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位置问题的解决方案的速度和质量，例如定位新的消防局以减少火灾损失或关闭人口管理社区的现有学校。这些称为位置分配（L-A）问题的挑战极为难以解决。这项研究调查了类似或不同属性值的趋势，例如住房浓度，以在地理上使用设施服务的地理位置使用。该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显着空白。这种新方法将使可以转化为其他研究领域的公共设施和私人设施的大型L-A问题，使得更好，更快的计算解决方案，为社会提供经济利益。描述SA与L-A问题的位置解决方案之间关系的文献很少。这项研究试图分析设施需求变量中SA之间的强烈可疑关系，以及解决L-A问题的解决方案。它将利用这种关系来重新制定L-A模型及其计算机解决方案方法。修订后的问题公式将集中于减少所检查的可能位置解决方案的数量，以便解决大尺寸的L-A问题和其他相关的位置问题变得可拖延和高效。对空间统计数据（例如SA）和空间优化（例如L-A）之间新界面的研究将提供新的知识和令人信服的证据，这些证据应刺激各种学科的进一步研究，包括运营研究，区域科学和地理。该奖项颁发了NSF的法定任务，并反映了通过评估的范围来审查构成的依据。