Emphasis Area Overview
Geospatial data science meets a number of important emerging technology and economic development challenges.
Increasingly location-based data will continue to power the next generation of location-aware services, applications & insight. MU’s Data Science & Analytics graduates will become among the world’s leading geospatial and location-driven focused data scientists with cutting-edge knowledge of geospatial data strategies including geospatial Big Data, GIS, geostatistical analysis & remote sensing.
Courses
This course will provide a practical overview of key issues encountered when working with and analyzing spatial data as well as an overview of major spatial analysis approaches. Discussions and laboratory work will focus on implementation, analysis, and interpretive issues given constraining factors that commonly arise in practice.
3 Credit Hours
This course provides an overview of theoretical and practical issues encountered when working with geospatial data for both the vector and raster data models with a focus on incorporating geospatial data into the data science lifecycle. Data access, indexing, retrieval, and other technical concepts are investigated. Important data storage paradigms such as enterprise geospatial databases and desktop GIS systems are explored along with scalable computational tools beyond desktop computing for Geospatial Big Data. Core issues in geospatial data storage, management, exploitation, and multi-data set entity resolution / correlation are examined.
3 Credit Hours
Introduction to the principles of remote sensing of the environment leading to information extraction from remote sensing geospatial raster data sets. Examines theoretical and practical issues associated with digital imagery from spacecraft, conventional and high-altitude aerial photography, thermal imaging, and microwave remote sensing. Covers standard processing techniques, including preprocessing and normalization, pixel-level feature extraction, information extraction, and structural/object extraction.
This is a planned future course and is not being offered on current schedules.
3 Credit Hours