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:
- 金额:$ 30万
- 依托单位:
- 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:
- 关键词:
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.
土地开发动态模型是城市和区域规划中的一个关键问题。土地使用的变化对环境、生活质量、公共财政和地方社区和较大规模区域的经济发展轨迹产生影响。此外,有必要评估和量化未来土地开发的多种情景所构成的威胁。土地利用模型既考虑到人类行为的关键驱动因素,又考虑到精细尺度的空间和时间依赖性,是各利益攸关方完成这一任务的宝贵工具。 该项目旨在开发方法和开放源码软件工具,用于建模、预测和评估土地使用变化的威胁。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主要驱动因素,并评估短期和长期发展和威胁对环境、经济和社会的影响。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,使结构经济地理学和相关模型的集成,与精细尺度时空数据驱动的模型。此外,它还严格解决了以下技术问题:(i)为拟议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,包括确定模型参数的变化情况,并根据动态编程算法将具有类似土地使用发展的地区聚类,以及(iii)制定一个框架,该框架可以纳入从概述未来威胁的情景中预测的路径,并预测相应的土地使用结果,以及评估其影响。该方法将通过一个高度分散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,从县税务审计员数据库中收集和策划。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
- DOI:10.1016/j.scs.2023.104390
- 发表时间:2023-03
- 期刊:
- 影响因子:11.7
- 作者:Emre Tepe;Abolfazl Safikhani
- 通讯作者:Emre Tepe;Abolfazl Safikhani
Machine learning application to spatio-temporal modeling of urban growth
- DOI:10.1016/j.compenvurbsys.2022.101801
- 发表时间:2022-06
- 期刊:
- 影响因子:0
- 作者:Yu-Seung Kim;Abolfazl Safikhani;Emre Tepe
- 通讯作者:Yu-Seung Kim;Abolfazl Safikhani;Emre Tepe
A General Modeling Framework for Network Autoregressive Processes
- DOI:10.1080/00401706.2023.2203184
- 发表时间:2021-10
- 期刊:
- 影响因子:2.5
- 作者:Hang Yin;Abolfazl Safikhani;G. Michailidis
- 通讯作者:Hang Yin;Abolfazl Safikhani;G. Michailidis
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George Michailidis其他文献
Asymptotics for <math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.gif" display="inline" overflow="scroll" class="math"><mi>p</mi></math>-value based threshold estimation under repeated measurements
- DOI:
10.1016/j.jspi.2016.01.009 - 发表时间:
2016-07-01 - 期刊:
- 影响因子:
- 作者:
Atul Mallik;Bodhisattva Sen;Moulinath Banerjee;George Michailidis - 通讯作者:
George Michailidis
Queueing Networks of Random Link Topology: Stationary Dynamics of Maximal Throughput Schedules
- DOI:
10.1007/s11134-005-0858-x - 发表时间:
2005-05-01 - 期刊:
- 影响因子:0.700
- 作者:
Nicholas Bambos;George Michailidis - 通讯作者:
George Michailidis
DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data
- DOI:
10.1186/s12859-024-05994-1 - 发表时间:
2024-12-18 - 期刊:
- 影响因子:3.300
- 作者:
Christopher Patsalis;Gayatri Iyer;Marci Brandenburg;Alla Karnovsky;George Michailidis - 通讯作者:
George Michailidis
Statistica Sinica Preprint No: SS-2022-0323
《统计》预印本编号:SS-2022-0323
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Abhishek Kaul;George Michailidis;Statistica Sinica - 通讯作者:
Statistica Sinica
Preface: Computational biomedicine
- DOI:
10.1007/s10479-018-3116-4 - 发表时间:
2019-01-14 - 期刊:
- 影响因子:4.500
- 作者:
Anton Kocheturov;Panos Pardalos;George Michailidis - 通讯作者:
George Michailidis
George Michailidis的其他文献
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{{ truncateString('George Michailidis', 18)}}的其他基金
ATD: Spatio-Temporal Modeling for Identifying Changes in Land Use
ATD:识别土地利用变化的时空模型
- 批准号:
2334735 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Change Point Detection for Data with Network Structure
网络结构数据变点检测
- 批准号:
2348640 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: ATD: Geospatial Modeling and Risk Mitigation for Human Movement Dynamics under Hurricane Threats
合作研究:ATD:飓风威胁下人类运动动力学的地理空间建模和风险缓解
- 批准号:
2319552 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: IMR: MM-1A: Scalable Statistical Methodology for Performance Monitoring, Anomaly Identification, and Mapping Network Accessibility from Active Measurements
合作研究:IMR:MM-1A:用于性能监控、异常识别和主动测量映射网络可访问性的可扩展统计方法
- 批准号:
2319593 - 财政年份:2023
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Change Point Detection for Data with Network Structure
网络结构数据变点检测
- 批准号:
2210358 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
CDS&E: Statistical Methodology for Analysis and Forecasting with Large Scale Temporal Data
CDS
- 批准号:
1821220 - 财政年份:2018
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
ATD: Collaborative Research: Extremal Dependence and Change-Point Detection Methods for High-Dimensional Data Streams with Applications to Network Cybersecurity
ATD:协作研究:高维数据流的极端依赖性和变点检测方法及其在网络网络安全中的应用
- 批准号:
1830175 - 财政年份:2018
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
BIGDATA: Collaborative Research: IA: F: Too Interconnected to Fail? Network Analytics on Complex Economic Data Streams for Monitoring Financial Stability
BIGDATA:协作研究:IA:F:互联性太强以至于不会失败?
- 批准号:
1632730 - 财政年份:2016
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
CyberSEES: Type 2: Collaborative Research: Tenable Power Distribution Networks
CyberSEES:类型 2:协作研究:可维持的配电网络
- 批准号:
1540093 - 财政年份:2015
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
Collaborative Research: Statistical Methodology for Network based Integrative Analysis of Omics Data
合作研究:基于网络的组学数据综合分析统计方法
- 批准号:
1545277 - 财政年份:2015
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
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