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%).

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 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.

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 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.

Searchedwords

InPortuguese InEnglish

Relationto thesurvey

Jogo Game Recreation

Treino PhysicalTraining PhysicalActivities

Livro Book Recreation/ Academic/SchoolActivities

EAD Distance Education Academic/SchoolActivities

Empréstimo Loan FinancialResources

Seguro desemprego Unemployment Insurance Assistance fromOtherPeople/ Financial Resources

Emprego Employment FinancialResources

Auxílio emergencial EmergencyAid Assistance fromOtherPeople/ Financial Resources

Figure 24 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.

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.

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).

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).

Searchedwords

InPortuguese InEnglish

Saúde mental Mental Health

Ansiedade Anxiety

Terapia Therapy

Preocupação Concern

Medo Fear

Relationto thesurvey

Feelingsafterthedisease

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.

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 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 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 5.4Guaratiba), 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 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.

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 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.

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.

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.

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 Laboratory of Geoprocessing and Social Cartography

11 Federal University of Ceará

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.

• 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.

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