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.
- GEOG 385: Spatial Data Analysis
- GEOG 580: Data Management for Geographic Information Systems
- GEOG 582: GIS Programming with Python
- GEOG 585: Quantitative Methods in Geographic Research
- GEOG 589: GIS-Based Decision Support Methods
- GEOG 683: Advanced Geographic Information Systems
- GEOG 683L: Geographic Information Systems Laboratory
- GEOG 780: Seminar in Techniques of Spatial Analysis
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.