Efficient regionalization for spatially explicit neighborhood delineation

Wei, Ran; Rey, Sergio J.; Knaap, Elijah

doi:10.1080/13658816.2020.1759806

Cited by 22 publications

(21 citation statements)

References 24 publications

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“…According to their findings, the tabu search is more likely to capture the best solution, while simulated annealing is more computationally efficient. Wei et al (2021) combined the two algorithms' advantages to solve the max‐ p ‐regions problem, achieving better‐quality solutions and higher‐efficiency computations.…”

Section: Solution Approachmentioning

confidence: 99%

“…The local search algorithm for max‐ p ‐compact‐regions is an extension of the algorithm in Wei et al (2021), and it is also designed with the integration of simulated annealing and a tabu list. However, the goal is to optimize the overall compactness of regions as formulated in Equation (9).…”

Section: Solution Approachmentioning

confidence: 99%

“…She, Duque, and Ye (2017) incorporated network structure into the max‐ p ‐regions problem, enabling a balance between the corridor regions formation and regional heterogeneity. Wei, Rey, and Knaap (2021) improved the heuristic approach of the max‐ p ‐regions problem by combining simulated annealing and tabu search to reach high accuracy and computational efficiency. In this article, we will extend the max‐ p ‐regions problem by integrating compactness as an explicit goal.…”

Section: Introductionmentioning

confidence: 99%

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The max‐p‐compact‐regions problem

Feng

Rey

Wei

2021

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The max‐p‐compact‐regions problem involves the aggregation of a set of small areas into an unknown maximum number (p) of compact, homogeneous, and spatially contiguous regions such that a regional attribute value is higher than a predefined threshold. The max‐p‐compact‐regions problem is an extension of the max‐p‐regions problem accounting for compactness. The max‐p‐regions model has been widely used to define study regions in many application cases since it allows users to specify criteria and then to identify a regionalization scheme. However, the max‐p‐regions model does not consider compactness even though compactness is usually a desirable goal in regionalization, implying ideal accessibility and apparent homogeneity. This article discusses how to integrate a compactness measure into the max‐p regionalization process by constructing a multiobjective optimization model that maximizes the number of regions while optimizing the compactness of identified regions. An efficient heuristic algorithm is developed to address the computational intensity of the max‐p‐compact‐regions problem so that it can be applied to large‐scale practical regionalization problems. This new algorithm will be implemented in the open‐source Python Spatial Analysis Library. One hypothetical and one practical application of the max‐p‐compact‐regions problem are introduced to demonstrate the effectiveness and efficiency of the proposed algorithm.

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Section: Solution Approachmentioning

confidence: 99%

Section: Solution Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The max‐p‐compact‐regions problem

Feng

Rey

Wei

2021

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“…These tunable parameters can be used to constrain the size or shape of clusters, or to avoid crossing administrative or geographical boundaries [12,13]. User preferences are also commonly incorporated into regionalization methods through the choice of a similarity or distance function between adjacent regions [14,15]. Additionally, as is the case with any clustering method, a key factor existing regionalization methods consider is the choice of the number of regions, which is typically fixed by the user [12,16] but is sometimes determined endogeneously based on user-defined thresholds for covariates of interest or other heuristics that depend or one's choice of dissimilarity between spatial units [15,17].…”

Section: Introductionmentioning

confidence: 99%

“…User preferences are also commonly incorporated into regionalization methods through the choice of a similarity or distance function between adjacent regions [14,15]. Additionally, as is the case with any clustering method, a key factor existing regionalization methods consider is the choice of the number of regions, which is typically fixed by the user [12,16] but is sometimes determined endogeneously based on user-defined thresholds for covariates of interest or other heuristics that depend or one's choice of dissimilarity between spatial units [15,17]. An increased level of user control is desirable for many applications of regionalization, as researchers can ensure that the identified regions are suitable for the task at hand and do not violate any necessary constraints.…”

Section: Introductionmentioning

confidence: 99%

Spatial regionalization as optimal data compression

Kirkley¹

2021

Preprint

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The process of aggregating areal units into contiguous clusters, known as regionalization, is central to the analysis of spatial data. Regionalization provides a means to reduce the effect of noise or outliers in sampled data, identify socioeconomically homogeneous areas for policy development, and simplify the visualization of data in maps among many other applications. Most existing regionalization methods require a substantial amount of manual input, such as the number of desired regions or a similarity measure among regional populations, which may be desirable for some applications but does not allow us to extract the natural regions defined solely by the data itself. Here we view the problem of regionalization as one of data compression. We define the optimal partition of spatial units with corresponding distributional data as the one that minimizes the description length required to transmit the data, and develop an efficient, parameter-free greedy optimization algorithm to identify this partition. We demonstrate that our method is capable of recovering planted spatial clusters in noisy synthetic distributional data, and that it identifies meaningful ethnoracial boundaries in real demographic data. Using our description length formulation, we find that the information contained in spatial ethnoracial data in metropolitan areas across the U.S. has become more difficult to compress over the period from 1980 to 2010, which reflects the rising complexity of urban segregation patterns of these metros. We identify the increasing overall diversity of these metros as a major contributor to this lower data compressibility, while the spatial scale of ethnoracial clustering does not appear to be a significant factor.

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