A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

Abstract

Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practical when applied to a large number of areas or problems for online aggregation, due to the long execution times using hardware with low resources. In this article, we present a framework for executing spatial regionalization tasks. The precondition for using our framework is the definition of a map describing the neighborhood relations-as spatial contiguity constraints-among the areas (or objects) to be regionalized. In our framework we implemented three clustering algorithms with spatial contiguity constraints: RegK-Means and Agglomerative Hierarchical Regionalization (AHR), our adaptation of k-means partition-based clustering and hierarchicalbased clustering algorithms, respectively; and the Automatic Zoning Procedure (AZP), a traditional algorithm for regionalization that also has the premise of simplifying the neighborhood relation representation. We conducted an exploratory analysis composed of two different experiments. Our results showed that our framework leads to a faster way of executing regionalization tasks in the experimental analysis, allowing us to observe a significant gain of AHR and RegK-Means over AZP in execution time, while showing better or similar results in other metrics, such as figure of merit.

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