Delimitation of Functional Regions Using a p-Regions Problem Approach

Author:

Kim Hyun1,Chun Yongwan2,Kim Kamyoung3

Affiliation:

1. Department of Geography, University of Tennessee at Knoxville, Knoxville, TN, USA

2. School of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA

3. Department of Geography Education, Kyungpook National University, Daegu, Korea

Abstract

Various spatial data analyses have been used for the identification of functional regions. Functional regions are identified by grouping many areal units into fewer clusters to classify the areal units in terms of similar properties, as well as to constrain the spatial contiguity of the areal units in each cluster. This article proposes a spatial optimization model, called the p-functional regions problem, to solve a regionalization problem by considering geographic flows. The magnitude of geographic flows, such as journey-to-work, is widely considered a good indicator of functional relationships between areas so that regionalization models incorporating various criteria, such as the maximum intraregion flows or the total inflows from other units, may be used to identify the p regions. We also propose an analytical target reduction approach to enhance the model tractability in generating optimal solutions to large problems and to demonstrate the effectiveness of the optimization model using journey-to-work data from Seoul (South Korea) and South Carolina (the United States).

Publisher

SAGE Publications

Subject

General Social Sciences,General Environmental Science

Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MNCD-KE: a novel framework for simultaneous attribute- and interaction-based geographical regionalization;International Journal of Geographical Information Science;2024-07-10

2. Location Planning of Emergency Medical Facilities Using the p-Dispersed-Median Modeling Approach;ISPRS International Journal of Geo-Information;2023-12-12

3. Statistical Inference for Spatial Regionalization;Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems;2023-11-13

4. PAGE: Parallel Scalable Regionalization Framework;ACM Transactions on Spatial Algorithms and Systems;2023-09-13

5. A Scalable Unified System for Seeding Regionalization Queries;Proceedings of the 18th International Symposium on Spatial and Temporal Data;2023-08-23

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3