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Integrating spatial autocorrelation into location-allocation problems

将空间自相关集成到位置分配问题中

基本信息

批准号：
1951344
负责人：
Yongwan Chun
金额：
$ 46万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951344&HistoricalAwards=false
关键词：
Integrating spatial autocorrelation into location

项目摘要

This project is about improving the speed and quality of solutions for network facility location problems, such as locating new fire stations to reduce fire loss or closing existing schools in population-declining communities. These challenges called location-allocation (L-A) problems are extremely difficult to solve optimally. This research investigates the tendency for similar or dissimilar attribute values, such as residential housing concentrations to cluster geographically near facility service usage. This clustering is called spatial autocorrelation (SA). These researchers will fill a significant gap in the spatial analysis literature by determining how SA information can facilitate more robust solutions in L-A analyses. This new approach will enable better and faster computational solutions to large L-A problems for location both public and private facilities that are translatable to other research domains, providing economical benefits to society. The literature describing relationships between SA and locational solutions to L-A problems is scant. This research seeks to analyze a strongly suspected relationship between SA in facility demand variables and solutions to L-A problems. It will exploit this relationship to reformulate L-A models and their computer solution methods. Revised problem formulations will focus on the reduction of the number of possible locational solutions examined so that solving large-size L-A problems and other related locational problems becomes tractable and efficient. This investigation of the novel interface between spatial statistics (e.g., SA) and spatial optimization (e.g., L-A) will provide new knowledge and convincing evidence that should spur further research in various disciplines, including operations research, regional science, and geography.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在提高网络设施选址问题解决方案的速度和质量，例如在人口减少的社区中定位新的消防站以减少火灾损失或关闭现有学校。这些被称为位置分配（L-A）问题的挑战极难以最佳方式解决。本研究探讨相似或不同属性值的趋势，例如住宅集中在地理上靠近设施服务使用。这种聚类称为空间自相关（SA）。这些研究人员将通过确定SA信息如何促进L-A分析中更强大的解决方案来填补空间分析文献中的重大空白。这种新方法将为公共和私人设施位置的大型L-A问题提供更好和更快的计算解决方案，这些问题可转化为其他研究领域，为社会提供经济效益。描述SA和L-A问题的定位解决方案之间关系的文献很少。本研究旨在分析设施需求变量中的SA与L-A问题的解决方案之间的强烈怀疑关系。它将利用这种关系来重新制定L-A模型及其计算机求解方法。修订后的问题提法将集中于减少所审查的可能的地点解决办法的数量，以便解决大型L-A问题和其他有关的地点问题变得易于处理和有效。对空间统计（如SA）和空间优化（如L-A）之间的新界面的研究将提供新的知识和令人信服的证据，这将促进包括运筹学、区域科学和地理学在内的各个学科的进一步研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。