A model-driven approach to regionalization and spatial change-of-support
Topics: Spatial Analysis & Modeling
, Geographic Information Science and Systems
, Quantitative Methods
Keywords: regionalization, scale, spatial statistics, modifiable areal unit problem
Session Type: Virtual Paper Abstract
Day: Saturday
Session Start / End Time: 2/26/2022 11:20 AM (Eastern Time (US & Canada)) - 2/26/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 33
Authors:
Tyler D Hoffman, School of Geographical Sciences and Urban Planning, Arizona State University
Taylor Oshan, Department of Geography, University of Maryland College Park
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Abstract
Regionalization refers to the design of areal zones by spatially aggregating smaller units into larger clusters. Algorithms to conduct regionalization typically require the desired number of clusters to be specified a priori, though a reasonable number is not always clear. Therefore, a heuristic is designed to endogenously determine the number of clusters in a supervised (i.e., model-driven) setting by balancing the fit of a spatial model and the average area of clusters used as input. Recent work in this direction has focused on similar procedures for inference in regressions; instead, this work uses these methods for studying functional regions. By designing more accurate spatial supports, one would be able to analyze the process on the proper scale and shapes with lessened reliance on the available data. In this way, the work directly confronts the modifiable areal unit problem (MAUP) in the special case where an analyst seeks to perform supervised learning or statistical inference on the process at hand. While rudimentary at present, such a heuristic paves the way for model-driven thinking about clustering and functional regions.
Additionally, a workflow is presented for integrating regionalization algorithms into larger spatial analyses for the delineation of functional regions. Finally, theory is introduced for expanding this method to a Bayesian estimation technique which would allow for robust inference on the mechanics of aggregation.
A model-driven approach to regionalization and spatial change-of-support
Category
Virtual Paper Abstract
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