Bivariate Choropleth Maps Research Articles

All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome.

Exploring the Potential Relationship between Cooling Green Space and Built-up area: Analysis of Community Green Space Characteristics Based on GWPCA

Rapid urbanization exacerbates urban heat island (UHI) problem. Although urban green space (UGS) can produce multiple benefits for mitigating UHI, the accompanying problems of urban development cause multiple pressures on greenery. Therefore, the pattern of green spaces should be adjusted to adapt to the current situation of the built-up area, fully utilizing its cooling effect, which is of great significance for achieving sustainable and balanced urban development. We selected 849 community units in Harbin city for the study, calculated the urban green space pattern index (UGSP) based on geographically weighted principal component analysis (GWPCA), And used bivariate choropleth map and Cartesian coordinate system to explore the spatial relationship between UGSP and associated characteristics of urbanization (e.g., land function diversity and development intensity). The results showed the following. (1) Compared with traditional PCA, GWPCA can capture local details of UGSP, especially proportion of tree area was the most sensitive factor in the urban core area, and FRAC_MN and LSI were the sensitive factors in the west and south of urban outer areas, respectively. (2) The matching degree between development intensity (I) and UGSP was slightly better than that between land function diversity (D) and UGSP, and showed a more egalitarian phenomenon. (3) The equilibrium value (dLow) presented a relatively random spatial distribution when UGSP was less than D/I, and the unbalanced value (dHigh) was scattered in the core area. Both dLow and dHigh were concentrated outside the urban core area when UGSP was greater than D/I. Our study provides an effective guidance for community greenery to mitigate UHI and realize the harmonious coexistence between city and nature.

Insights at a Glance: Unravelling Spatial Trends with Bivariate Choropleth Mapping

Insights at a Glance: Unravelling Spatial Trends with Bivariate Choropleth Mapping

Meteorological drought in the upper Noteć catchment area (Central Poland) in the light of NDVI and SPI indicators

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland.

Examining rehabilitation access disparities: an integrated analysis of electronic health record data and population characteristics through bivariate choropleth mapping.

Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients' social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients' social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. This was a retrospective cohort study of patients' access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions. Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care.

Association between social vulnerability factors and unintentional fatal injury rates – United States, 2015–2019

Background: Differences in social and environmental factors can contribute to disparities in fatal injury rates. The purpose of this study was to examine the relationship between social and environmental factors and unintentional fatal injury across counties in the United States and how this relationship varies by geography. Methods: County-level vital statistics on age-adjusted unintentional fatal injury rates for 2015–2019 were linked with county-level data from the 2018 Social Vulnerability Index (SVI), a dataset identifying socially vulnerable communities. We conducted linear regression to examine the association between SVI and unintentional fatal injury, overall and by Census region/division. We mapped county-level data for SVI and unintentional fatal injury rates in bivariate choropleth maps using quartiles. Results: SVI was positively associated with unintentional fatal injury (β = 18.29, p < 0.001) across U.S. counties. The geographic distribution of SVI and unintentional fatal injury rates varied spatially and substantially for U.S. counties, with counties in the South and West regions having the greatest levels of SVI and rates of unintentional fatal injury. Conclusions: Our findings demonstrate that the social vulnerability of counties is associated with unintentional fatal injury rates. Modification of the SVI for injury research could include additional social determinants and exclude variables not applicable to injuries. A modified SVI could inform unintentional injury prevention strategies by prioritizing efforts in areas with high levels of social vulnerability. Practical Applications: This study is the first step in combining the SVI and injury mortality data to provide researchers with an index to investigate upstream factors related to injury.

The Geography of Mental Health, Urbanicity, and Affluence

Residential location has been shown to significantly impact mental health, with individuals in rural communities experiencing poorer mental health compared to those in urban areas. However, the influence of an individual's social group on the relationship between residential location and mental health outcomes remains unclear. This study disaggregates the rural-urban binary and investigates how geography and social groupings interact to shape mental health outcomes. Merging data from PLACES and Claritas PRIZM, we conducted a hotspot analysis, generated bivariate choropleth maps, and applied multiscale geographically weighted regressions to examine the spatial distribution of mental health and social groupings. Our findings reveal that mental health is influenced by complex interactions, with social groups playing a critical role. Our study highlights that not all rural and urban areas are alike, and the extent to which social groups influence mental health outcomes varies within and across these areas. These results underscore the need for policies that are tailored to meet the unique mental health needs of individuals from different social groups in specific geographic locations to inform policy interventions that more effectively address mental health disparities across diverse communities.

Measuring crime: a new paradigm

PurposeThe purpose of this exploratory study is to expand on a previously developed crime harm index – the California Crime Harm Index (CA-CHI) – by discussing the development of the CA-CHI and presenting a comparison of the distribution of crime count and harm in a large Western city in the United States.Design/methodology/approachThis study used descriptive analyses, spatial univariate mapping and bivariate choropleth maps to analyze the distribution of Part I crime counts and harm.FindingsResults of the analysis show that while there was some variation in the distribution of crime count and harm city wide, spatial mapping and statistics reveal that the geographical distribution of crime count and harm across census tracts are largely consistent.Research limitations/implicationsMinor discrepancies between the distribution of crime count and harm indicate the potential for the CA-CHI to inform law enforcement practices. However, the distributions remain largely similar at the census tract level. There is room for further development of the CA-CHI to better distinguish between the distribution of crime harm and volume.Originality/valueNo other study has used spatial mapping techniques like bivariate choropleth mapping to examine the distribution of crime volume and crime harm based on the CA-CHI in any location in California.

Major Changes in Spatiotemporal Trends of US Rotavirus Laboratory Detections After Rotavirus Vaccine Introduction-2009-2021.

For the 15 years before rotavirus vaccine introduction in 2006, annual rotavirus activity in the United States showed a distinct spatiotemporal pattern, peaking first in the Southwest and last in the Northeast. We modeled spatiotemporal trends in rotavirus laboratory detections from 2009 to 2021. Laboratories reporting to the National Respiratory and Enteric Virus Surveillance System were eligible for inclusion in a given surveillance year (July to June) if ≥1 polymerase chain reaction or enzyme immunoassay rotavirus test per week was reported during ≥26 weeks and totaling ≥100 annual tests. For each laboratory, the season peak was the week with the highest 7-week moving average of the number of rotavirus positive tests during the national season, defined as the period with a 3-week moving average of >10% rotavirus positivity lasting ≥2 consecutive weeks. We input peak week as a continuous variable and the geospatial coordinates of each laboratory into a spherical variogram model for Kriging spatial interpolation. We also created a state-level bivariate choropleth map using tertiles of the 2010-2019 average birth rates and rotavirus vaccine coverage. Following the established biennial trend, the 2010-2011, 2012-2013, 2014-2015, 2016-2017, and 2018-2019 surveillance years had >10% rotavirus positivity for ≥2 weeks and were included in the geospatial analysis. During all 5 seasons included in the geospatial analysis, the earliest peak week occurred in Oklahoma, Arkansas, and the western Gulf coast, a pattern markedly different from prevaccine seasons. These states also had the average lowest rotavirus vaccine coverage and highest birth rate, suggesting that more rapid accumulation of susceptible children drives annual rotavirus season activity. Increasing vaccine coverage remains a key tool in reducing rotavirus burden.

Future global conflict risk hotspots between biodiversity conservation and food security: 10 countries and 7 Biodiversity Hotspots

Balancing biodiversity conservation and food security is the key to global sustainable development. However, we know little about the future global conflict risk hotspots between biodiversity and food security at both country and Biodiversity Hotspots (BHs) levels. First we calculated land use intensity index (LUII) based on future land use simulation, incorporated data on species richness(including birds, mammals and amphibians) and introduced the Global Food Security Index (GFSI). Then we used local indicators of spatial association (LISA) and bivariate choropleth map to identify the future global conflict risk hotspots between biodiversity conservation and food security. These include 10 countries (including Congo (Kinshasa), Sierra Leone, Malawi, Togo, Zambia, Angola, Guinea, Nigeria, Laos, Cambodia) and 7 BHs (Eastern Afromontane, Guinean Forests of West Africa, Horn of Africa, Indo-Burma, Mediterranean Basin, Maputaland-Pondoland-Albany and Tropical Andes). Special attention needs to be paid to these hotspots to balance biodiversity conservation and food security.

The unequal commute: Comparing commuting patterns across income and racial worker subgroups

The spatial dimension of the journey-to-work has important implications for land use and development policymaking and has been widely studied. One thrust of this research is concerned with the disaggregation of workers into subgroups for understanding disparities in commute. Most of these studies, however, were limited to the disaggregation by single socioeconomic class. Hence, this research aims to examine commuting disparities across commuter subgroups stratified by two socioeconomic variables—income and race—using a visual analytics approach. By applying the doubly constrained spatial interaction model to the 2014 Longitudinal Employer-Household Dynamics data, this research first synthesizes commuting flows for Downtown Houston workers across income-race subgroups at the tract level in Harris County, Texas, USA. It then uses bivariate choropleth mapping to visualize the spatial distributions of major Downtown Houston commuter neighborhoods by income-race classes, and significant commuting disparities are identified across income-race subgroups. The results highlight the importance of considering income and race simultaneously for commuting research. The visualization could help policymakers clearly identify the unequal commute across worker subgroups and inform policymaking.

Leveraging Hazard, Exposure, and Social Vulnerability Data to Assess Flood Risk to Indigenous Communities in Canada

This study integrates novel data on 100-year flood hazard extents, exposure of residential properties, and place-based social vulnerability to comprehensively assess and compare flood risk between Indigenous communities living on 985 reserve lands and other Canadian communities across 3701 census subdivisions. National-scale exposure of residential properties to fluvial, pluvial, and coastal flooding was estimated at the 100-year return period. A social vulnerability index (SVI) was developed and included 49 variables from the national census that represent demographic, social, economic, cultural, and infrastructure/community indicators of vulnerability. Geographic information system-based bivariate choropleth mapping of the composite SVI scores and of flood exposure of residential properties and population was completed to assess the spatial variation of flood risk. We found that about 81% of the 985 Indigenous land reserves had some flood exposure that impacted either population or residential properties. Our analysis indicates that residential property-level flood exposure is similar between non-Indigenous and Indigenous communities, but socioeconomic vulnerability is higher on reserve lands, which confirms that the overall risk of Indigenous communities is higher. Findings suggest the need for more local verification of flood risk in Indigenous communities to address uncertainty in national scale analysis.

Drought vulnerability and risk assessment in India: Sensitivity analysis and comparison of aggregation techniques

Long term drought management requires proper assessment and characterization of drought hazard, vulnerability and risk. This is particularly important for an agriculture-dependent, highly-populated, developing country such as India. However, the regulation of drought vulnerability and drought risk assessment in the country is mostly region-specific and ad-hoc, considering only a limited number of vulnerability indicators. In this study, a comprehensive, fine-resolution, country-wide drought risk assessment is carried out considering drought hazard in a multivariate framework, and using reliable drought vulnerability indicators that account for exposure, sensitivity and adaptive capacity. Further, multiple aggregation techniques including subjective, objective and comprehensive methods are employed for vulnerability assessment, and their performance assessed and compared. The Analytic Hierarchy Process (AHP)+Entropy and TOPSIS methods, which are comprehensive aggregation techniques are found to be better performing, TOPSIS being the most robust method. A bivariate choropleth map based on the TOPSIS-derived drought vulnerability shows regions of Punjab, Haryana, Uttar Pradesh and Tamil Nadu subjected to drought hazard-driven risk, while risk in other regions such as Rajasthan, parts of Central India, Orissa and parts of Maharashtra are driven more by drought vulnerability. Parts of Western Rajasthan, Vidharbha, North-East India, Chattisgarh, Tamil Nadu and Karnataka are under severe drought risk resulting from an interplay of hazard and vulnerability. Irrigation index, water body fraction, and groundwater availability are found to be the most significant indicators for assessing drought vulnerability in India. The above findings can aid decision makers and government bodies to plan region-specific line of action for building drought resilience.

COVID-19 MORTALITY IN NEW YORK CITY ACROSS NEIGHBORHOODS BY RACE, ETHNICITY, AND NATIVITY STATUS

ABSTRACT New York City has lost more lives from covid-19 than any other American city. This study examines variation in covid-19 deaths across neighborhoods as it relates to variation in the racial, ethnic, and nativity-status composition of neighborhoods. This topic has received little scholarly attention and is imperative to explore, given the absence of racial and ethnic specific covid-19 mortality rates by neighborhood. New York City is a racially and ethnically segregated city, and a longstanding destination of immigrants, making some neighborhoods more susceptible to greater levels of covid-19 mortality than others. Using ZCTA-level data on covid-19 deaths and demographic data from the American Community Survey, our descriptive and bivariate choropleth mapping analyses reveal that a racial, ethnic, and nativity-status hierarchy exists in the geographic distribution of covid-19 mortality. Implications of these findings are discussed as they relate to residential segregation and persistent spatial inequalities faced by neighborhoods of color.

Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.

Can social vulnerability indices predict county trauma fatality rates?

Social vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US. We evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI. A total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01). While the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research. Epidemiological, level IV.

Bivariate choropleth map documenting land cover intensity and population growth in Poland 2006–2018

ABSTRACT Bivariate choropleth mappings are proposed in this paper as an effective GIS-based approach that supports the monitoring of Sustainable Development Goals, listed in the Agenda 2030, namely build-up area expansion and population dynamics, the Tier II indicator for SDG 11 (11.3.1). This paper focuses on the application of a multidimensional approach to mapping land use efficiency in Poland between 2006 and 2018. The resulting data are assigned to counties and visualized as choropleth maps categorizing the counties based on the strength of land cover intensity and population growth. The proprietary class interdependence index (CII) based on the relationship between these two variables is used to evaluate class ranges of the bivariate choropleth map. The proposed cartographic presentation might be a useful and important tool in SDG monitoring as it provides information about the relation between population and land consumption rate, portraying the most correlated data in classes along the diagonal.

An Exploratory Analysis of Networked and Spatial Characteristics of International Natural Resource Trades (2000–2016)

Natural resources, as the material basis of human life and production, play a crucial role in national economic and social development. It is essential to reveal the structural characteristics of global natural resource supply and demand, which has become one of the most critical factors affecting every country’s policy strategy and economic development. However, mining the characteristics of international natural resource trades is a huge challenge because of the availability and quality of trade data. In this study, the international natural resource trade system is modeled as networks based on the available bilateral trade data from 2000 to 2016. Complex network methods and spatial analysis are utilized to explore the networked and spatial characteristics of different international natural resource trade networks (INRTNs). First, we quantitatively present the overall evolution trend of INRTNs by calculating several indicators of network features at the macrolevel. Then, as the intermediate-level characteristics of INRTNs, the core–periphery structures are explored by applying hierarchical clustering and a visual matrix heatmap. Finally, at the microlevel, the imbalance in direction is detected through the combination of node importance in a complex network with bivariate choropleth maps of spatial analysis. The empirical evidence from INRTNs of different product types in this paper will help governments and business administrations to perceive the complex natural resource trade environment, which can instruct policymakers to formulate effective import–export policies and ensure national resource security and sustainable development.

Evidence from Google Trends of a Widening Second-level Digital Divide in Brazil. Even Worse with the Covid-19

Background: While Brazilian governmental initiatives focused on home broadband Internet access, availability of computers for students in schools has been drastically reduced since 2010. Furthermore, schools usually prohibits mobile Internet access in its premises, contrarily to the migration of students’ access to smartphones. Objectives: This study investigates the impact of the increasing home and mobile Internet access on the existing educational inequalities. Design: This study made use of quantitative, locally statistical research to investigate the reproduction or closing of existing educational digital divide across already contrasting Brazilian regions. Setting and Participants: Child or adolescent from 9 to 17 years of age and their guardians, interviewed by CETIC.br. Data collection and analysis: Data was obtained from the CETIC.br data portal and the Google Trends webpage. Data were analysed by means of local geostatistical measures of spatial autocorrelation and inequality, as well as bivariate choropleth maps. Results: Our results suggest that the Brazilian school system is failing to cultivate in their students the more productive use of Internet access and therefore contributing to the widening of the existing second-level digital divide between regions and social classes. Conclusions: This digital divide was critically exacerbated by the arrival of the ongoing COVID-19 pandemic and the suspension of presential classes.Brazilian policymakers should concentrate efforts and resources in addressing this large-scale second-level digital divide, possibly by equipping educators and students with the knowledge and skills towards the educational, productive and responsible use of the Internet, as well as allowing mobile Internet access in school premises.

Análise da Distribuiçao Espacial da COVID-19 em Minas Gerais

A COVID-19 é uma doença respiratória aguda que foi classificada como pandemia em 11 de março de 2020, pela Organização Mundial da Saúde. Muitos esforços têm sido despendidos com o intuito de acompanhar e apresentar a distribuição espacial dos casos dessa doença e em várias escalas. São observadas abordagens metodológicas das mais diferenciadas, como mapas coropléticos e cartogramas, mapas com símbolos proporcionais, dinâmicos ou estáticos, além de gráficos e tabelas. Cada recurso adaptado às especificidades da escala ou do contexto regional. Com o objetivo de apresentar contribuições metodológicas para a representação de dados epidemiológicos, apresentamos a distribuição espacial dos casos confirmados da COVID-19 em Minas Gerais, até a data de 21 de junho de 2020, com uso de Cartogramas de Áreas Contíguas Univariados, de um Mapa Coroplético Bivariado e de um Mapa de Símbolos Proporcionais para a representação de dados relativos e absolutos. Os resultados evidenciaram a tendência de municípios com maiores populações ou maiores densidades demográficas apresentarem mais casos confirmados da doença e as medidas que relativizam os números absolutos de casos confirmados com a população residente, quando representados pelos cartogramas ou pelo mapa Coroplético Bivariado, revelaram informações que ficavam visualmente ocultadas pelas diferenças das áreas dos municípios.