Geospatial

Geospatial Program 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.

3 Credit Hours

Outcomes

Graduates of the Master of Science in Data Science and Analytics who pursue the Geospatial Emphasis Area will achieve the following educational objectives, in addition to the core program objectives while becoming immersed in Geospatial Big Data computational ecosystems:

Students will have a firm understanding of the structure of spatial data and its integration with spatial analysis tools.
Student will develop a robust understanding of the caveats that can be encountered in geospatial data structures and analysis.
Students will have a firm understanding of geospatial data structures such as vector and raster data and their use in data analytics.
Students will develop expertise in designing, managing, accessing, and manipulating geospatial data repositories.
Students will gain knowledge and experience with the exploitation of geospatial data that is stored in a variety of formats and source locations, as well as experience developing geospatial visualizations of data, blending multiple geospatial data layers as well as non-spatial data.
Students will have a solid understanding of the basic concepts, principles, and techniques in remote sensing.
Students will understand the spatial and spectral characteristics of remote sensing data for passive, active, thermal, and other sensor phenomenologies.
Students will have an ability to acquire and exploit remote sensing data using a variety of tools and techniques for real-world applications.

Geospatial Faculty

Elizabeth Garwood

Assistant Teaching Professor, Course Coordinator

246 Naka Hall
(573) 882-9007
garwoode@missouri.edu

Timothy Matisziw

Associate Professor

E2509 Lafferre Hall
(573) 882-8370
matisziwt@missouri.edu

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Grant Scott

Assistant Research Professor

W3038 Lafferre Hall
(573) 884-6400
grantscott@missouri.edu

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Sample Course Path

Students move through 8-week modules completing core courses and then progressing through emphasis area courses directly applicable to their area of study.