Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

Spielman, Seth E.; Folch, David C.

doi:10.1371/journal.pone.0115626

Cited by 76 publications

(53 citation statements)

References 20 publications

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“…Thus, we provide a measure to characterize such error formally and a principled way to obtain an optimal (in terms of spatial aggregation error) regionalization defined over the generic continuous domain

D_{s} \subset R^{d}

. Regionalization has traditionally been solved by using techniques outside the realm of statistics (Duque et al ., ; Spielman and Logan, ; Folch and Spielman, ) and our work offers a new perspective that respects the uncertainty of spatial random processes. Consequently, our methodology can significantly impact federal statistics, survey methodology, geography, spatial statistics and remote sensing or data acquisition settings.…”

Section: Discussionmentioning

confidence: 83%

“…These data can be downloaded from http://factfinder2.census.gov/. This is an important example because there has been a growing interest in regionalizing data from the ACS (Spielman and Logan, ).…”

Section: Application: Median Household Income From the American Commumentioning

confidence: 99%

“…Regionalization is a topic that has been considered primarily by geographers. The current state of the art is the deterministic ‘max‐ p algorithm’ (Duque et al ., ; Spielman and Logan, ; Folch and Spielman, ; Spielman and Logan, ). In general, the max‐ p algorithm is a greedy search algorithm (using any desired criterion) that groups data defined on

n_{A}

areal units into p

(⩽ n_{A})

contiguous regions.…”

Section: Introductionmentioning

confidence: 99%

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Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error

Bradley

Wikle

Holan

2016

Journal of the Royal Statistical Society Series B: Statistical Methodology

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The modifiable areal unit problem and the ecological fallacy are known problems that occur when modeling multiscale spatial processes. We investigate how these forms of spatial aggregation error can guide a regionalization over a spatial domain of interest. By "regionalization" we mean a specification of geographies that define the spatial support for areal data. This topic has been studied vigorously by geographers, but has been given less attention by spatial statisticians. Thus, we propose a criterion for spatial aggregation error (CAGE), which we minimize to obtain an optimal regionalization. To define CAGE we draw a connection between spatial aggregation error and a new multiscale representation of the Karhunen-Loéve (K-L) expansion. This relationship between CAGE and the multiscale K-L expansion leads to illuminating theoretical developments including: connections between spatial aggregation error, squared prediction error, spatial variance, and a novel extension of Obled-Creutin eigenfunctions. The effectiveness of our approach is demonstrated through an analysis of two datasets, one using the American Community Survey and one related to environmental ocean winds.

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D_{s} \subset R^{d}

Section: Discussionmentioning

confidence: 83%

Section: Application: Median Household Income From the American Commumentioning

confidence: 99%

n_{A}

areal units into p

(⩽ n_{A})

contiguous regions.…”

Section: Introductionmentioning

confidence: 99%

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Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error

Bradley

Wikle

Holan

2016

Journal of the Royal Statistical Society Series B: Statistical Methodology

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“…13 We compared the age, gender, race/ethnicity and insurance status of children in our ED population versus Census estimates.…”

Section: Methodsmentioning

confidence: 99%

Identifying Local Hot Spots of Pediatric Chronic Diseases Using Emergency Department Surveillance

Lee

Fong

et al. 2017

Academic Pediatrics

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Objective To use novel geographic methods and large-scale claims data to identify the local distribution of pediatric chronic diseases in New York City. Methods Using a 2009 all-payer emergency claims database, we identified the proportion of unique children aged 0 to 17 with diagnosis codes for specific medical and psychiatric conditions. As a proof of concept, we compared these prevalence estimates to traditional health surveys and registry data using the most geographically granular data available. In addition, we used home addresses to map local variation in pediatric disease burden. Results We identified 549,547 New York City children who visited an emergency department at least once in 2009. Though our sample included more publicly insured and uninsured children, we found moderate to strong correlations of prevalence estimates when compared to health surveys and registry data at pre-specified geographic levels. Strongest correlations were found for asthma and mental health conditions by county among younger children (0.88, p=0.05 and 0.99, p<0.01, respectively). Moderate correlations by neighborhood were identified for obesity and cancer (0.53 and 0.54, p<0.01). Among adolescents, correlations by health districts were strong for obesity (0.95, p=0.05), and depression estimates had a non-significant, but strong negative correlation with suicide attempts (−0.88, p=0.12). Using SaTScan, we also identified local hotspots of pediatric chronic disease. Conclusions For conditions easily identified in claims data, emergency department surveillance may help estimate pediatric chronic disease prevalence with higher geographic resolution. More studies are needed to investigate limitations of these methods and assess reliability of local disease estimates. What’s New This study demonstrated how emergency department surveillance may improve estimates of pediatric disease prevalence with higher geographic resolution. We identified 29% of New York City children with a single year of data and identified local hotspots of pediatric chronic diseases.

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“…The most straightforward solution is to map the difference estimates and uncertainty information with GIS tools to more easily convey each area’s reliability (Sun and Wong ; Koo, Chun, and Griffith ; Wei, Tong, and Phillips ). Another approach involves a regionalization, which aggregates small areas to minimize margins of error of each region through sample size increase (Spielman and Folch ; Sun and Wong ). This can minimize sampling errors and heterogeneity of samples, but critically degrades spatial granularity, which is precisely one of the features of ACS data.…”

Section: Uncertainty Of Acs Estimatesmentioning

confidence: 99%

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

Jung

Thill

Issel

2018

Geographical Analysis

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We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.

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Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

Cited by 76 publications

References 20 publications

Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error

Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error

Identifying Local Hot Spots of Pediatric Chronic Diseases Using Emergency Department Surveillance

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

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