ArticleAnnals of the Association of American Geographers. Association of American Geographers
A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis.
Article in Annals of the Association of American Geographers. Association of American Geographers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Physics-Informed Graph Learning for Spatially Contiguous and Capacity-Constrained Hospital Service Area Delineation.Computers, environment and urban systems · 2026Article
- Association between snack intake behaviors of children and neighboring women: A population-based cross-sectional analysis with spatial regionalization.SSM - population health · 2024Article
- Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward.JMIR medical informatics · 2022Article
- Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information.Annals of the American Association of Geographers · 2022Article
- Rurality and Origin-Destination Trajectories of Medical School Application and Matriculation in the United States.ISPRS international journal of geo-information · 2021Article
- Why Public Health Needs GIS: A Methodological Overview.Annals of GIS · 2020Article
- Where there's smoke: Cigarette use, social acceptability, and spatial approaches to multilevel modeling.Social science & medicine (1982) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.