A Map Algebra Approach to Analyzing Spatiotemporal Data

Authors: Shaowen Wang, Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, David Tarboton, Department of Civil and Environmental Engineering, Utah State University, Mike Hodgson, Department of Geography, University of South Carolina, Eric Shook, Department of Geography, Environment, and Society, University of Minnesota, Xingong Li*, Department of Geography & Atmospheric Science, University of Kansas
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Cyberinfrastructure
Keywords: map algebra, spatiotemporal data, parallelization
Session Type: Paper
Day: 4/5/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Roosevelt 3, Marriott, Exhibition Level
Presentation File: No File Uploaded

Time series of geospatial grids (TSSG) are important and widely available spatiotemporal data for monitoring, modeling and predicting environment systems and human-environment interactions. Processing and analyzing TSSGs are somewhat cumbersome, if not impossible, with traditional GIS data analysis frameworks and tools which were developed, conceptually, for handling individual snapshot grids and, computationally, for limited geographical extent and resolutions. We propose a spatiotemporal map algebra framework which is developed based on redefined types of spatial neighborhoods and the interactions between spatial and temporal neighborhoods. We define spatial scopes as neighborhoods and classify spatial scopes based on the spatial relationships among the neighborhoods. Under this new perspective, map algebra operations can be consistently defined based on the types of neighborhoods on which they perform. We then define temporal neighborhoods and construct spatiotemporal neighborhoods using a bottom-up approach based on the interactions between their component spatial and temporal neighborhoods. We also distinguish and identify the operations that perform computation in their spatiotemporal neighborhoods and the operations that derive spatiotemporal relationships. As in the case of parallelizing conventional map algebra, spatiotemporal map algebra operations would benefit greatly from parallel computing for analyzing TSSGs. The time dimension in TSSGs brings challenges and opportunities as the parallelization can occur in space, time or both. We will identify key challenges, strategies, and possible solutions for implementing parallelized spatiotemporal map algebra operations.

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