Identifying Sustainable Development Areas for Housing Development in Stockholm by Melanie Hierl

Identifying Sustainable Development Areas for Housing Development in Stockholm

Melanie Hierl Maria Henriksen AG2130 â&#x20AC;&#x201C; 10.12.2018

Table of Contents 1. Introduction and Motivation .................................................................................................................1 2. Aim and Objectives ..............................................................................................................................1 3. Area of Study .......................................................................................................................................2 4. Data sources .......................................................................................................................................2 4.1 Data from SLU ..............................................................................................................................2 4.1.1 Description of “Fastighetskartan” ........................................................................................................................... 2 4.1.2 Description of “Översiktskartan” ............................................................................................................................. 2

4.2 Data from Trafikverket ...................................................................................................................3 4.3 Data from SMHI ............................................................................................................................3 5. Method - a literature review on Multi-Criteria Analysis ..........................................................................3 5.1 What is Multi-Criteria Analysis ........................................................................................................3 5.2 The Multi-Criteria Analysis Process.................................................................................................3 5.3 Benefits and challenges with MCA .................................................................................................4 6. The MCA-process in Identifying Sustainable Development Areas for Housing Development in Stockholm ................................................................................................................................................................5 6.1 Data Management and Motivation .................................................................................................6 6.1.1 Climate ........................................................................................................................................................................ 6 6.1.2 Land use..................................................................................................................................................................... 6 6.1.3 Infrastructure ............................................................................................................................................................. 7 6.1.4 Education ................................................................................................................................................................... 7 6.1.5 Leisure ........................................................................................................................................................................ 8 6.1.6 Services ...................................................................................................................................................................... 8

6.2 Structuring Data ............................................................................................................................8 6.3 Criteria and Constraint Matrix.........................................................................................................8 6.3.2 Process of creating constraints layer ..................................................................................................................... 9 6.3.3 Process of creating weighted overlay map ......................................................................................................... 13 6.3.4 Motivation for factors and weights ...................................................................................................................... 13

7. Results...............................................................................................................................................17 8. Reflection on results and method .......................................................................................................19 9. Concluding remarks ...........................................................................................................................20 References .............................................................................................................................................21

1. Introduction and Motivation Urbanization can be traced back to the beginning of industrialization, where large parts of the population living in peripheral areas were moving to cities in order to find employment. Since then, cities have been growing constantly both vertically and horizontally in order to provide housing for the new residents. In Stockholm, the traces of urbanization can be found embodied in specific patterns of built environment, such as the 1920s suburbs, the miljonprogramomrĂĽden (Million Housing Programme Areas) but also the merging of the city of Stockholm and the wider Stockholm region. Today, Stockholm is facing a housing shortage with especially rental and affordable housing missing. Since the demographic forecasts predict an increase in the population of Stockholm, new areas for housing development need to be identified in order to tackle the shortage. Stockholms politicians agreed on a number of 140.000 new housing until the year 2030 (Stockholm stad, 2018). In order to ensure that these developments will be sustainable both in regards of environmental and social sustainability, a multi-criteria analysis will help to identify suitable areas for development. In order to prevent urban sprawl, it is prioritized to densify rather than to expand the city horizontally. This will not only prevent the continuing of sealing of land but also decrease the costs of infrastructure adaptation. With Sweden being centered towards Stockholm, it is quite unlikely that the demand for housing will decrease in the next decades. Even if new telecommunication technologies are changing working patterns and travel patterns accordingly, this does not seem to have a big impact on the building infrastructure of Stockholm yet. In light of this, smart planning solutions for sustainable future development of Stockholm is needed. Furthermore, since Stockholm is located right between the Baltic Sea and the Lake MĂ¤laren, the city and its municipalities will be affected by climate change in form of sea level rise but also changes in the micro-climate. Thus, planning for sustainable future developments needs to contain strategies for dealing with these issues and threats. Our work will not only be of value for Stockholm City and the larger region of Stockholm but also contribute to Swedenâ&#x20AC;&#x2122;s aims in adapting the Sustainable Development Goals. With planning for sustainable cities/communities and climate action and live on land, we try to fulfil goals number 11 and 13.

2. Aim and Objectives The aim is to identify new sustainable areas for housing development in Stockholm in regard of different future changes. Areas complying different factors of environmental and social sustainability will be analyzed and compared in order to suggest the most suitable areas. This means not only to identify vacant land to build on but also to consider climate change and demographic changes and the effects they will have on the development areas.

Research questions

Methodological contribution

What areas in Stockholm are suitable for sustainable future housing development?

How many areas become suitable? Is MCA an appropriate technique? What are the pros and what are the cons?

3. Area of Study As the area of study, the administrative and territorial area of Stockholm Municipality, was chosen due to several reasons. Working within one administrative area makes it easier to find and access data whereas, on the other hand, the combination of data from several different municipalities of the wider Stockholm region can cause distortions. Since Swedish municipalities are autonomous regarding planning decisions, carrying out a comprehensive analysis on the Stockholm region might interfere with municipalities’ own plans.

4. Data sources The data used in this project is available online and provided by Swedish public authorities and universities. Some of the data accessed can be used without any changes except for clipping it to the size of the research area to minimize data pollution. However, some of the data needs to be compiled to make it tailored to the project. One can do so by accessing the shape files and exporting the relevant attributes to a new and individual shapefile. This process is described in chapter 6.1, “Data Management and Motivation”. Following, a general description of the data sources used in this project is presented. 4.1 Data from SLU Most of the data used for the analysis was downloaded from the download service GET provided by the Swedish University SLU. GET is a so-called metadata catalog, and provides data from several different Swedish authorities, such as maps, aerial photographs and satellite remote sensing data from Lantmäteriet, geological data from SGU, and population statistics from SCB. The data we downloaded from GET was vector data. The data apprehended from this service was mainly from Lantmäteriet, and the downloaded data derives from 3 different datasets; Fastighetskartan, Översiktskartan and “Planer, bestämmelser och rättigheter”. 4.1.1 Description of “Fastighetskartan” The data in ”Fastighetskartan” dataset is divided into 5 sub categories; Kommunikationer, Markdata, Bebyggelse, Övrigt, containing vector data. For our analysis, data from all these sub category maps where downloaded. In the sub category Markdata, the shape file“my_get.shp” contains data on property structure and topography. The ones taken for the MCA are building structure and squares, open land, wetland, forest, nature reserves and farm land. From the category Bebyggelsedata and the shape file “by_get.shp”, layers containing information on buildings, education, culture and leisure were extracted. More generally, the data set contains data on the property structure, topography, buildings, communications, land and hydrography. On building scale, the different functions uses of the buildings, such as schools, universities, kindergartens, cultural, entertainment and sport facilities, as well as hospitals and health centers are captured - just to name a few.

Data on nature reserves was taken from the Övrigt category and the shape file “ny_get.shp”. Communication infrastructure such as train, tram and metro lines were taken from the shape files “jl_get.se”, whereas information on train, metro and tram stations was taken from the “js_get.shp” layer within the Kommunikationer sub category. 4.1.2 Description of “Översiktskartan” The information used from this shape file are lakes and sea, contained in the “my_get.shp” shape file. Data on streams was extracted from “hl_get.shp”. Generally, this data set contains data on different types of surfaces and land uses such as territorial and administrative borders, 2

hydrography such as coast line and water surfaces, different types of roads, dense areas, power lines, railways, military areas, nature and cultural reserves, animal protection areas and isolines. 4.2 Data from Trafikverket Lastkajen 5.9 is a system provided by the Swedish transport authority; Trafikverket. Lastkajen 5.9 is a system for data downloading, in which data on Swedish road and railway networks can be apprehended. The shape files VagdataTrafikverketVIS_DKStorstadsvag.shp, VagdataTrafikverketNVDB_DKVagtrafiknat.shp and VagdataTrafikverketNVDB_DKHastighetsgrans.shp have been used to extract data on bus stations, roadnetworks, roads, main roads and speed limits from. Other than that, the data set that has been assembled individually by preference choices, also contains bike lanes, pedestrian paths, lowemission zones. 4.3 Data from SMHI SMHI climate data contains layers of different scenarios of sea level rise, both by year and intensity. Two different time frames, the years 2050 and 2100, are included with two different intensity levels of sea level rise, varying from rcp45 and rcp85. For the MCA, the scenario of rcp85 for the year 2100 was applied, according to the assumed life span of the housing construction of 70+ years. Further detailed description of the data downloaded from this sources follows in chapter 6.

5. Method - a literature review on Multi-Criteria Analysis 5.1 What is Multi-Criteria Analysis In Geographical Information Systems, Multi-criteria analysis (MCA) is an analysis technique which is used as a way of considering several different criteria in decision making. For example, the use of MCA is beneficial in site selection processes where there is usually a need to consider multiple factors such as site location, land use, distance to populated areas, demographics, proximity to transport and road infrastructure and environmentally sensitive areas (GIS-People, 2018). Furthermore, one key feature of Multi-Criteria Analysis is the emphasis on the judgment of the decision makers. For example, objectives and criteria need to be established in the MCA process, as well as an estimation of relative importance weights. Thus, a certain level of subjectivity marks MCA as a method (Department for Communities and Local Government, 2009). Moreover, Multi-Criteria Analysis can facilitate a reflected analysis in decision-making, where there are usually conflicting and different groups involved in the process (San Cristobal, 2012; Chen, 2014). When using MCA as a analytical tool, both geographical data and the preferences of various stakeholders can be assigned quantifiable values for evaluation and further decisions (Malczewski 2004). 5.2 The Multi-Criteria Analysis Process To be able to weigh the different criteria, a performance matrix ought to be created. The performance matrix consists of two main stages; scoring and weighting. The scoring is done by assigning a numerical value to the different options, or criteria, which for example can range from 1-9, or 1-100. Thus, the less preferred option would score lower and the more preferred options would score higher (Department for Communities and Local Government, 2009; San Cristobal, 2012). In the weighting, numerical weights (0-100 %) are assigned to each criterion. 3

Furthermore, a constraint analysis, meaning assigning the values 1 (=OK) and 0 (=Not OK) to criteria that are to be excluded from the final outcome map, such as for example toxic waste facilities close to new housing developments, or water surfaces which cannot be built on. Moreover, San Cristobal (2012) argues that there are two levels in Multi-criteria-decision making processes; the managerial level and the engineering level. On the managerial level, the goals are defined and chooses the final suitable option. On the engineering level, the alternatives are defined, the consequences of each option pointed out, and the multi-criteria ranking of alternatives performed (San Cristobal, 2012). The Department for Communities and Local Government (2009) defines 8 individual steps in the MCA process. The first step is to establish the decision context, meaning to define the aims of the MCA, as well as the decision makers and key stakeholders. The second step is to identify the options or criteria that will be used in the MCA. Further, the third step is to identify the objectives and criteria that reflects the value associated with the consequences of each option. The fourth step is a description of the expected performances of each criterion. Following the fifth step is the weighting, and the sixth step is to combine the weights and scores, in order to evaluate. The last two steps involve examining the results, and conducting a sensitivity analysis of the results, if changes in weights or scores were to be made (The Department for Communities and Local Government (2009). Similarly, San Cristobal (2012) divides the MCA process into steps, although arguing that it consists of five main stages; i) defining the problem, ii) generating alternatives and establishing criteria, iii) criteria selection, iv) criteria weighting, v) evaluation, vi) selecting the appropriate multi-criteria method and finally, vii) ranking the alternatives. 5.3 Benefits and challenges with MCA According to The Department for Communities and Local Government (2009), some of the benefits of the use of MCA is that it is an open and explicit process, and that the choice of objectives and criteria are open to analysis or change if they are deemed inappropriate. Further, they argue that the scores and weights used in MCA are explicit and developed in accordance with established techniques, which is beneficial for the outcome. Too, according to The Department for Communities and Local Government (2009), MCA as both a process and outcome can provide important means of communication, both within the decision-making body, and between the decision-making body and the wider community. Furthermore, several scholars argue that one of the main advantages of MCA is that it allows the alternative solutions being considered to be ranked in order of suitability (The Department for Communities and Local Government, 2009; San Cristobal, 2012; GIS People, 2018; Malczewski, 2004; Chen, 2014). Nonetheless, there are some challenges with the use of Multi-Criteria Analysis in decision making. First, one limitation of the analysis tool is that is fails to show that one option adds more to welfare than it detracts. For example, The Department for Communities and Local Government (2009) notes that unlike Cost-Benefit Analysis, there is no explicit motivation or requirement for a Pareto Improvement rule that benefits should exceed costs. Thus, in MCA, the â&#x20AC;&#x2DC;bestâ&#x20AC;&#x2122; option can be inconsistent with improving welfare, so doing nothing could in principle be preferable (The Department for Communities and Local Government (2009). Furthermore, as previously mentioned, a certain degree of subjectivity marks the MCA process, according to The Department for Communities and Local Government (2009), this is a matter of concern.

6. The MCA-process in Identifying Sustainable Development Areas for Housing Development in Stockholm With in the frame of this project, the research question posed was â&#x20AC;&#x153;What areas in Stockholm are suitable for sustainable future housing developmentâ&#x20AC;?? To answer this question, a MultiCriteria Analysis was performed in ArcGIS. In this chapter, a detailed step-by-step description on how the data was prepared for the MCA, as well as how the MCA principles and tools were used to create a final suitability map for Stockholm Municipality. The following flowchart illustrate this process, see figure 1.1.

Figure 1.1. Flowchart illustrating data management and MCA process in ArcGIS

6.1 Data Management and Motivation First, all the data available were clipped to the shape of Stockholm municipality by using the spatial analysis tool ‘clip’. The shape of Stockholm municipality was found by selecting and exporting “Stockholm Municipality” from Översiktskartan provided by the GET service. By using this tool, all features of a shape file are clipped to the shape of a specific polygon that functions as a blue print. A new shapefile is the outcome. In order to work concisely with the data accessed and tailored, a geodatabase will be created with feature classes and shapefiles that have been specifically preprocessed to match the research area of Stockholm municipality. An explanation of the data located in the feature classes follows. 6.1.1 Climate Factors for planning for climate adaptation and climate change are the sea level rise of Lake Mälaren and sea level rise for the Baltic Sea. Since Stockholm is located on the estuary between the two semi-enclosed seas, any change in the level of the sea affects the coast line. Thus, the data provided by SMHI on scenario data was applied to our analysis. The data regards scenarios for future sea-level rise for different RCP scenarios. RCP scenarios means scenarios for how greenhouse-gases will amplify in the future (SMHI, 2018). For this analysis, RCP 8,5 was selected. This is a “worst case scenario”, and practically means a continued increase of high level of greenhouse-gas emissions. Furthermore, the se level rise data was selected for a long-time perspective, meaning the projected levels with RCP 8,5 in year 2100. Thus, the coastline used in our analysis is marked by this scenario.

Precipitation and temperature data have not been regarded since they were found to be not particularly relevant for decisions on development areas. However, their values are included in the data for future sea level rise. 6.1.2 Land use By attaining an overview of the land use and displaying different attributes of the Markdatalayer (my_get.shp), it became clear that military areas are not lying within the area of Stockholm Municipality and can therefore be ignored. Different water surfaces such as streams, lakes and the sea are important recreational factors (swimming, owning a boat) and very characteristic for Stockholm. The data for lakes and seas were extracted from the layer file my_get.shp from the Översiktskarta. Data about stream were exported from hl_get.shp, in the same data set. Furthermore, there are different types of land and uses of land that are either suitable or unsuitable for building. As we wanted one shapefile for water surfaces, the individual polygons “sea”, “lake” and “stream” were merged using the tool “merge” in ArcGIS. The output shapefile was then named “MWater”.

Areas classified as open land (extracted from my_get.shp) are suitable for new housing development, whilst the ground on wetlands (extracted from my_get.shp) is not stable enough to carry heavier constructions. In order to avoid forest clearance due to ecological, climate and recreational reasons, forest areas (extracted from my_get.shp) are not to be built on. However, a closer proximity to forestral areas is increasing the suitability. Farming land closer to urban areas partly ensures food supply of the urban population and decreases transportation distances and CO2 pollution. Therefore, farming land (extracted from my_get.shp) needs to be remained and will not be built on. Nature reserves function as green lungs, filtering and binding CO2. Close to urban areas, nature reserves do not only have a ecological and health function but also a recreational function for the residents. Nature reserves, like farming land, need to be preserved but the closer the proximity to the development area, the higher the recreational value. 6

Sustainability, as mentioned before, is directly connected to the built environment, the density of a city, corresponding travel distances and travel modes. Closer proximity to already existing urban areas (densifying, fill-ins) and infrastructure are therefore weighed higher as dispersed housing and remote areas. Data on the building structure (extracted from my_get.shp) and the buildings (by_get.shp) provides information on existing housing areas and possible areas for densification and infills. In order to be able to distinguish open land from squares, a squares layer (extracted from my_get.shp) was created to exclude these polygons from the analysis. In order not to build on any excluded areas, such as biotops, animal protection area, preliminary injunction areas, areas of characteristic landscape protection, natural heritage protection, nature reserve, culture reserve, notable building (baukultur), where there is a construction prohibition, excavation prohibition, or railway construction plans, groundwater protection, surface water protection, road construction plans or extended prohibition for new development, another land use layer called excluded areas is included in the analysis. It contains both the protection of existing valuable land regarding both culture and nature as well as plans for development of different kinds (roads, railways, housing). In order not to interfere with existing but not yet applied plans and protected areas, the excluded areas layer will make sure such areas are a constraint for future housing development. However, areas that are of certain national interest (riksintressen) could unfortunately not be found. 6.1.3 Infrastructure Data on infrastructure encompasses different modes of traveling ranging from road networks, to public transport networks of different kinds to pedestrian networks. This feature class includes variables such as pedestrian streets, bike lanes, streets and motorways, bus stops and bus routes, metro and tram stations and the metro and tram lines as well as commuter train stops and the commuter train lines within the Stockholm region.

The road network was downloaded from NVDB (Nationell Vägdatabas). Furthermore, a “speed limit” layer was added to the gdb. Within the speed limit layer, we exported a new shapefile; 70+ km/h, to include in the constraint. For this layer, we then created a buffer of 100 meters to create a buffer zone for noise pollution. Other transportation infrastructures such as trainlines, metro lines and tramlines and stations (extracted from ly_get.shp), as well as bus stations, are also important factors when deciding suitability for new housing development with a sustainability aim. Both in terms of noise pollution from operation (social sustainability) and proximity to public transport stations to limit need for cars (ecological sustainability). Thus, buffers of 0-300 meters for train and tramlines, and 0-100 meters for metro lines were created for limiting noise pollution affecting personal health. 6.1.4 Education As proximity to education is important factor for social sustainability in new housing developments, all forms for education was selected from the building layer. First, we selected “societal function” = schools to extract all the schools, and cut it after “Stockholm”. Then, in the attribute table, we selected all universities and högskolor to create a separate layer. Finally, we had two different layers for educations; all schools and higher education. However, it is important to note that we did not look into the details of each school, as it would have taken too much time to research all 2500 schools, and thus only going by if the name included was “skola” can lead to unintentionally including for example institutions that are not elementary, middle, or highschools. Same goes for the universities, there is a risk of unintentionally missing out some smaller universities that it is not clear from the name that they are universities. 7

6.1.5 Leisure For recreational value, relative proximity to entertainment arenas and cultural facilities are seen as important considerations in a suitability analysis. Thus, similar to the process for education, entertainment arenas such as “Tele2 Arena”, “Globen” (extracted from by_get.shp) etc. was manually selected and then exported to a separate layer. Similarly, the cultural facilities were selected based on the classification in the field “societal function”, and then manually evaluated before being exported to a separate layer.

Furthermore, other aspects of the built environment that are perceived to have a recreational value in terms of sports was selected. The various sport facilities of Stockholm, such as swimming pools, indoor ice rinks and horseback riding facilities, are displayed as different attributes of the layer by_get.shp and was thus extracted. 6.1.6 Services We argue that there are two service functions that are vital for new housing developments to have proximity to; hospital and health centers. Hence, two layers were created from data exported from the building layer (by_get.shp) using the same procedure as in the creation of shapefiles for leisure and schools.

6.2 Structuring Data After selecting and managing our datasets, a Geodatabase (gdb) was constructed in ArcCatalog, which was named “Suitable Development Areas”. Within the database, we create 6 new feature datasets using the SWREF99TM coordinate system. These are; Climate, Leisure, Education, Infrastructure, Land Use, and Services. Then, these datasets are filled with different feature classes with the tool “Feature Class to Feature Class”. The reason why we constructed a Geodatabase was due to its positive implications on the structure and performance of data, as well as data management. According to Childs (2009, p. 12), there are 9 reasons for why building up a Geodatabase is important; 1. 2. 3. 4. 5. 6. 7. 8. 9.

Improved versatility and usability Optimized performance Few size limitations Easy data migration Improved editing model Storing rasters in the geodatabase Customizable storage configuration Allows updates to spatial indexes Allows the use of data compression

6.3 Criteria and Constraint Matrix After all the data was structured, the next step was to create a matrix with factors for the criteria and constraints that would be used in the analysis. These factors are used as constraints and factors respectively in the multi-criteria analysis to identify very distinct no-development areas and to gradually interpret other areas that might be suitable. The matrixes for the constraints and criteria respectively was created in excel (see table 1.1 and 1.2). First, the 17 constraints and 17 factors which can be seen in the same table were identified and defined. Following, the steps that were taken in ArcGIS are presented.

6.3.1 Buffers First, to prepare for the constraint analysis, we used the analysis tool “Buffer” (dissolve: none) to create buffers of 100 meters around the shapefiles; lakes, sea, streams. The reason this was done was to account for the beach protection law that is stated in the Swedish Building Act. Second, using the same tool (dissolve: all), we created 300-meter buffers around train lines and tram lines, to minimize noise pollution. Around the metro line we created a 100-meter buffer, as the noise pollution is assumed to be less due to the underground operation of the metro. However, vibration might still occur why a protection area from 100 meters is thought to be suitable. Following this, we created a buffer of 100 meter for the roads with a speed limit of more than 70 km/h, to limit housing areas being affected by noise pollution. The buffer layers created this way are in a later part of the process converted to raster files to be used for the constraint analysis.

Furthermore, in preparation of the weighted analysis, we created three buffers by using the analysis tool “multiple ring buffer” (dissolve: none) of 100-300m, 301-700m and 701-1300m around the shapefiles Lakes, Streams and Sea. We do the same for Forest, where we have 0300, 301-700m and 701-1300m as our buffers. Further, using the same tool, we created buffers of 100-300m, 301-700, and 700-1300m for the shapefile Coast Line. Then, we created buffers for the layer All Schools and Universities of 0-1000m, 1001-2000m and 2001-3000m. For Kindergartens, we created buffers of 0-500m, 501-800m, and 801-1300m because we wanted walking distance to these facilities. Later on, the kindergartens layer was not included due to inadequate data. For the shapefiles Culture Facilities and Entertainment Arenas and Sport Facilities, we created buffers of 0-2000m, 2001-3000m and 3001-5000m. For the shapefile Hospital we created buffers of 0-1000m, 1001-2000m and 2001-5000m. For the shapefile Health Centre the buffers were set to 0-500m, 501-800m and 801-1300m, as we similarly wanted distances that could be covered by foot. Lastly, for the shapefiles Tram Stations and Train Stations we created buffers of 0-1000m, 1001-2000m and 2001-3000m. For the shapefile Metro Stations and Bus Stops we created buffers of 0-500m, 501-800m and 801-1300 m, as we argue it is more important to have walking distance to this type of transport modes. When all buffers are created for our constraint analysis and the weighted analysis, we used the tool “clip” to clip the buffers to our study area Stockholm. However, later on in the process of creating constraints layers, we realized that the multiple ring buffers could not be used. Therefore, the same original layers were transformed into raster files to which buffers were added using the spatial analyst tool “Distance”. The following section explains this step even further. 6.3.2 Process of creating constraints layer First, all shapefiles needed to be converted to raster-files. This was done through the use of the tool “Polygon to Raster” and “Polyline to Raster”. This tool uses the cell center to establish the value of a raster pixel. The “feature to raster tool” converts lines, points or polygon features to raster data, while the previously mentioned tools provide more control of the feature; converting only polygons or polylines to raster data. Except for the shapefiles Road network, Roads and MainRoads which are polylines, the shapefiles were polygons, since we for the other lines (speed limit, tram line, metro line and train line) had created single buffers for noise pollution and thus were polygons.

Following the conversion from polygons and polylines to raster, the output datafiles where reclassified using the “reclassify tool”, where the field value was set to 0 and 1. As a result of these, the values were set to 0 for all our constraints (see table 1.1), and 1 was set for open land. Since there must not be “NoDAta” in the layers, the opponent values were classified 1 and 0 respectively. First, we did not think about setting the value of the features to either 1 or 0 and the remaining data to 0 or 1 respectively instead of NoData. Furthermore, some layers that were specified on small areas, e.g. the sea layer displaying the polygon for the Baltic Sea on the eastern part of Stockholm, did not span the whole Stockholm layer. As a result, the whole step of reclassifying the raster layers had to be redone: this time valuing NoData as either 1 or 0, depending on the layer and setting the processing extent as the Stockholm layer. All constraint layers are then joined to one constraints layer by multiplying them using the raster calculator. In this process, raster pixels overlapping each other are being multiplied, meaning values of 1*1=1, 0*1=0 and 1*0=0 are created in the outcome layer (see figure 1.2).

Table 1.1. Constraint Matrix

Figure 1.2: Constraints map showing areas for building and areas excluded for housing development.

6.3.3 Process of creating weighted overlay map As in the process of converting polygons and points to raster, the tools “polygon to raster” and “point to raster” were used to transform vector into raster data. The layers containing polygones used for the suitability map are the BuildingStrcuture of housing areas, Forest, a merged layer of different Water surfaces (lakes, streams, sea), AllSchools, Universities, CultureFacilities, EntertainmentArenas, SportFacilities, Hospital, HealthCentre. For the layers containing point features, TrainStations, MetroStations, TramStations and BusStations were converted to raster layers.

Next, the different Euclidian distances to these features were created to enable a factor weighing in the following step. In contrast to the assumption of using multiple ring buffers which have been created in the very beginning, these multiple ring buffers cannot be displayed in raster layers. Therefore, the spatial analyst tool “Distance” has been applied, using maximum distances ranging between 1300, 3000 and 5000 meters. According to the following table, the distances have been created. Using the reclassify tool, the distances have been assigned a specific value according to the factors 1-9 (see table 1.2). In other step, the output layers have been overlayed in a weighted overlay, assigning not only different factors (1-9) according to the matrix, but also different percentages as weights (1100%). The outcome was a suitability map, displaying different areas of Stockholm as least to very suitable for new housing development (see figure 1.3). 6.3.4 Motivation for factors and weights Both the matrix and its outcome as a suitability map, are grounded on values of how the factors are valued and weighted (see table 1.2 and figure 1.3). For the factors ranging from 1, not data, to 3, least suitable, to 9, most suitable, considerations such as common, intersubjective values e.g. close proximity to forestal areas and water accounting for recreational value have been taken into account. A closer proximity to existing building structure and infrastructure have been considered being more sustainable. Therefore, the closer the distance to these factors, the higher the value (9) and equally, the farer the distance, the lower the value (3). Distances of 0500m from already existing building structure were valued highest with 9 points, 501-800m accounted for 6 points and 501-1300m for 3 points. The closest proximity category for forest areas, 0-300m was assigned 9 points, 301-700m distance were assigned 6 points and 7011300m 3 points. For all types of water (lakes, sea, streams), a strandskydd (beach protection) of 100m was considered. Therefore, the closest proximity to water one could possibly built on is 100-300m, equaling 9 points. 301-700m distance makes up for 6 points, whereas 3 points are assigned to areas with a distance of 701-1300m to water surfaces.

To build as sustainable as possible, sustainable commuting options such as public transport should be in close proximity to any new housing development. Both bus stations and metro stations were thought to be the most frequently used public transportation modes which is why a proximity of 0-500m was assigned 9 points, 501-800m equals 6 points while 801- 1300m got 3 points. Regarding train and tram as sustainable transportation modes, their frequency of use was assumed to be less as well as their degree of capacity utilization. Therefore, a distance of 0-1000m to train and tram stations was assigned 9 points, while 1001-2000m got 6 points and 2001-3000m equals 3 points. 13

Proximity to education and health service were thought to be an important soft location factor. While it was assumed that pupils and students enrolled at high schools (gymnasium) and universities would commute longer distances own their own in contrast to e.g. parents which drop of their children at kindergartens or elementry schools, distances of 0-1000m were assigned 9 points, 1001-2000m 6 points and 2001-3000m equal 3 points. Traveling to a hospital was assumed not to happen as frequent compared to health centres (vĂĽrdcentral) which cover the primary health care. Therefore, it was decided that the distances to health centres needs to be shorter than to hospitals: 0-500m/ 0-1000m assign for 9 points, 501-800m/ 1001-2000m assign for 6 points and 801-1300m/ 2001-5000m equal 3 points. Leisure factors, as well, have been ranked regarding their frequency of use and their distance. It can be supposed that citizens travel greater distances to leisure facilities compared to education and health services, which is why the proximity of 0-2000m to culture facilities, entertainment arenas and sport facilities has been assigned 9 points, 2001-3000m equal 6 points and 3001-5000m were assigned 3 points. For all factors, No Data has been assigned 1 point. In order to distinguish even further between the distance each factor has been given, a percentage enabling a weighing between different factors according to their importance has been applied. Similarly to assigning points to factors, the same assumptions and values for close proximity to existing built structure and infrastructure being considered more sustainable have been applied by assigning 10% of the total weight to built structure (housing areas), train stations, metro stations and bus stations. Proximity to recreational values such as water and forest were assigned 5%. Recreation and closeness to nature is found to be increasing in its importance for mental health in the future. Equal importance, and therefore percentage, have sport facilities, culture facilities and sport arenas which also function as factors of leisure. Closer proximity to all schools has been found to be more important (10%) than to universities (5%). This is because general education is more important, and more children need to have access to than specialized education such as academic education. Similarly, closer proximity to health centers is of greater importance as these are frequented more.

Table 1.2 Weighted Overlay Matrix

Figure 1.3: Weighted overlay map showing areas suitable for housing development according to the matrix.

To combine this suitability map with the constraint map displaying areas that were assigned for either building (1) or no development (0), the raster calculator has been used to multiply the two named layers while at the same time calculating in the assigned weight for each layer (0-100%). The result of this step is shown in the following chapter (see figure 1.4.)

7. Results With this location analysis the most suitable areas for housing development in Stockholm Municipality being both environmentally resilient, and social sustainable are identified. For this, the before created maps of suitable areas and excluded areas are combined in an weighted overly where specific factors are given different weighing. Figure 1.3 is showing the suitable areas for new housing development based on an index displaying the values of 0-9 in different shades of green, whereas the most suitable areas do have the most dark green colour. In order to simplify the visual communication of the map, categories of 0, 0,01-5, 5,01-6 and 6,01-9 are displayed. In order to make orientation around the research area easier, Stockholms building structure and communications, such as train, metro and tram lines as well as metro stations are visualised as well. Furthermore, streams, lakes (including Lake Mälaren) and the Baltic Sea are included in the map to visualise the regional context. Having a closer look at each separate value of the suitability map, the only two patches of land receiving a 9 were a small park in front of a governmental building at Kungsholmen, close to Stadshuset, and Norra Bantorget, a park at the end of Vasagatan. Furthermore, some smaller areas in the northeastern part of Stockholm, having a suitability of 8, can be identified as existing urban parks as well. This was found by evaluating the spaces receiving a value of 9, based on our own knowledge from visiting these areas, as well as analyzing the environments using the “street view” function in Google Maps. Inner city parks function as common good that not only have a recreational and health value for residents, but also an ecological value. Managing stormwater, these unsealed grounds are important infiltration sources for inner cities. Therefore, these parks will be preserved in their function. Not being able to designate areas of value 9 for new housing development, the next values of suitability (8 and 7) are chosen to be suitable after closely inspecting these areas. Bigger vacant lots such as Gärdet in the north, Järvafältet in the northwest of Stockholm and Årstafältet in the south of Stockholm have been identified as suitable. Other lots receiving lower values were excluded from our final result as they were deemed too small for any valuable contribution to the housing shortage. On the two latter plots of considerable size which have a lower value according to our analysis, Stockholm Municipality is currently constructing new housing developments. Gärdet, however, which got a higher value (8) according to our MCA is not designated for new housing development yet.

Figure 1.3 Suitable areas for housing development (raster calculation from constraints and suitability map).

8. Reflection on results and method The conducted Multi-Criteria-Analysis (MCA) contains a great, thus not exhausted, variety of soft and hard location factors, such as infrastructure, land use, culture and education. Regarding distances to these factors, a matrix of suitability helps to identify areas that are more suitable for housing development while at the same time excluding previously defined constraints. Both constraints and factors ground on a subjective matrix conducted by the authors. This makes the result of this MCA uncertain to the extent that certain values can vary according to personal preferences. Also, the matrix does not base on research outcomes nor on objective parameters. However, the authors matrix aims at being intersubjective, taking cultural preferences into consideration. Such preferences are close proximity to water surfaces and forest areas, having a high recreational value for Stockholmers. Additionally, close proximity to infrastructure such as metro, bus and tram stations have been considered, as well as varying distances to amenities regarding their frequency of use. Negative factors and legal regulations such as noise pollution levels, beach protection (strandskydd) and future sea level rise have been taken into consideration by excluding risk areas. Nonetheless, the suggest suitable areas for future sustainable housing development are based on the authors values and limited by the decision on which land use to be designated for building. By excluding forest areas from the suitability analysis due to environmental and ecological reasons, the amount of resulting areas is limited to those of open land. By decreasing the level of strictness and enabling construction on forest areas, more potential areas could have been identified. Furthermore, old industrial and harbor areas have not been considered for future development due to a lack of data. However, it is worth to include industrial areas in order to prevent additional sealing of new, untouched ground in order to leave areas for infiltration of storm water. As noted by The Department for Communities and Local Government (2009), the subjectivity of authors is a general concern with Multi-Criteria Analysis, and can also be seen in the analysis conducted by the authors. Furthermore, the Multi-Criteria Analysis performed within the scope of this project is concentrated to the municipal boarders of Stockholm. Thus, the results are also impacted by this geographical delimitation. Whereas the constraints are municipally bound, e.g if a constraint factor is in an area just outside the focus area it will not impact the result of an MCA, this is not the case for the criteria factors. Thus, as several of the criteria selected in this analysis do transcend municipal boarders, neighborhood effects are likely to occur. By this, we mean that if we for example were to include all entertainment arenas (which is one of our criteria) that are also located in neighboring municipalities such as Huddinge, Danderyd, Solna etc in the MCA, the weighted overlay result map could differ from the result that we got looking at only the facilities within Stockholm Municipality. However, some criteria do not transcend municipal boards (such as elementary schools or health centers), as your home municipality is linked to which school and health center you belong to. Nonetheless, it is important to note this aspect in the discussion of the areas that are deemed suitable using an MCA, and highlights the need to extensively think through the criteras chosen, and the need to evaluate and reconsider the results, and not seeing the output map of an MCA as a â&#x20AC;&#x153;perfect solutionâ&#x20AC;?. As mentioned in the results discussion, three main areas were identified as being suitable development areas by the authors, based on the result of the Multi-Criteria Analysis, as well as the subsequent analysis of the result. This process highlights the benefits, as well as the 19

challenges of using Multi-Criteria Analysis in decision making processes. Although providing valuable options for the decisionmakers, as well as excluding areas that are not relevant for development, there is still need for further analysis and discussions of the results. For example, Gärdet received the highest value in the MCA, however it is the only one of the three areas identified where very limited development plans are present. In fact, there is a near non-existent political will to build here, as the values created from the large green area close to the inner city is deemed more important than the need for housing. Nonetheless, the positive aspects of Multi-Criteria Analysis as a method for decision making are numerous, as found both in previous research and in the analysis performed within the scope of this project. For example, it allows for several factors of sustainability to be evaluated and areas to be ranked accordingly. It is also an open and explicit process, and if the project where to move forward and be included in an actual municipal planning process, the analysis and subsequent result would have communicative qualities, that enables further discussion and decision making based on sustainability principles. Hence, we argue that Multi-Criteria Analyis is a highly relevant method when exploring the question of which areas within a larger geographical area that are suitable for sustainable future housing development. This due to the nature of MCA, providing several options for suitable areas, in this project. Furthermore, that 2 out of the 3 large continuous surfaces that were found to have the highest values are also areas where the municipality of Stockholm has decided to develop as new housing areas accredits the analysis, as there are extensive research behind a development planned and approved by the municipality.

9. Concluding remarks Returning to the aim stated in the beginning of this report, asking what areas in Stockholm are suitable for sustainable future housing development, the result and subsequent discussion reveals that the areas that are most suitable according to the Multi-Critieria Analysis are inner city parks, and that further evaluation of the areas given the highest values is needed, finally resulting in three large open land areas; Gärdet, Årstafältet and Järvafältet. Furthermore, the objectives and methodological contribution stated in the same chapter concern the MCA method, asking how many areas become suitable, if the technique is appropriate for answering the research question, and what the pros and cons of MCA are. The finding in this report is that the Multi-Criteria Analyis provides a large number of areas that are suitable for sustainable future housing development, ranging from very suitable (value 9) to unsuitable (value 0). Nonetheless, further analysis of the outcome was required, resulting in three areas that were most suitable in accordance with the aim. This is an indicator of the relevance of the method for answering the research question, however it also demonstrates the quality of MCA in providing several ranked options for decision makers.

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Appendix - Overview of all data used in this report.