Geospatial Data Analytics Concentration

Geospatial Data Analytics Concentration Program Overview

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Geospatial analytics has gained prominence across industries due to the abundance of geospatial data available, such as location coordinates and satellite images. The convergence of data science and geospatial analytics presents numerous opportunities for applications in environmental planning, urban development, logistics, public health, and a broad range of other fields. Advances in cloud computing, models, and sensing technologies have also enhanced geospatial data analysis capabilities, enabling students to gain a deeper understanding of complex environmental and social patterns.

The Concentration in Geospatial Data Analytics equips students with a comprehensive skillset for diverse career paths and further academic pursuits. By integrating geospatial data into data science and analytics, students gain the ability to uncover valuable insights and patterns that traditional analytical approaches may overlook. The program provides the tools, techniques, and theoretical foundations necessary to tackle real-world challenges in a rapidly evolving job market.

Students will receive specialized training in geospatial analysis, including Geographic Information Systems (GIS), remote sensing, and deep learning techniques applied to Earth observation data. Graduates of the program are well-prepared for careers as data scientists, geospatial analysts, spatial data engineers, or consultants in diverse industries such as environmental planning, urban development, transportation, public health, and natural resource management. The program also provides a strong foundation for further academic studies or research.

Geospatial Data Analytics Concentration Requirements

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The Concentration in Geospatial Data Analytics is designed to equip students with the skills and knowledge needed to analyze and interpret geospatial data effectively. Through a combination of core courses in quantitative methods, GIS, remote sensing, computing, and data science, students will gain a solid foundation in geospatial analysis techniques. The elective courses offer specialized knowledge in advanced GIS and remote sensing topics, allowing students to tailor their studies to their specific interests and career goals.

 

Core Courses (6 courses):

GEOG 110 - Introduction to Quantitative Methods  

GEOG 190 - Introduction to Geographic Information Science  

GEOG 293 - Introduction to Remote Sensing  

CSCI 120 - Introduction to Computing  OR CSCI 124 - Honors Introduction to Computing  

DSCI 105 - Applied Data Analytics  OR DSCI 125 - Introduction to Data Science  

DSCI 215 - Applying Deep Learning to Earth Observation  

 

Elective Courses (Choose 1 course):

GEOG 213 - Advanced Geospatial Analytics with Python  

GEOG 246 - Geospatial Analysis with R  

GEOG 260 - GIS & Land Change Models  

GEOG 279 - GIS & Map Comparison  

GEOG 282 - Advanced Remote Sensing  

GEOG 287 - New Methods in Earth Observation  

GEOG 296 - Advanced Raster GIS  

ID 296 - Advanced Vector GIS  

 

Note: There is no capstone requirement for this concentration.

Geospatial Data Analytics Concentration Faculty

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Lyndon Estes, Ph.D. (Co-Director)

John Rogan, Ph.D. (Co-Director)

Hamed Alemohammad, Ph.D.

Ken Basye, Ph.D.

Rinku Roy Chowdhury, Ph.D.

Li Han, Ph.D.

Gary Holness, Ph.D.

Yelena Ogneva-Himmelberger, Ph.D.

Robert Pontius, Ph.D.

Florencia Sangermano, Ph.D.

Catalin Veghes, M.S.