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

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.