ATD: Spatio-Temporal Modeling for Identifying Changes in Land Use

ATD：识别土地利用变化的时空模型

基本信息

批准号：
2124507
负责人：
George Michailidis
金额：
$ 30万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124507&HistoricalAwards=false
关键词：
ATD Spatio Temporal Modeling Identifying

项目摘要

Modeling land development dynamics represents a key problem in urban and regional planning. Land use changes have impact on the environment, the quality of life, public finances and economic development trajectories of local communities and larger scale regions. Further, there is a need to assess and quantify threats posed through multiple scenarios about future land developments. Land use models that account both for key drivers of human behavior, as well as fine scale spatial and temporal dependencies are valuable tools to various stakeholders for this task. The project aims to develop methods and open source software tools for modeling, predicting and assessing threats in land use change. It will provide various stakeholders, community organizations, regional planners, policymakers, businesses as well as diverse scientific fields with new capabilities to gain insights into key drivers of land use changes and also assess the environmental, economic and social impact of both short and longer term developments and threats. In addition, the project provides research training opportunities for graduate students. To achieve the stated goals, the project leverages a modeling framework that enables integration of structural economic geography and related models, with fine scale spatiotemporal data driven models. In addition it rigorously addresses the following technical issues: (i) development of fast estimation and statistical inference methods for the proposed models, (ii) development of techniques to perform unsupervised learning tasks including identifying regime changes in the parameters of the models and clustering regions with similar land use developments based on dynamic programming algorithms, and (iii) development of a framework that can incorporate projected paths from scenarios outlining future threats, and predict the corresponding land use outcomes, as well as assess their impact. The methodology will be tested and illustrated through a highly dis-aggregated spatiotemporal data set that contains detailed information for each land parcel in the state of Florida, assembled and curated from county tax auditor databases.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.

土地开发动态建模是城市和区域规划中的一个关键问题。土地利用变化对当地社区和更大范围区域的环境、生活质量、公共财政和经济发展轨迹产生影响。此外，需要评估和量化未来土地开发的多种情景所带来的威胁。土地利用模型既考虑了人类行为的关键驱动因素，又考虑了精细尺度的空间和时间依赖性，对于各个利益相关者来说是完成这项任务的宝贵工具。该项目旨在开发用于建模、预测和评估土地利用变化威胁的方法和开源软件工具。它将为各种利益相关者、社区组织、区域规划者、政策制定者、企业以及不同的科学领域提供新的能力，以深入了解土地利用变化的关键驱动因素，并评估短期和长期的环境、经济和社会影响事态发展和威胁。此外，该项目还为研究生提供研究培训机会。为了实现既定目标，该项目利用了一个建模框架，该框架能够将结构经济地理学和相关模型与精细时空数据驱动模型相集成。此外，它严格解决了以下技术问题：（i）为所提出的模型开发快速估计和统计推断方法，（ii）开发执行无监督学习任务的技术，包括识别模型参数和聚类区域的状态变化基于动态规划算法的类似土地利用开发，以及（iii）开发一个框架，该框架可以纳入概述未来威胁的情景中的预测路径，预测相应的土地利用结果，并评估其影响。该方法将通过高度分解的时空数据集进行测试和说明，该数据集包含佛罗里达州每个地块的详细信息，这些信息是从县税务审计数据库中收集和整理的。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。