Spatial Data Analysis

Annual Review of Public Health
Sudipto Banerjee

Abstract

With increasing accessibility to geographic information systems (GIS) software, statisticians and data analysts routinely encounter scientific data sets with geocoded locations. This has generated considerable interest in statistical modeling for location-referenced spatial data. In public health, spatial data routinely arise as aggregates over regions, such as counts or rates over counties, census tracts, or some other administrative delineation. Such data are often referred to as areal data. This review article provides a brief overview of statistical models that account for spatial dependence in areal data. It does so in the context of two applications: disease mapping and spatial survival analysis. Disease maps are used to highlight geographic areas with high and low prevalence, incidence, or mortality rates of a specific disease and the variability of such rates over a spatial domain. They can also be used to detect hot spots or spatial clusters that may arise owing to common environmental, demographic, or cultural effects shared by neighboring regions. Spatial survival analysis refers to the modeling and analysis for geographically referenced time-to-event data, where a subject is followed up to an event (e.g., death or o...Continue Reading

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Citations

Sep 23, 2020·BMC Pediatrics·Arthur Lauriot Dit PrevostDyuti Sharma
Nov 14, 2019·Conflict and Health·P Gregg Greenough, Erica L Nelson
Jan 15, 2021·The Journal of Trauma and Acute Care Surgery·Jay J DoucetTodd Costantini
Jul 10, 2021·Birth Defects Research·Ian W TangVerónica M Vieira

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Software Mentioned

GMCAR
ARPH
SANOVA
MCAR
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