Improving Population Mapping and Exposure Assessment: Three-Dimensional Dasymetric Disaggregation in New York City and São Paulo, Brazil

Maroko, Andrew; Maantay, Juliana; Machado, Reinaldo Paul Pérez; Barrozo, Lígia Vizeu

doi:10.1080/23754931.2019.1619092

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…For instance, when quantifying the association between a health outcome and population density, using a county scale could obscure the heterogeneity of both the outcome of interest (e.g., there may be very high rates in one part of the county, and low rates everywhere else) and the associated variable (e.g., one neighborhood in the county could house a large number of residents in high-rise buildings and the remainder could be lowintensity single-family homes and park land). This issue, often called the modifiable area unit problem (MAUP), has been documented in multiple research domains, including health [21,22], exposure estimation [23], measures of access [24], and environmental justice [25,26]. A related phenomenon is the choice of the overall study area.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots

2020

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There have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities associated with neighborhood attributes, such as the proportion of racial and ethnic minorities, migrants, and the lower income households. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020 using data from the New

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Section: Introductionmentioning

confidence: 99%

COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots

2020

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show abstract

“…For instance, when quantifying the association between a health outcome and population density, using a county-scale could obscure the heterogeneity of both the outcome of interest (e.g., there may be very high rates in one part of the county, and low rates everywhere else) and the associated variable (e.g., one neighborhood in the county could house a large number of resident in high-rise buildings and the remainder could be low intensity single family homes and park land). This issue, often called the modifiable area unit problem (MAUP), has been documented in multiple research domains, including health (19, 20), exposure estimation (21), measures of access (22), and environmental justice (23, 24). A related phenomenon is the choice of the overall study area.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots

Maroko

Nash

Pavilonis

2020

Preprint

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There have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities, including well-researched social determinants of health, such as racial and ethnic minorities, migrants, and the economically challenged. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020.In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas) demonstrated typical protective factors associated with the social determinants of health and the ability to social distance. These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations. Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) also had similarities, such as lower rates of college graduates and higher proportions of people of color. It also appears to be larger households (more people per household), rather than overall population density, that may to be a more strongly associated with hot spots.Findings suggest important differences between the cities' hot spots as well. They can be generalized by describing the NYC hot spots as working-class and middle-income communities, perhaps indicative of service workers and other occupations (including those classified as "essential services" during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago's hot spot neighborhoods, on the other hand, are among the city's most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.

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“…Looking specifically at Brazil, satellite images have been used mainly to investigate if they are able to detect slums (Hofmann et al 2008;Nadalin and Mation 2018), estimate population (Amaral et al 2005(Amaral et al , 2006Tomás et al 2016;Neves et al 2017;Maroko et al 2019;Campos et al 2020) and map Human Development Index (Charris et al 2019).…”

Section: Satellite Images Applied To Brazilmentioning

confidence: 99%

Predicting socioeconomic indicators using transfer learning on imagery data: an application in Brazil

Castro

López

2022

GeoJournal

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Improving Population Mapping and Exposure Assessment: Three-Dimensional Dasymetric Disaggregation in New York City and São Paulo, Brazil

Cited by 17 publications

References 14 publications

COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots

COVID-19 and Inequity: a Comparative Spatial Analysis of New York City and Chicago Hot Spots

Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots

Predicting socioeconomic indicators using transfer learning on imagery data: an application in Brazil

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