title: A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis.
abstract: Similar geographic areas often have great variations in population size. In health data management and analysis, it is desirable to obtain regions of comparable population by decomposing areas of large population (to gain more spatial variability) and merging areas of small population (to mask privacy of data). Based on the Peano curve algorithm and modified scale-space clustering, this research proposes a mixed-level regionalization (MLR) method to construct geographic areas with comparable population. The method accounts for spatial connectivity and compactness, attributive homogeneity, and exogenous criteria such as minimum (and approximately equal) population or disease counts. A case study using Louisiana cancer data illustrates the MLR method and its strengths and limitations. A major benefit of the method is that most upper level geographic boundaries can be preserved to increase familiarity of constructed areas. Therefore, the MLR method is more human-oriented and place-based than computer-oriented and space-based.
authors.affiliation: Department of Geography, University of Georgia.
authors.forename: Lan
authors.initials: L
authors.lastname: Mu
authors.affiliation: Department of Geography and Anthropology, Louisiana State University.
authors.forename: Fahui
authors.initials: F
authors.lastname: Wang
authors.affiliation: Louisiana Tumor Registry, Louisiana State University Health Sciences Center.
authors.forename: Vivien W
authors.initials: VW
authors.lastname: Chen
authors.affiliation: Louisiana Tumor Registry, Louisiana State University Health Sciences Center.
authors.forename: Xiao-Cheng
authors.initials: XC
authors.lastname: Wu
keywords: health data analysis; mixed-level regionalization (MLR); modified Peano curve algorithm (MPC); modified scale-space clustering (MSSC); place-oriented, space and place