Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

Jung, Paul H.; Thill, Jean-Claude; Issel, L. Michele

doi:10.1080/13658816.2018.1554811

Cited by 25 publications

(12 citation statements)

References 34 publications

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“…2014 ). Large margins of error in block group estimates may systematically introduce bias into the HVIs ( Jung et al. 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Few heat vulnerability studies have considered the impact that large margins of errors, especially for block group level geographies, may have on identifying spatial patterns of heat vulnerability (Spielman et al 2014). Large margins of error in block group estimates may systematically introduce bias into the HVIs (Jung et al 2019). Block group estimates are calculated as 5-y averages, rather than as point-in-time estimates.…”

Section: Environmental Health Perspectivesmentioning

confidence: 99%

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Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

Conlon

Mallen

Gronlund

et al. 2020

Environ Health Perspect

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BACKGROUND: Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping. OBJECTIVE: We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat-health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA. METHODS: We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May-September, 2000-2009. We used PCA to construct HVIs using: a) "unsupervised"-PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) "supervised"-PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles). RESULTS: Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract-level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups. DISCUSSION: HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions.

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“…2014 ). Large margins of error in block group estimates may systematically introduce bias into the HVIs ( Jung et al. 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Section: Environmental Health Perspectivesmentioning

confidence: 99%

Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

Conlon

Mallen

Gronlund

et al. 2020

Environ Health Perspect

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“…Also, it is well known that denominator data that rely on annual data sampling to apprehend the at-risk population (denominator)-such as annual American Community Survey data-is affected by large and spatially variable margins of error across the urban region. While some methods have been developed to provide unbiased estimates of statistics [64,65], many analyses continue to ignore this important and impactful data uncertainty [66].…”

Section: Discussionmentioning

confidence: 99%

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

2019

Self Cite

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Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

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“…Considering the estimate reliability is a major challenge in spatial analysis when using data from ACS and other population and health surveys, such as the US Census's Current Population Survey (CPS), the US Center for Disease Control and Prevention's Behavioral Risk Factor Surveillance System (BRFSS) data and the US National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program. During the past decade, researchers have made some headway in incorporating estimate error in geovisualizing estimates statistically , determining class breaks in choropleth maps using simple statistical concepts (Sun et al, 2015(Sun et al, , 2017 or sophisticated optimization methods (Koo et al, 2017;Mu & Tong, 2019;Wei & Grubesic, 2017), and measuring spatial autocorrelation (Jung et al, 2019;Koo et al, 2019). Therefore, we currently have some tools to analyze population survey data spatially with more honesty by considering error associated with estimates (Koo et al 2018).…”

Section: David W S Wongmentioning

confidence: 99%

Looking Back, Looking Forward: Progress and Prospect for Spatial Demography

et al. 2021

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In 2011 a specialist meeting on the "Future Directions in Spatial Demography" was held in Santa Barbara, California (Matthews, Goodchild, & Janelle, 2012). 1 This specialist meeting was the capstone to a multi-year National Institutes of Health training grant that had supported workshops in advanced spatial analysis methods increasing used by population scientists. 2 Early-career scholars who had participated in the training workshops and senior demographers and geographers drawn from across the United States participated in the specialist meeting. 3 The application process to attend the 2011 meeting, required that each of the forty-one attendees submit a statement that reviewed challenges and identified new directions for spatial demography, including gaps in current knowledge regarding innovations in geospatial data, spatial statistical methods, and the integration of data and models to enhance the science of spatial demography in population and health research. Reading again some of the

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Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

Cited by 25 publications

References 34 publications

Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Looking Back, Looking Forward: Progress and Prospect for Spatial Demography

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