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.

建模土地发展动态是城市和区域规划中的一个关键问题。土地使用变化会影响当地社区和大规模地区的环境，生活质量，公共财政和经济发展轨迹。此外，有必要通过多种关于未来土地发展的情况来评估和量化威胁。土地使用模型既说明人类行为的关键驱动力，又是详尽的空间和时间依赖性，这对各种利益相关者来说都是有价值的工具。该项目旨在开发用于建模，预测和评估土地使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，区域规划师，政策制定者，企业以及多样化的科学领域提供新的能力，以深入了解土地利用变化的主要驱动力，并评估短期和更长期限的发展和长期发展和威胁的环境，经济和社会影响。此外，该项目为研究生提供了研究培训机会。为了实现既定的目标，该项目利用一个建模框架，可以将结构经济地理和相关模型与精细的时空数据驱动模型进行整合。此外，它严格解决以下技术问题：（i）开发提议模型的快速估计和统计推理方法，（ii）开发执行无监督的学习任务，包括确定模型参数和聚类区域的参数中的制度变化，并具有基于动态编程式途径的相似土地使用的开发，并能够符合（III）的未来（iii），并将威胁并预测相应的土地使用结果，并评估其影响。该方法将通过高度散布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土地包裹的详细信息，并由县税务审计师数据库组装和策划。这奖反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的crietia crietia crietia crietia crietia crietia crietia crietia crietia crietia crietia crietia cristia cripitia cripitia cristia cristia cristia cristia and crigitia cristia cristia cristia cristia cripitia cristia crigitia均值得一评论。