title: Iso-population partition: An innovative epidemiological approach to mapping and analyzing spatially aggregated data.
abstract: In epidemiology, data are often aggregated using administrative boundaries or regular spatial lattices. Iso-population partitioning methods allow the aggregation of small units for which population data are available into larger units that are contiguous, as compact as possible, and have a similar population size. The objective of this paper was to study the influence of three spatial data aggregation approaches on data visualization and data analysis: iso-populated units (IPUs), administrative units, and iso-geometric units. This study was conducted using results and simulations from the brucellosis clinical surveillance system for dairy cattle in France. Our findings indicate that using spatial partitioning methods for generating IPUs enhances the ability to interpret the spatial distribution of epidemiological indicators under study. In addition, it provides information on population density and improves the consistency of the power of statistical tests across units. By defining the target population size per spatial unit, IPUs can be used to control the statistical power of a study. Finally, by adding criteria based on environmental factors to generate spatial units, they can be used to control the variation of exposure to these factors within the units.
authors.affiliation: ANSES-Lyon, Lyon, France.
authors.forename: Anne
authors.initials: A
authors.lastname: Bronner
authors.affiliation: ANSES-Lyon, Lyon, France.
authors.forename: Eric
authors.initials: E
authors.lastname: Morignat
authors.affiliation: ANSES-Lyon, Lyon, France.
authors.forename: Emilie
authors.initials: E
authors.lastname: Gay
authors.affiliation: Royal Veterinary College, Hatfield, Hertfordshire, UK.
authors.forename: Timothée
authors.initials: T
authors.lastname: Vergne
authors.affiliation: Royal Veterinary College, Hatfield, Hertfordshire, UK.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Fournié
authors.affiliation: Royal Veterinary College, Hatfield, Hertfordshire, UK.
authors.forename: Dirk U
authors.initials: DU
authors.lastname: Pfeiffer
authors.affiliation: ANSES-Lyon, Lyon, France. Electronic address: didier.calavas@anses.fr.
authors.forename: Didier
authors.initials: D
authors.lastname: Calavas
mesh_terms: D000818:Animals; D002006:Brucellosis / Q000453:epidemiology / Q000382:microbiology / Q000662:veterinary*; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology* / Q000382:microbiology; D003612:Dairying; D003625:Data Collection / Q000379:methods*; D062665:Epidemiological Monitoring / Q000662:veterinary*; D005602:France / Q000453:epidemiology; D062206:Spatial Analysis
keywords: Partitioning methods; Spatial analysis; Spatial data aggregation