Acta Polytechnica CTU Proceedings https://doi.org/10.14311/APP.2024.46.0047 Acta Polytechnica CTU Proceedings 46:47–52, 2024 © 2024 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague GENERAL QUALITY OF URBAN GREENERY AS A CITY INDICATOR IN BRAZIL IN COMPARISON WITH CENTRAL EUROPE Jobson Larrubia de Almeida Júniora,∗, Michael Pondělíčekb a Instituto Militar de Engenharia (IME), Chemistry Department of Postgraduate, Praça Gen. Tibúrcio, 80 – Urca, Rio de Janeiro, Brazil b Czech Technical University in Prague, Masaryk Institute of Advanced Studies, Kolejní 2637/2a, 160 00 Prague 6 – Dejvice, Czech Republic ∗ corresponding author: eng.ambiental.jobsonlarrubia@gmail.com Abstract. The evidence for climate change and its associated consequences, such as severe droughts, floods, and storms, exposed the need to investigate the ways that countries can cope with them, especially Brazil, which is a continental nation. Moreover, the COVID-19 pandemic contributed to the urgency to consider global uncertainties. To audit it, the Pondělíček method, an indicator of the general quality of urban greenery (IOKZM), was employed. This approach considers four factors to indicate if one city is sustainable: city altitude [m], average annual temperature [°C], average annual precipitation [mm], and the total greenery area [km2] per inhabitant per year. The outcomes demonstrate that, regardless independently of the Brazilian region, geographic localization, estimated altitude range, or precipitation range may not strongly influence the BIOKZM. The impact measured in the indicator is significantly influenced by the greenery areas in Brazil. Those estimated areas are much smaller than European Union ones. Teresina’s Fkes demonstrates that if the greenery area is more than 5 (i.e., more than 50 percent), the IOKZM will be a positive outcome. The IOKZM calculated for Brazilian cities is interesting precisely because of their location in the southern hemisphere, as all the evidence of climate change subtly suggests that the climate in the southern hemisphere changes in a different way. Hence, this article can be used as an initial tool to assess whether the chosen Brazilian cities (Belém, São Luís, Guarulhos, Campinas, Porto Alegre, Teresina, and João Pessoa) can be considered “smart cities”. It is a step towards guiding mayors and other public figures in charge to create and maintain smart cities in Brazil. In addition, further studies should be conducted to contribute to this collaborative effort. Keywords: Green cities, climate change, global warming, Brazil, smart cities. 1. Introduction It is projected that global warming will exceed 1.5 °C during the 21st century, making it harder to limit warming below 2 °C. By 2100, Global Warming will have led to a temperature increase of 2.8 °C [1]. This global event will generate climate impacts which can threaten the culture, infrastructure, and economy of many countries. According to the United Nations Of- fice for Disaster Risk Reduction (UNISDR) [2], seismic events and deep scarcities are continually projected. However, exponential rises in storms and floods have been registered since 1970. The floods and storms are direct climate consequences of this global varia- tion, as opposed to seismic events and deep droughts. UNISDR’s estimation shows that extreme events such as heatwaves, heavy precipitation, droughts, and trop- ical cyclones were provoked by human influence. Ac- cording to the Intergovernmental Panel on Climate Change (IPCC) [1], human influence has been the main cause of the rise of global sea levels and at- mospheric temperatures since at least 1971. This issue is of even more significance due to the global population growing rapidly. It is projected that the population will increase by more than one billion peo- ple within the next 15 years, reaching 8.5 billion in 2030, and to rise further to 9.7 billion by 2050 and 11.2 billion by 2100. Roughly 10 % of the global popu- lation lives in Europe (738 million) and 9 % in Latin America (634 million). Thus, rapid urbanization, cli- mate change, inadequate maintenance of water infras- tructures, and poor solid waste management may lead cities to flooding, water scarcity, water pollution, and adverse health effects. The rehabilitation costs may overwhelm the resilience of cities, and the expenditure of inaction is high. These megatrends highlight the urgent challenges facing cities [3]. The localization of the countries around the globe are determinants for the dangerously escalating cli- mate impacts, such as precipitation. Thus, the ocean level can rise due to its proportionality, which can impact urban systems and the resilience strategies applied in the cities [2]. Approximately 3.3–3.6 billion people live in areas that are highly vulnerable to climate change. And the increasing weather and ex- treme climate events have exposed millions of people to acute food insecurity, reduced water security, and other impacts [1]. Furthermore, the COVID-19 pan- 47 https://doi.org/10.14311/APP.2024.46.0047 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en Jobson Larrubia de Almeida Júnior, Michael Pondělíček Acta Polytechnica CTU Proceedings demic has brought into focus the range of challenges faced in responding to a wide range of uncertainties, while also highlighting the need to prioritize local needs in order to facilitate a just and inclusive transi- tion. The pandemic has hit cities around the world and has exposed many vulnerabilities that need to be addressed in order to ensure better resilience to similar future adverse events [4]. The global impacts of both COVID-19 and climate change have caused substantial economic damages, particularly when the two are combined. Climate change has been detected in climate-exposed and affected sectors such as agri- culture, forestry, fishing, energy, and tourism. The rising extreme heat events have driven hundreds of local losses of species. Some tropical, coastal, polar, and mountain ecosystems have reached hard adapta- tion limits. Nevertheless, approaches such as urban greening, wetland restoration, and upstream forest conservation have been effective in reducing flood risk and urban heat [1]. The implementation of eco-technologies can upgrade the discussions of management tools such as indicators, standards, budgets, and rating systems, which incen- tivizes cities to make an effort to achieve their green objectives [5]. That growth of the global population contributes to the importance of smart cities, which involve natural elements. As highlighted by JÓŹWIK and JÓŹWIK [6], sustainable development largely en- compasses ecological, physical, and visual or aesthetic issues. The United Nations Sustainable Development Goals, particularly Goal 11, emphasize the need to take urgent actions in cities to address multiple social, economic, and environmental concerns [4]. Accord- ing to ISO/FDIS 37122:2019 [7], smart cities provide social, economic, and environmental sustainability outcomes and respond to challenges such as climate change and rapid population growth. This type of city uses data information and modern technologies to deliver better services and quality of life to the residents, businesses, and visitors of the city without disadvantages for others and natural environmental degradation. Smart city is a term that encompasses the following topics: sustainability, technology, urban mobility, transport, planning, digitalization, innova- tion, resilience, climate change, urbanization, artificial intelligence, big data analytics, public health, remote sensing, social media, information technology, deep learning, internet of things (IoT), energy demand, cloud computing, efficiency, renewable energy, energy management, optimization, blockchain, wireless sensor networks, security, and privacy [4]. Data sources from different cities can vary depend- ing on them [7]. Nowadays, all of these matters remain to integrate public and private services us- ing technological innovation, which typically involves information and communication technologies (ICT) in planning, governance, transportation, water treat- ment, electricity distribution, and public oversight areas. They can draw people with different cultures and qualifications to exchange their abilities and ex- pertise. Besides, they can lead the world to a new era of sustainability by reducing the high levels of carbon in the atmosphere, potentializing the mobility and energy sectors, and adapting to climate change [5]. In this context, the global smart cities market is ex- pected to exceed $2 trillion (USD) by 2025 [8]. Smart cities are treated as countries’ progress strategies [5], because their structures prevent attitudes to avoid the interruption, incapacitation, or destruction of net- works and infrastructure, which can result in serious social problems, waste treatment issues, and economic consequences. They can be applied as a strategy in stage two of the Diagnostic and Evaluation of cities’ risk, especially approach number 6, which involves analyzing the environment and engaging with local stakeholders [2]. As such, this article proposes to analyze seven Brazilian cities using the Pondělíček method as a step to offer assistance to mayors and public in creating and maintaining smart cities in Brazil. 2. Research Methods From the 18th of February until April, a literature review was conducted and encountered in Google Scholar, SpringerLink, and ScienceDirect databases based on this article. Likewise, the Pondělíček method for calculating the indicator of the general quality of urban greenery (IOKZM) the method will be applied in Brazilian cities. The IOKZM indicator is impor- tant and based on an understanding of environmental gradients (biotic and abiotic nature, even their intra and inter actions) for the predominant types of urban greenery, and these are also considered to be predom- inantly euryvalent species and dated to urban condi- tions (ecologically less demanding and tolerant) and not stenovalent (ecologically intolerant). Therefore, quantities describing the basic factors for generally conceived greenery in cities are part of the construc- tion of the indicator. Briefly, the IOKZM indicator, according to Equation (1), is a constructed indicator that is then a real composite indicator of the level of general quality of greenery in cities in the Czech Republic and has across-the-board information based on geographic and meteorological data. The indicator mathematically creates an area between four axes, according to Figure 1, on which logarithmized values of individual components of the indicator are plotted. The total and target value of the indicator is there- fore given by the area of the resulting quadrilateral, formed inside by four right triangles. IOKZM = 1 2[ (log Fkes · log Fprt) + (log Fprt · log Fprs) + (log Fprs · log Fnmv) + (log Fnmv · log Fkes)], (1) 48 vol. 46/2024 General quality of urban greenery as a city indicator . . . Figure 1. Construction of the OKZM indicator. Figure 2. Localization of Selected Cities in Brazil. where Fnmv is the city’s altitude data [m], Fprt is the average annual temperature in the city [°C], and Fprs is the average annual precipitation in the city [mm]. To apply this indicator in Brazilian cities (BIOKZM), seven cities were chosen strategically to analyze the IOKZM influence in different Brazilian regions (North, Northeast, South, and Southeast), as seen in Figure 2 and Table 1. These have around one million inhabitants, according to the 2022 Brazilian Geography and Statistic Institute (IBGE) [9] census (Table 1), and they were elected because they can represent the variety of influence of the factors ac- cording to their regions. Moreover, the factor Fkes corresponds to the percentage of the total of greenery areas calculated in square km per inhabitant annu- ally. Brazilian greenery area data was registered in IBGE’s database in square meters (Fkes). Those were converted into square km and transformed in per- centage to be applied in Pondělíček’s calculus. In addition, the IBGE computes the Fkes in different years, according to Table 2. All data were taken from the Climate Data [10], Sustainable Cities Insti- tute [11], https://topographic-map.com [12], and IBGE [9, 13] databases, according to Table 3, and the IOKZM variables were calculated in consonance with them, according to Table 3. City Brazilian region Inhabitants 1 North 1 367 336 2 Northeast 1 061 374 3 Northeast 868 523 4 Northeast 889 618 5 Southeast 1 383 272 6 Southeast 1 170 247 7 South 1 404 269 Table 1. Brazilian cities data according to the 2022 census from the Brazilian Geography and Statistic Institute (IBGE) database. City Fkes Census Year 1 23.1761 2008 2 1 014 837 2015 3 184 359 971 2013 4 83.3303 2012 5 44.6744 2012 6 9.8843 2015 7 47.11 2014 Table 2. Brazilian greenery areas data registered in IBGE’s database in square meters (Fkes). 3. Results The BIOKZMs were calculated according to the afore- mentioned databases and the results are shown in Table 3. 4. Discussion The measured impact of Brazilian greenery areas on the indicator was found to be significant, despite the fact that these estimated areas are much smaller than those in the European Union. Thus, greenery areas which do not represent a high significant number in square km, results in Fkes that are close to zero. Ac- cording to Table 3, independently of the Brazilian region, these outcomes indicate that geographic lo- calization, estimated altitude range, or precipitation range result in low values in Table 4, which make it difficult to compare BIOKZM with European Region IOKZM. The Brazilian IOKZM and European Region IOKZM are shown in the Table 5. It is important to establish which Brazilian outcomes exposed a situa- tion based on archaic data for greenery areas. It was not possible to find city censuses on recent greenery areas. If it was possible to calculate the recent Brazil- ian IOKZM, these BIOKZM might possibly decrease too much. The human growth and fast urbanization with insufficient environmental management plan for those cities indicate that a decrease is provoked in the indicator [14, 15], and these issues had happened in Brazil. The correlation between Fkes and tem- perature (Fprt) is directly evidenced by the IOKZM, with deforestation in Brazilian cities contributing to higher temperatures [16]. The range of temperature 49 https://topographic-map.com Jobson Larrubia de Almeida Júnior, Michael Pondělíček Acta Polytechnica CTU Proceedings City log 1 × log 2 log 2 × log 4 log 3 × log 4 log 3 × log 1 BIOKZM 1 1.109 0.684 1.593 2.582 2.984 2 1.54 -2.457 -5.736 3.596 -1.528 3 3.073 1.141 2.497 6.723 6.717 4 1.947 -2.277 -4.854 4.151 -0.516 5 3.751 -1.669 -4.111 9.239 3.605 6 3.747 -2.46 -5.862 8.928 2.176 7 1.829 -1.432 -3.54 4.521 0.689 Table 3. Variables of BIOKZM equation. Brazil European Region City Fprs City Fprs Belém 2,085 Prague 480 São Luís 2,156 Munich 938 Teresina 1,447 Vienna 675 João Pessoa 1,462 Budapest 563 Guarulhos 1,580 Berlin 535 Campinas 1,451 Krakow 675 Porto Alegre 1,019 Table 4. Comparison of Brazilian and European Union precipitation range. Brazil European Region City IOKZM City IOKZM Belém 2.984 Prague 7.3776 São Luís -1.528 Munich 8.9197 Teresina 6.717 Vienna 7.3308 João Pessoa -0.516 Budapest 6.7725 Guarulhos 3.605 Berlin 6.0678 Campinas 2.176 Krakow 7.6091 Porto Alegre 0.689 Table 5. Comparison of Brazilian and the European Union IOKZM. in Teresina is almost like other cities in the European Union, according to Table 3 and Table 6. Even with the higher temperature range of Brazilian cities, the BIOKZM of some cities was maintained in negative outcomes. GOMES (2022) demonstrated that deforestation disrupts the balance of hydrological systems, espe- cially the flow of rivers in the Amazon rainforest and the Cerrado. As a consequence, the water flow was reduced because of the decrease in water recharge in the soil [17]. This hydrological disturbance affects the temperature range and the plumiometric range. According to the results in Table 3, the pluviometrics can reduce the IOKZM. However, comparing these outcomes within Table 6, the European Union has cities with lower grades in pluviometric terms, and their IOKZM are positive. Even considering São Luis, which has the top Fprs, the BIOKZM are not positive. According to Table 3, Fprt, Fprs, and Fmnv combined Brazil European Region City Fprt City Fprt Belém 26.7 Prague 13 São Luís 26.8 Munich 13 Teresina 19.2 Vienna 9,5 João Pessoa 21.3 Budapest 11.2 Guarulhos 19.7 Berlin 12 Campinas 27.9 Krakow 9.2 Porto Alegre 25.8 Table 6. Comparison of Brazilian and European Union temperatures range. were not sufficient to turn the IOKZM to a high num- ber. On the other hand, the precipitation indicator that is correlated with negatives BIOKZM influences this indicator directly, as can be observed in the out- comes from cities number 4 and 6 from Table 3. The altitude above sea level slightly influences the IOKZM when applied in different countries. This is clear in the Pondělíček study, where the cities selected from the European Union have altitudes slightly similar (47, 127, 200, 233, 239, 520 m) compared to the Brazilian ones (6, 12, 24, 26, 134, 665, 841 m). According to Table 3, Teresina is the highest city analyzed, and this associated outcome caused a positive BIOKZM. Compared to the Pondělíček method for calculating the IOKZM of the European Union, we can observe that all five European Union cities selected for this study exposed higher grades compared to Brazilian IOKZM, according to Table 3. BIOKZM revealed the influence of the temperature of the cities investigated. The five cities inspected from the IOKZM of the Euro- pean Union have temperatures under 13 °C, which is the maximum measured between the European Union sample. In contrast, the Brazilian minimum and max- imum average temperatures analyzed are 19.2 °C and 27 °C, respectively. This article and thesis aims to look at the geograph- ical and climatic conditions of cities differently and to obtain an overview of the quality of selected Central European cities in terms of conditions for developing green infrastructure within cities. The selected Indi- cator of the General Quality of Green Cities (OKZM Indicator) was used to determine the relative potential of selected cities for further support of green infras- 50 vol. 46/2024 General quality of urban greenery as a city indicator . . . tructure of the city (the indicator works similarly to IQ, with the comparison of values among themselves). To measure the potential of the need to care for urban vegetation in Europe, the cities closest to Prague with populations of more than 1 million in absolute num- bers and linked to the Central European area were selected: Munich, Vienna, Budapest, Krakow, and Berlin (Berlin and Krakow are peripheral cities, but subject to similar development patterns as the others, and their influence on the development of Central Europe is obvious). Similarly, in Brazil, selected cities from different zones, from the equator to the tem- perate zone in the southern hemisphere, were chosen for the BIOKZM: Belém, Campinas, Guarulhos, Joao Pessoa, Porto Alegre, Sao Luiz, and Teresina. In Cen- tral Europe, Munich and Krakow dominated, taking into account the number of local inhabitants and thus the density of inhabitants in cities. The best in terms of BIOKZM in Brazil were the southern hemisphere cities of Campinas, Porto Alegre, and Teresina, which are practically in similar conditions as cities in the northern hemisphere and in Europe. Based on a com- parison of the results of IOKZM and BIOKZM, we conclude that cities in the temperate zone are practi- cally suitable and comparable for assessment thanks to IOKZM and its calculation, whereas cities on the equator have extreme conditions and their vegetation within the city cannot be evaluated similarly. In prin- ciple, calculations have proven that IOKZM works at this level [18–21]. This comparison between IOKZM and BIOKZM from temperate zones signifies a significant advance of international collaboration efforts to minimize global warming and climate change impacts. Thus, EU and Brazilian temperate zones can be studied with more accuracy in these terms. Moreover, this paper gives the scientific community resources to investigate some countries which are in temperate zones as well as to discover if the OKZM indicator can be used on them too. Countries in the southern hemisphere, such as South Africa, the south of Australia, New Zealand, Chile, Argentina, and countries in the northern hemi- sphere, such as the USA, Canada, Morocco, China, Mongolia, North Korea, and South Korea are in the temperate zones and represent an important part of the global economy. And this fact is related to sus- tainability. In addition, the aggregation of data from these countries could propose to the scientific commu- nity and the governments an OKZM network which reflects international cooperation. Some secondary data could be created from these preliminary country- specific data, correlating OKZM with specific issues such as the influence on continents, the economy, the relationship with the country’s position in the world (north or south), global demographics, climate change per hemisphere, and governments’ efforts to attain a state of environmental well-being combined with societal cooperation. 5. Conclusions IOKZM calculated for Brazilian cities is interesting precisely because of their location in the southern hemisphere, as all the evidence of climate change subtly suggests that the climate in the southern hemi- sphere changes in a different way. According to some authors, this is due to the Pacific Ocean and also Antarctica as a land mass with a large glaciation, which cools South America more. The purpose of the BIOKZM calculation is now mainly to identify trends in the cities, and when comparing data from five years ago to the current data, it can indicate how the geo- morphological and climatic conditions are changing. The final use of BIOKZM is in the field of vegeta- tion care and maintenance in cities. According to BIOKZM, we can assess the level of care required for vegetation and predict its demands based on the influ- ence of the climate. In particular, financial demands are costs that are important to the care of urban veg- etation. High-quality vegetation and functional trees fulfil their role in improving the microclimate of the city better and dampening all extremes. The main role of BIOKZM is therefore to look for trends in the care of urban vegetation and to help find a trend where it is possible to reduce or increase investments in greenery, depending on the city’s population density. References [1] K. Calvin, D. Dasgupta, G. Krinner, et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland, 2023. https://doi.org/10.59327/ipcc/ar6-9789291691647 [2] The United Nations Office for Disaster Risk Reduction (UNISDR). How to build resilient cities: a guide for public local managers., 2012. [2023-12-18]. https://www.unisdr.org/files/26462_ handbookfinalonlineversion.pdf [3] S. H. A. Koop, C. J. van Leeuwen. The challenges of water, waste and climate change in cities. Environment, Development and Sustainability 19(2):385–418, 2016. https://doi.org/10.1007/s10668-016-9760-4 [4] A. Sharifi, P. Salehi (eds.). Resilient Smart Cities: Theoretical and Empirical Insights. The Urban Book Series. Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-95037-8 [5] V. Mendes. Climate smart cities? Technologies of climate governance in Brazil. Urban Governance 2(2):270–281, 2022. https://doi.org/10.1016/j.ugj.2022.08.002 [6] R. Jóźwik, A. Jóźwik. Influence of environmental factors on urban and architectural design – example of a former paper mill in Nanterre. Sustainability 14(1):86, 2021. https://doi.org/10.3390/su14010086 [7] International Organization for Standardization. ISO/FDIS 37122:2019 Sustainable cities and communities: Indicators for smart cities, 2019. [2023-12- 18]. https://www.iso.org/standard/69050.html 51 https://doi.org/10.59327/ipcc/ar6-9789291691647 https://www.unisdr.org/files/26462_handbookfinalonlineversion.pdf https://www.unisdr.org/files/26462_handbookfinalonlineversion.pdf https://doi.org/10.1007/s10668-016-9760-4 https://doi.org/10.1007/978-3-030-95037-8 https://doi.org/10.1016/j.ugj.2022.08.002 https://doi.org/10.3390/su14010086 https://www.iso.org/standard/69050.html Jobson Larrubia de Almeida Júnior, Michael Pondělíček Acta Polytechnica CTU Proceedings [8] E. Leite. Innovation networks for social impact: An empirical study on multi-actor collaboration in projects for smart cities. Journal of Business Research 139:325–337, 2022. https://doi.org/10.1016/j.jbusres.2021.09.072 [9] Database of Instituto Brasileiro de Geografia e Estatística (IBGE). Ibge, 2023. [2023-04-14]. https://www.ibge.gov.br [10] Climate Data. Database climate-data, 2023. [2023-03-10]. https://pt.climate-data.org/ [11] Instituto Cidades Sustentáveis. Database of sustainable cities program, 2023. [2023-03-07]. https: //www.cidadessustentaveis.org.br/inicial/home [12] TessaDEM. Database of topographic-map.com, 2023. [2023-03-20]. https://pt-br.topographic-map.com/ [13] Instituto Brasileiro de Geografia e Estatística (IBGE). Preview of population cities based on demographic cense 2022 collected until 25th December of 2022, 2023. [2023- 03-20]. https://www.ibge.gov.br/en/statistics/ social/labor/22836-2022-census-3.html [14] P. de Souza Nascimento. Impactos socioambientais em áreas de expansão urbana de barreiras (ba): análises consolidadas. Anais do XVI Simpósio Nacional de Geografia Urbana-XVI SIMPURB 1:3978–3993, 2019. [15] V. C. S. MOURA. Impactos ambientais da urbanização: esforços da pesquisa brasileira e mapeamento e percepção de moradores na cidade de Santarém, Pará. Ph.D. thesis, Universidade Federal do Oeste do Pará, 2019. [16] P. dos Santos Ribeiro, D. J. C. Gomes, E. B. de Souza, et al. Influência do desmatamento na temperatura do ar. Revista Brasileira de Geografia Física 16(01):165–176, 2023. [17] D. J. C. Gomes, M. M. M. Nascimento, F. M. Pereira, et al. Variabilidade da vazão na bacia hidrográfica do rio araguaia influenciada pela precipitação em anos extremos e desmatamento. Brazilian Journal of Environmental Sciences (RBCIAMB) 57(3):451–466, 2022. [18] Copernicus Urban Atlas. Copernicus programme 2018. [2023-02-07]. https://land.copernicus.eu/local/urban-atlas [19] H. Dreiseitl, B. Wanschura. Strenghtening the blue-green infrastructure in our cities, 2016. [2023-02-07]. https://ramboll.com/- /media/38fc23d12a5d47dcb7b3821716d69270.pdf [20] ESPON. Zelená infrastruktura v urbánních oblastech – TEZE POLITIK. ÚÚR, 2020. ISBN 978-80-87318-99-7. [21] K. Gupta, P. Kumar, S. Pathan, K. Sharma. Urban neighborhood green index – a measure of green spaces in urban areas. Landscape and Urban Planning 105(3):325–335, 2012. https: //doi.org/10.1016/j.landurbplan.2012.01.003 52 https://doi.org/10.1016/j.jbusres.2021.09.072 https://www.ibge.gov.br https://pt.climate-data.org/ https://www.cidadessustentaveis.org.br/inicial/home https://www.cidadessustentaveis.org.br/inicial/home https://pt-br.topographic-map.com/ https://www.ibge.gov.br/en/statistics/social/labor/22836-2022-census-3.html https://www.ibge.gov.br/en/statistics/social/labor/22836-2022-census-3.html https://land.copernicus.eu/local/urban-atlas https://ramboll.com/-/media/38fc23d12a5d47dcb7b3821716d69270.pdf https://ramboll.com/-/media/38fc23d12a5d47dcb7b3821716d69270.pdf https://doi.org/10.1016/j.landurbplan.2012.01.003 https://doi.org/10.1016/j.landurbplan.2012.01.003 Acta Polytechnica CTU Proceedings 46:47–52, 2024 1 Introduction 2 Research Methods 3 Results 4 Discussion 5 Conclusions References