105547

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

Spatial analysis of COVID-19 and its impacts in Rio de Janeiro, Brazil Análisis espacial de COVID-19 y sus impactos en Río de Janeiro, Brasil by/por

Ana Maria Lopes Bragança Silva 11938075 A thesis submitted in partial fulfilment of the requirements of the degree of Master of Science – MSc Advisor ǀ Supervisor:

Leonardo Zurita Arthos PhD

Rio de Janeiro - Brazil, February 07, 2022

SCIENCE COMMITMENT Through this document, including my personal signature, I certify and guarantee that my thesis is completely the result of my own work. I have cited all the sources that I have used in my thesis, and in all cases I have specified their origin.

Rio de Janeiro, Brazil, February 07, 2022

ACKNOWLEDGMENTS I thank my husband and family for all their encouragement and support. I thank Professor Dr. Anton Eitzinger for his guidance and the UNIGIS professors for giving me the necessary knowledge to develop this research. I am grateful to Dr. Rodrigo Batista Lobato and the Veiga de Almeida University (UVA) for embracing my idea and giving me the necessary support.

DEDICATED I dedicate this work to my husband, who has always been by my side, encouraging me throughout all the decisions of my life. I also dedicate it to the victims of COVID-19 and their families, especially in Brazil, a place where the main public authorities did less than expected to save lives.

ABSTRACT COVID-19, a disease resulting from contamination by the Sars-Cov-2 virus, was declared a pandemic on March 11, 2020. The definition of a pandemic has a strong geographical component: for it to be declared, the presence of infected people must be detected on more than one continent. Thus, due to an increasingly connected world and how easy the transmission of viral infections has become, an accelerated evolution in the number of contagions and deaths caused by the disease was observed in COVID-19 pandemic, challenging public and health authorities in search for solutions for the global health emergency. The generation of information is essential for public managers' decisionmaking while conducting responses to the pandemic, as well as the development of protocols and actions to deal with the health emergency, especially in an environment of social inequality. In this context, the present study demonstrates the results obtained through an online survey conducted in the city of Rio de Janeiro, Brazil, between April 7 and May 11, 2020. The survey included 15 questions related to people's perception of the impacts caused due to the pandemic and used GIS tools and applications, such as Survey123 Connect for ArcGIS, ArcGIS Online, Story Map, and Dashboard for ArcGIS. The data obtained from analysis were presented in map-format, word verifications were related to online research on Google Trends, and the construction of a scatterplot matrix were carried out aiming to identify and quantify the impacts endured by Rio de Janeiro's population after the arrival of the COVID-19 pandemic. In addition to the information obtained in the research, the official databases related to COVID-19 and data provided by Rio de Janeiro's city hall were used. Despite the limitations of the research in terms of the sample, representativeness, and spatial diversity, it was possible to verify the impacts upon the population through a survey carried out according to the city's different planning regions. Some differences were observed according to the socioeconomic level of the planning region. There was also concern about the loss of income caused by the pandemic and effects on the mental health of the population.

Keywords: COVID-19, impacts, online survey, Rio de Janeiro, spatial analysis

5

RESUMEN COVID-19, una enfermedad resultante de la contaminación por el virus Sars-Cov-2, fue declarada una pandemia el 11 de marzo de 2020. Una pandemia, por su naturaleza, tiene un fuerte componente geográfico, ya que, para ser declarada, la presencia de personas infectadas debe detectarse en más de un continente. Así, debido a un mundo cada vez más conectado y a la facilidad de transmisión del virus, lo que se observó fue una evolución acelerada en el número de contagios y muertes provocadas por la enfermedad, desafiando a las autoridades públicas y de salud en la búsqueda de soluciones para la emergencia global. La generación de información es fundamental para la toma de decisiones de los gestores públicos en la conducción de la pandemia, así como en la definición de protocolos y acciones para atender la emergencia sanitaria, especialmente en un ambiente de fuerte desigualdad social. En este contexto, el presente estudio demuestra los resultados obtenidos a través de una encuesta en línea realizada en la ciudad de Río de Janeiro, Brasil, entre el 7 de abril y el 11 de mayo de 2020. La encuesta realizó 15 preguntas relacionadas con los impactos de la pandemia sentida por la población de la ciudad utilizando herramientas y aplicaciones GIS, como Survey123 Connect for ArcGIS, ArcGIS Online, Story Map y Dashboard for ArcGIS. Los datos obtenidos fueron tratados en forma de mapas, verificación de palabras relacionadas con la investigación en línea en Google Trends y construcción de un scatterplot matrix, con el objetivo de identificar y cuantificar los impactos sufridos por la población de Río de Janeiro luego de la llegada de la pandemia de COVID-19. Además de la información obtenida en la investigación, se utilizaron las bases de datos oficiales relacionadas con COVID-19, así como los datos proporcionados por la municipalidad de Río de Janeiro. A pesar de las limitaciones de la investigación en cuanto a muestra, representatividad y diversidad espacial, fue posible verificar los impactos sufridos por la población a través de la encuesta realizada según las diferentes regiones de planificación de la ciudad. Se observaron algunas diferencias según el nivel socioeconómico de la región de planificación. También se observaron la preocupación con la pérdida de ingresos provocada por la pandemia, así como algunos efectos sobre la salud mental de la población.

Palabras clave: análisis espacial, COVID-19, encuesta online, impactos, Río de Janeiro

6

RESUMO A COVID-19, doença resultante da contaminação pelo vírus Sars-Cov-2, foi declarada uma pandemia em 11 de março de 2020. Uma pandemia, por natureza, possui uma forte componente geográfica, pois, para ser declarada, a presença de infectados tem que ser detectada em mais de um continente. Assim, devido a um mundo cada vez mais conectado e à facilidade de transmissão do vírus, o que se observou foi uma evolução acelerada em número de contágios e de óbitos ocasionados pela doença, desafiando as autoridades públicas e sanitárias na busca de soluções para a emergência de saúde global que se instalou. A geração de informação é essencial para a tomada de decisão por gestores públicos na condução da pandemia, assim como na definição de protocolos e ações de enfrentamento da emergência sanitária, principalmente em um ambiente de forte desigualdade social. Nesse contexto, o presente estudo demonstra os resultados obtidos através de uma pesquisa online realizada na cidade do Rio de Janeiro, no Brasil, entre 7 de abril e 11 de maio de 2020. A pesquisa realizou 15 questionamentos relacionados aos impactos da pandemia sentidos pela população da cidade utilizando ferramentas e aplicativos de SIG, como Survey123 Connect for ArcGIS, ArcGIS Online, Story Map e Dashboard for ArcGIS. Os dados obtidos foram tratados na forma de mapeamentos, verificação de palavras relacionadas à pesquisa online no Google Trends e construção de um scatterplot matrix, visando identificar e quantificar os impactos sofridos pela população do Rio de Janeiro após a chegada da pandemia da COVID-19. Além das informações obtidas na pesquisa, utilizou-se as bases de dados oficiais relacionadas à COVID-19 assim como dados fornecidos pela prefeitura da cidade do Rio de Janeiro. Apesar das limitações da pesquisa em termos de amostra, representatividade e diversidade espacial, foi possível verificar os impactos sofridos pela população através do levantamento realizado de acordo com as diferentes regiões de planejamento da cidade. Observou-se algumas diferenças de acordo com o nível socioeconômico da região de planejamento. Também foi observada a preocupação com a perda de renda ocasionada pela pandemia, assim como alguns efeitos na saúde mental da população.

Palavras-chave: análise espacial, COVID-19, impactos, pesquisa online, Rio de Janeiro

7

TABLE OF CONTENTS SCIENCE COMMITMENT ................................................................................................................. 6 ACKNOWLEDGMENTS ..................................................................................................................... 7 DEDICATED ....................................................................................................................................... 8 ABSTRACT ......................................................................................................................................... 5 RESUMEN .......................................................................................................................................... 6 RESUMO ............................................................................................................................................ 7 TABLE OF CONTENTS ....................................................................................................................... 8 GLOSSARY ....................................................................................................................................... 10 LIST OF FIGURES ............................................................................................................................. 11 1 INTRODUCTION........................................................................................................................... 13 1.1 BACKGROUND...................................................................................................................... 13 1.2 OBJECTIVES .......................................................................................................................... 16 1.2.1 GENERAL OBJECTIVE ........................................................................................... 16 1.2.2 SPECIFIC OBJECTIVES ........................................................................................... 16 1.2.3 RESEARCH QUESTION .......................................................................................... 16 1.2 HYPOTHESIS ......................................................................................................................... 16 1.4 JUSTIFICATION ..................................................................................................................... 17 1.5 SCOPE.................................................................................................................................... 18 2 LITERATURE REVIEW .................................................................................................................. 20 2.1 COVID-19 IN THE CONTEXT OF RIO DE JANEIRO............................................................. 20 2.2 SPATIAL ANALYSIS AND PUBLIC HEALTH ......................................................................... 24 2.3 SPATIAL ANALYSIS AND COVID-19 .................................................................................... 28 2.4 MONITORING OF COVID-19 USING GEOPORTALS.......................................................... 33 2.5 GIS AND COLLABORATIVE MAPPING ................................................................................ 37 2.6 SURVEY123 FOR ARCGIS..................................................................................................... 39 2.6.1 AN OVERVIEW ..................................................................................................... 39 2.6.2 CASE STUDIES ...................................................................................................... 41 3 METHODOLOGY .......................................................................................................................... 42 3.1 AREA OF STUDY ................................................................................................................... 42 3.2 DATA USED IN THE STUDY ................................................................................................. 43 8

9

3.3 FLUXOGRAM ........................................................................................................................ 44 3.4 METHODOLOGY DESCRIPTION .......................................................................................... 45 3.4.1 AN OVERVIEW ..................................................................................................... 45 3.4.2 ONLINE SURVEY ELABORATION .......................................................................... 46 3.4.3 TREATMENT OF DATA OBTAINED ....................................................................... 51 4 RESULTS AND DISCUSSION ........................................................................................................ 52 4.1 GENERAL RESULTS............................................................................................................... 52 4.2 MAPPING DATA RELATED TO COVID-19 .......................................................................... 56 4.3 MAPPING DATA RELATED TO COVID-19 IMPACTS ......................................................... 60 4.4 VARIABLES RELATION ......................................................................................................... 69 4.5 ANALYSES OF RESULTS ....................................................................................................... 71 5 CONCLUSIONS ............................................................................................................................. 77 6 REFERENCES ................................................................................................................................ 81 7 APPENDIX..................................................................................................................................... 87

GLOSSARY AUDITASUS

Unified Health System's Analytical Audit in Hospitalizations (Brazil)

BRICS

Brazil, Russia, India, China, and South Africa

CNES

National Register of Health Establishments (Brazil)

COVID

Corona virus disease

CSV

Comma-separated values

ESRI

Environmental Systems Research Institute

FIDA

International Fund for Agricultural Development

FIOCRUZ

Oswaldo Cruz Foundation (Brazil)

GIS

Geographic Information Systems

GPS

Global Positioning System

GWR

Geographically Weighted Regression

IBGE

Brazilian Institute of Geography and Statistics

ICU

Intensive Care Unit

INEP

National Institute of Educational Studies and Research Anísio Teixeira (Brazil)

IPP

Pereira Passos Institute (Brazil)

JHU

John Hopkins University

LABOCART

Laboratory of Geoprocessing and Social Cartography (Brazil)

LEGO

Laboratory of Geospatial Studies at Veiga de Almeida University (Brazil)

MGWR

Multiscale Geographically Weighted Regression

NGO

Non-governmental Organization

SUS

Unified Health System (Brazil)

UFC

Federal University of Ceará (Brazil)

UN

United Nations

UVA

Veiga de Almeida University (Brazil)

VGI

Volunteered Geographic Information

WASH

Water, sanitation, and hygiene

WHO

World Health Organization

10

LIST OF FIGURES Figure 1. Confirmed deaths in selected countries. . ............................................................ 21 Figure 2. Country Preparedness Capacity for COVID-19. . .................................................. 22 Figure 3. Spatial Analysis. .................................................................................................... 24 Figure 4. John Snow's map of cholera cases in London.. .................................................... 25 Figure 5. Fields of application of GIS in Public Health.. ....................................................... 27 Figure 6. Real-time dashboard (a) and responses' locations (b).. ....................................... 32 Figure 7. Challenges in spatiotemporal analysis using GIS.................................................. 34 Figure 8. Mapping initiatives related to COVID-19. Prepared by the author based on geoportals information........................................................................................................ 36 Figure 9. 1-2-3 process in Survey123. ................................................................................. 40 Figure 10. Study area location ............................................................................................. 42 Figure 11. Data Source ........................................................................................................ 43 Figure 12. Methodological steps ......................................................................................... 45 Figure 13. Survey123 Connect (a) and XLSForm (b) ............................................................ 47 Figure 14. Story Map which brings the survey .................................................................... 48 Figure 15. Real-time dashboard .......................................................................................... 49 Figure 16. Survey’s questions. ............................................................................................. 50 Figure 17. Location of Survey’s answers ............................................................................. 53 Figure 18. Proportion of age groups.................................................................................... 54 Figure 19. Number of answers and population density. ..................................................... 56 Figure 20. Related cases in the questionnaire and official data on COVID-19 cases .......... 57 Figure 21. Related deaths in the questionnaire and official data on COVID-19 deaths ..... 59 Figure 22. Income and type of occupation .......................................................................... 61 Figure 23. Items affected by the pandemic......................................................................... 63 Figure 24. Searched words in Google Trends ...................................................................... 64 Figure 25. Graphics created from the searched words in Google Trends .......................... 65 Figure 26. Graphics created from the searched words in Google Trends .......................... 65 Figure 27. Sentiment provoked by the pandemic ............................................................... 67 Figure 28. Searched words in Google Trends ...................................................................... 68

11

12

Figure 29. Graphics created from the searched words in Google Trends .......................... 68 Figure 30. Scatterplot matrix using social isolation and income responses in the survey and official data ................................................................................................................... 70 Figure 31. Graph with the answers obtained in Survey123. ............................................... 74

1 INTRODUCTION 1.1 BACKGROUND The new coronavirus (Sars-CoV-2), responsible for the disease COVID-19, was initially detected in China after the identification of numerous and unusual cases of pneumonia in the city of Wuhan (Belforte et al., 2020). The identification of a new coronavirus occurred in January 2020, and a pandemic was declared on March 11 by the World Health Organization (WHO), based on the detection of the disease in different continents of the world.

Exactly one year after the pandemic was declared, there are more than 115 million accumulated cases of COVID-19 in the world, with more than 2.6 million deaths (Our World in Data, 2021), which characterizes the serious public health problem that humanity is experiencing. Brazil has the second-highest number of accumulated deaths globally, second only to the United States, totaling more than 11.12 million cases and more than 260 thousand deaths by COVID-19.

In Brazil, the first confirmed case of the disease occurred on February 26, in the city of São Paulo, which is the largest city in the country and is part of the metropolitan region that has the primary airline hub in South America (Aguiar, 2020), being an important pathway across Brazilian cities and the rest of the world. This is a relevant point to understand the spread of the pandemic and its important space-time component. As said by Sposito & Guimarães, “(…) circulation and connectivity between different places are just as important as the territorial location in the process of spatial diffusion of phenomena of all kinds, showing the relevance of Milton Santos' theory, for which space is an inseparable set of action systems and object systems1” (Sposito & Guimarães, 2020, ¶ 5).

1

The original text was translated from Portuguese by the author and can be read at <https://www2.unesp.br/portal#!/noticia/35626/por-que-a-circulacao-de-pessoas-tem-peso-na-difusao-dapandemia>. Retrieved November 06, 2020.

13

14

In the second-largest city in Brazil, Rio de Janeiro, the first registration of COVID-19 took place on March 5. The state of Rio de Janeiro's first death was a 63-year-old woman, on March 17, in the municipality of Miguel Pereira, who contracted the disease in the city of Rio de Janeiro due to her work activity. In the present day (March 11, 2021), the city of Rio de Janeiro has reached more than 210 thousand accumulative cases of the disease and 19 thousand deaths (Ministério da Saúde2, 2021), which would place the city, if it was a country, in the 25th position of deaths registered by countries due to the pandemic in the world (Worldometer, 2021).

The city's demographic conditions impact the way the pandemic spreads and is combated.

There is a predominance of inadequate housing conditions in the city (slums, known as favelas), in which there is a lack of basic sanitation and health resources, and a high concentration of people, which favors non-compliance with disease prevention measures.

These conditions facilitate contagion and increase the risk of death caused by the disease (Cobre et al., 2020).

From March 12, 2020, the day after the disease was classified as a pandemic by WHO, the mayor of the city began issuing decrees3 in response to the pandemic, such as social isolation and quarantine measures, formalizing remote work, closure of non-essential services, restriction of people in public transport services, suspension of school activities, among others.

These measures have been made more flexible over the months, with no clear guidelines to be followed or effective coordination between different government levels (municipal, state, and federal). Thus, these measures were not enough to stop the spread of the disease, given that the city of Rio de Janeiro ranks 5th in the highest mortality rates due to the disease in the country (more than 9%) (AUDITASUS, 2021), being the first city among Brazilian capitals. Additionally, there is a high occupancy rate of Intensive Care Unit (ICU) beds for COVID-19 (surpassing 90% in some moments of the pandemic) with the establishment of hospital waiting lines for those seeking treatment for the disease.

2

Ministry of Health in Brazil.

Municipal decrees on COVID-19: <https://pge.rj.gov.br/covid19/municipal/decretos#:~:text=DECRETO%20RIO%20Nº%2047429%20DE,19%2 C%20e%20dá%20outras%20providências>. Retrieved January 28, 2021.

3

15

In Brazil, the pandemic was initially felt by the wealthier classes, who brought the virus when returning to the country from trips abroad, mainly from Europe. The country, a peripheral economy, initially watched the development of the pandemic and other nations' governments’ decisions as an onlooker who would only really experience the virus's devastating effects many weeks after the leading global economies.

In March, at the beginning of the pandemic, not much was known about its real effects on the global economy and people's lives, whether in terms of impacts on prices related to consumption and the availability of products, such as food, hygiene products, hospital products, among others, or even about the preservation of jobs and the upkeep of the purchasing power of families. Besides, it was not clear yet how fast people would be infected by the virus around the world and how much time the virus would be active in society, as well as how long it would take to come up with a solution to fight the disease, via drugs or vaccines. In this uncertain scenario, the most effective actions adopted to contain the pandemic were social isolation and mass testing.

The spatialization of the cases responds in an integrated way to the spatial question, where the infected people are, as well as to monitor their temporal spread. Therefore, several initiatives to visualize the pandemic in space-time began to emerge since the virus is quite contagious and further driven by the intense flows of people between the world's countries. The importance of the spatial component for its understanding was realized.

Thus, this study aims to use the data produced from an online survey that was carried out at the beginning of the pandemic via social media that aimed to use Geographic Information Systems (GIS) tools in a collaborative mapping to understand the dynamics and impacts of the disease in Rio de Janeiro, Brazil. The questionnaire developed addresses a variety of aspects drawn upon people's perception of the pandemic. Finally, other data produced in different databases related to the evolution of the pandemic in Rio de Janeiro will be presented to evaluate the strength of the tool and complement the spatial information presented.

1.2 OBJECTIVES 1.2.1 GENERAL OBJECTIVE Identify and quantify impacts, such as economic, social, and psychological impacts endured by the population of Rio de Janeiro, Brazil, after the arrival of COVID-19 until May 2020.

1.2.2 SPECIFIC OBJECTIVES 1. Analyze the results from the collaborative mapping on COVID-19 carried out between April 7 and May 11, 2020, in the city of Rio de Janeiro.

2. Relate the data obtained in the collaborative mapping to the disease's official data and demographic data by planning regions of the city.

3. Verify interest to words associated with the pandemic on the internet, and that may be related to the data obtained in the collaborative mapping.

1.2.3 RESEARCH QUESTION Some research questions guided the present investigation, as can be seen below: •

Do the results reflect the impacts felt by the population as a result of the pandemic?

•

Do the results reflect people's interest in certain words related to the pandemic on Internet research?

The research questions will be answered, as far as possible, based on the procedures adopted to achieve the objectives.

1.2 HYPOTHESIS The hypothesis is that there is evidence that the pandemic of the new coronavirus caused social, economic, and psychological impacts on the population of the city of Rio de Janeiro that can be verified in collaborative mapping.

16

1.3JUSTIFICATION The new coronavirus SARS-Cov-2, responsible for the disease COVID-19, was initially detected in China. Being declared a pandemic caused by the virus in March 2020, it was observed, at that time, that there was an effort to provide spatial information about the pandemic, but the main focus was essentially on the number of contagions and deaths related to the disease in a given location. This information, while extremely important, didn't reveal much about how people were navigating the pandemic, how they felt, and the impact of the disease on their lives, other than the impact of the disease itself.

However, during the onset of COVID-19 cases in Brazil, a platform to monitor the impacts of the pandemic on people's daily lives was not established, since the main focus was on monitoring the number of contaminated and deaths over time in different locations where the virus was present.

The use of geotechnologies in this scenario, besides generating the necessary information to understand the development of the pandemic and its impacts, can also support the decisions of public managers and civil society entities in facing the consequences of the disease, as one of the keys to coping with the pandemic is the generation of information (Franch-Pardo et al., 2020).

Social inequalities mark the city of Rio de Janeiro. About 19.28% of households in Rio de Janeiro are located in slums (the favelas), called subnormal agglomerations (IBGE, 2019).

This refers to the fact that the disease affects each location differently, considering sanitation conditions and access to drinking water, housing, air, and soil pollution (MundoGEO, 2020a). Thus, the COVID-19 pandemic accentuate the inequality in Brazil and, in the case of this study, in Rio de Janeiro. The study of data produced at the beginning of the pandemic, in addition to the analysis of spatial information developed for the city since then, is relevant to understand the spatial dynamics of COVID-19 in the city of Rio de Janeiro, especially in understanding the socioeconomic and psychological impacts experienced by the population.

With this concern, an online survey was developed using GIS resources, such as Survey123 Connect, ArcGIS Online, Story Map and Dashboard for ArcGIS. This research aimed to gather information perceived by the general population related to the pandemic.

17

18

Thus, in the span of year of research, what is intended in this study is to analyze the data obtained in the survey, performing the necessary segmentation and treatments to understand the results of this research. It is also intended to verify what types of spatial information data were produced concerning COVID-19 in Rio de Janeiro's municipality since a year has passed from the pandemic's announcement. These two pieces of information will be evaluated together, as one can complement the other.

The analysis of this information is relevant for public managers' decision-making, for the development of public policies counteracting the impacts felt by the population as a result of the pandemic, and bringing transparency to the different civil society actors (Cardoso et al., 2020).

1.5 SCOPE This study's scope is to assess the impacts of the COVID-19 pandemic perceived by the population of the city of Rio de Janeiro, Brazil, by analyzing the data collected in a collaborative mapping carried out between April 7 and May 11, 2020. The research was launched via social media and contained 15 questions elaborated in Survey123 Connect for ArcGIS. The questions dealt with some demographic data, issues related to the activities and items affected by the pandemic, impacts related to mental health, and the interviewee's location.

Through these data, analyses and mappings will be produced to visualize the results in space according to the city's planning regions. In addition, in a complementary way, official data from COVID-19 and people's interest in the questionnaire's words was used.

The research did not propose to constitute a representative sample of Rio de Janeiro's population, with no control over the diversity of the constituted sample.

Besides being another source of information about the pandemic, bringing a different approach than the mapping initiatives presented at the beginning of the health emergency (focused more on the evolution of the number of cases and deaths of COVID-19 in different regions), the study is part of an initiative carried out in conjunction with a research group from a Rio de Janeiro university (LEGO - Laboratory of Geospatial Studies at Veiga de

19

Almeida University). Thus, it is expected that the content produced with the research will be used in other studies and serve as a basis for conducting new investigations and mapping regarding COVID-19 and its impacts.

2 LITERATURE REVIEW 2.1 COVID-19 IN THE CONTEXT OF RIO DE JANEIRO When the public health emergency of COVID-19 was declared a pandemic on March 11, 2020, WHO took the lead concerning the initial guidelines that should be followed in addressing the health emergency. As a definitive solution to fight the disease was not available through the development of drugs or vaccines, the possible path of prevention would be adopting practices related to water, sanitation, and hygiene (WASH) (WHO, 2020). This provides for adequate sanitary facilities so that hand hygiene is carried out properly with drinking water and that contact surfaces are also adequately sanitized.

Another essential aspect of preventing the spread of the disease is the adoption of social isolation and individual protection measures, such as the use of masks.

These measures end up affecting the functioning of cities, which, in some cases, even adopted lockdowns, with the closing of restaurants, cinemas, theaters, schools, gyms, among others public facilities, that is, an extreme effort in which most non-essentials activities are imposed some restrictions and people are confined to their homes.

Thus, countries adopted procedures related to individual guidelines, such as social isolation, hygiene measures and the use of masks, and collective actions, such as activity restriction and mass testing. The way in which these measures were adopted and how the population was oriented had an impact on the performance of each country in facing the pandemic.

What can be seen in the graph below (Figure 1) is that Brazil's performance in tackling the pandemic is not considered acceptable in comparison to the largest global economies and the BRICS bloc (Brazil, Russia, India, China, and South Africa). It is possible to verify that Brazil is the only one of the selected countries to have shown a worsening trend in terms of the pandemic after a year of confrontation.

20

21

Figure 1. Confirmed deaths in selected countries. From Our World in Data, 2021.

In Rio de Janeiro, schools were closed in March, and many of them adopted remote education. This situation is set amidst the lack of proper environments in which to carry out classes and adequate equipment, such as computers and the internet, a problem that can be posed to both students and teachers. According to data from Censo Escolar4 (INEP, 2019) and Instituto Brasileiro de Geografia e Estatística (IBGE)5 (2018), more than 60% of students enrolled in the municipality are in the public school system, precisely the one that lacks the most significant resources. In addition to the difficulty in continuing educational activities, there are impacts on young people's mental health with the loss of social activities carried out in schools. Students are also expected to lose their bonds to school due to a height in unemployment rates and the need to boost families’ income, in addition to the migration of students from the private to the public school system (Pereira, 2020).

4 5

School Census.

Brazilian Institute of Geography and Statistics.

22

Health-wise, Brazil has a Unified Health System (Sistema Único de Saúde - SUS) that guarantees public and free care to the entire population. However, due to its inability to provide quality care, part of the population with the highest income uses private health care. With the pandemic and its impact on employment and income, a migration of a part of the population served by the private health system to the public health system must be observed.

In the following map (Figure 2), it is possible to verify that the country had a good response capacity to COVID-19. However, each region of the country's situation is quite heterogeneous, in which the best health resources are often found in large urban centers.

Figure 2. Country Preparedness Capacity for COVID-19. From the UN, 2020.

In the city of Rio de Janeiro, according to the Cadastro Nacional de Estabelecimentos de Saúde (CNES)6 (2019), in the public system, there are approximately 7.86 ICU beds per 100 thousand inhabitants and 79.69 hospital beds per 100 thousand inhabitants. When the total numbers are observed (public and private health systems), the number of ICU beds increases to 38.5 per 100 thousand inhabitants and beds to 162.41 per 100 thousand inhabitants.

6

National Register of Health Establishments.

23

On the employment side, more than 68% of Rio de Janeiro's economic activity is dependent on the service sector, encompassing commerce, public administration, transport, telecommunications, rentals, and services provided to companies and families (Barboza et al., 2020). The country experienced a recession between 2015 and 2016, which generated significant unemployment. In 2019, while many places in the country witnessed a surge of job openings, in Rio de Janeiro, more than 7 thousand jobs were lost. COVID-19, in this scenario, dramatically affects the city's economy because of the high contribution of the service sector.

Furthermore, even before COVID-19, in 2019, unemployment in the city affected more vulnerable groups, such as women, young people, the poor, and the low-skilled. Regarding informality, the rate reached almost 36% of the city workforce (Ottoni et al., 2020). Even in March 2020, these groups were already identified as those who would be suffering the greatest risk in the face of the health crisis (UN, 2020). Thus, according to the predominant type of work that is carried out in the city, the pandemic maximizes the issue of unemployment as a large portion of the population does not have occupations compatible with remote work.

The sanitation issue is another point of attention related to the COVID-19 pandemic in Rio de Janeiro, considering that hygiene is an essential factor in combating the pandemic.

Sanitation in the city is not universal. According to Instituto Trata Brasil7 (2020), 97.41% of the population has access to water supply and 85.14% to sewage supply. However, the percentage of collected sewage that is treated in comparison to water consumption is 42.87%. Thus, among the 100 largest cities in Brazil, Rio de Janeiro ranks 52 in the sanitation ranking.

Finally, the adoption of social isolation as a measure to combat COVID-19 finds an unfavorable housing scenario. As already mentioned in item 1.4, almost 20% of households are in slums with a high population density. It is estimated that more than 10% of households in the city have three or more residents per bedroom (IBGE, 2010).

7

Treat Brazil Institute.

2.2 SPATIAL ANALYSIS AND PUBLIC HEALTH The conception that we have of GIS nowadays began to emerge in the 1960s, with the development of computerized map systems and the launch of remote sensing satellites of the Earth's surface. According to Smith et al. (2021, ¶ 2), The term ‘GIS’ is widely attributed to Roger Tomlinson and colleagues, who used it in 1963 to describe their activities in building a digital natural resource inventory system for Canada (Tomlinson 1967, 1970). The history of the field has been charted in an edited volume by Foresman (1998) containing contributions by many of its early protagonists. A timeline of many of the formative influences upon the field is provided in Longley et al. (2015, p. 20). The research makes the unassailable point that the success of GIS as an area of activity has been driven by the success of its applications in solving real world problems.

Previous to the conception of GIS, but nonetheless part of its nature is spatial analysis, which can answer several questions related to data that have a geographical component.

The table below (Figure 3) provides some examples of the types of questions that may appear in spatial analysis.

Analysis

General Question

Example

Condition

What is...?

What is the population of this city?

Localization

Where is...?

What are the areas with a slope above 20%?

Trend Routing

What has changed...?

Where to go...?

Was this land productive five years ago?

What is the best way to the subway?

Pattern

What is the pattern...?

What is the distribution of dengue (disease) in Fortaleza?

What is the impact on the climate if we deforest the Amazon?

Figure 3. Spatial Analysis. Adapted from INPE, 2006.

Model

What happens if...?

In the health care field, these questions can also be asked to answer, for example, who fell ill, and where or when the disease occurred (Hino et al., 2006).

24

25

The spatial analysis conducted by John Snow in 1854, aiming to study the relationship between the occurrence of cholera in London and the supply of water in different supply pumps in the city (Snow, 1999), is a classic study of spatial analysis and can also be applied to public health. By mapping the locations of cholera deaths and whether the water supply came from a collection point upstream or downstream of the sewage discharge into the River Thames (Figure 4), Snow was able to demonstrate the relationship between the disease and water contamination conclusively.

Figure 4. John Snow’s map of cholera cases in London. Adapted from Tufte, 1983 apud INPE, 2006.

The applications of GIS in public health are diverse, especially in the field of epidemiology, where geographic information can be used to search for causes and effects related to the health of the population (León, 2007).

26

One of the main approaches of mapping in epidemiology is identifying risks according to geographic regions, merging information related to the frequency of occurrence, spatial distribution, and factors that offer health hazard and that do not have a homogeneous distribution in all social groups. Knowledge of this information is essential to reduce or eliminate the health risks to a specific population and, in a preventive sense, avert the emergence of dangerous situations in public health.

According to the Ministério da Saúde (2006), the main applications of spatial analysis in public health are: - Disease mapping, through the construction of maps of epidemiological indicators; - Ecological studies, measuring the association between aggregated indicators; - Health and environment, relating different layers of data on environment and health; - Detection of clusters, identifying clusters related to the highest incidence of diseases and variables of interest; - Diffusion processes, evaluating the evolution of the spatial distribution of diseases in time and space; - Study of trajectory between locations, aiming to analyze health care networks; Going further, these analyses can be grouped into three major fields of knowledge (Figure 5), assisting in the planning, evaluation, and monitoring of programs implemented in public health and decision-making by politicians and managers.

27

pidemiological Surveillance

Spa al distribu on of diseases and determina on of pa erns elimita on of ris areas apping of basic health indicators nalysis of research hypotheses from the understanding of di usion and e posure to speci c agents lanning of disease preven on and control ac vi es.

ealth Services valua on

rbani a on and nvironment

nalysis of the spa al distribu on of health services lanning and op mi a on of health resources ccessibilitystudy se of health services nalysis of the pa ent ow to determine areas of demand for health resources.

rban disease ecology Study of social, demographic and environmental factors in ci es nalysis of how pollu on, overpopula on, stress and poverty a ect human health in ci es i erences in the use of space by di erent social groups due to economic and poli cal pressures on society.

Figure 5. Fields of application of GIS in Public Health. Adapted from RIPSA, 2000 apud León, 2007.

These possibilities of using GIS in public health allow essential questions to be answered to understand important phenomena in the field of health surveillance (Ministério da Saúde, 2006), such as •

what is the pattern of case distribution of a disease in space;

•

whether there is any relationship between a disease, possible sources of contamination, or means of dissemination;

•

what is the evidence about the transmissibility of a disease, whether it is transmitted from individual to individual or through a common source.

Regarding the methods used in the spatial analysis of information related to public health, according to Hino et al. (2006), they can be divided into: - Visualization: the mapping of health events is the primary tool, ranging from the point distribution of events to complex overlays of disease incidence maps which describe the distribution of certain variables of interest.

28

- Exploratory data analysis: used to describe spatial patterns and the relationship between maps. Some exploratory techniques will take the form of graphs (histograms, scatterplots, among others), while others will be cartographic in nature.

- Modeling: used when it is intended to formally test a hypothesis or estimate relationships, such as, for example, between the incidence of a given disease and environmental variables.

Thus, it is possible to verify the adherence in the use of spatial analyzes with a focus on public health, especially with the use of GIS tools. However, some problems were noticed (Abdullahi et al., 2010), such as the lack of standardization and information about the source of the data produced, given that there are many different sources in the health field that produce geographic information; some information produced in the health field is generated by specific applications, making integration with other databases difficult, which also causes problems related to interoperability.

In addition to these factors, in Brazil, the lack of databases for specific health information, as well as other types of information that are added to spatial analyzes, such as socioeconomic and demographic data, can be mentioned as a historical problem for spatial analyzes in public health (Chiaravalloti-Neto, 2016). This problem has been gradually overcome with the greater availability of geographic databases and health information systems.

2.3 SPATIAL ANALYSIS AND COVID-19 At the beginning of the pandemic, the studies carried out to understand its evolution in space and time have as background several different applications. In Brazil and internationally, these studies aim to characterize the dynamics and dissemination of the pandemic in the territory and its flows to provide a more significant base of information for society and support governmental decisions. There is also the concern to relate the data of COVID-19 to other social and demographic variables in order to flesh out the vulnerabilities

29

of certain groups in facing the pandemic. The use of online mapping systems and GIS tools are fundamental in understanding a pandemic that has quickly spread in space.

In the Brazilian context, the spatiotemporal analysis of COVID-19, using the official government databases, was used to understand the spread of the disease in the municipalities of the state of Rondônia. Through the mappings performed, it was observed that the number of ICU beds offered by the state was insufficient, which could result in the collapse of the health system (Belforte et al., 2020).

Considering that the number of infected and victims of COVID-19 is not enough information to understand the disease’s spatial and temporal dynamics, Cardoso et al. (2020) report the initiative for a COVID-19 mapping portal in the municipality of São Gonçalo, in the state of Rio de Janeiro. In addition to informing the population about the evolution of the disease in space, the maps produced aimed to assist public managers in responding to health emergencies.

Aguiar (2020) seeks to understand, through mappings, how the transit of people between cities impacted the spread of the virus in the world, according to the routes traveled and accounting for those arriving by air travel in Brazil. In the country, at first, the virus followed a path of dissemination through air transport, later reaching more distant regions through highways and waterways. Knowledge and control of disease flows are essential for an efficient allocation of resources to face the pandemic.

The spatiotemporal evolution of the COVID-19 cases in Niterói, in the state of Rio de Janeiro, was studied in relation to the city’s neighborhoods, relating some social indicators, such as income, number of elderly people, and hospitals’ bac drop. The elaboration of maps based on the study carried out by Leal et al. (2020) identifies the neighborhoods in which the pandemic had the most remarkable development related to elderly people’s presence in those places. Also, the study demonstrates the principal axes for the spread of the disease in the city. It is observed that the neighborhood with the highest prevalence of cases is the one with the highest concentration of elderly people and the highest concentration of income in the city at the beginning of the pandemic.

At the international level, Sodoré et al. (2020) conduct a study similar to that of Aguiar (2020) when carrying out mappings to understand the dynamics of flows and spatial

30

diffusion of COVID-19 in Burkina Faso, West Africa, which is an impoverished country, with social problems and poor health infrastructure, and it has one of the highest mortality rates

by COVID-19 on the African continent. As in Brazil, in that country, the wealthier classes imported the virus from trips abroad, with the city of Paris, France, being observed as the main hub for the dissemination of COVID-19 to the capitals of countries in western Africa.

Burkina Faso’s capital, Ouagadougou, is the epicenter of the disease’s spatial spread, which ends up spreading the virus to smaller cities within a radius of influence of about 100 km.

Boulos & Geraghty (2020) discuss how GIS has helped to fight the pandemic, presenting some initiatives that have been developed since its emergence. Thus, Johns Hopkins University’s initiative can be mentioned, which uses Esri’s applications to provide dashboards with the pandemic information around the world. The World Health Organization has made available a dashboard for monitoring the pandemic, also based on Esri’s applications. Another initiative, HealthMap compiles real-time information about COVID-19 based on different data sources, as does BlueDot, which uses machine learning and natural processing language to extract information from COVID-19 and its developments from different sources around the world. China has also launched some platforms, such as the “close contact detector”, which helps in the identification and proximity to possible people who are contaminated by COVID-19 or suspected, helping in decision making by the user. There is also a tracking system in the Guangzhou metro that helps in tracking contact in the event of possible contagion. Finally, WorldPop and EpiRisk use mobility data to build predictive risk maps. In addition to providing accessible information to the general population, these GIS-based mapping platforms help combat misinformation about the pandemic.

In Pakistan, Sawar et al. (2020) list some challenges related to the use of GIS to monitor the pandemic in the country, among them, the development and application of GIS tools; the identification of pandemic outbreaks in space, with case monitoring, segmentation, and dynamic mapping of the evolution of the pandemic; the management of the data that are important for the understanding of the pandemic and that will be used in the mapping; making predictions based on hypothesis testing and risk maps concerning the demands for medicines; monitoring the chain supply and risk transportation; keep track of how the spread of social sentiment in space and other relevant discoveries take place. The study

31

concludes that the best use of GIS tools provides a faster and more effective response by the country’s authorities in combating the pandemic, such as, instead of decreeing a widescale lockdown throughout the country, identifying areas that need to adopt more restrictive measures.

Using spatial statistics, Kim & Bostwick (2020) verify the correlation, in the community areas of Chicago, USA, between the risk of death by COVID-19, the social vulnerability index, the health risk score constructed for the city, and the population concentration of African-Americans, exposing how COVID-19 affects social strata differently, being yet another factor in exposing inequalities.

A similar approach is carried out by Tao et al. (2020) in the state of Florida, USA, an analysis relating the accessibility to COVID-19 tests to certain groups, such as African-Americans, Asians, low-income groups, and the elderly, verifying that an effort should be made to safeguard the most vulnerable population more efficiently.

At the national level in the USA, Mollalo et al. (2020) list 35 variables in different groups (socioeconomic, environmental, behavioral, topographic, and demographic) that can explain the spatial variability of the incidence of cases of COVID-19, using models such as the geographically weighted regression (GWR) and the multiscale geographically weighted regression (MGWR). The variables that can explain the variability in the incidence of cases of COVID-19, four from the initial group of 35, which are median household income, income inequality, percentage of nurse practitioners, and percentage of black female population.

Maps were built with the spatial distribution of the coefficients found for each variable, as well as a map with the local distribution related to the four selected variables. It was found that the MGWR model being the most appropriate to explain the greatest variations, reaching an R2 of 68.1%.

Antoniou et al. (2020) note the use of crowdsourcing as a tool for mass surveys in Greece.

For this, a simple question was asked using Survey123 for ArcGIS if the person had had any symptoms of COVID-19. The interviewee provided his location after selecting one of the three possible responses (No, Not sure, and Yes). The survey was released on March 16, 2020, through social media, e-mails, and some appearances on TV and communication with the press. The responses fed a real-time dashboard (Figure 6a). In about a month, more

32

than 200,000 responses were obtained across the country, most of them concentrated in the first days after the release, verifying the relationship between the number of responses, the population concentration, and the number of COVID-19 cases based on official numbers. Thus, it was verified the application of this initiative in the identification of hot spots of the disease, and it can be a tool to be used by the public power in securing quick responses by the population as an alternative to the use of tracing apps. In Figure 6b in the sequence, it possible to see the responses’ locations obtained in the survey all over Greece.

(a)

(b)

Figure 6. Real-time dashboard (a) and responses’ locations (b). Adapted from Antoniou et al., 2020.

2.4 MONITORING OF COVID-19 USING GEOPORTALS The rapid spread of the new coronavirus, which has a high rate of contagion, driven by the large circulation of people globally through harbors and airports, ensured a geographical dimension different from that experienced in other pandemics. The few months that separate the detection of Sars-Cov-2 in December 2019 in China, until the pandemic decree

by WHO in March 2020, was accompanied by a series of initiatives to monitor the evolution of the disease in space and in time, being one of the best known on a global level the Johns Hopkins University (JHU) (2020) initiative, named COVID-19 Dashboard, which already has more than 1.6 billion unique accesses (MundoGEO, 2020b) and provides information on the evolution of the disease in real-time.

The tool presents various information about the evolution of the pandemic, such as active and accumulated cases, deaths, and recoveries in 191 countries and regions of the world.

Also, in January 2020, data were collected and processed manually (Dong et al., 2020), with the need to make the tool more automated with the development of the disease. In addition, to provide transparency to the general population, authorities, and researchers regarding the pandemic’s evolution, the initiative makes available for downloading the data used to feed the dashboard in a repository.

On these initiatives to monitor the evolution of the pandemic in space in real-time, Silva (2020, ¶ 2) points out that: (...) they are extremely important to inform the population about the contagion of the disease, whether at the local, regional or global level. Thus, it is possible to verify both the evolution of contagion and the occurrence of deaths resulting from infection by COVID-19. Reading and interpreting this scenario provides support for decision making at the government level, such as the adoption of restrictive measures and social isolation8.

8

The original text was translated from Portuguese by the author and can be read at https://mundogeo.com/2020/04/08/artigo-geotecnologias-na-identificacao-de-impactos-sociais-devido-acovid-19/. Retrieved November 11, 2020.

33

34

Observing this large volume of information being produced daily, from several different locations, it can be noticed the problem of processing the information obtained and the integration of different databases. In this regard, it is possible to state that many monitoring platforms of COVID-19 need to work with information from the perspective of spatial big data. Thus, in Figure 7, it is possible to observe some challenges related to the use of GIS with big data from a space-time perspective.

Figure 7. Challenges in spatiotemporal analysis using GIS. Adapted from Zhou et al. (2020)

In the ten challenges presented, it is possible to highlight some points raised by the authors, such as the integration of bases from different sources produced in different contexts, scales, and spatial references. Furthermore, the short time for updating information and developing applications, given the rapid development of the pandemic and taking into account that they need to be friendly to the general public and present large volumes of data on multiple scales (continents, countries, regions, counties, cities, etc.). This information is extremely relevant to recognize the path and speed of the spread of the disease and to make predictions of how it will behave in the future and in different contexts. Finally, this perspective on mental health and feelings related to the pandemic is relevant in the context of GIS, considering that humanity experienced an unexpected

35

health emergency (at least, for a large portion of the population), generating an unprecedented level of uncertainty, which can be perceived in different ways depending on the context and location.

There are initiatives by different social segments in Brazil in the online mapping of COVID19 in space and time. As the country has continental dimensions and is divided into federations (states), the number of COVID-19 mapping initiatives is quite extensive, being developed by universities, public and private institutions, state governments and city halls, NGOs, among others. As a very heterogeneous country, the spread of COVID-19 was felt in different ways in each region.

Thus, with the focus of the present study being Rio de Janeiro, Figure 8 below shows some initiatives that have been developed to tackle the COVID-19 situation. The number of portals for mapping the disease is not intended to be exhaustive, only to present the main initiatives that reach the municipal level of Rio de Janeiro or that were developed specifically for the city. The figure lists the main approaches related to COVID-19 and how spatial information is presented.

36

Site Name

Particular Subject

Analysis of COVID19 and its impact Ideia Big Data on politics, behavior, among others COVID-19 active Covid por CEP cases and deaths – Rio de according to postal Janeiro code Provides information on COVID-19 cases, Observatório deaths, contagion COVID-19 BR dynamics, occupation of the hospital network, among others Provides information on confirmed cases, Painel Rio recoveries, deaths COVID-19 and active cases of COVID-19, among others Daily cases, accumulated cases, MonitoraCovid deaths, combat -19 measures, statistical analysis, among others COVID-19 cases Portal and deaths, GEOCOVID-19 projections, Brasil isolation, and reports COVID-19 Analytics

O IBGE apoiando o combate à COVID-19

Coronavírus Brasil

Pandemic-related statistics and forecasting models Data and statistics related to the pandemic, such as unemployment rate, impacts on the health network.

Data and statistics on daily and accumulated cases, recoveries, deaths, among others

Region

Brazil and the World

Spatial Information There are maps to illustrate some analyzes, but it is not a mapping platform

Developers

Link

Private company (IDEIA)

https://ideiabigdata.com/covid19/

Rio de Janeiro

Geoportal

Independent initiative based on official data

https://covidporcep.rio.br/

Brazil, states, and municipalities

Depending on the subject there are thematic maps to represent the data

Independent initiative by several researchers

https://covid19br.github.io

Rio de Janeiro

Geoportal

Rio de Janeiro City Hall

https://experience.arcgis.com/exp erience/38efc69787a346959c9315 68bd9e2cc4

Brazil, states, and municipalities

There are interactive maps depending on the theme, but it is not a geoportal

Oswaldo Cruz Foundation (FIOCRUZ)

https://bigdatacovid19.icict.fiocruz.br

Brazil, states, and municipalities

Geoportal

Interinstitutional network initiative

http://covid.mapbiomas.org

Brazil

There are maps to illustrate some analyzes, but it is not a mapping platform

Pontifical Catholic University of Rio de Janeiro (PUCRJ) and others

https://covid19analytics.com.br

Brazil, states, and municipalities

Website that collects a variety of information. There are links to interactive maps.

Brazilian Institute of Geography and Statistics (IBGE)

https://covid19.ibge.gov.br

Brazil, regions, states, and municipalities

There are interactive maps, but it is not a geoportal

Ministry of Health

https://covid.saude.gov.br

Figure 8. Mapping initiatives related to COVID-19. Prepared by the author based on geoportals information.

Retrieved November 8, 2020.

2.5 GIS AND COLLABORATIVE MAPPING Collaborative mapping plays an extremely relevant role in connecting traditional communities and all the knowledge they have accumulated over time with the decisionmaking process related to the territory’s use. In this way, often-marginalized demographics assume a role that guarantees autonomy and preservation of their identity, besides approximating local knowledge to the technical-academic environment (Araújo & Nascimento, 2012).

Even in this context, GIS are understood as one of the instruments used in collaborative mapping (FIDA, 2009, p. 17), being increasingly accessible to a large part of the population, making the process more democratic and participatory.

Collaborative mapping in the context of the technologies available today has evolved into the so-called Volunteered Geographic Information (VGI). According to Goodchild (2007), some technologies were essential for the development of VGI, such as Web 2.0, georeferencing, geotags, GPS, graphics, and broadband communication: - The advent of Web 2.0 allowed greater user interaction on the network, which allowed them to be not only a consumer of content but also someone capable of feeding content to the network. This context culminated in the concept of Geoweb, in which it is possible to share geographic information over the internet (Bravo & Sluter, 2018). It was observed, then, the generation of different collaborative mapping products, such as OpenStreetMap. - The georeferencing of several systems allows interoperability between platforms, such as, for example, between data collected in the field on a device that uses GPS, an online mapping platform, and GIS software.

- The insertion of geographic information through geotags allows the inclusion of geographic location on websites, photographs, among others.

- The incorporation of GPS in general-purpose devices, allowing the acquiring of coordinates, location, measurements, etc.

- The improvement in the quality of graphics and map visualizations, including in 3D.

37

38

- The ability to communicate through a fast internet connection, which allows the transfer of large volumes of data.

Thus, a new way of observing phenomena that occurs on the Earth’s surface has developed in recent decades. This context allowed people who did not necessarily have knowledge in cartography to become engaged in mapping endeavors or events of collective interest, acting in the mapping of disasters, reporting traffic events, robberies, among others.

VGI ensures greater agility in the production and dissemination of information. The collection of local information, on a large scale, for example, would require a high investment of time and financial resources that public authorities would probably not be able to invest, considering the number of demands and specific situations in a given location. Thus, it is possible that with the offer of current technologies, information will be produced and made available at a more compatible speed with the occurrence of the fact, enabling more timely responses to solve problems, including from the government.

If, on the one hand, VGI empowers ordinary people to generate geographic information from their perspective of observation (Goodchild & Glennon, 2010), on the other hand, it segregates, even more, those on the margins of the digital inclusion process (Haworth & Bruce, 2015), being yet another factor in the deepening of inequalities.

Besides, there are factors in the generation of VGI that may cause unease and mistrust, related to the privacy and security of data provided voluntarily (Haworth & Bruce, 2015), as well as the fact that non-expert individuals provide information that can end up generating low-quality information and credibility (Elwood et al., 2012).

According to Bravo (2018), it is necessary to differentiate some related concepts, such as “crowdsourcing”, collaborative mapping, and VGI. The first deals with online data collection from multiple sources; the second deals more with the empowerment of communities, as previously mentioned, and information sharing; finally, the VGI is information generated voluntarily and shared by people who do not necessarily have specific knowledge in mapping.

The increasingly intuitive and friendly systems have given the possibility not only of the professional with technical knowledge to be part of this collective construction but also of

39

the layperson, in what came to be called VGI (Volunteered Geographic Information), favoring the quick obtainment of geographic information, which can be relevant in the construction of warning systems in place in cities (related to floods, public safety, among others), as well as in the context of a public health emergency such as the one the world is going through. Thus, ordinary people can become a progressively important link in the process of producing information, administration, and decision-making in their locality.

2.6 SURVEY123 FOR ARCGIS 2.6.1 AN OVERVIEW Survey123 for ArcGIS is a solution developed by the Environmental Systems Research Institute (Esri) for creating, sharing, and analyzing surveys (Esri, 2021a). The tool allows the information to be collected via the web or by mobile devices, enabling several research applications that involve a community engagement process. According to Smith (2017), the software has features that help in the community engagement process, being a solution that presents easy integration with ArcGIS desktop and with Esri online platforms, allowing a deep spatial data analysis, in addition to being an intuitive software for the research developer and friendly for the interviewee.

The method of using the tool encompasses the steps described as 1-2-3 process (Esri, 2019): (1) ask questions, (2) make answers, and (3) make better decisions, as shown in Figure 9 below, which presents a schematic of this process.

40

Figure 9. 1-2-3 process in Survey123. Adapted from Esri (2019)

The survey can be developed in two ways using the tool, through the Survey123 website or the desktop application, using Survey123 Connect, which allows for more sophisticated questionnaire creation using an XLSform.

The response capture process can take place through a field app that allows responses to be given online and offline. In this case there is a restriction, since access to research through the app is only performed by users who have an account with Esri. There is also the possibility of replying via the web browser.

Regarding data analysis’s possibilities to support decision-making, the Survey123 website allows the visualization of several statistics related to data capture. However, if Survey123 Connect is used, depending on the type of question elaborated in the questionnaire, the data analysis performed on the website is not understandable for forms elaborated in XLSform.

The responses given to Survey123 are stored as a hosted feature service, allowing the preparation of analyzes and maps in ArcGIS Online and integration with other Esri apps, such as Operations Dashboard, StoryMaps, and Collector. The data can also be exported in different formats, such as comma-separated values (CSV), shapefile, or geodatabase, making it possible to carry out various analyses.

41

2.6.2 CASE STUDIES Survey123 can be used in different knowledge areas related to applications in the environment, health, and urban organization.

In the state of Montana, in the United States, the application was used to assist the work of Citizen scientists at Glacier National Park in field observation of loons, part of the Common Loon Citizen Science Project (Wold, 2021). The use of Survey123 by the researchers provides a database in which the researcher himself can follow the results of his actions by visualization of maps created instantly. Besides, it allows decision-making to occur more quickly so that the park remains healthy.

In the United States also, on the northeast Pacific coast, PBS Engineering and Environmental collects data in the field using Survey123 related to environmental site assessments and stormwater inspections for reporting, adding on-site photographs and maps (Esri, 2021b). In addition to automating data collection, one of the positive aspects of using the tool is that data collection does not require that whoever is collecting it to have GIS experience.

Survey123 is also used by the Lebanese Red Cross, where volunteers survey various issues of interest, such as recruiting blood donors and assessing refugees’ health (Esri, 2021c).

The data collected in the entity’s projects were integrated with ArcGIS Dashboards, which gives a real-time view of the data obtained and in ArcGIS for Power BI, making it possible to carry out data analysis.

Rio de Janeiro’s city hall used the data collected in a Survey123 to verify the interest of the local population in downtown neighborhoods (Prefeitura do Rio de Janeiro, 2021). The results feed into a Dashboard for ArcGIS that presents the main data collected. The results obtained from gathering the information help managers in making decisions about how to revitalize the city center.

3 METHODOLOGY 3.1 AREA OF STUDY The study area of the present work corresponds to the municipality of Rio de Janeiro in Brazil. The city is located in the southeast region of the country and is bathed by the Atlantic Ocean. Rio de Janeiro is the second-largest city in the country in terms of population, with about 6.7 million people (IBGE, 2020), distributed over an area of 1,200.33 km 2, which results in a population density of 5,597.55 inh./km². The municipality area is administratively divided into five planning areas, subdivided into 16 planning regions, 33 administrative regions, and 163 neighborhoods (IPP, 2019). To evaluate the online survey results, the planning regions’ geographic division will be used, as they have a certain parity with the population’s socioeconomic conditions. In Figure 10 below, it is possible to see the city’s location in Brazil and the 16 planning regions used in the study.

Figure 10. Study area location

42

3.2 DATA USED IN THE STUDY The present study uses primary data collected in an online questionnaire applied between April 7 and May 11, 2020. The questionnaire has 15 questions that aim to verify how the COVID-19 pandemic has affected people in the city of Rio de Janeiro. The data obtained address issues such as income, employment, activities that were affected by the start of the pandemic, people’s feelings about the pandemic, among others.

Additionally, some geographical regions of the city will be used, particularly the planning regions, available at the public geographic database of the city hall (Instituto Pereira Passos – IPP). These data have been geoenriched with the ArcGIS demographic data from the year 2018. Of the variables present in the database, the number of men and women, age, and population density for each planning region in the city will be used.

Concerning the COVID-19 data, the public bases for mapping the pandemic will be used, especially the data provided by the city hall of Rio de Janeiro, considering that in this data information is included by the planning regions used in the present study. The database fed by the Ministério da Saúde (Ministry of Health), for example, does not reach the level of detail necessary to carry out the planned analyzes.

Finally, to verify how the questions related to the questionnaire were searched over time, the Google Trends database will be used, as well as the official public databases related to the pandemic in order to situate it in time and space in the analysis performed.

In the table below (Figure 11), it is possible to verify the data and sources used for the present study’s analyses.

Data

Source

Answers to the questionnaire Rio de Janeiro’s Planning Regions Geoenriched City Hall’s COVID-19 Dashboard Google Trends

ArcGIS Survey123 (https://survey123.arcgis.com/surveys/a9ade036619c4f04b29d0e33b8849dfd/overview) Data Rio – Instituto Pereira Passos (https://www.data.rio/datasets/3033f988051a4ce38a396202bc205a2e_0) Painel Rio COVID-19 (https://experience.arcgis.com/experience/38efc69787a346959c931568bd9e2cc4) Package gtrends in R

Figure 11. Data Source

43

3.3 FLUXOGRAM The methodology developed in the present study aimed to be implemented quickly due to the pandemic propagation speed. Thus, it was necessary to find tools that could be integrated easily, without great development effort and the need for programming experts.

Since the beginning, as it was conceived to be a collaborative tool, it was fundamental that the adjustments between the first and second rounds could also be easily done.

In Figure 12, it is possible to verify the development of the steps of the methodology proposed in the present investigation. It can be assessed that the survey was developed to accept suggestions after a first round. The definition of the study area occurred in the second round of application.

44

45

Figure 12. Methodological steps

3.4 METHODOLOGY DESCRIPTION 3.4.1 AN OVERVIEW The present study had as its initial problem the development of a collaborative mapping from an online survey, addressing issues with a quantitative and qualitative focus, related to COVID-19 within a GIS context, something that was not observed in the initial months of the pandemic. Thus, it is possible to interpret certain impacts related to the pandemic beyond the advance of the number of cases and deaths related to COVID-19 in space and time.

46

A first version of the mapping was carried out without defining a specific study area, testing the tool’s functionality, and collecting feedback from respondents, having been disseminated on the main social media platforms, such as WhatsApp, LinkedIn, Instagram, and Facebook.

3.4.2 ONLINE SURVEY ELABORATION The primary data used in this research were obtained through independent and collaborative mapping. The steps in obtaining these data are described below.

As aforementioned, several platforms were monitoring the evolution of the pandemic in space and time as soon as the WHO declared it. However, there was no follow-up research focused on the impacts to people's daily lives. In this context, an online survey was developed on Survey123 Connect for ArcGIS, with questions structured in different possible answer formats, such as single choice or multiple-choice answers. An inserted question asked for the interviewee's location, which then generated a coordinate (x, y).

For later integration with a Dashboard for ArcGIS, a decision was made to develop the questionnaire in XLSForm, as explained in item 2.6.1, because it is a more sophisticated way to develop the questionnaire in Survey123. The main question that guided the questionnaire was: "How has COVID-19 affected you?", followed by the questions elaborated in XLSForm. Thus, some questions were asked about how the pandemic was affecting people's lives. In Figure 13a below, it is possible to view the survey creation environment in Survey123 Connect, as well as the XLSForm in which the questions were developed (Figure 13b).

47

(a)

(b)

Figure 13. Survey123 Connect (a) and XLSForm (b)

The questionnaire was incorporated in a link into a Story Map, which provided some information about the pandemic (Silva, 2020a). The WHO dashboard related to the

48

pandemic was embedded in the Story Map as a source of information about cases and deaths of COVID-19 worldwide.

Figure 14. Story Map which brings the survey

At the end of the Story Map, a dashboard was presented, and it was being fed by the answered questionnaires in real-time. As previously mentioned, to be able to integrate the dashboard with the survey data, it was necessary to develop it in XLSForm, as two questions were multiple-choice, in which the interviewee could answer more than one option. Thus, if the questionnaire was created in Survey123 using the web designer, these fields would become text fields (esriFieldTypeString), making integration with the dashboard impossible without manipulating the data. Thus, in order not to require manual data update, the XLSForm was created. The dashboard created (Figure 15) addressed the questions inserted in the questionnaire, and the variables had filters between them, which could make the visualization more interesting for those who were manipulating the data.

The management of data obtained in the survey, story map, and the dashboard was carried out in ArcGIS Online.

49

Figure 15. Real-time dashboard

As explained, the first round of the survey did not have a specific area of interest and was disclosed through social media. With the first data obtained, the initiative was described in an article published on a geotechnology portal (Silva, 2020b).

From the beginning, the research was open to contributions. Thus, after the first round and disclosure, feedback was given by them to improve some questions. There was also reinforcement in the dissemination part through a research group from Veiga de Almeida University (UVA), members of the Geospatial Studies Laboratory (LEGO) in Rio de Janeiro.

In this way, after some specific research adjustments, a new round was made, focusing on data collection in Rio de Janeiro. The research was then applied between April 07 and May 11, 2020. The 15 questions asked and the types of each question are shown in Figure 16 below. All questions were mandatory.

50

How has COVID-19 affected you?

(The objective of this survey is to map how the coronavirus has affected people's lives, what are the impacts and the needs that the population has felt) How old are you?

Up to 25 years Between 45 - 60 years Between 25 - 35 years More than 60 years Between 35 - 45 years Gender?

Female I prefer not to inform Male How many people with whom you have had personal contact have contracted the coronavirus (including you)?

These may be unconfirmed suspected cases I didn't get the disease and I don't know anyone Two people who got it One person Three or more people Are there deaths related to the disease in your personal circle?

These may be unconfirmed suspected cases There are not Yes, two people Yes, one person Do you find yourself in quarantine (it may be self-isolation)?

Yes No If so, since when?

What is your main source of income?

Salaried with stability Informal worker Salaried without stability Retired Self-employed Unemployed Entrepreneur What is your average family income bracket?

I don't have income 5 to 10 minimum wages Up to 1 minimum wage More than 10 minimum wages 1 to 5 minimum wages Did the consequences of the pandemic impact you financially? If so, how much?

No 5 to 10 minimum wages Up to 1 minimum wage More than 10 minimum wages 1 to 5 minimum wages Do you contribute financially to services you used before the pandemic that you are no longer using?

Domestic services (cleaning, gardening, others), gym, schools, therapies, etc.

Yes No Have you been looking to consume from local merchants?

Yes No Would you say the pandemic has put you in a delicate financial position?

Yes No From the following items, select the ones which were hampered after the onset of the disease.

Health Care Physical activities Financial resources Recreation Food Academic/School Activities Transport Labor Activity Assistance from Other People (family members, Other employees) Hygiene Items None What have you been feeling after the disease started?

Anxiety Sadness Depression Fear Loneliness Other Concern None Anguish What is your location?

Figure 16. Survey’s questions.

Type

Single choice from drop-down list

Single choice

Single choice

Single choice

Single choice Date

Single choice

Single choice

Single choice

Single choice

Single choice Single choice

Multiple-choice

Multiple-choice

Map

3.4.3 TREATMENT OF DATA OBTAINED The primary data obtained in the online survey are already spatialized. However, it is necessary to verify the data obtained both exclusively in the municipality of Rio de Janeiro and in the period of interest between April 7 and May 11, 2020. It is essential to clarify that, from the beginning, it was not intended to constitute a representative sample of the city of Rio de Janeiro nor to impose a geographical limitation, which results in this first analysis and filtering of the data obtained.

These data will be segmented according to the geographic division of the city's planning areas, which is information provided by the city hall of Rio de Janeiro. Thus, some analyzes and mappings will be carried out with the results obtained in the research.

Besides, it is necessary to add some demographic and socioeconomic data to this segmentation (Urban & Nakada, 2020) and the official data related to COVID-19 to verify the adherence between the data obtained in the mapping and the variables related to these data observed in reality. For this analysis, mapping and a scatterplot matrix will be built in ArcGIS Pro in order to evaluate the relationships between variables.

As previously explained, the data used with the demographic information of the Rio de Janeiro population will be obtained on the geospatial basis of the 16 planning regions of the city that were geoenriched with data from 2018.

Finally, some qualitative questions in the questionnaire are not addressed by official data related to the pandemic and in another GIS visualization. Thus, this information can be verified through the interest of people over time, observed in the search for certain subjects on Google, via Google Trends (Biasi, 2020), seeking the gradual evolution of words related to mental health after the arrival of the pandemic. For example, it can be a way to assess which feelings were arisen in the population due to COVID-19.

51

4 RESULTS AND DISCUSSION 4.1 GENERAL RESULTS The research's general results present the quantitative data obtained from responses to Survey123 after the definition of the application area in the city of Rio de Janeiro and dissemination in the main social media. It can also be seen how these answers were obtained according to the city’s planning regions and the relation between these variables and some demographic data of the city.

As a result of applying the survey, 250 responses were obtained for Rio de Janeiro’s municipality in the target period of application, between April 7 and May 11, 2020 (Figure 17). On the map below, it is possible to check the interviewees’ points regarding their location and the limits of the city’s planning regions. These locations were informed by the interviewees in the Survey123.

The region in which the highest number of responses were obtained was region 2.2 (Tijuca) with 65 interactions, followed by region 2.1 (Zona Sul), with 37 interactions, and region 1.1 (Centro), with 35 interactions. Region 5.4 (Guaratiba) was the only planning region in which no answers were collected. All answers compiled in the survey are consolidated by the planning region in the appendix section of this work.

52

53

Figure 17. Location of Survey’s answers

Among these responses, 73.20% were given by women, with the remaining 26.80% being answered by men. The proportion found in the city, considering the geoenriched data obtained in the city hall website, is about 47% of men and 53% of women. Among the interviewees' age, groups up to 25 years (31%) and between 45 and 60 years (32%) stood out (Figure 18).

54

6% 31% 32% Up to 25 years Between 25 - 35 years Between 35 - 45 years

13% 18%

Between 45 - 60 years More than 60 years

Figure 18. Proportion of age groups

The city hall's enriched demographic data present a proportion of around 20% for each referenced age group, concentrating about 22% of the population between 15 and 29 years old and 23% in the range between 30 and 44 years old, being these groups a higher population proportion (Table 1).

Table 1. Rio de Janeiro's population age. Adapted from Data Rio – Instituto Pereira Passos, 2018.

2018 Total Population Age 0-14

2018 Total Population Age 15-29

2018 Total Population Age 30-44

2018 Total Population Age 45-59

2018 Total Population Age 60+

17,43%

21,90%

22,82%

19,58%

18,26%

It is a little challenging to compare the data obtained in Survey123 with the city hall's information, given that the adopted age groups are different. However, in the official database, it is possible to affirm that the ranges had more balanced figures considering that it was a representative sample, and significantly different percentages between the ranges can be perceived when comparing to the survey data. Additionally, it is possible to verify that the two age groups that correspond (45-59/60 and 60+) have quite different percentages between what was found in the research and reality.

Continuing the analysis of the data obtained in the survey, the following map (Figure 19) was built based on two pieces of information: population density in each planning region, based on the city hall’s data, and the number of responses obtained in the online questionnaire.

55

The analysis performed considers whether the mapped variables have a positive relationship with each other, with low-low, medium-medium, or high-high values in each one, or if the relationships between the variables have different results. This approach will be used in some of the mappings in the sequence.

Some regions have a positive relationship between the variables used, such as regions 5.3 (Santa Cruz), 5.2 (Campo Grande), and 4.2 (Barra da Tijuca) in which there are low values in both variables; in regions 5.1 (Bangu) and 4.1 (Jacarepaguá) the values are intermediate; in 2.1 (Zona Sul) they have high values. On the other hand, there are cases of high values of population density and few responses, as in 3.1 (Ramos), 3.6 (Pavuna), and 3.4 (Inhaúma), as well as a fact verified in 3.7 (Ilha do Governador), with intermediate population density and few responses. In 3.2 (Méier), 3.3 (Madureira), and 3.5 (Penha), there are intermediate values of responses, being places of high population density. Finally, in 2.2 (Tijuca) and 1.1 (Centro), there is a high incidence of answers to the questionnaire, being places of intermediate population density. The region with no answers in the questionnaire, 5.4 (Guraratiba), has the lowest population density in the city.

56

Figure 19. Number of answers and population density

4.2 MAPPING DATA RELATED TO COVID-19 The data related to the official record of cases will be used to build a map that relates this information to the known cases reported by the interviewees in the survey. It is noteworthy that the official data regarding the number of COVID-19 cases in Rio de Janeiro were the cumulative data recorded up to the end of the research, on May 11, 2020. According to the official data, there were 22,115 accumulated cases of COVID-19 in the city of Rio de Janeiro until the end date of the survey.

Thus, performing a similar analysis that was made in Figure 19 for the sum of the cases reported in the questionnaire and the official data related to COVID-19 displayed in the city

57

hall dashboard (Figure 20), a certain adherence is noted in the number of cases in the following regions: 3.4 (Inhaúma), 3.6 (Pavuna) and 3.7 (Ilha do Governador), which is low for both variables, and 1.1 (Centro), 3.2 (Méier) and 3.3 (Madureira), with intermediate values. The highest occurrence happens in places of accumulated cases at an intermediate level, and which had a low number of responses on this item in the questionnaire, representing six areas. There is a caveat here in the number of cases added to the questionnaire since the maximum number that the interviewee could answer was three or more known people who had contracted COVID-19 (as explained in the table shown in Figure 15). Thus, the number of cases obtained in the questionnaire may be underestimated.

Figure 20. Related cases in the questionnaire and official data on COVID-19 cases

58

The official data related to deaths in Rio de Janeiro will be used in the following map. As done before, the number of deaths considered is the accumulated number until May 11, 2020. According to the official data, there was a total of 5,459 deaths in Rio de Janeiro by the end of the survey.

Observing what happens with the numbers of deaths (Figure 21), there is an adherence between the official data and that collected in the survey in the following regions: 3.4 (Inhaúma), 3.7 (Ilha do Governador), 4.2 (Barra da Tijuca) and 5.3 (Santa Cruz), presenting low figures, 1.1 (Centro), 3.2 (Méier) and 3.6 (Pavuna), which have intermediate figures and 2.1 (Zona Sul), 3.3 (Madureira) and 5.1 (Bangu), presenting high figures. There are also cases where the accumulated deaths by COVID-19 from official data presented intermediate figures, but the questionnaire data added a high number of deaths, in 2.2 (Tijuca) and 3.5 (Penha), or a low number of deaths, in 3.1 (Ramos) and 5.2 (Campo Grande) values. Finally, in region 4.1 (Jacarepaguá), the official data values are high, while in the survey they are intermediate. In this research variable, the same caveat made in regard to case data is appropriate, as the option with the highest amount in the questionnaire was whether the interviewee knew 2 or more people who died by COVID-19 (as shown in the survey’s questions in Figure 16).

59

Figure 21. Related deaths in the questionnaire and official data on COVID-19 deaths

4.3 MAPPING DATA RELATED TO COVID-19 IMPACTS Figure 22 contains a mapping illustrating only the questions in the questionnaire. It is possible to verify three pieces of information from the questionnaire, (1) what is the primary source of income of the interviewees, (2) their family income (in minimum wages), and (3) what is the impact of the pandemic on their income (also in minimum wages).

The bars show the interviewees' occupation by planning region. It is also possible to view the total data of occupations of all respondents. The colors of the polygons of the planning regions, as in previous maps, relate two variables. In this case, the variables of average income and impact on income.

There is a predominance of people who have their income from a stable job (83 people, corresponding to 33.20% of the total), with the smallest group being entrepreneurs (13 people or just over 5%).

60

61

Figure 22. Income and type of occupation

Regarding the city's regions, it is observed that people have a high income in region 2.2 (Tijuca), but there is also a report of significant impact on income. In 4.2 (Barra da Tijuca), low impact and high income are reported. Also, this region does not have salaried with stability as a predominant occupation. According to information obtained in the enriched data in the city hall source, this is the region where the population has the second-highest purchasing power in the city. 3.1 (Ramos), 3.4 (Inhaúma), and 3.7 (Ilha do Governador) were the places where the interviewees had the lowest income and had suffered a high impact from the pandemic. Out of these three, areas 3.1 (Ramos) and 3.4 (Inhaúma) are in the group of the five regions with the lowest purchasing power in the city. The three areas

62

in which the results were low for both income and the impacts of the pandemic on income – as observed in 5.3 (Santa Cruz), 5.2 (Campo Grande), and 3.6 (Pavuna) – have the highest number of non-stable workers (43.75%).

The predominance of workers who enjoy professional stability in regions close to the city's downtown may reflect that most of the (stable) public jobs in Rio de Janeiro are located in the center's vicinity. Thus, it is expected that interviewees who responded to this item are close to the central region.

Continuing the analysis, the following map (Figure 23) presents information regarding the items that the interviewee considers to have been affected by the pandemic. This information is presented both by the planning regions and by the total that respondents across the city answered. This question in the questionnaire allowed the choice of multiple items.

The most mentioned item among the options was recreation (totaling 202 responses), followed by physical activities (with 167 answers), academic/school activities (with 160 responses), assistance from other people (136 responses), and financial resources (134 replies). Among the available items, the least mentioned was related to hygiene items, only mentioned by 25 respondents. People who mentioned that other items had affected them due to the pandemic and were not covered in the survey totaled 31; people who said that nothing was affected totaled 6.

In places where recreation was not predominant were regions 5.1 (Bangu), 3.3 (Madureira), and 4.1 (Jacarepaguá), in which the outstanding item was academic/school activity, and 3.2 (Méier), in which the most marked items were physical activities and financial resources.

In the other areas, the recreation option was the one which was selected the most (alone or in conjunction with another item). It is noted that in places where this item was chosen the most are regions that are not bathed by the sea, an important place of recreation in the city.

63

Figure 23. Items affected by the pandemic

The issues addressed in the survey can be understood as having affected the population by studying related words that were searched on Google. Thus, in order to complement the data presented in the previous map, the following graphs relate the most reported items in the survey for what was affected by the pandemic with the search for associated words in Google Trends.

The graphs were generated in R, and the Google Trends package is limited geographically since it is not possible to posit the query only for the municipality of Rio de Janeiro, being the state level the lowest possible one. As Rio de Janeiro is the capital of the state of Rio

64

de Janeiro, being the most populated city of the state, a decision was made to investigate the researched words even with this circumscription.

The first graphs are related to the most reported items in Figure 23, which are recreation, physical activity, academic/school activity, assistance from other people, and financial resources. The chosen words related to these items have their correspondence in English presented in the table below (Figure 24). The vertical lines that appear in each graph (Figures 25 and 26) correlate to the dates of the beginning of the pandemic (in blue), declared on March 11, 2020, and the date of application of the survey (the two black lines), between April 7 and May 11, 2020.

Searched words In Portuguese In English Jogo Game Treino Physical Training Livro Book EAD Distance Education Empréstimo Loan Seguro Unemployment desemprego Insurance Emprego Employment Auxílio Emergency Aid emergencial Figure 24. Searched words in Google Trends

Relation to the survey Recreation Physical Activities Recreation / Academic/School Activities Academic/School Activities Financial Resources Assistance from Other People / Financial Resources Financial Resources Assistance from Other People / Financial Resources

65

Figure 25. Graphics created from the searched words in Google Trends

Figure 26. Graphics created from the searched words in Google Trends

It is noticed that the words like game, physical training, and employment were not found in a moment of greater demand in the period of the research. However, they were the items that were most sought after at a later point in the pandemic. On the other hand, topics such as book research, distance education, loans, unemployment insurance, and emergency aid saw intense online search in this period.

66

Survey123's question related to feelings caused by the pandemic was another issue where the interviewee could check more than one option. Among the possibilities that could be selected, those most frequently picked were concern (with 208 responses), anxiety (with 160 answers), fear (with 143 responses), and anguish (with 116 responses). Depression is the least reported feeling among respondents. Some interviewees reported that other feelings were caused by the pandemic (with 22 replies), and others reported that no feelings were caused by the pandemic (with 11 responses).

The following map (Figure 27) shows the total of each feeling among those available in the questionnaire, as well as the feelings by city planning region. In all planning regions, the feeling of concern was the most selected. In some regions, such as in region 5.2 (Campo Grande) and region 5.1 (Bangu), concern was mentioned as much as anxiety. Region 3.3 (Madureira) was the one with the most balanced selection, except for areas where there was a low number of participants, such as region 3.7 (Ilha do Governador), 3.1 (Ramos), and 3.4 (Inhaúma), who had only 1 (in the first two) or 2 (in the last one) interviewees.

67

Figure 27. Sentiment provoked by the pandemic

As previously done, to better understand the relationship between these reports on people’s feelings and the reality of what was being researched at the time, search words on Google that have a relationship with the feelings addressed in the research were also selected. The defined words were the three most reported ones in the survey (concern, anxiety, and fear).

The words used and their English counterparts are shown in Figure 28. The graph was generated in R from the word search on Google Trends with words related to the interviewees' most reported feelings in the survey, being shown below (Figure 29).

68

To give a better date reference, the generated graph shows, in vertical lines, the pandemic's start date (in orange) and the start and end dates of the research application (in black).

Searched words In Portuguese In English Mental Saúde mental Health Ansiedade Anxiety Terapia Therapy Preocupação Concern Medo Fear

Relation to the survey

Feelings after the disease

Figure 28. Searched words in Google Trends

Figure 29. Graphics created from the searched words in Google Trends

It is noticed that anxiety, fear, and therapy were the words that people were interested in researching in the state of Rio de Janeiro. Mental health peaked slightly in the survey period but saw a significant peak in searches in June 2020.

4.4 VARIABLES RELATION The last survey data analysis from Survey123 will address items related to income and quarantine (or social isolation). The way to analyze this data will be through the construction of a scatterplot matrix to verify the relationships between the variables.

Questions from official COVID-19 data were incorporated, such as the cumulative number of cases and deaths from the disease in Rio de Janeiro's municipality. As already mentioned, the number of cases and deaths that are used in this study are the accumulated number until the last day that the Survey123 questionnaire was available to receive answers.

The questionnaire information that was used to construct the matrix is: •

Do you find yourself in quarantine (it may be self-isolation)?

•

What is your average family income bracket?

•

Did the consequences of the pandemic impact you financially? If so, how much?

•

Do you contribute financially to the services you used before the pandemic that you are no longer using?

•

Have you been looking to consume from local merchants?

•

Would you say the pandemic has put you in a delicate financial position?

Thus, the matrix below (Figure 30) was generated with the answers to these questions, in addition to official data related to the cases and deaths by COVID-19, in each planning region of the city.

69

70

Figure 30. Scatterplot matrix using social isolation and income responses in the survey and official data

The measures that appear in the table show the values of R2, representing how closely the variables are related. Thus, the highest R2 value (0.99) appears in two relations, between the consumption of local merchants and quarantine (or self-isolation) and the consumption of local merchants and the upkeep of payments for services that were used before the pandemic. This means that local merchants' consumption explains 99% of the variation in the other two variables.

It can be noticed that the values obtained for R2 among the variables from the survey are relatively high, the lowest being 0.82, which relates the interviewee's total family income to be in a delicate financial situation due to the pandemic. In this sense, it is feasible to think that the higher the one’s family income is, the lesser is the chance that the person is in a delicate financial situation. Thus, it makes sense that this ratio is smaller than the others, even if it still has a high value.

When verifying the answers to the questionnaire and its relationship with the official data of COVID-19, one can realize that the values of R2 diminished quite a lot. The highest value occurs between the number of cases and deaths (0.69), which means that the highest value does not have a relation with the data collected in the Survey123 questionnaire. When these two variables are compared to that found in the questionnaire, there is a more

71

significant relationship between people who have adopted quarantine or social isolation and the accumulated number of deaths. In other words, the cumulative number of deaths from COVID-19 explains 22% of the variation in quarantine or social isolation.

The results that presented the lowest values of R2 relate the variables of cases and deaths

by COVID-19 and the total impact on the income of the interviewees, both having a value of 0.05. Thus, there is an established relationship between the variables separately: the information collected in the questionnaire has a strong correlation, as well as between the two variables that relate the number of cases and deaths based on official numbers. There is no high association when analyzing the relationships between variables together.

4.5 ANALYSES OF RESULTS Answering the research questions that were asked in section 1.2.3: •

Do the results reflect how the impacts felt by the population as a result of the pandemic?

Although the research has some limitations, which will be explained below, the results were able to reflect some of the impacts felt by the population after the early onset of the pandemic.

•

Do the results reflect people's interest in Internet research in certain words related to the pandemic?

Again, considering research limitations, some words that reflected the most affected items in the search were related to the Google search pattern for the state of Rio de Janeiro.

The questionnaire initially had the prerogative of mapping in space impacts on Rio de Janeiro's population daily life due to the pandemic of COVID-19. With the total evaluation of the data obtained in the research, it was possible to perceive the spatial dynamics of the responses to the survey by the 16 planning regions of Rio de Janeiro's city.

The mapping of most of the 15 questions asked in the survey was carried out, addressing the spatial information obtained in different ways, relating to the demographic data of the city, such as the official data related to COVID-19 concerning the number of cases and

72

deaths, and establishing a link between some impacts selected and the relevance of the items questioned in Google searches.

As already mentioned, the questionnaire was not developed with the intention of constituting a representative sample of the city. This limitation can be observed in how the answers were obtained, more concentrated in certain regions of the city without reflecting the planning regions' population density. This can be perceived visually in the observation of the map shown in Figure 17.

The lack of relationship between the responses obtained and the reality was also observed in the uneven distribution of answers by sex and age of the interviewees, which do not reflect the proportion of these variables found in the city.

As mentioned, the planning region that obtained the highest number of responses to the survey was region 2.2 (Tijuca). Consequently, it was the region with the highest number of cases and deaths reported in the questionnaire. However, when compared with the official data of cases and deaths by COVID-19, the region is in the eighth place among the 16 planning regions.

The region with the highest number of cases and deaths until the survey's end date was region 2.1 (Zona Sul), the region with the highest concentration of income in the city and the second region in terms of survey responses.

In addition, in the regions that are located on the west side of the city, such as 4.1 (Jacarepaguá) and 4.2 (Barra da Tijuca), there is not a high occurrence of responses and, consequently, there are few reports of cases and deaths, despite the significant numbers during the research application period.

On the other hand, in one region, there was no answer to the questionnaire (region 5.4 Guaratiba), and in three regions, the number of responses was relatively low (3.7 - Ilha do Governador, 3.1 - Ramos, and 3.4 - Inhaúma). This situation ends up affecting comparisons and hampers the establishment of connections that can be made between different planning regions through the data obtained.

Among the maps presented, the one that reports the population density and the number of responses (Figure 19) shows that six of the 15 planning regions that had responses have

73

a positive relationship between the variables, considering a low-low, medium-medium or high-high correspondence between the observed values.

Regarding the number of cases and deaths obtained in the questionnaire and its relationship with the official data (Figures 20, and 21 respectively), the map with the number of deaths seems to present a greater adherence between research and what is found in reality, with eight planning regions having a positive relationship. In the map that shows the number of cases, the positive relationship between the variables occurs in 6 planning regions.

As the pandemic was still in its early stages, there were just under 5,500 deaths in the city and only over 22,000 cases, high numbers compared to other realities in the world but still considered low for the Brazilian experience. Thus, it was expected a low occurrence related to deaths in the questionnaire.

As Antoniou et al. (2020) reported, constant disclosure is important in this type of initiative because a decrease in the number of responses over the time the survey was available was noticed. It is also necessary to consider the means of disseminating the research. As much as the research has been disseminated in several online media platforms (social media), it is necessary to recognize that a more significant number of responses could have brought more robust results. Thus, the use of other media can be important for this type of approach.

The lack of this consistency in the application of the survey can be seen in the graph below (Figure 31), which is a graph obtained in the web version of Survey123. It is possible to observe the variation in compiling answers throughout the period of application of the survey, between April 7 and May 11, 2020. The moments when the dissemination intensified appear in well-defined peaks in the graph and the effects of isolated campaigns last a few days.

74

Figure 31. Graph with the answers obtained in Survey123.

There is also a bias in the sample obtained, which can be seen both in the concentration of the points that were obtained (which became sparser towards the west), as well as in the survey of information regarding the occupation (most of the stable workers) and regarding the average income (very high by Brazilian standards). The exclusion of marginalized people in this type of initiative, as already pointed out by Haworth & Bruce (2015), can be another infortune imposition of the country's inequality.

This relationship between occupation and average income and the pandemic’s impact on income can be seen in the map in Figure 22, in which the planning area that has the highest level of income in the city, 2.1 (South Zone). The area has a high average income among respondents, medium impact on income due to the pandemic and most people have a stable occupation as their primary source of income.

As the pandemic was still beginning when the research was launched, there was a significant lack of accurate information about the disease and a lack of mass testing. This gap in knowledge may also have influenced how participants were responding to the questionnaire, as many people ended up not being sure about the manifestation of the disease because the symptoms were not well known, and testing was insufficient.

As for the items affected by the pandemic (Figure 23) and the responses to the affected feelings (Figure 27), it is clear, in the first, that the pattern of responses may be related to the bias of the constituted sample, considering that the most relevant item listed was recreation.

When the questionnaire was applied, there was a shortage of masks for individual protection and hand sanitizers, alongside the previously established problem of lack of

75

sanitation in the city. Even in this context, the category related to hygiene items ranked last among the items considered affected within these groups.

Nonetheless, words that reflected the most affected items were related to Google's search pattern for the state of Rio de Janeiro. In Google Trends, words related to education and income or items reported as having suffered impacts by the pandemic’s outcome increased in Google search during the survey period.

When it came to feelings provoked by the pandemic, anxiety and fear were words searched within the survey’s availability period. However, concern, which was the most chosen feeling among the interviewees, was not much sought. There was a peak in the search for mental health a few months after the survey was applied.

Overall, the survey revealed that respondents suffered several impacts related to the pandemic since the options of "none" that were present in both questions were chosen by few interviewees.

The absence of a relationship between the variables obtained in the questionnaire and the other variables used can be seen in the scatterplot matrix constructed between the questionnaire's variables and the disease's official data.

What was found was that the variables in the questionnaire have a very high R2, meaning a percentage in the relationship between variables in which one variable can explain the variation of the other. When the questionnaire's variables are analyzed together with the official data, this correlation becomes very fragile. Among them, the variables of cases and deaths by COVID-19 have a relevant R2.

Regarding the questionnaire developed in Survey123, the limitation pointed out by Smith (2017) was noticed when new questions were added after the feedback of the first round of questions, since after a first publication the questionnaire cannot be modified.

In order to keep the first data in the same database even after updating with new questions, a decision was made to keep the previous data, which required that a date and location filter be made, considering also that in that second moment an option was made to limit the questionnaire to the city of Rio de Janeiro.

76

Survey123 allows geographic limitation for a given location, but even if configured to do so in XLSForm, respondents from outside the municipality could respond, which generates later filtering work.

One of the advantages of this type of initiative, the speed of implementation, was observed during the research application, and the items related to feelings and mental health were suggestions of the first people who answered the questionnaire, having been quickly incorporated into it in a second round, which this work proposes to analyze. However, it is noticed that an approach to survey this type of information regarding mental health and people’s daily life in GIS is not common in Brazilian reality.

There are some initiatives related to understanding some aspects of the pandemic, such as the online survey conducted by Fundação Oswaldo Cruz9 (FIOCRUZ) (2021), which is a traditional health research institution in Brazil. An online survey was carried out in a similar period to the present study (from April 24 to May 8, 2020) to understand some aspects of the pandemic, in which the results identified that 40% of people feel sad/depressed and that 54% reported feeling anxious/nervous frequently (Silva & Lobato, 2020). Despite presenting perspectives associated with the one addressed in the present study, they do not add the spatial component to their results.

9

Oswaldo Cruz Foundation

5 CONCLUSIONS As demonstrated throughout this study, despite its limitations in terms of sample, representativeness, and spatial diversity, the research carried out fulfills its objective of evaluating the data obtained with the questionnaire's application.

Different analyses were performed to visualize the results, such as through mappings, construction of graphs in R, using the Google Trends package, and developing a scatterplot.

Complementarily, the use of official data on the disease’s action in the city and demographic rates were also accessed.

The assessment of impacts suffered by the population in a spatial approach is important in understanding the pandemic and the guidelines to be issued by public authorities, especially in a context of a very high social inequality as seen in Rio de Janeiro, as the impacts perceived in wealthier areas of the city are different from the impacts perceived in more impoverished locations.

The mapping carried out, aggregating different variables by the city's planning regions, helps to understand in an intertwined way how the impacts were felt by the people who answered the questionnaire.

With a larger and more representative sample of the city, the analysis level could have been more detailed. Instead of using the divisions of the 16 planning regions, the division by the 33 administrative regions or 163 neighborhoods could have been used, which would end up highlighting the differences found within the city’s perimeter because rich and poor localities end up practically sharing the same space in the scale used in the present study.

In terms of the impacts upon the population, the research accentuates some concerns regarding loss of income and mental health effects. On the other hand, it fleshed out that even if people were isolated and often prevented from maintaining certain services and consumption patterns, fomenting a concern in terms of offering financial support so that these activities can survive during a scenario of restrictions and exceptions.

The words searched on Google partially reflect the information obtained from the search.

This may be related to the words chosen, some directly linked to the terms used in the investigation and others not.

77

78

The methodology chosen for this type of analysis can be improved, as suggested by the study carried out by the Laboratório de Geoprocessamento e Cartografia Social10 (LABOCART) at Universidade Federal do Ceará11 (UFC) (2020), in which a collaborative mapping related to COVID-19 was carried out in the city of Fortaleza, Brazil. For the definition of words that would justify non-compliance with social isolation, the webQDA software was used, and a cloud of 100 words was built that would be related to this issue.

Therefore, this adopted methodology could help in a more structured way in the definition of words related to the issues addressed in the research and related to the results obtained.

Thus, through the treatment of the data observed in the research, it was possible to identify and quantify the impacts endured as a result of the COVID-19 pandemic. One of the limitations of the research, as already mentioned, refers to the sample's constitution. This context does not favor the statistical treatment of data, which could be an approach used in data analysis.

Other limitations verified are related to the lack of understanding of effects of COVID-19, considering that the pandemic was still at the beginning when the questionnaire was applied. In addition to that, the lack of precision of the geographical location, considering that the geographic limiter of Survey123 was not effective in preventing some points from being marked in the middle of the ocean, for example, meant that some samples had to be completely disregarded from the sample.

Nevertheless, it is clear that the research demonstrated, through a collaborative mapping, the perceptions of impact resulting from the pandemic in the city of Rio de Janeiro. The information obtained in this type of initiative, within a context of GIS, can help to understand the development of the pandemic in space and time, given that the dynamics of the disease shape its nuances in accordance with a highly unequal scenario, pointing out paths for a safer resumption of activities in a "new normal" context.

A year after the start of the pandemic, the conduction of a new round, with another understanding of the disease, may bring about new perspectives on the development and impacts of the pandemic.

10 11

Laboratory of Geoprocessing and Social Cartography Federal University of Ceará

79

Also, a better understanding obtained with the collaborative mapping carried out in this study will assist in the definition of more objective and concise questions in the elaboration of the questionnaire, as well as in the definition of more efficient ways of disseminating the research in order to obtain a more representative sample of reality.

The return to the Laboratory of Geospatial Studies (LEGO) research group of the Veiga de Almeida University (UVA) is expected to define new strategies to be developed in a new research round. It is worth mentioning that, at the end of 2020, the initiative received an Honorable Mention from the University as "Best Work in the Undergraduate Course in Geography".

After a year of pandemic, some reflections about the conduct of the health emergency in Brazil may give rise to new perspectives in elaborating other collaborative mappings. Some highlights that can generate further investigations are the following:

•

Expectation regarding the application of vaccines.

Unfortunately, there was no efficient schedule regarding the purchase of sufficient doses of vaccine for the Brazilian population. Currently, on March 15, 2021, two types of vaccines are being applied in Brazil. However, not even priority groups have been able to get vaccinated, and in many cities, including Rio de Janeiro, the vaccination campaign has been suffering constant interruptions due to lack of doses.

•

Emergence of new variants and worsening of the disease.

Brazil has become the new epicenter of the disease, as can be seen in the graph shown in Figure 1, which illustrates that while the world has managed to keep the disease under control, the country has been experiencing the worst peak of the pandemic. This ends up reflecting upon the emergence of new variants of the virus, which can be even more dangerous. It has been verified that Brazil has about 10% of deaths by COVID-19 in the world, having less than 3% of the world population. This lack of control has caused the depletion of health resources, with ICUs reaching more than 100% occupancy in several cities.

80

•

Disinformation about the disease data.

The country has gone through several moments of disinformation about the disease, treatment, and its effects. There were also setbacks and embarrassing mishaps regarding the disclosure of official state data on the disease. This context led to the creation of several independent pandemic monitoring initiatives, which are also important tools for informing the population.

•

Treatment of sequelae.

Many recovered from the disease have persistent sequelae that require medium or longterm treatment. The depletion of health resources makes it challenging to care for these patients. Also, the current situation has prevented other diseases from being treated, and many surgeries and medical procedures have been canceled.

•

Health professionals' burnout.

Health professionals were in high demand throughout the last year, and the physical and mental exhaustion of these professionals has been reported. At a time when the pandemic has intensified in Brazil, this is a point of great concern.

Thus, these are some of the most relevant points that have been dealt with in Brazil's current situation, all of which have consequences in the city of Rio de Janeiro as well. Some of them can be evaluated in a GIS context, through a collaborative mapping, generating the information needed to understand the pandemic's effects and in turn, helping public managers make better-informed decisions.

6 REFERENCES Aguiar, S. (2020). COVID-19: A doença dos espaços de fluxos. GEOgraphia, 22(48), 51.

Adbullahi, F. B., Lawal, M. M. & Agushaka, J. O. (2010). Design and implementation of a Webbased GIS for public healthcare decision support system in Zaria metropolis. International Journal of Recent Research and Applied Studies (IJRRAS), 4(4).

Antoniou, V., Vassilakis, E. & Hatzaki, M. (2020). Is Crowdsourcing a Reliable Method for Mass Data Acquisition? The Case of COVID-19 Spread in Greece During Spring 2020.

International Journal of Geo-Information, 9, 605. https://doi.org/10.3390/ijgi9100605 Araújo, N. S., & Nascimento, D. M. C. (2012). Mapeamento participativo e sua importância na identidade territorial de Barro Vermelho, Santo Amaro - Bahia. Extensio: Revista Eletrônica de Extensão, 9(13), 51–63. https://doi.org/10.5007/1807-0221.2012v9n13p51 AUDITASUS. (2021). COVID-19 Brasil: Ranques dos municípios com as maiores taxas de mortalidade, por faixa de casos confirmados. Retrieved January 28, 2021, from https://auditasus.com.br/destaques/covid19-braasil/covid19-brasil-ranques-municipiostx-mort?uf=RJ&nomemun=RIO%20DE%20JANEIRO-RJ Barboza, R. M., Borges, B., Sant'Anna, A., & Bragança, A. (2020). Capítulo 3: Economia, desenvolvimento e bem-estar. In E. B. Mello, A. G. Vieira & R. M. Barboza (org).

Maravilhosa para todos: Políticas públicas para o Rio de Janeiro. São Paulo: Editora Lux. Belforte, L. C. M., Reis, R. D. S. P., Silva, G. P. da, & Cavalcante, M. M. D. A. (2020). Leitura Geográfica no Contexto da COVID-19 em Rondônia ao Norte do Brasil. Revista Tamoios, 16(1), 145–153. https://doi.org/10.12957/tamoios.2020.50379 Biasi, L. C. K. (2020). Estimation of New COVID-19 Cases in Brazil Using Google Search Data. Poster presented in Ken Kennedy Institute Data Science Conference. Retrieved October 30, 2020, from https://cpb-use1.wpmucdn.com/blogs.rice.edu/dist/d/11215/files/2020/10/Estimation-of-COVID-19Cases-in-Brazil-Using-Google-slides.pdf Boulos, M. N. K. & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID - 19 / severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. International Journal of Health Geographics, 19:8. https://doi.org/10.1186/s12942-020-00202-8 Bravo, J. V. M, & Sluter, C. R. (2018). O Mapeamento Colaborativo: seu surgimento, suas características e o funcionamento das plataformas. Revista Brasileira de Geografia Física, 11(05), 1902-1916.

81

82

CNES, Cadastro Nacional de Estabelecimentos de Saúde. (2019). Recursos Físicos DATASUS Hospitalar - Leitos de Internação - Brasil. Retrieved November 18, 2020, from http://tabnet.datasus.gov.br/cgi/deftohtm.exe?cnes/cnv/leiintbr.def Cardoso, P. V., Seabra, V. D. S., Bastos, I. B., & Porto Costa, E. D. C. (2020). A importância da análise espacial para tomada de decisão: Um olhar sobre a pandemia de COVID-19. Revista Tamoios, 16(1), 125–137. https://doi.org/10.12957/tamoios.2020.50440 Chiaravalloti-Neto, F. (2016). O Geoprocessamento e Saúde Pública. Arquivos de Ciência da Saúde, 23(4), 01-02.

Cobre, A. F., Böger, B., Fachi, M. M., Vilhena, R. O., Domingos, E. L., Tonin, F. S. & Pontarolo, R. (2020). Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil. Ciencia e Saude Coletiva, 25, 4131-4140. https://doi.org/10.1590/1413-812320202510.2.26882020 COVID-19 Analytics. (2020). Retrieved November 8, 2020, from https://covid19analytics.com.br Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S14733099(20)30120-1 Elwood, S., Goodchild, M. F., Sui, D. Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. (2012). Annals of the Association of American Geographers, 102(3), 571–590.

Esri, Environmental Systems Research Institute. (2019). Our 1-2-3 guide to Survey123. Retrieved March 11, 2021 from https://resource.esriuk.com/blog/our-1-2-3-guide-to-survey123/ Esri, Environmental Systems Research Institute. (2021a). ArcGIS Survey123. Retrieved March 11, 2021 from https://www.esri.com/en-us/arcgis/products/arcgis-survey123/overview Esri, Environmental Systems Research Institute. (2021b). Engineering Firm Streamlines Report Generation with Data Collection App. Retrieved March 11, 2021 from https://www.esri.com/en-us/landing-page/product/2020/pbs-case-study Esri, Environmental Systems Research Institute. (2021c). Lebanese Red Cross Uses ArcGIS Apps to Manage Humanitarian Aid Projects. Retrieved March 11, 2021 from https://www.esri.com/en-us/customer-stories/lebanon/lebanese-red-cross FIDA, Fondo Internacional de Desarrollo Agrícola. (2009). Buenas prácticas en cartografía participativa. 1-59. Retrieved November 26, 2020, from http://www.conectadel.org/wpcontent/uploads/downloads/2013/03/5cartografiaparticipativa-121121113649phpapp02.pdf Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 739. https://doi.org/10.1016/j.scitotenv.2020.140033 FIOCRUZ, Fundação Oswaldo Cruz. (2020). MonitoraCovid-19. Retrieved November 8, 2020, from https://bigdata-covid19.icict.fiocruz.br

83

FIOCRUZ, Fundação Oswaldo Cruz. (2021). ConVid - Pesquisa de Comportamentos. Retrieved March 15, 2021 from https://convid.fiocruz.br Goodchild, M. F. & Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth 3(3), pp. 231–241.

Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), pp. 211–221.

Haworth, B. & Bruce, E. (2015). A Review of Volunteered Geographic Information for Disaster Management. Geography Compass, 5, 237–250.

Hino, P., Villa, T. C. S., Sassaki, C. M., Nogueira, J. A. & Santos, C. B. (2006). Geoprocessamento Aplicado à Área da Saúde. Revista Latino-Americana de Enfermagem, 14(6), 939-943. https://doi.org/10.1590/S0104-11692006000600016 IDEIA. (2020). COVID-19. Retrieved November 8, 2020, from https://ideiabigdata.com/covid19/ IBGE, Instituto Brasileiro de Geografia e Estatística . (2010). Censo Demográfico. Retrieved November 18, 2020, from https://www.ibge.gov.br/estatisticas/sociais/populacao/22827-censo-2020censo4.html?=&t=o-que-e IBGE, Instituto Brasileiro de Geografia e Estatística. (2018). IBGE Cidades - Rio de Janeiro.

Educação. Retrieved November 18, 2020, from https://cidades.ibge.gov.br/brasil/rj/riode-janeiro/panorama IBGE, Instituto Brasileiro de Geografia e Estatística. (2019). Painel Síntese por Município COVID19. Retrieved November 10, 2020, from https://covid19.ibge.gov.br/paineis-sintese/ IBGE, Instituto Brasileiro de Geografia e Estatística. (2020). IBGE Cidades - Rio de Janeiro.

População. Retrieved August 20, 2020, from https://cidades.ibge.gov.br/brasil/rj/rio-dejaneiro/panorama IBGE, Instituto Brasileiro de Geografia e Estatística. (2020). O IBGE apoiando o combate à COVID19. Retrieved November 8, 2020, from https://covid19.ibge.gov.br INEP, Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira. (2019). Censo Escolar Resultados e Resumos.

Retrieved November 18, 2020, from http://portal.inep.gov.br/web/guest/resultados-e-resumos INPE, Instituto Nacional de Pesquisas Espaciais. (2006). Análise Geográfica. Retrieved March 24, 2021 from http://www.dpi.inpe.br/spring/portugues/tutorial/analise.html IPP, Instituto Pereira Passos. (2019). Data.Rio. Regiões de Planejamento - Indicadores 2018.

Retrieved January 20, 2021, from https://www.data.rio/datasets/regiões-deplanejamento-indicadores-2018?geometry=-44.463%2C-23.138%2C-42.428%2C22.695&orderBy=CS20PRM_CY&orderByAsc=false&selectedAttribute=CS20IDX_CY Instituto Trata Brasil. (2020). Ranking do Saneamento 2020. Retrieved November 18, 2020, from http://www.tratabrasil.org.br/estudos/estudos-itb/itb/ranking-do-saneamento-2020

84

JHU, Johns Hopkins University. (2020). COVID-19 Dashboard. Retrieved August 10, 2020, from https://coronavirus.jhu.edu/map.html Kim,

S. J. & Bostwick, W. (2020). Social Vulnerability and Racial Inequality in COVID-19 Deaths in Chicago. Health Education & Behavior, 47(4) 509–513. https://doi.org/10.1177/1090198120929677

Leal, T. S., Leão, O. M. R., Barros, R. S. & Rezende, P. O. B. (2020). Dinâmica Espaço-Temporal na Disseminação da COVID-19 em Niterói (RJ): Uma Contribuição Geográfica na Fase Inicial da Pandemia.

Revista Tamoios, 16(1), 108-124. https://doi.org/10.12957/tamoios.2020.50510 León, M. E. S. (2007). SIG na Saúde Pública - Estudo de Caso: Mortalidade Infantil em Dom Pedrito/RS. Dissertação de Mestrado, Universidade Federal de Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brasil.

Mesentier, T. (2020). Covid por CEP - Rio de Janeiro. Retrieved November 8, 2020, from https://covidporcep.rio.br/?fbclid=IwAR0gZFPPKgj4Zwlu0ymh_McPNqYR2r4irb1cds7pLZtiqnVsOvBibEezI4 Ministério da Saúde. (2006). Abordagens Espaciais na Saúde Pública. Série: Capacitação e atualização em geoprocessamento em saúde, Volume 1, Brasília, Distrito Federal, Brasil.

Ministério da Saúde. (2021). Coronavírus Brasil. Retrieved March 11, 2021, from https://covid.saude.gov.br/ Mollalo, A., Vahedi, B. & Rivera, K. M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the Total Environment, 728, 138884. https://doi.org/10.1016/j.scitotenv.2020.138884 MundoGEO. (2020a). Padrão de disseminação da Covid-19 reproduz desigualdades territoriais.

Retrieved November 8, 2020, from https://mundogeo.com/2020/05/22/padrao-dedisseminacao-da-covid-19-reproduz-desigualdades-territoriais/ MundoGEO. (2020b). Mapeamento da Covid-19 atinge marca de 1,6 bilhão de acessos. Retrieved November 11, 2020, from https://mundogeo.com/2020/05/29/mapeamento-da-covid19-atinge-marca-de-16-bilhao-de-acessos/ Observatório COVID-19 BR. (2020).

Retrieved https://covid19br.github.io/sobre.html

November

8,

2020,

from

Ottoni, B., Duque, D., & Ulyssea, G. (2020). Capítulo 4: Mercado de trabalho. In E. B. Mello, A. G. Vieira & R. M. Barboza (org). Maravilhosa para todos: Políticas públicas para o Rio de Janeiro. São Paulo: Editora Lux. Our World in Data. (2021). Coronavirus Data Explorer. Retrieved March 14, 2021, from https://ourworldindata.org/coronavirus-data-explorer Pereira, V. (2020). Capítulo 1: Educação. In E. B. Mello, A. G. Vieira & R. M. Barboza (org).

Maravilhosa para todos: Políticas públicas para o Rio de Janeiro. São Paulo: Editora Lux.

85

Portal

GEOCOVID-19 Brasil.

(2020). http://covid.mapbiomas.org

Retrieved

November

8,

2020,

from

Prefeitura do Rio de Janeiro. (2020). Painel Rio COVID-19. Retrieved November 8, 2020, from https://experience.arcgis.com/experience/38efc69787a346959c931568bd9e2cc4 Prefeitura do Rio de Janeiro. (2021). Reviver Centro. Retrieved March 11, 2021, from https://reviver-centro-pcrj.hub.arcgis.com Sarwar, S., Waheed, R., Sarwar, S. & Khan, A. (2020). Science of the Total Environment COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions? Science of the Total Environment, 730, 139089. https://doi.org/10.1016/j.scitotenv.2020.139089 Silva, A. M. L. B. (2020a). Story Map: Como a COVID-19 tem te afetado? Retrieved August 20, 2020, from https://storymaps.arcgis.com/stories/648d1264be724e529353b1cfb85e7084 Silva, A. M. L. B. (2020b). Artigo: Geotecnologias na identificação de impactos sociais devido à Covid-19.

MundoGEO. Retrieved November 11, 2020, from https://mundogeo.com/2020/04/08/artigo-geotecnologias-na-identificacao-deimpactos-sociais-devido-a-covid-19/ Silva, A. M. L. B. & Lobato, R. B. (2020). Uso de SIG para Compreensão dos Efeitos da Pandemia da COVID-19 na Vida da População do Rio de Janeiro. Anals of V Jornada de Geotecnologias do Estado do Rio de Janeiro (JGEOTEC 2020). (509-518) Niterói, RJ/Brazil: GEOPARTNERS. Smith, J. (2017). Exploring Innovative Technologies to Enhance the Park Planning Process: A Case Study of Duval Park, Alachua County, Florida. Master Thesis, University of Florida, the United States.

Smith, M. J., Goodchild, M. F. & Longley, P. A. (2021). Geospatial Analysis 6th Edition. Retrieved March 24, 2021 from https://www.spatialanalysisonline.com/HTML/index.html?introduction_and_terminolog y.htm Snow, J. (1999). Sobre a Maneira de Transmissão do Cólera. São Paulo: Hucitec/Abrasco Sodoré, A. A., Monié, F. & Pouya, L. P. (2020). Distribuição Geográfica E Difusão Espacial Do Coronavírus/Covid-19 No Burquina Fasso (África Ocidental). Revista Tamoios, 16(1), 167187. https://doi.org/10.12957/tamoios.2020.50604 Sposito, M. E. B., & Guimarães, R. B. (2020). Por que a circulação de pessoas tem peso na difusão da pandemia. Notícias UNESP. Retrieved November 06, 2020 from https://www2.unesp.br/portal#!/noticia/35626/por-que-a-circulacao-de-pessoas-tempeso-na-difusao-da-pandemia Tao, R., Downs, J., Beckie, T. M., Chen, Y. & Mcnelley, W. (2020). Examining spatial accessibility to COVID-19 testing sites in Florida. Annals of GIS, 26(4), 319-328. https://doi.org/10.1080/19475683.2020.1833365

86

UFC, Universidade Federal do Ceará. (2020). Dados preliminares do Mapeamento participativo da Covid-19 em Fortaleza (CE).

Retrieved March 15, 2021 from http://www.labocart.ufc.br/?page_id=798 UN, United Nations. (2020). Shared Responsibility, Global Solidarity: Responding To the SocioEconomic Impacts of Covid-19. March, 1–26. Retrieved April 22, 2020, from https://www.un.org/sites/un2.un.org/files/sg_report_socioeconomic_impact_of_covid19.pdf Urban, R. C., & Nakada, L. Y. K. (2020). GIS-based spatial modelling of COVID-19 death incidence in São Paulo, Brazil.

In Environment and Urbanization. https://doi.org/10.1177/0956247820963962 WHO, World Health Organization. (2020). Water, sanitation, hygiene and waste management for the COVID-19 virus. March, 1–9.

Wold, N. (2021). The Survey123 App: Enhancing the Citizen Science Experience. Retrieved March 11, 2021, from https://www.nps.gov/articles/survey123.htm Worldometer. (2021). Coronavirus Update (Live): COVID-19 Virus Pandemic. Retrieved March 11, 2021, from https://www.worldometers.info/coronavirus/ Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., Cao, Z., Wang, J., Yuan, W., Zhu, Y., Song, C., Chen, J., Xu, J., Li, F., a, T., Jiang, L., Yan, F., Yi, J., u, Y., … Xiao, H. (2020). COVID-19: Challenges to GIS with Big Data. Geography and Sustainability, 1(1), 77–87. https://doi.org/10.1016/j.geosus.2020.03.005

7 APPENDIX Gender RP

Average Female Age

Source of Income

Male

Cases Deaths

Quarantine / Selfisolation

Average Quarantine Days

Salaried with stability

Salaried without stability

SelfEntrepreneur employed

Informal worker

Retired Unemployed

Average Income

Average Impact on Income

1.1

43,14

25

10

20

7

26

34,12

13

3

7

3

1

4

4

5,50

1,89

2.1

39,12

28

9

27

10

35

32,86

11

9

9

0

0

3

5

6,85

1,55

2.2

46,35

50

15

52

12

54

35,83

26

6

15

4

3

6

5

6,98

2,59

3.1

40,00

1

0

3

0

0

1

0

0

0

0

0

0

3,00

3,00

3.2

36,90

12

9

16

8

17

43,35

9

1

2

1

1

2

5

4,69

1,88

3.3

32,50

16

4

19

10

18

38,15

3

2

3

3

1

0

8

3,00

1,25

3.4

25,00

2

0

0

0

2

32,00

1

0

0

0

0

0

1

1,50

3,00

3.5

37,50

15

1

29

11

11

37,09

6

3

1

0

4

0

2

2,47

1,56

3.6

33,93

6

1

8

6

6

37,67

0

3

3

0

0

0

1

2,79

1,00

3.7

25,00

0

1

1

0

1

19,00

1

0

0

0

0

0

0

3,00

3,00

4.1

30,00

12

2

8

4

14

36,50

5

0

3

1

1

1

3

4,04

2,25

4.2

46,00

2

3

0

1

4

33,25

2

0

1

0

0

1

1

7,10

0,60

5.1

32,06

10

7

10

9

15

40,47

3

1

2

1

3

1

6

3,29

1,53

5.2

29,17

2

4

3

2

6

41,00

1

4

0

0

0

0

1

2,67

0,50

5.3

25,00

2

1

5

1

2

41,50

1

0

0

0

1

0

1

3,00

0,00

Total

39,17

183

67

201

81

211

36,43

83

32

46

13

15

18

43

5,20

1,86

87

88

Items which were harmed after the pandemic Consume Contribution from Local to Services Merchants

Delicate Financial Position

Health Care

Financial Resources

Food

Assistance Transport from other people

Hygiene Items

Physical activities

Recreation

Academic/School Activities

Labor Activity

Other

None

21

28

20

11

16

8

11

12

0

22

26

19

14

3

1

28

35

18

11

17

12

10

22

5

28

29

21

17

6

2

55

62

34

23

28

11

20

43

6

46

58

37

28

4

0

0

1

1

1

1

0

1

0

0

0

1

1

0

0

0

11

16

15

7

17

8

8

10

5

16

15

12

5

3

0

16

19

11

9

12

4

13

13

2

15

16

18

2

1

1

0

2

2

0

2

1

2

1

0

1

2

1

0

0

0

12

15

15

6

12

6

11

11

2

9

13

11

4

5

1

4

5

6

3

4

3

3

2

0

1

6

5

2

1

0

0

0

1

0

0

0

1

1

0

1

1

1

0

1

0

11

13

10

6

10

3

8

6

2

9

9

12

2

1

0

4

5

1

3

1

0

2

4

0

4

5

3

1

0

0

11

17

13

9

9

5

10

8

1

10

12

13

1

5

1

1

6

5

1

4

2

1

2

1

3

6

3

1

1

0

2

2

0

2

1

1

2

1

1

2

3

3

0

0

0

176

226

152

92

134

64

103

136

25

167

202

160

77

31

6

89

Feelings caused by the pandemic Anxiety

Depression Loneliness

Concern

Anguish

Sadness

Fear

Other

None

20

6

9

30

11

10

20

2

0

20

4

9

31

19

14

16

4

2

40

7

8

54

26

19

44

2

3

0

0

0

1

1

0

1

0

0

17

7

5

18

9

11

9

2

1

10

3

1

13

8

10

10

4

4

2

2

2

2

2

2

2

0

0

13

2

6

14

10

11

9

3

0

5

2

3

7

5

3

5

1

0

1

0

1

1

1

1

1

0

0

7

1

3

11

6

3

7

1

1

4

1

2

5

3

1

3

0

0

13

2

5

13

9

10

11

2

0

5

0

1

5

3

2

3

1

0

3

1

1

3

3

2

2

0

0

160

38

56

208

116

99

143

22

11