Geostatistical analysis of health data with different levels of spatial aggregation

Goovaerts, Pierre

doi:10.1016/j.sste.2012.02.008

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…The Spearman's rank correlation coefficient values suggest that although all distance measures are significantly related to one another ( p < 0.01), the strength of this relationship becomes weaker as the spatial unit increases in size. This supports findings in the literature [9] , [40] , [41] . If individual level data is not available, we recommend that the smallest unit of aggregation be used.…”

Section: Discussionsupporting

confidence: 93%

Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

et al. 2015

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The aim of this study was to quantify the error associated with different accessibility methods commonly used by public health researchers. Network distances were calculated from each household to the nearest GP our study area in the UK. Household level network distances were assigned as the gold standard and compared to alternate widely used accessibility methods. Four spatial aggregation units, two centroid types and two distance calculation methods represent commonly used accessibility calculation methods. Spearman's rank coefficients were calculated to show the extent which distance measurements were correlated with the gold standard. We assessed the proportion of households that were incorrectly assigned to GP for each method. The distance method, level of spatial aggregation and centroid type were compared between urban and rural regions. Urban distances were less varied from the gold standard, with smaller errors, compared to rural regions. For urban regions, Euclidean distances are significantly related to network distances. Network distances assigned a larger proportion of households to the correct GP compared to Euclidean distances, for both urban and rural morphologies. Our results, stratified by urban and rural populations, explain why contradicting results have been reported in the literature. The results we present are intended to be used aide-memoire by public health researchers using geographical aggregated data in accessibility research.

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Section: Discussionsupporting

confidence: 93%

Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

et al. 2015

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“…In most datasets, there will inevitably be records that cannot be geocoded to the individual address level. Methods are being developed to combine data geocoded at different levels in order to allow researchers to use all records (Goovaerts 2012; Hibbert et al 2009). Simultaneously pursuing the further development of such methodologies and efforts to improve our ability to geocode administrative data at highly resolved spatial scales through improved reference layers and address reporting will create a multitude of new research opportunities in spatial public health analysis.…”

Section: Discussionmentioning

confidence: 99%

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Edwards

Strauss

Miranda

2013

Transactions in GIS

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Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, zip code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population-level datasets at three spatial resolutions – zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban-rural spectrum. Our results suggest highly resolved spatial data architectures for population-level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.

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“…Apesar de ter sido criada, inicialmente, para tratar de variaveis relacionadas à mineração (Krige, 1951), os benefıćios fornecidos pela Geostatıśtica 4izeram com que seus conceitos fossem expandidos a diversas áreas de estudo (Bayraktar e Turalioglu, 2005;Goovaerts, 2012;Oliver e Webster, 1986;Zimmerman et al, 1998). Na engenharia de transportes, o emprego dos conceitos geoestatıśticos já foi veri4icado na modelagem de acidentes/segurança viária (Gomes et al, 2018;Gundogdu, 2014;Majumdar et al, 2004;Manepalli e Bham, 2011) e, mais recentemente, na estimativa de variaveis de demanda por transportes.…”

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Estimativa do volume de passageiros ao longo de uma linha de transporte público por ônibus a partir da geoestatística

Marques

Pitombo

2019

TRANSPORTES

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A modelagem clássica da demanda por transportes ignora um importante aspecto normalmente presente na estrutura das variáveis de interesse: a autocorrelação espacial. Pesquisas recentes reconhecem e incluem tal característica à estimativa da demanda, mas há limitações referentes aos elementos básicos de tratamento utilizados nas abordagens. No intuito de superar alguns problemas e restrições associados aos estudos anteriores, o presente trabalho se valeu da dependência espacial entre as observações de Embarques e Desembarques, por ponto de parada, e Carregamento nos trechos, ao longo de uma linha de transporte público, a fim de gerar estimativas dessas variáveis em pontos e trechos que não seriam amostrados por ocasião da pesquisa sobe/desce. Tal previsão foi realizada por meio da aplicação de Krigagem Ordinária a uma linha de ônibus da cidade de São Paulo, Brasil. Os resultados obtidos confirmaram a adequabilidade do ferramental geoestatístico à estimativa de variáveis de demanda por transportes ao longo de uma linha de ônibus.

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Geostatistical analysis of health data with different levels of spatial aggregation

Cited by 18 publications

References 33 publications

Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

Quantifying the Error Associated with Alternative GIS-based Techniques to Measure Access to Health Care Services

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Estimativa do volume de passageiros ao longo de uma linha de transporte público por ônibus a partir da geoestatística

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