists, institutions and specific software tools. A complete development of a SDSS for GAP analysis requires a detailed documentation of every activity that produces spatial outputs. This research focused on two research questions: What output information of the 2013 GAP Analysis is relevant and can be selected for creating a Spatial Support System for Biodiversity? What elements are considered to structure a web user interface capable of including spatial and thematic information permitting the interpretation of the results of the biodiversity conservation gap analysis? The first question pursued understanding the output information of the biodiversity conservation gap analysis. Its results are essentially geographic scenarios composed of several indicators. These scenarios are capable of defining important locations for biodiversity conservation at Ecuadorian continental territory. The geographic layer considered for structuring the SDSS was the output hexagonal layer. This layer contains an attribute table with all the input variables considered for this specific analysis and the main best results of the simulated scenarios that has high biodiversity locations. Consequently, this single layer is the main input for creating an SDSS. The result itself has multiple variables existing in the table of contents. There are numerous and in some cases are difficult of interpreting because it is expressed using hexagonal polygons and the names of fields are sometimes confusing. In order of presenting this fields, it was necessary adding geographic layers for contextualizing, understanding and visualizing the main variables used for the analysis. These selected layers were points and polygons capable of expressing by itself clearly locations of sources of anthropic pressure corresponding to points of mining concessions, oil wheels, oil spills and polygons of population center. In addition, the polygons layers

corresponding to official government of the natural protected areas and the political division of the Ecuadorian provinces were used for highlighting these places of the Ecuadorian territory. The relevant variables of biodiversity were contained inside of the hexagonal output resulting layer, consequently some specific fields were isolated and exported as single shapefile of each scenario: best solution, best solution including protected areas, and the intersection of best solution and protected areas. The remnant vegetation layer was classified on categories for best presentation and comprehension. These layers are expressed and displayed as hexagons. The second question evaluated the identified elements in order of structure a web user interface. The layers and its information were classified in two main components a dynamic map and reports. The dynamic map collects the vector geographic layers and the reports are composed of a static graphic and a summary table of an individual indicator. The web dynamic map interface displays the layers: protected areas, remnant vegetation, best solution lock in PANE, best solution, oil spills, oil wells, mining concession, population centers and Ecuadorian provinces. The background presents an online OpenStreetMap (Open Street Map, 2020) map centered at Ecuador. The reports are clustered in two button options gap analysis results and source of pressure. Each web button presents fixed queries of explicit variables and a general static graphic. The option gap analysis result has these side menu buttons: Hexagons / province Best Solution, Hexagons / province best Solution including PANE area, Hexagons / region Best Solution, Hexagons /region Best Solution including PANE area, Hexagons / province Solutions Intersection, Hexagons / province Protected Areas and Hectares /province remaining vegetation. They present the total number of hexagons per Ecuadorian province or Ecuadorian region of the solutions, remnant vegetation, and protected areas. The source of pressure has these side menu buttons: mining concessions, oil activities and population. The compounded queries totalize the number of locations in the case of mining and oil. The mining queries presents the number hexagons, total concessions, granted concession, concession in progress, and registered concession measured in

hectares and grouped per province. The oil queries present the number of spills and wells per province. The population queries present hectares of Urban Area and statics about of density, variance and average with 2010 data per province. Each web page of these queries has static graphics presenting the main layer of the corresponding source of anthropic pressure or biodiversity scenario or indicator. This research sought to accomplish two goals. The first goal was generating spatial and thematic queries that allow analyzing the existing geo-database at different political scales and areas of interest. The second was structuring an accurate user interface for this DSS that allows the interpretation of different results of the spatial queries and criteria of biodiversity variables for this particular procedure. The first goal was partially completed. Predefined queries were created using explicitly the resulting scenarios of the biodiversity gap analysis. The queries where created with the purpose of summaries relevant variables of source of pressure and biodiversity at provincial level as areas of interest. However, it was not possible systematize complex queries at database level because a lack of interaction with decision-makers or scientists that could contribute with feedback or detailed criteria for its construction. The second was structuring an accurate user interface for this DSS that allows the interpretation of different results of the spatial queries and criteria of biodiversity variables for this particular procedure. This goal was completed. The web interface was created and it contains customized buttons that triggers the queries using the spatial database and a Web-GIS capable of displaying layers of sources of pressure and biodiversity that can be zoomed and activated at the end user will for its interpretation. This research presented a methodology of creating open source software infrastructure and a spatial database based on an entity-relationship model, capable of solving future complex database relationships and displaying the spatial information. This methodology could be improved creating a detailed diagram about the processes of the selection of priority biodiversity areas and a complete schema of the methodology criteria for prioritization for Ecuadorian territory. This information could be used for a best selection

of the variables and creating complex database relationships and predefined queries that responds to the necessities of scientists and decision-makers. Furthermore, this work accomplished a detailed example of a customized Web GIS frontend using exclusively open source software. This platform displayed and provided the basic operation using specific spatial layers for individual or overlayer analysis. In a global context, this first prototype of an SDSS is capable of creating predefined queries giving some relevant information and displaying selected and classified spatial information. As any prototype needs experts and end usersâ&#x20AC;&#x2122; feedback for identifying, improving and automatizing the required process of the biodiversity gap analysis.

6. Conclusion Biodiversity GAP represents the problem of the absence of conservation at locations that have biodiversity importance. This study was an effort of complementing the GAP analysis in Ecuador managing and publishing relevant data creating a SDSS prototype. Currently, it displays the sources of pressure, context variables and the results of the GAP analysis at once for confronting the major challenge for conservation biology that is halting the ongoing and accelerating decline of biodiversity. Moreover, this structure has the purpose of showing an assessment of the extent to which a protected area system meets protection goals set by a nation or region to represent its biological diversity. The spatial information that contains the results of the biodiversity conservation gap analysis has been identified as the central input for this application. It contains the majority of relevant variables for this academic exercise. This information has been articulated in a derived software application which structure is this prototype SDSS. It has accomplished its goal of generating spatial and thematic queries that allow analyzing the existing geo-database at different political scales and areas of interest. And, its WEB GIS user interface presents an accurate structure that allows the interpretation of different results of the spatial data and criteria of biodiversity variables for this particular procedure. This study has presented the theoretical elements of a SDSS, GIS technology and applied methodology for developing a SDSS prototype for the Ecuadorian biodiversity conservation gap analysis. This prototype of SDSS requires a WEB GIS SDSS module for sharing, publishing and analyzing its results with a wide audience. Its developed visual WEB interface could be transfer to a cloud hosting with this purpose. Implementing this module requires increasing the selection of variables that are capable of presenting specific elements, analysis and reports. The queries and web pages could be reviewed in detail with experts creating scenarios, layer combinations and suggestions about the data requests for a formal feedback and the methodology could be applied again for improving the model. The system could be used periodically with each Ecuadorian gap analysis. However, the development of the SDSS requires detailed and organized documentation of the process and its results from the beginning.

7. Bibliography APACHE. (2015). APACHE HTTP Server project Retrieved 20/08/2015, from http://httpd.apache.org/download.cgi Baquero, F. (2013). Geodatabase final proyecto ivpc-2013. Bareth, G. (2009). GIS-and RS-based spatial decision support: structure of a spatial environmental information system (SEIS). International Journal of Digital Earth, 2(2), 134-154. Bennett, D. A. (1997). A framework for the integration of geographical information systems and modelbase management. International Journal of Geographical Information Science, 11(4), 337-357. doi: 10.1080/136588197242301 Black, S. A., Meredith, H. M. R., & Groombridge, J. J. (2011). Biodiversity conservation: Applying new criteria to assess excellence. Total Quality Management & Business Excellence, 22(11), 1165-1178. doi: 10.1080/14783363.2011.624766 Boroushaki, S., & Malczewski, J. (2010). ParticipatoryGlS: A Web-based Collaborative GIS and Multicriteria Decision Analysis. Urisa Journal, 22(1), 23. Corral, K., Schuff, D., & St. Louis, R. D. (2006). The impact of alternative diagrams on the accuracy of recall: A comparison of star-schema diagrams and entity-relationship diagrams. Decision Support Systems, 42(1), 450-468. doi: http://dx.doi.org/10.1016/j.dss.2005.02.003 Cuesta-Camacho, F., Peralvo, M., Luna, S., & Campos, F. (2007). Análisis de Vacíos y áreas prioritarias para la conservación de la biodiversidad en el Ecuador Continental. Instituto Nazca de Investigaciones Marinas, EcoCiencia, Ministerio del Ambiente, The Nature Conservancy, Conservación Internacional, Proyecto GEF: Ecuador Sistema Nacional de Áras Protegidas, BirdLife International y Aves & Conservación, Quito. Ecuador. Cuesta-Camacho, F., Peralvo, M., Muriel, P., Bustamante, M., & Torres, O. (2013). Identificación de vacíos y prioridades de conservación en el ecuador continental. V 4.0 (pp. 62). Quito: Consorcio para el Desarrollo Sostenible de la Ecoregión Andina (CONDESAN),Pontificia Universidad Católica del Ecuador-Escuela de Ciencias Biológicas,Ministerio del Ambiente del Ecuador- Dirección Nacional de Biodiversidad (DNB), GIZ – Programa GESOREN Cuesta, F., Peralvo, M., Merino-Viteri, A., Bustamante, M., Baquero, F., Freile, J. F., . . . TorresCarvajal, O. (2017). Priority areas for biodiversity conservation in mainland Ecuador. Neotropical Biodiversity, 3(1), 93-106. doi: 10.1080/23766808.2017.1295705

Densham, P. J. (1991). Spatial decision support systems. Geographical information systems: Principles and applications, 1, 403-412. Druzdzel, M. J., & Flynn, R. R. (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent. Marcel Dekker, Inc. Last Login, 10(03), 2010. Dudley, N. (2008). Guidelines for applying protected area management categories: IUCN. ESRI, Environmental Systems Research Institute. (2013). ARGIS: Resources Spatial Join Retrieved 7/9/2013, from http://resources.arcgis.com/EN/HELP/MAIN/10.1/index.html#//00080000000q000000 Farnaghi, M., Mansourian, A., & Toomanian, A. (2009). Integration of rendering technologies and visualization techniques to improve 3D Mobile GIS applications. Geneletti, D. (2004). A GIS-based decision support system to identify nature conservation priorities in an alpine valley. Land Use Policy, 21(2), 149-160. GEOEXT2. (2012a). GeoExt 2 â&#x20AC;&#x201D; JavaScript Toolkit for Rich Web Mapping Applications Retrieved 24/11/2015, from https://geoext.github.io/geoext2/ GEOEXT2. (2012b). GeoExt Legend Tree Retrieved 24/11/2015, from https://geoext.github.io/geoext2/examples/tree/tree-legend.html GEOSERVER. (2015a). WCS reference Introduction Retrieved 25/11/2015, from http://docs.geoserver.org/latest/en/user/services/wcs/reference.html GEOSERVER. (2015b). WFS basics Retrieved 25/11/2015, from http://docs.geoserver.org/stable/en/user/services/wfs/basics.html GEOSERVER. (2015c). WMS reference Introduction Retrieved 25/11/2015, from http://docs.geoserver.org/stable/en/user/services/wms/reference.html GIS STACKEXCHANGE. (2015). Geographic Information Systems. How to connect OpenLayers to PostGIS data? Retrieved 5/6/2015, from http://gis.stackexchange.com/questions/52818/how-to-connect-openlayers-to-postgisdata GITHUB. (2015a). Downloads Retrieved 24/11/2015, from https://github.com/geoext/geoext2/releases/tag/v2.0.3

GITHUB. (2015b). loader.js Retrieved 24/11/2015, from https://geoext.github.io/geoext2/examples/loader.js GITHUB. (2015c). tree-legend.js Retrieved 24/11/2015, from https://geoext.github.io/geoext2/examples/tree/tree-legend.js Gorry, G. A., & Scott Morton, M. S. (1971). A framework for management information systems. Gould, W. A., & Solórzano, M. (2009a). Multiple scale integrated range maps for modeling predicted distributions of vertebrate species in the US Virgin Islands. Gap Analysis Bulletin. 16: 9-20., 16, 9-20. Gould, W. A., & Solórzano, M. (2009b). Multiple scale integrated range maps for modeling predicted distributions of vertebrate species in the US Virgin Islands. GAP analysis bulletin, 9. Hijmans, R. J., & Elith, J. (2014). Species distribution modeling with R. INEC, Instituto Ecuatoriano de Estadísticas y Censos. (2016). Resultados Provinciales. Retrieved 14/06/2016, from http://www.ecuadorencifras.gob.ec/censo-de-poblacion-y-vivienda/ Jacobs, J. (2016). Megadiverse Countries Retrieved 11/11/2016, from http://geography.about.com/od/physicalgeography/a/Megadiverse-Countries.htm JavaScript, S. (2015). The JavaScript Source Retrieved 24/6/2015, from http://www.javascriptsource.com/ Joly, K., & Myers, W. L. (2001). Patterns of mammalian species richness and habitat associations in Pennsylvania. Biological Conservation, 99(2), 253-260. Karnatak, H., Saran, S., Bhatia, K., & Roy, P. S. (2009). Geospatial database organization and spatial decision analysis for biodiversity databases in Web GIS environment. Geocarto International, 25(1), 3-23. doi: 10.1080/10106040802677045 Kashta, L. (2010). Protected Areas Gap Assessment and Marine Protected Ares Development Project (pp. 195): Tirana. Keystone-Center. (1991). Final Consensus Report on the Keystone Policy Dialogue on Biological Diversity on Federal Lands: Keystone Center.

Larrea, C. (2013). Extractivism, economic diversification and prospects for sustainable development in Ecuador. In Latin America and Shifting Sands of Global Power Conference, Australia National University, Canberra (pp. 11-12). Larson, B. D., & Sengupta, R. R. (2004). A spatial decision support system to identify speciesspecific critical habitats based on size and accessibility using US GAP data. Environmental Modelling & Software, 19(1), 7-18. doi: http://dx.doi.org/10.1016/S1364-8152(03)000987 Lonly Planet. (2019). Ecuador: When to go and weather Retrieved 16/12/2019, from https://www.lonelyplanet.com/ecuador/weather Lynch, P. J., & Horton, S. (2009). Web Style Guide, 3rd edition: Basic Design Principles for Creating Web Sites: Yale University Press. MAE, Ministerio del Medio Ambiente. (2013). Mapa de Deforestacion HistĂłrica del Ecuador.Gobierno del Ecuador. Maina, M., Amin, M., & Yazid, M. (2014). Web geographic information system decision support system for irrigation water management: a review. Acta Agriculturae Scandinavica, Section Bâ&#x20AC;&#x201D;Soil & Plant Science(ahead-of-print), 1-11. McFarland, D. S. (2012). Dreamweaver CS6: The Missing Manual (Missing Manuals) Microsoft. (2012). Scenario: Configuring a Production Environment for Web Deployment Retrieved 20/02/2020, from https://docs.microsoft.com/en-us/aspnet/webforms/overview/deployment/configuring-server-environments-for-webdeployment/scenario-configuring-a-staging-environment-for-web-deployment Monde Geospatial. (2015a). WebMapping application using PostGIS, Geoserver and GeoExt - Part 1 Retrieved 23/11/2015, from https://www.youtube.com/watch?v=l5Vez5itais Monde Geospatial. (2015b). WebMapping application using PostGIS, Geoserver and GeoExt - Part 2 Retrieved 23/11/2015, from https://www.youtube.com/watch?v=EVQuNhX6xL8#t=47.84 Monde Geospatial. (2015c). WebMapping application using PostGIS, Geoserver and GeoExt - Part 3 Retrieved 23/11/2015, from https://www.youtube.com/watch?v=86q5sSvkEPg Murgante, B., Borruso, G., & Lapucci, A. (2009). Geocomputation and urban planning Geocomputation and Urban Planning (pp. 1-17): Springer Berlin Heidelberg.

MYSQL. (2015). MY SQL. Fetching Spatial Data Retrieved 19/8/2015, from https://www.mysql.com/ Open Source Geospatial Foundation GEOSERVER. (2015a). GEOSERVER Data Stores Retrieved 17/03/2020, from https://docs.geoserver.org/latest/en/user/data/app-schema/datastores.html Open Source Geospatial Foundation GEOSERVER. (2015b). GEOSERVER Workspace Retrieved 17/03/2020, from https://docs.geoserver.org/latest/en/user/data/webadmin/workspaces.html Open Street Map. (2020). Map: Ecuador Retrieved 19-feb-2020, from https://www.openstreetmap.org/#map=7/-1.783/-78.132 OPENLAYERS2. (2011). Free Maps for the Web Retrieved 24/11/2020, from http://openlayers.org/two/ OPENSOURCE. (2015). The Open Source Definition Retrieved 19/8/2015, from http://opensource.org/ Ozan, E., Kauffmann, P., & Sireli, Y. (2003). How to design Multicriteria Spatial Decision Support Systems. Paper presented at the Proceedings of 2003 National Conference, American Society for Engineering Management. Parrish, J., & Dudley, N. (2011). What does gap analysis mean? A simple framework for assessment. Phillips, S. J., & DudĂk, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175. POSTGRESQL. (2015). Tutorial About Retrieved 13/7/2016, from https://www.postgresql.org/about/ POSTGRESQL. (2019). Solutions for the Enterprise 2NDQUADRANT: PostgreSQL vs MySQL Retrieved 11/12/2019, from https://www.2ndquadrant.com/es/postgresql/postgresql-vsmysql/ Power, D. J. (2002). Decision support systems: concepts and resources for managers: Greenwood Publishing Group.

Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M., & Wilson, K. A. (2007). Conservation planning in a changing world. Trends in Ecology & Evolution, 22(11), 583-592. doi: http://dx.doi.org/10.1016/j.tree.2007.10.001 Ramirez, R. G., Ching, C., & St Louis, R. D. (1993). Independence and mappings in model-based decision support systems. Decision Support Systems, 10(3), 341-358. Salafsky, N., Salzer, D., Stattersfield, A. J., Hilton-Taylor, C., Neugarten, R., Butchart, S. H. M., . . . Wilkie, D. (2008). A Standard Lexicon for Biodiversity Conservation: Unified Classifications of Threats and Actions. Conservation Biology, 22(4), 897-911. doi: 10.1111/j.15231739.2008.00937.x Sarkar, S., Pressey, R. L., Faith, D. P., Margules, C. R., Fuller, T., Stoms, D. M., . . . Andelman, S. (2006). Biodiversity conservation planning tools: Present status and challenges for the future. [Review; Book Chapter]. Annual Review of Environment and Resources, 31, 123159. doi: 10.1146/annurev.energy.31.042606.085844 Soulé, M. E. (1985). What is conservation biology. Bioscience, 35(11). SQLITE. (2015). The SQLite R*Tree Module Retrieved 19/8/2015, from https://www.sqlite.org/ Sugumaran, R., & Degroote, J. (2010). Spatial decision support systems: principles and practices: Crc Press. Sugumaran, V., & Sugumaran, R. (2007). Web-based Spatial Decision Support Systems (WebSDSS): evolution, architecture, examples and challenges. Communications of the Association for Information Systems, 19(1), 40. TECNOPEDIA. (2020a). Entity-Relationship Model (ER Model) Retrieved 16/02/2020, from https://www.techopedia.com/definition/7057/entity-relationship-model-er-model TECNOPEDIA. (2020b). Star Schema Retrieved 16/2/2020, from https://www.techopedia.com/definition/14724/star-schema Terán, M. C., Clark, K., Suárez, C., Campos, F., Denkinger, J., Ruiz, D., & Jiménez, P. (2006). Highpriority Areas Identification and Conservation Gap Analysis of the Marine Biodiversity from Continental Ecuador: Imprenta Flores, Quito, Ecuador. TERRA. (2019). Terra Andina Ecuador: Clima del Ecuador Retrieved 16/12/2019, from http://www.ecuador-viaje.com/clima.html

The PHP Group. (2015). Documentation Retrieved 24/6/2015, from https://www.php.net/docs.php UT, University of Texas at Austin. (2014). Web Design Process Retrieved 07/01/2019, from https://www.utexas.edu/learn/designprocess/index.html Veronica, R. R. (2006). Decision Support Systems: Concepts, Definitions And Typologies. JOURNAL STUDIA UNIVERSITATIS BABES-BOLYAI NEGOTIA. Veronica, R. R. (2007). Decision support systems development. Journal of the Facultyof Economics, 2, 882-885. Vignoli, L., Bombi, P., Bologna, M., Capizzi, D., & Salvi, D. (2013). Landscape of amphibian diversity in Latium Region: peaks, valleys and gaps of conservation priority. Italian Journal of Zoology, 80(4), 586-595. Volk, M., Hirschfeld, J., Schmidt, G., Bohn, C., Dehnhardt, A., Liersch, S., & Lymburner, L. (2007). A SDSS-based Ecological-economic Modelling Approach for Integrated River Basin Management on Different Scale Levels â&#x20AC;&#x201C; The Project FLUMAGIS. Water Resources Management, 21(12), 2049-2061. doi: 10.1007/s11269-007-9158-z Watts, & Possingham. (2013). Marxan.net: Cloud infrastructure for systematic conservation planning Retrieved 15/12/2019, from http://marxan.net Williams, J. (2001). Biodiversity Theme Report. Canberra ACT 2601 Australia: RMIT University.

8. Annexes

Annex 1: Information Sources And Gap Analysis Variables Table A: Ecuadorian Institutions and their provided information Acronym INEC

PRAS

ARCON

SENPLADES

MAE

IGM

SENAGUA

CONELEC

PUCE

CONDESAN

Meaning Instituto Nacional de Estadísticas y Censos http://www.inec.gob.ec/estadisticas/ National Institute of Statistics and Censuses Programa de reparación ambiental http://www.ambiente-pras.gob.ec/ Environmental remediation program Agencia de regulación y control minero http://www.arcom.gob.ec/ Agency of Mining Regulation and Control Secretaría Nacional de Planificación http://www.planificacion.gob.ec/ National Secretary of Planning Ministerio del Ambiente Ecuador http://www.ambiente.gob.ec/ Ministry of Environment Ecuador Instituto Geográfico Militar http://www.igm.gob.ec/work/index.php Military Geographical Institute Secretaría Nacional del Agua http://www.agua.gob.ec/ National Secretary of Water Consejo Nacional de Electricidad http://www.conelec.gob.ec/ National Electricity Council Pontificia Universidad Católica del Ecuador http://www.puce.edu.ec/ Pontifical Catholic University of Ecuador Consorcio para el Desarrollo Sostenible de la Ecoregión Andina – Sede Quito http://www.condesan.org/portal/ Consortium for Sustainable Development of the Andean Eco Region

Information Census and socioeconomic variables.

Type Government

Ecuadorian petroleum activities, locations and environment remediation activities. Ecuadorian mining activities

Government

Ecuadorian government planning information Environmental Ecuadorian Information Ecuadorian geographic and land use Information Ecuadorian water Information

Government

Ecuadorian electricity Information

Government

Ecuadorian Biodiversity databases and species samples Ecuadorian Biodiversity databases and species samples

Private

Government

Private

Table B: International research institutions Acronym NASA

WORLDCLIM

CCAF CGIAR

Meaning National Aeronautics and Space Administration http://www.nasa.gov/ https://climate.nasa.gov/vital-signs/carbondioxide/ WorldClim is a set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer. http://www.worldclim.org/ Climate Change, Agriculture and Food Security http://ccafs.cgiar.org/ Consortium for spatial information http://srtm.csi.cgiar.org/

Information Carbon Dioxide

Free climate data for ecological modeling and GIS Climate Change Scenarios Physiographic Variables

Table C: Total of spatial variables and geographic information Variable Name

Number Year Description

Census and household

Census and household Census and household

1 1

Climate WORLDCLIM 1950 -2000

2001 , 2010 2013 2013

Cantons, Locations, Parishes, Provinces, Blocks, Sectors at Urban Zones, Sectors at Disperse Zones, and Urban Zones

Spatial Information Data Type

Geometry Scale

Datum Projection Zone

Vector Polygon

1:50000 WGS84

UTM

17S

Vector Polygon Vector Polygon

1:50000 WGS84 1:50000 WGS84

UTM UTM

17S 17S

Raster pixel

1 KM WGS84

UTM

17S

Raster pixel

2.5 MIN WGS84

Comparison between land cover of parishes years 2001 and 2010 Crossing features between land cover of parishes and CGA hexagons Annual Temperature 1950-2000 (Mean, Seasonally, Maximum and Minimum), Annual Precipitation (Mean, Seasonaly, Wettest Quarter and Driest Quarter), ombrothermic index, ombrothermic index 2005 monthly, Monthly Precipitation total (years between 1950 and 2000), Average Temperature years 1950 -2000 (Annual and Monthly), Average Temperature years 1950 -2000 (Annual and Monthly), Regional (Annual Precipitation, Total Annual Temperature, Total 2005 Average Temperature) Decimal Degrees 2013 Climate Change Models 2050

Raster pixel

Climate Change Models

2013 Climate Change Models 2050

Raster pixel

Physiographic Variables

2013 Elevation, Slope, Aspect

pixel

Mining Concessions

2013

Polygon

NATION WGS84 UTM AL

Oil Industry

2010

Polygon

NATION WGS84 AL

UTM

17S

Accessibility

2010

pixel

NATION WGS84 AL

UTM

17S

Carbon

2013

pixel

NATION WGS84 AL

UTM

17S

PANE

2012

Polygon

NATION WGS84 AL

UTM

17S

Species Models

2013

pixel

NATION WGS84 AL

UTM

17S

Prioritization

2013

Polygon Hexagon

NATION WGS84 UTM AL

17S

Land Use and Land Cover change

2010

Polygon

NATION WGS84 UTM AL

17S

Land Use and Land Cover change

2013

pixel

NATION WGS84 UTM AL

17S

Other variables

2013

Line

NATION WGS84 AL

UTM

17S

Other variables

2010 , 2012 , 2013

Polygon

NATION WGS84 AL

UTM

17S

Climate WORLDCLIM 1950 -2000 Climate Change Models

Total

264

Raster File Geodat Mining Concessions abase Featur e Class File Geodat Water training, Spills, Seasons, Lighters, swimming pools, Platforms, abase Wells and PRAS Original Files. Featur e Class File Geodat Accessibility (In Hours), Ecosystems, Double Rivers, Categorized abase ways, Urban areas, and Original Files of Accessibility Model. Featur e Class File Geodat abase Biomass above ground Raster Datase t File Geodat PANE, Protecting Forests of Ecuador, Individual and collective abase properties (Socio Bosque). Featur e Class File Biodiversity richness by groups. Potential amphibians, potential Geodat angiosperms, potential birds, potential ferns, potential reptiles, abase remnants amphibians, remnants angiosperms, remnants birds, Raster remnants birds, remnants ferns, and remnants reptiles Datase t File Geodat Coverage Variables and Prioritization of Areas using Hexagons abase Featur e Class File Geodat Coverage of land use and vegetation 1990 and 2008 abase Featur e Class File Bioregions changes 1990-2008, Land use changes 1990-2008, Geodat Combined coverage 1990-2008, Biogeographic sectors, Coverage's abase 1990 and 2008 100 m, Combined coverage 1990-2008 of 100m, and Raster Coverage changes 1990-2008 of 100m Datase t File Geodat Roads, trails and simple rivers abase Featur e Class File Double rivers, urban areas, land use coverage 1990-2000-2008, Geodat Biogeographic sectors, Reforestation areas, Ecosystems of Ecuador, abase Reservoirs of Ecuador, Electrical Generation Projects, OCP, and Featur Heavy Crude Oil Pipeline e Class

GEOGRAP HIC UTM GEOGRAP 2.5 MIN WGS84 HIC 1 KM WGS84 UTM 1 KM

WGS84

NA 17S NA 17S

17S

Annex 2: Layer Publishing Process Geoserver The first step for publiblishing the layers using GEOSERVER is creating the conection between the WORKSPACE and the exiting datasource. In this case, using the option NEW STORE\ NEW DATA SOURCE\ POSTGIS DATABASE\ was used for constructing THIS connection. The workspace parmeters in this case are: the workspace named TESIS, the data source is the name of the POSGRESQL database named SDSS. The conection parameters are the host defined as localhost, the database port of conecition 5432, nome of the database (SDSS), the squema is public, the database username is postgres, the password is Passw0rd, and the namespace is http://localhost:8088/tesis. Then it is necessary to define the services editing the workspace using the Edit Workspace option, the parameters of name (in this case tesis) and the namespace URI (http://localhost:8088/tesis) must be reviewed and the services WCS, WFS and WMS has to be enabled. The next step is adding a new layer from the menu LAYERS\NEW LAYER from tesis:SDSS. Once a layer is added, the layer must be edited from the optio EDIT\LAYER. This menu has four submenus, however to publishing the used option was DATA in which most of the parameters are left with its default values. The parameters that must be modified are the coordinate reference systems using the number 32717 (WGS 84/ UTM Zone 17s). Using these mandatory parameters for this application the parameter BOUNDING BOXES and LAT / LON BOUNDING BOX recalculates its values. After a layer is added this LAYER has to be PREVIEWED using the option DATA\LAYER PREVIEW using the option OPEN LAYERS for displaying a specific layer. If the layer style has to be modified the changes must be done using QGIS connecting the database table with this application.

Annex 3: Structured and Tested Queries At Postgres Mining Parameters per Provinces Global Query /*Comparative Table Mining Parameters per Provinces*/ select count(min_nuc) as "Num Hex", round(sum(min_tot),2) as "Tot Conc/Ha", round(sum(min_oto),2) as "Granted Conc/Ha ", round(sum(min_tra),2) as "In Progress", round(sum(min_ins),2) as "Registered", dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results a CSV file Num Hex 5968 1323 3773 730 2275 3856 4879 894 88 3409 3385 634 4146 1299 1832 1336 1247 2700

Tot Conc/Ha 5535.06 5479.74 51052.54 1495.38 161555.51 2937.21 143862.06 65249.09 571.82 56200.08 23098.82 19633.20 7457.53 24203.61 169175.01 6879.51 160873.09 38202.72

Granted Conc/Ha 0.00 19.00 3733.48 270.99 191.00 0.00 120.00 16.00 0.00 1332.98 0.00 6.00 757.07 503.98 66.99 0.00 3484.92 6251.04

In Progress 177.80 152.83 36352.08 64.49 818.25 656.51 10252.58 3775.51 1.70 3421.40 1427.51 3315.22 2537.23 831.13 32460.47 553.16 801.60 4360.68

Registered 5357.25 5307.92 10966.97 1159.88 160546.27 2280.70 133489.43 61457.59 570.12 51445.72 21671.34 16312.01 4163.28 22868.51 136647.59 6326.33 156586.55 27590.95

596 2113 597 1831 917 855

18205.47 257753.20 635.26 28170.15 31701.58 23214.96

288.00 0.00 0.00 11897.09 2792.90 124.40

671.67 32155.27 65.89 5254.93 9637.96 17597.69

17245.80 225597.91 569.35 11018.18 19270.75 5492.86

Province PASTAZA CHIMBORAZO SUCUMBIOS SANTA ELENA LOJA MANABI MORONA SANTIAGO BOLIVAR ZONA NO DELIMITADA ESMERALDAS GUAYAS CAÑAR ORELLANA COTOPAXI AZUAY LOS RIOS EL ORO NAPO SANTO DOMINGO DE LOS TSACHILAS ZAMORA CHINCHIPE TUNGURAHUA PICHINCHA IMBABURA CARCHI

GAP Solutions Parameter Query /*Total Hexagons solution 174 per Province*/ select sum(solution_1) as "Best Solution/ Hex",dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results on a CSV file Best Solution/ Hex 1524

Province PASTAZA

154 359

CHIMBORAZO SUCUMBIOS

428

SANTA ELENA

397 360 781

LOJA MANABI MORONA SANTIAGO

182

BOLIVAR

0 922

ZONA NO DELIMITADA ESMERALDAS

109 88 628

GUAYAS CAÃ‘AR ORELLANA

422 437 51

COTOPAXI AZUAY LOS RIOS

62 466

EL ORO NAPO

SANTO DOMINGO DE LOS TSACHILAS

293 98 239

ZAMORA CHINCHIPE TUNGURAHUA PICHINCHA

155

IMBABURA

CARCHI

Parameter Query /*Total Hexagons solution 175 per Province*/ select sum(solution_2) as"Best Solution lock in PANE", dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results Best Solution lock in PANE 1292 319 1581 163 501 474 1317 183 4 1213 320 128 1809 482 429 36 123 1485 18 656 263 369 213 60

Province PASTAZA CHIMBORAZO SUCUMBIOS SANTA ELENA LOJA MANABI MORONA SANTIAGO BOLIVAR ZONA NO DELIMITADA ESMERALDAS GUAYAS CAÃ‘AR ORELLANA COTOPAXI AZUAY LOS RIOS EL ORO NAPO SANTO DOMINGO DE LOS TSACHILAS ZAMORA CHINCHIPE TUNGURAHUA PICHINCHA IMBABURA CARCHI

Parameter Query /*Total Hexagons solution 174 per Region*/ select sum(solution_1) as "Best Solution",region as "Region" from ec_hexprov group by region

Output Results Best Solution 3856 2817

Region Sierra Amazonia

1601

Costa

Parameter Query /*Total Hexagons solution 175 per Region*/ select sum(solution_2) as"Best Solution lock in PANE", region as "Region" from ec_hexprov group by region

Output Results Best Solution lock in PANE 6910 4664 1864

Region Sierra Amazonia Costa

Parameter Query /*Total Hexagons Intersection of Solutions 174 y 175 outside PANE*/ select sum(solmar_pri), dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results sum 88 24 0 101 230 78 212 105 0 127 15 11 4 64 158 22 23 1 16 66 0 102 55 0

Parameter Query /*Total Hectares on Protected Areas per Province*/ select round (count(pane),2)as "Pane Ha", dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results Pane Ha 5968.00 1323.00 3773.00 730.00 2275.00 3856.00 4879.00 894.00 88.00 3409.00 3385.00 634.00 4146.00 1299.00 1832.00 1336.00 1247.00 2700.00 596.00 2113.00 597.00 1831.00 917.00 855.00

Parameter Query /*remaining vegetation Ha*/ select round(sum(veg_rem_ha),2) as "Remaining vegetation Ha",dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results Remaining vegetation Ha 2806302.51 302992.65 1477473.21 249421.68 611106.12 636988.05 1954073.16 133692.93 5729.13 872600.04 424727.55 121581.81 1813274.10 260282.16 490139.10 26086.86 172254.60 1155718.53 27559.44 798561.18 180438.03 397400.58 207470.97 202670.91

Oil Activities Global Query /*Comparative Table Oil Activities*/ select round(sum(pras_der),2) as "Oil spills",round(sum(pras_poz),2) as "Oil Wells",dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results on CSV File Oil spills 1.00 0.00 422.00 178.00 0.00 0.00 0.00 0.00 0.00 11.00 17.00 0.00 447.00 0.00 0.00 0.00 0.00 15.00

Oil Wells 44.00 0.00 1022.00 2930.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 1126.00 0.00 0.00 0.00 0.00 75.00

3.00 0.00 0.00 3.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00

Population Global Query /*Population variables Hexagon GAP Analysis INEC/CONDESAN*/ select sum(area_urb)as"Urban Area/ Ha", round(sum(dpob_pri),2) as "Pop Den Hab_km2_2010 >= 81.88 AND Loc_2010 >1",round(sum(vdpob_0110),2) as"Pop Var Pvar_Dpob>= 0.2522 AND Loc_2010 >1",count(phog_pri) as "Avg Peo/ Hou Per_PHog_10>= 4.2 AND Loc_2010 >1",dpa_despro as "Province" from ec_hexprov group by dpa_despro

Output Results on CSV file

Urban Area/ Ha 144780000000000,00 222083000000000,00 1250140000000000,00 23797400000000000,00 23680800000000000,00 6130340000000000,00 6395900000000000,00 351190000000000,00 0.00000000000 10941100000000000,00 192271500000000000,00 5456200000000000,00 7324000000000000,00 11563600000000000,00 3122300000000000,00 1858350000000000,00 3037090000000000,00 131690000000000,00 106544000000000,00 223040000000000,00 1593250000000000,00 16449330000000000,00 2703410000000000,00 9028900000000000,00

Pop Den Hab_km2_2010 >= 81.88 AND Loc_2010 >1 25.00 214.00 68.00 38.00 105.00 882.00 14.00 154.00 0.00 155.00 1085.00 175.00 65.00 317.00 210.00 489.00 249.00 38.00

Pop Var Pvar_Dpob>= 0.2522 AND Loc_2010 >1 275.00 182.00 546.00 171.00 125.00 436.00 590.00 18.00 0.00 984.00 760.00 26.00 361.00 150.00 295.00 345.00 133.00 239.00

175.00 0.00 211.00 368.00 206.00 119.00

198.00 147.00 58.00 497.00 41.00 76.00

Avg Peo/ Hou Per_PHog_10>= 4.2 AND Loc_2010 >1 5968,00 1323,00 3773,00 730,00 2275,00 3856,00 4879,00 894,00 88,00 3409,00 3385,00 634,00 4146,00 1299,00 1832,00 1336,00 1247,00 2700,00 596,00 2113,00 597,00 1831,00 917,00 855,00

Province PASTAZA CHIMBORAZO SUCUMBIOS SANTA ELENA LOJA MANABI MORONA SANTIAGO BOLIVAR ZONA NO DELIMITADA ESMERALDAS GUAYAS CAÃ&#x2018;AR ORELLANA COTOPAXI AZUAY LOS RIOS EL ORO NAPO SANTO DOMINGO DE LOS TSACHILAS ZAMORA CHINCHIPE TUNGURAHUA PICHINCHA IMBABURA CARCHI

Annex 4: Html, Php and Js Code Web Pages and Query Pages Upper Menu: Home

File name: index.html <!doctype html> <html> <head> <meta charset="utf-8"> <title>SDSS GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="sdss.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="about.html">About SDSS GAP Analysis Data</a></li> <li><a href="Contact.html">Contact Information</a></li> </ul> </td> <td> <img src="images/EC_Provincias.jpg" width="692" height="799" usemap="#Map"> </td> </tr> <tr class="logo">  <td colspan="2"> <a href="http://www.condesan.org/" target="_blank"><img src="01 logo condesan horiz.jpg" width="230" height="105"></a>

<a href="http://www.unigis.net/" target="_blank"><img src="../images/UNIGIS.jpg" width="230" height="105"></a> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: About

File name: about.html <!doctype html> <html> <head> <meta charset="utf-8"> <title>About SDSS GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="sdss.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="About_SDSS.html">About SDSS GAP Analysis Data</a></li> <li><a href="Contact.html">Contact Information</a></li> </ul>

</td> <td> <h2>Project Information</h2> <p class="about">The Spatial Decision Support System for Biodiversity conservation in Ecuador using data from the GAP analysis 2013 generates relevant information for decision support and research activities. The creation of this system is important because it implements queries that focus on specific indicators. They identify important biodiversity areas derived from the GAP Analysis solution. It locates the major conflicting sites in which biodiversity conservation and socioeconomic activities are confronted using several existing indicators collected on the geo-databases. Also, the SDSS displays the protected areas and the best solution of the GAP Analysis 2013 threatened by several human activities.</p> <p> </p> <p class="about">Also, this SDSS integrates queries and images that facilitates the decision making process at a finer scale in this case province level. The main advantage of this kind of analysis is that the results can support land use planning processes at national and sub-national levels. This information could be also used by actors such as NGOs, GADS (Gobiernos autรณnomos desentralizados), Ministerio del Medio Ambiente (Ecuadorian Enviroment Ministry), and Universities, that may need to identify specific areas for future biodiversity studies and conservation plans.</p> <p class="about">The selected variables and its sources for building the database were:</p> <img src="images/SDSS Database Definitive.jpg" width ="686" height="437"> </td> </tr> <tr class="logo">  <td colspan="2"> <a href="http://www.condesan.org/" target="_blank"><img src="01 logo condesan horiz.jpg" width="230" height="105"></a> <a href="http://www.unigis.net/" target="_blank"><img src="../images/UNIGIS.jpg" width="230" height="105"></a> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: Contact

File name: contact.html <!doctype html> <html> <head> <meta charset="utf-8"> <title>Contact</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css">

</head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="sdss.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="about.html">About SDSS GAP Analysis Data</a></li> <li><a href="Contact.html">Contact Information</a></li> </ul> </td> <td> <h2>Contact information:</h2> <p>Linda Grijalva</p> <p>linda_grijalva@hotmail.com</p> <p>UNIGIS student</p> <p>Laure Collet</p> <p>laure.collet@team.unigis.net</p> <p>UNIGIS Latin America</p> <p>Manuel Peralvo</p> <p>Coordinador de proyecto Bosques Andinos</p> <p>manuel.peralvo@condesan.org</p> <p>CONDESAN</p> </td> </tr> <tr class="logo">  <td colspan="2"> <a href="http://www.condesan.org/" target="_blank"><img src="01 logo condesan horiz.jpg" width="230" height="105"></a>

Upper Menu: GAP Analysis Results

File: GAP_Results.html <!doctype html> <html> <head> <meta charset="utf-8"> <title>GAP Analysis Results</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li>

Side Menu: Hexagons / province Best Solution

File name: Q2_Hex_S1_PRO.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png">

</td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li> <li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul> </td> <td> <img src="images/Q2_Sol_174_Prov.jpg" width="1076" height="815" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Hexagons/ province</h3> <p>This query represents the optimum GAP solution. This solution illustrates the selection of the location of high importance for biodiversity at continental Ecuador. The output table organizes the Hexagons of this solution per province.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n";

exit; } $q1 = 'select sum(solution_1) as "Best Solution/ Hex",dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: Hexagons / province Best Solution including PANE area

File name: Q2_Hex_S2_PRO.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title>

<p>This query represents the GAP solution all hexagons that are inside of the Protected Area System (PANE) plus the areas that need to be protected outside the PANE. It shows the distribution of these hexagons per province.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select sum(solution_2) as"Best Solution lock in PANE", dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body>

</html>

Side Menu: Hexagons / region Best Solution

File name: Q2_Hex_S1_REG.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li> <li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul>

</td> <td> <img src="images/Q2_Sol_174_Pane_Reg.jpg" width="1076" height="815" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Hexagons/ regions</h3> <p>This query represents the optimum GAP solution. This solution illustrates the selection of the location of high importance for biodiversity at continental Ecuador. The output table organizes the Hexagons of this solution per regions.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select sum(solution_1) as "Best Solution",region as "Region" from ec_hexprov group by region'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td>

Side Menu: Hexagons / region Best Solution including PANE area

File name: Q2_Hex_S2_REG.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li>

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<li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul> </td> <td> <img src="images/Q2_Sol_175_Pane_Reg.jpg" width="1076" height="815" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Hexagons/ regions</h3> <p>This query represents the optimum GAP solution. This solution illustrates the selection of the location of high importance for biodiversity at continental Ecuador. The output table organizes the Hexagons of this solution per regions.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select sum(solution_2) as"Best Solution lock in PANE", region as "Region" from ec_hexprov group by region'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC);

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for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: Hexagons / province Solutions Intersection

File name: Q2_Inter_Sol.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td>

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</tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li> <li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul> </td> <td> <img src="images/Q2_Inter_Sol_Prov.jpg" width="1076" height="815" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Solutions Intersection Hexagons/Provinces</h3> <p>This query represents the Intersection between the best solution and the solution that includes the system of protected areas PANE. These are areas with high biodiversity importance. This information had been classified per provinces and the unit is the hexagon.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select sum(solmar_pri), dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result);

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$col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: Hexagons / province Protected Areas

File name: Q2_PANE_area.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>

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<tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li> <li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul> </td> <td> <img src="images/Q2_PANE_Prov.jpg" width="1076" height="815" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: PANE Hexagon Hectares/Province</h3> <p>This query totalize the hexagons and its area that are included in the system of protected areas (Patrimonio de Ă reas Naturales del Estado PANE) per province.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; }

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$q1 = 'select round (count(pane),2)as "Pane Ha", dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Side Menu: Hectares /province Remaining vegetation

File name: Q2_Veg_rem.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query GAP Analysis</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center">

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<tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q2_Hex_S1_PRO.php">Hexagons / province Best Solution </a></li> <li><a href="Q2_Hex_S2_PRO.php">Hexagons / province Best Solution including PANE area</a></li> <li><a href="Q2_Hex_S1_REG.php">Hexagons / region Best Solution </a></li> <li><a href="Q2_Hex_S2_REG.php">Hexagons / region Best Solution including PANE area</a></li> <li><a href="Q2_Inter_Sol.php">Hexagons / province Solutions Intersection</a></li> <li><a href="Q2_PANE_area.php">Hexagons / province Protected Areas</a></li> <li><a href="Q2_Veg_rem.php">Hectares /province Remaining vegetation</a></li> </ul> </td> <td> <img src="images/Q2_Veg_Rem.jpg" width="1029" height="1024" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Hectares of Remaining Vegetation per Hexagon / Province</h3> <p>These query summaries the hectares of remaining vegetation per Hexagon and province.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr>

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<?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select round(sum(veg_rem_ha),2) as "Remaining vegetation Ha",dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

Upper Menu: Source of Pressure

File name: Pressure.html <!doctype html> <html> <head>

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Side menu Mining Concessions

File name: Q1_Mining.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query Mining</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q1_Mining.php">Mining Concessions </a></li> <li><a href="Q3_Oil_Act.php">Oil Activities</a></li> <li><a href="Q4_Population.php">Population </a></li>

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</ul> </td> <td> <img src="images/Q1_Mining.jpg" width="704" height="779" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Mining</h3> <p>This compound query represents a comparative table of the totalized variables on mining concessions and its different states of registration: registered, granted, and pending of concession. The complementary parameters are the total area measured in hectares per hexagon and the number of concessions. These parameters are listed per province.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select count(min_nuc) as "Num Hex", round(sum(min_tot),2) as "Tot Conc/Ha", round(sum(min_oto),2) as "Granted Conc/Ha ", round(sum(min_tra),2) as "In Progress", round(sum(min_ins),2) as "Registered", dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; }

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Side menu Oil Activities

File name: Q3_Oil_Act.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query Oil Activities</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr> <td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png">

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<ul class="navegacion2"> <li><a href="Q1_Mining.php">Mining Concessions </a></li> <li><a href="Q3_Oil_Act.php">Oil Activities</a></li> <li><a href="Q4_Population.php">Population </a></li> </ul> </td> <td> <img src="images/Q3_Oil.jpg" width="1002" height="1117" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Oil Activities</h3> <p>These query summaries the hexagons related to oil activities which are oil spills and wells listed per provinces.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select round(sum(pras_der),2) as "Oil spills",round(sum(pras_poz),2) as "Oil Wells",dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){ list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n";

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} echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html> Side menu Population File name: Q4_Population.php <!doctype html> <html> <head> <meta charset="utf-8"> <title>Query Population</title> <link href="Estilo_H.css" rel="stylesheet" type="text/css"> </head> <body> <table class="general" align="center"> <tr class="encabezado">  <td> <td colspan="2"> <img class="foto_lateral"src="../images/Encabezado_GAP.fw.png"> </td> </tr>  <tr class="menu_sup"> <td colspan="2"> <ul class="navegacion"> <li><a href="index.html">Home</a></li> <li><a href="GAP_Results.html"> GAP Analysis results</a></li> <li><a href="Pressure.html">Sources of Pressure</a></li> <li><a href="SDSS.html">SDSS</a></li> </ul> </td> </tr>  <tr>

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<td class="c1"> <img class="foto_lateral"src="../images/Menu_Lateral.fw.png"> <ul class="navegacion2"> <li><a href="Q1_Mining.php">Mining Concessions </a></li> <li><a href="Q3_Oil_Act.php">Oil Activities</a></li> <li><a href="Q4_Population.php">Population </a></li> </ul> </td> <td> <img src="images/Q4_Population.jpg" width="1076" height="841" usemap="#Map"> </td> </tr> <tr class="query">  <td> <h3> Query: Oil Activities</h3> <p>These query summaries the hexagons related to oil activities which are oil spills and wells listed per provinces.</p> </td>  <td colspan="2"> <table width=600 border="1"cellpadding="1" cellspacing="1"> <tr> <?php $conn = pg_connect("host=localhost dbname=SDSS user=postgres password=Passw0rd"); if (!$conn) { echo "Error en conexion a base de datos.\n"; exit; } $q1 = 'select sum(area_urb)as"Urban Area/ Ha", round(sum(dpob_pri),2) as "Pop Den Hab_km2_2010 >= 81.88 AND Loc_2010 >1",round(sum(vdpob_0110),2) as"Pop Var Pvar_Dpob>= 0.2522 AND Loc_2010 >1",count(phog_pri) as "Avg Peo/ Hou Per_PHog_10>= 4.2 AND Loc_2010 >1",dpa_despro as "Province" from ec_hexprov group by dpa_despro'; $result = pg_query($conn,$q1); if (!$result) { echo "Error en seleccion de nombre y correo.\n"; exit; } $num = pg_numrows($result); $col_num = pg_numfields($result); for($k=0;$k<$col_num;$k++){ $fieldName = pg_field_name($result, $k); echo"<td>" . $fieldName . "</td>".PHP_EOL; } for ($i=0; $i<$num; $i++) { echo"<tr>"; $registro = pg_fetch_array($result, $i, PGSQL_ASSOC); for ($j=0; $j<$col_num; $j++){

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list($col_name, $col_value) =each($registro); print "\t\t<TD><FONT SIZE=2 FACE='Calabri'>$col_value</FONT></TD>\n"; } echo "</tr>"; } ?> </tr> </table> </td> </tr> <tr class="pie"> <td colspan="2"> <h6>Todos los derechos reservados. UNIGIS 2015. www.unigis.net</h6> </td> </tr> </table> </body> </html>

WEB HTML and GIS GEOEXT JS code Upper Menu: SDSS

File name: sdss.html <!DOCTYPE html> <html> <head> <title>GeoExt Legend Tree</title>  <script type="text/javascript" src="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/includeext.js"></script> <script type="text/javascript" src="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/optionstoolbar.js"></script>  <link rel="stylesheet" type="text/css" href="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/example.css" />  <link rel="stylesheet" type="text/css" href="../shared/example.css" />  <script src="Libs/OpenLayers-2.13.1/OpenLayers.js"></script> <script type="text/javascript" src="loader.js"></script> <script type="text/javascript" src="q1_tree-legend.js"></script> <style type="text/css"> .legend { padding-left: 18px; } .x-tree-elbow, .x-tree-elbow-end { width: 3px !important;

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} .gx-tree-layer-icon { display: none !important; } </style> </head> <body> <div id="desc"> <h1>SDSS Ecuador System</h1> <p>This example shows how to add legends to the layer nodes in a tree.</p> <p>The js is not minified so it is readable. See <a href="q1_tree-legend.js">q1_tree-legend.js</a>.</p> </div> </body> </html>

Geoext and Open Layer modified files

File name: loader.js Ext.Loader.setConfig({ enabled: true, disableCaching: false, paths: { GeoExt: "Libs/geoext2-2.0.3/src/GeoExt", Ext: "https://cdn.sencha.com/ext/gpl/4.2.1/src" } }); File name: q1_tree-legend.js Ext.require([ 'Ext.container.Viewport', 'Ext.layout.container.Border', 'GeoExt.tree.Panel', 'Ext.tree.plugin.TreeViewDragDrop', 'GeoExt.panel.Map', 'GeoExt.tree.OverlayLayerContainer', 'GeoExt.tree.BaseLayerContainer', 'GeoExt.data.LayerTreeModel', 'GeoExt.tree.View', 'GeoExt.container.WmsLegend', 'GeoExt.tree.Column', // We need to require this class, even though it is used by Ext.EventObjectImpl // see: http://www.sencha.com/forum/showthread.php?262124-Missed-(-)-dependency-reference-to-aExt.util.Point-in-Ext.EventObjectImpl 'Ext.util.Point' ]); Ext.application({

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name: 'Tree Legend', launch: function() { var mapPanel = Ext.create('GeoExt.MapPanel', { region: "center", center: [-78.465, -0.175], zoom: 7, layers: [ new OpenLayers.Layer.WMS("OpenStreetMap WMS", "https://ows.terrestris.de/osm/service?", {layers: 'OSM-WMS'}, { attribution: '&copy; terrestris GmbH & Co. KG <br>' + 'Data &copy; OpenStreetMap ' + '<a href="http://www.openstreetmap.org/copyright/en"' + 'target="_blank">contributors<a>', buffer: 0, // exclude this layer from layer container nodes displayInLayerSwitcher: false } ), new OpenLayers.Layer.WMS("Ecuadorian Provinces", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'nxprovincias', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Population Centers", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'location', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Mining Concession", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'mining', format: 'image/png', transparent: true }, { singleTile: true }

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), new OpenLayers.Layer.WMS("Oil Wells", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'oilwell', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Oil Spills", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'oilspill', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Best Solution", "http://localhost:8088/geoserver/tesis/wms?", { layers: 's174_hex', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Best Solution lock in PANE", "http://localhost:8088/geoserver/tesis/wms?", { layers: 's175_hex', format: 'image/png', transparent: true }, { singleTile: true } ), new OpenLayers.Layer.WMS("Remnant Vegetation", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'veg_ha', format: 'image/png', transparent: true },

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{ singleTile: true } ), new OpenLayers.Layer.WMS("Protected areas", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'pane', format: 'image/png', transparent: true }, { singleTile: true } ) ] }); var store = Ext.create('Ext.data.TreeStore', { model: 'GeoExt.data.LayerTreeModel', root: { plugins: [{ ptype: "gx_layercontainer", loader: { createNode: function(attr) { // add a WMS legend to each node created attr.component = { xtype: "gx_wmslegend", layerRecord: mapPanel.layers.getByLayer(attr.layer), showTitle: false, // custom class for css positioning // see tree-legend.html cls: "legend" }; return GeoExt.tree.LayerLoader.prototype.createNode.call(this, attr); } } }] } }); var tree = Ext.create('GeoExt.tree.Panel', { region: "east", title: "Layers", width: 250, autoScroll: true, viewConfig: { plugins: [{ ptype: 'treeviewdragdrop', appendOnly: false }] },

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store: store, rootVisible: false, lines: false }); Ext.create('Ext.Viewport', { layout: "fit", hideBorders: true, items: { layout: "border", items: [ mapPanel, tree, { contentEl: desc, region: "west", width: 250, bodyStyle: {padding: "5px"} } ] } }); } });

CSS Style File name: Estilo_H.css @charset "utf-8"; /* CSS Document */ h1,h2,h6{ font-family:Arial, Helvetica, sans-serif; } .general { width:1010px; } .encabezado { background: #EEEAD6; height:100px; width:1010px; } .c1{ background-color:#EEEAD6; width:260px; } .menu_sup { background-color: #EEEAD6; width:1010px; height:30px; }

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.navegacion{ margin-left:80px; } .navegacion li { display: inline; margin: 12px; background-color: #8AC007; padding: 10px; border-radius: 5px 5px 5px 5px; transition: all 1s; } .navegacion li:hover { display:inline; margin:12px; background-color: #F93; padding:10px; border-radius:5px 5px 5px 5px; transition: all 1s; } .navegacion li a{ color:white; text-decoration:none; font-family:Arial, Helvetica, sans-serif; font-weight:bold; } .c1 ul li{ /*Eliminar vinetas de menu*/list-style:none; text-align:left; color:#033; background-color:#8AC007; /*Creas espacios entre menu items*/ margin:8px; /*Crea separacion dentro de caja*/ padding:5px; width:170px; } .c1 ul li:hover{ /*Eliminar vinetas de menu*/list-style:none; text-align:left; color:#033; background-color: #F93; /*Creas espacios entre menu items*/ margin:8px; /*Crea separacion dentro de caja*/ padding:5px; width:170px; transition: all 1s; } .c1 ul li a { color:white; text-decoration:none;

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font-size:16px; font-family:Arial, Helvetica, sans-serif; font-weight:bold; cursor:pointer; } .foto_lateral{ margin-left:50px; } .logo{ background: #EEEAD6; height:105px; width:1010px; } .query{ background: #EEEAD6; } .about{ background-color: #FFF; font-family:Arial, Helvetica, sans-serif; text-align: left; } .pie{ background-color: #EEEAD6; height:50px; color:green; font-family:Arial, Helvetica, sans-serif; text-align:center; }

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Annex 5: Geoext GEOEXT is the chosen software for this application. On its website the selected style in this case Legend Tree was clicked and displayed. Once the Legend Tree website (GEOEXT2, 2012b) was display a RIGHT CLICK event over the screen was done to seeing the code application. The option VIEW PAGE SOURCE on any WEB BROWSER was used for viewing the code. Once HTML is display it is important to locate the parts of the HTML document. The coding presents several parts that are highlighted over the original GEOEXT LEGEND TREE document that is shown in the following table: <!DOCTYPE html> <html> <head> <title>GeoExt Legend Tree</title>  <script type="text/javascript" src="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/includeext.js"></script> <script type="text/javascript" src="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/optionstoolbar.js"></script>  <link rel="stylesheet" type="text/css" href="https://cdn.sencha.com/ext/gpl/4.2.1/examples/shared/example.css" />  <link rel="stylesheet" type="text/css" href="../shared/example.css" />  <script src="../../website-resources/OpenLayers-2.13.1/OpenLayers.js"></script> <script type="text/javascript" src="../loader.js"></script> <script type="text/javascript" src="tree-legend.js"></script> <style type="text/css"> .legend { padding-left: 18px; } .x-tree-elbow, .x-tree-elbow-end { width: 3px !important; } .gx-tree-layer-icon { display: none !important; } </style> </head> <body> <div id="desc"> <h1>GeoExt Legend Tree</h1> <p>This example shows how to add legends to the layer nodes in a tree.</p> <p>The js is not minified so it is readable. See <a href="tree-legend.js">tree-legend.js</a>.</p> </div> </body> </html>

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Summarizing, the highlighted part of the HTML document had been identified using colors. The parts of this HTML document were: CODE PART

HTML PART Head Head Head

EXTJS LIBRARY CSS STYLE OPEN LAYERS LIBRARY JAVA SCRIPT

Head

HTML DIV

Body

FUNTION

COLORED

Library Style Library

Yellow Turquoise Bright Green Light Gray

Code for loading GEOEXT library HTML DIV which displays a description in the user interface

Dark Yellow

Once the GEOEXT HTML CODE document had been reviewed the next step is copying the code from the LAYER TREE HTML web page and pasting it on a local editor for customizing the application with the selected and stylizing layers of the SDSS system. Using a local editor like NOTEPAD++, the pasted code was saved on a file with an HTML extension. The directory structure and the saved file at this point were: C:\GeoExt Tutorial\SDSS.HTML In order of customizing the HTML code was necessary identified the relative URL for OPEN LAYERS and JAVASCRIPTS. The relative URL lines of HTML code were: <script src="../../website-resources/OpenLayers-2.13.1/OpenLayers.js"></script <script type="text/javascript" src="../loader.js"></script> <script type="text/javascript" src="tree-legend.js"></script>

This libraries were saved locally by downloading the asked version from the original code in this case the OPEN LAYERS 2.13.1 from its web page (OPENLAYERS2, 2011). The OPEN LAYERS libraries were unzipped and moved on a created directory named LIBS directory. The directory structure is: C:\GeoExt Tutorial\LIBS\OPENLAYER-2.13.1\. The HTML code was changed pointing to the created directory the parameter SRC with the local directory structure as it is presented on the following line: <script src="Libs/OpenLayers2.13.1/OpenLayers.js"></script> The JAVA SCRIPTS files used on this web GIS application were downloaded from OPEN LAYERS WEB PAGE source code by clicking each one of them as it is shown in the next graphic:

125

First, clicked the hyperlink loader.js(GITHUB, 2015b), this event opened the original code of this file as it is shown in the next box: Ext.Loader.setConfig({ enabled: true, disableCaching: false, paths: { GeoExt: "../../src/GeoExt", Ext: "https://cdn.sencha.com/ext/gpl/4.2.1/src" } });

These lines of code were pasted at a new document of NOTEPAD ++ and the extension JS of JAVA SCRIPT format. This JAVA SCRIPT required the downloading of the GEOEXT 2.0.3 library from its web page (GITHUB, 2015a). The downloaded files had to be moved to the created directory structure: C:\GeoExt Tutorial\Libs\geoext2-2.0.3 The original file loader.js was modified pointing the SRC parameter to the local directory. Consequently, this file shows this structure: Ext.Loader.setConfig({ enabled: true, disableCaching: false, paths: { GeoExt: "Libs/geoext2-2.0.3/src/GeoExt", Ext: "https://cdn.sencha.com/ext/gpl/4.2.1/src" } });

Second, clicked the hyperlink tree-legend.js from its web page (GITHUB, 2015c)and the contend was copied to a local editor. The result of customizing this JAVA SCRIPT file is the end user that is presented through the HTML. Ext.require([ 'Ext.container.Viewport', 'Ext.layout.container.Border', 'GeoExt.tree.Panel', 'Ext.tree.plugin.TreeViewDragDrop', 'GeoExt.panel.Map', 'GeoExt.tree.OverlayLayerContainer', 'GeoExt.tree.BaseLayerContainer', 'GeoExt.data.LayerTreeModel', 'GeoExt.tree.View', 'GeoExt.container.WmsLegend', 'GeoExt.tree.Column', // We need to require this class, even though it is used by Ext.EventObjectImpl // see: http://www.sencha.com/forum/showthread.php?262124-Missed-(-)-dependency-reference-toa-Ext.util.Point-in-Ext.EventObjectImpl 'Ext.util.Point' ]); Ext.application({ name: 'Tree Legend', launch: function() {

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// see tree-legend.html cls: "legend" }; return GeoExt.tree.LayerLoader.prototype.createNode.call(this, attr); }

}

}] });

}

var tree = Ext.create('GeoExt.tree.Panel', { region: "east", title: "Layers", width: 250, autoScroll: true, viewConfig: { plugins: [{ ptype: 'treeviewdragdrop', appendOnly: false }] }, store: store, rootVisible: false, lines: false });

}

Ext.create('Ext.Viewport', { layout: "fit", hideBorders: true, items: { layout: "border", items: [ mapPanel, tree, { contentEl: desc, region: "west", width: 250, bodyStyle: {padding: "5px"} } ] } });

});

This file has tree parts that had been highlighted. The first part was identifying the EXTJS application that had been yellow highlighted on the table for identifying the beginning of the application. The second important parameter is the CENTER that was green highlighted because it centered and zoomed the map when it is loaded. The third parameter is LAYERS that had turquoise highlighted because it loads the published layers through the WMS service. This last to parameters are used for changing and creating the SDSS geo portal. The CENTER parameter was modified to center on Ecuador using the latitude and longitude coordinates [-78.465, -0.175] and the zoom was downsized to 7 as it is shown in the following box:

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region: "center", center: [-78.465, 0.175], zoom: 7,

The second important parameter used for creating this application was LAYERS in the following box the modification using the local GEOSERVER service at 8088 port and the workspace tesis and the WMS service is presented. Also, the description of the Layer with the name Ecuadorian Provinces was modified for descripting the layer. new OpenLayers.Layer.WMS("Ecuadorian Provinces", "http://localhost:8088/geoserver/tesis/wms?", { layers: 'nxprovincias', format: 'image/png', transparent: true }, { singleTile: true } ),

The tree-legend.js file had been modified with the selected published layers at GEOSERVER that are part of this SDSS. On the other hand, the SDSS.HTML file was modified for invoking the modified tree-legend.js and load.js at the local machine changing the parameters SRC as it is show in the next box: <script type="text/javascript" src="loader.js"></script> <script type="text/javascript" src=" tree-legend.js"></script>

At the same time, at SDSS.HTML the body of the HTML code was modified for identifying the system <body> <div id="desc"> <h1>SDSS Ecuador System</h1> <p>This example shows how to add legends to the layer nodes in a tree.</p> <p>The js is not minified so it is readable. See <a href=" tree-legend.js"> tree-legend.js</a>.</p> </div> </body>

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Annex 6: Validation Process The validation process has two components the checklist of the elements and the tested user interface. The first component completes a matrix with the elements and its events. The second component captures the screens of the executed code. Check List: Elements, Events and Functionality Element HTML Button HTML Button HTML Button HTML Button

Purpose

Name

File Name

Event

Working

Display

Upper Menu

Home

index.html

On click

Yes

Display

Side Menu

About

about.html

On click

Yes

Display

Side Menu

Contact

contact.html

On click

Yes

Display

Upper Menu

GAP_Results.html

On click

Yes

PHP Button

Query

Side Menu

Q2_Hex_S1_PRO.php On click

Yes

PHP Button

Query

Side Menu

Q2_Hex_S2_PRO.php On click

Yes

PHP Button

Query

Side Menu

Q2_Hex_S1_REG.php On click

Yes

PHP Button

Query

Side Menu

Q2_Hex_S2_REG.php On click

Yes

PHP Button

Query

Side Menu

Q2_Inter_Sol.php

On click

Yes

PHP Button

Query

Side Menu

Q2_PANE_area.php

On click

Yes

PHP Button PHP Button PHP Button PHP Button PHP Button HTML Button Checkbox Checkbox

Query Query Query Query Query

Side Menu Upper Menu Side Menu Side Menu Side Menu

Gap Analysis Results Hexagons / province Best Solution Hexagons / province Best Solution including PANE area Hexagons / region Best Solution Hexagons / region Best Solution including PANE area Hexagons / province Solutions Intersection Hexagons / province Protected Areas Hectares /province Remaining vegetation Source of Pressure Mining Concessions Oil Activities Population

Q2_Veg_rem.php Pressure.html Q1_Mining.php Q3_Oil_Act.php Q4_Population.php

On click On click On click On click On click

Yes Yes Yes Yes Yes

Display Display Display

Upper Menu SDSS Published Layer Protected Areas Published Layer Remnant Vegetation Best Solution lock in Published Layer PANE Published Layer Best Solution Published Layer Oil Spills Published Layer Oil Wells Published Layer Mining Concessions Published Layer Population Centers Published Layer Ecuadorian Provinces Displayed Layers Zoom in Displayed Layers Zoom out

sdss.html sdss.html sdss.html

On click On click On click

Yes Yes Yes

sdss.html sdss.html sdss.html sdss.html sdss.html sdss.html sdss.html

On click On click On click On click On click On click On click

Yes Yes Yes Yes Yes Yes Yes

sdss.html

On click

Yes

sdss.html

On click

Yes

Checkbox Checkbox Checkbox Checkbox Checkbox Checkbox Checkbox HTML Button HTML Button

Display Display Display Display Display Display Display Zoom in Zoom out

130

Combo Box

Change Theme Change Theme Change Theme Change Theme

Displayed Layers Displayed Layers Displayed Layers Displayed Layers

Checkbox

Display

Published Layer Protected Areas

sdss.html

Checkbox

Display

Published Layer Remnant Vegetation

sdss.html

Checkbox

Display

Best Solution lock in Published Layer PANE

sdss.html

Checkbox

Display

Published Layer Best Solution

sdss.html

Checkbox

Display

Published Layer Oil Spills

sdss.html

Checkbox

Display

Published Layer Oil Wells

sdss.html

Checkbox

Display

Published Layer Mining Concessions

sdss.html

Checkbox

Display

Published Layer Population Centers

sdss.html

Checkbox

Display

Published Layer Ecuadorian Provinces

sdss.html

Combo Box Combo Box Combo Box

Theme Neptune

sdss.html

On click

Yes

Theme Classic

sdss.html

On click

Yes

Theme Gray

sdss.html

On click

Yes

Theme Accessibility

sdss.html

On click On Drag and drop On Drag and drop On Drag and drop On Drag and drop On Drag and drop On Drag and drop On Drag and drop On Drag and drop On Drag and drop

Yes

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Tested User Interface HTML pages and PHP queries

The following screenshot are the executed code HTML and PHP. There were tested for validation at CHROME and INTERNET EXPLORER BROWSERS. Upper Menu: Home Side Menu: About

Side Menu: Contact

Upper Menu: Gap Analysis Results Side Menu: Hexagons / province Best Solution

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Side Menu: Hexagons / province best Solution including PANE area

Side Menu: Hexagons / region Best Solution

Side Menu: Hexagons / region Best Solution including PANE area

133

Side Menu: Hexagons / province Solutions Intersection

134

Side Menu: Hexagons / province Protected Areas

Side Menu: Hectares /province remaining vegetation

135

Upper Menu: Source of Pressure Side Menu: Mining Concessions Side Menu: Oil Activities

Side Menu: Population

136

WEBGIS SDSS Upper Menu: SDSS Published Layer: Protected Areas

Published Layer: Remnant Vegetation

137

Published Layer: Best Solution lock in PANE

Published Layer: Best Solution Published Layer: Oil Spills

138

Published Layer: Oil Wells

Published Layer: Mining Concessions

Published Layer: Population Centers

139

Published Layer: Ecuadorian Provinces

Displayed Layers: Zoom in

The button ZOOM IN represented by the plus symbol had been used for focus the attention in some particular location of the Ecuadorian map. The following screen shows the layer mining combined with provinces and zoomed in:

Displayed Layers: Zoom out

The button ZOOM OUTrepresented by the minus symbol had been used for focus the attention in some particular location of the Ecuadorian map. The following screen shows the layer oil wells and provinces and the option zoomed out:

Combo Box: Change Theme and Drag and drop options (Check box layers)

The layers can be displayed in four-layer styles: Neptune (Default option), Classic, Gray and accessibility. The layers could be check at the user choice. Also, the layers can be moved by the mouse option drag and drop overlaying them at the user wishes for facilitating the analyzed indicators. These screenshots show several combinations of the themes and drag and drop option.

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Displayed Layers: Theme Neptune

The next graphic shows the layers Ecuadorian provinces that had been dragged and dropped at the first spot of the layer’s display. The oil spills had been placed at second place, the protected areas at third place and finally the best solution at the fourth place. These layers are using the theme Neptune.

Displayed Layers: Theme Classic

The next graphic shows the layers Ecuadorian provinces that had been dragged and dropped at the first spot of the layer’s display and remnant vegetation at the bottom of the selected analysis at the third place. The second layer protected areas had been unchecked. The used theme is classic.

Displayed Layers: Theme Gray

The selected theme is gray. The selected layers are Ecuadorian Provinces and Best Solution lock in PANE that had been placed at third place and fourth place each one for the user’s analysis.

Displayed Layers: Theme Accessibility

The selected theme at the combo box is accessibility. The selected layers are Ecuadorian Provinces and Mining Concessions. They had been placed through drag and drop mouse event at second and third place for this particular user’s analysis.

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Annex 7: Installation Processes DATA BASE: POSTGRESQL 9.4 and POSTGIS 2.1.5

The following text summaries the relevant steps and screenshots of the installation process of POSTGRESQL 9.4 and POSTGIS 2.1.5. It starts with the initial screenshot fo installing POSTGRESQL and click NEXT. Then is necessary the definition of user name and password. This set up uses the Username: postgres and password: Passw0rd. Select the default port is 5432 for the installation of POSTGRESQL database. Continue the installation using the regional configuration leaving the default option. After the definition of these parameters, the installation of the libraries starts. Then is necessary the definition of the connection port with the APACHE SERVER. The default port of connection is 8080. The application verifies the installation details using the loop back IP ADDRESS (127.0.0.1), the port 5432 and its default home directory is C:\Program Files\PostgresSQL\9.4. After the installation of the main database is necessary the installation of POSTGIS. This is done using the set-up package postgis_2_1_pg94.exe. The installation confirms the port connection database (5432) and its password for starting. The environment variable GDAL_DATA is register. It will overwrite the existing settings. Its default value is setting on YES so, the installation continues accepting this option. Also, it is necessary to enable the environment variable POST_GIS_ENABLE_OUTDB_RASTERS with the value 1. Enable this variable and the out db raster requires to choose the option yes. After these updates the installation finishes. WEB PUBLISHER: Installation process of GEOSERVER

The installed version for this deployment of GEOSERVER is 2.7.1.1. The installation program asks for the destination folder, its default is C:\Program Files (x86)\ GeoServer 2.7.1.1. The next screen will ask to choose Start Menu Folder in this case: GeoServer 2.7.1.1. The data directory is created at this path is C:\Program Files (x86)\ GeoServer 2.7.1.1\data_dir. The server administration sets up the credentials for its administrator user. This installation uses the USERNAME: admin and PASSWORD: geoserver. The connection port to the geo-server web server is set up on port 8088. It had been changed from the default port 8080 since APACHE WEB server is using this port. Once the installation is completed is necessary to select the option Finish. The installed version of GEOSERVER had been installed at localhost:8088. WAMP Simulator: BITNAMI and Console integration with POSTGRES

The package has a specific component for PHPPGADMIN that creates the connection with POSTGRES DATABASE. The application requires selecting a directory for its installation. The default directory is C:\Bitnami\wappstack-5.4.39-0. The connection with POSTGRES asks the Database Server port defined as 5432. It ask the credential of the database: Username: postgres and password: Passw0rd. Finally, BITNAMI Console for POSTGESQL can be lounched from the option phpPgAdmin.