We apply computational theories, methods, tools, and technologies to understand complex spatiotemporal processes and solve geographical problems.
Spatial Database Management: Knowledge of database is a highly desirable skill in GIS to efficiently and effectively store, manage, and access big spatial data for data analytics and geospatial application development. Key research and technical themes include relational and object-oriented database design, query languages and performance, distributed database management system, and heterogeneous data integration.
High-Performance Computing: Unprecedented amount of geospatial data have been collectively generated everyday via ubiquitously distributed geosensor networks, location aware devices, and social media. High-Performance Computing (HPC), often in the form distributed and/or parallel computing such as cluster computing, grid computing, supercomputing, and cloud computing enables to efficiently and effectively collect, store, process, query, analyze, visualize, model, update, share and integrate big geospatial datasets.
More than half of fatal traffic crashes occur due to aggressive driving according to AAA (American Automobile Association) Foundation for Traffic Safety. Ubiquitous technology has made it possible to monitor driver behavior at a high frequency for a long period of time. This provides an opportunity for researchers to investigate risky driving behavior at a large scale.
Collaborating with the National Safety through Disruption (Safe-D) University Transportation Center (UTC), this project aims to develop a big data analytics framework and visualization tool to conduct spatiotemporal modeling and classify and visualize aggressive driving behavior using data from emerging technology. As an essential safety planning tool in the era of big data, this framework/tool can be used to identify where and when aggressive driving occurs.
The American Association of Geographers (AAG) launched its “Encoding Geography” Initiative in 2018, a long-term commitment to expand geo-computational thinking at all levels of geography education. This initiative will inform a research strategy to design geo-computational curricula that (1) is inclusive, (2) supports teacher learning, and (3) can be measured for effectiveness. Such efforts will require collaborative work and dialogue between different stakeholders. The National Center for Research in Geography Education (NCRGE) is providing funds to initiate a Research Coordination Network to initiative the long term objectives of AAG's Encoding Geography initiative.
Analysis of spatially distributed data including computer applications. Spatial sampling, descriptive statistics for areal data, inferential statistics, use of maps in data analysis.
PostgreSQL, PostGIS, and open source databases to store, manage, and query geospatial data.
Automating geocoding processes by Python scripting, managing vector and raster data, and preprocessing geospatial data.
Application of statistical techniques to geographic research to include simple regression and correlation, multiple regression, geographically weighted regression, classification, factor analysis, and computer applications.
Integration of Geographic Information Systems (GIS) with discrete and continuous multiple criteria decision making (MCDM) methods. Applications of MCDM in land use planning, site selection, and resource management spatial decision problems.
Scripting techniques with Python for automating geoprocessing tasks and developing GIS tools. Use of Bayes’ Theorem in spatial modeling.
Geoprocessing Python scripting techniques with applications to spatial modeling and analysis.
Spatial analytic techniques from image processing, remote sensing, geographic information systems, cartography or quantitative methods. May be repeated with new content. See Class Schedule for specific content.