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A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination
Lan Mu1, Fahui Wang2, Vivien W Chen3
1Department of Geography, University of Georgia.
Annals of the Association of American Geographers. Association of American Geographers
|August 8, 2015
Summary
This study introduces a mixed-level regionalization (MLR) method for health data analysis. MLR creates geographic areas with similar populations, balancing spatial variability and data privacy.
Area of Science:
- Geographic Information Systems (GIS)
- Spatial Analysis
- Public Health Informatics
Background:
- Geographic areas exhibit significant population variations, complicating health data management.
- Decomposing large population areas and merging small ones is crucial for spatial variability and data privacy.
Purpose of the Study:
- To propose a novel mixed-level regionalization (MLR) method for creating geographic areas with comparable populations.
- To address challenges in health data management and analysis by balancing spatial variability and data privacy.
Main Methods:
- Utilizes the Peano curve algorithm and modified scale-space clustering.
- Integrates spatial connectivity, compactness, attributive homogeneity, and population/disease count criteria.
Main Results:
- The MLR method successfully constructs geographic areas with comparable population sizes.
- Demonstrates strengths and limitations through a case study using Louisiana cancer data.
Conclusions:
- The MLR method is human-oriented and place-based, preserving existing geographic boundaries.
- Offers a practical approach for health data regionalization that enhances familiarity and utility.
Keywords:
health data analysismixed-level regionalization (MLR)modified Peano curve algorithm (MPC)modified scale-space clustering (MSSC)place-oriented, space and placeMore Related Videos
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