Ph.D. in Geospatial Analytics

Students examine why sustainable solutions to grand societal challenges need geospatial analytics. Emphasis is placed on the roles that location, spatial interaction and multi-scale processes play in scientific discovery and communication. Discussion of seminal and leading-edge approaches to problem-solving is motivated by grand challenges such as controlling the spread of emerging infectious disease, providing access to clean water and creating smart and connected cities. Students also engage in several written and oral presentation activities focused on data science communication skills and professionalization.

Applied experience in the architecture of geospatial data management, including open source options. The course introduces students to: (i) spatial and temporal data types (OGC specification, GPS and accelerometer matching), (ii) spatial predicates, (iii) spatial indices and (iv) spatial query processing. In addition, students will be exposed to modern spatial data management systems like NoSQL and graph databases, and data integration principles including protected health information (PHI/HIPAA).

Advanced understanding of physical principles of remote sensing, image processing and applications from earth observations. Awareness of tradeoffs between earth observing sensors, platforms and analysis techniques will help prepare the students to critically assess remote sensing products and devise solutions to environmental problems. Students will be able to communicate the complexities of image analysis and will be better prepared to integrate earth observations into their areas of expertise. Topics include electromagnetic energy and radiative transfer; US and international orbital and suborbital data acquisition platforms; passive and active imaging and scanning sensors; spatial, spectral, radiometric, and temporal resolutions; geometric corrections and radiometric calibrations; preprocessing of digital remotely sensed data; advanced image analysis including enhancement, enhancement, classification, geophysical variable retrieval, error and sensitivity analysis; data fusion; data assimilation; and integration of remotely  sensed data with other data types in a geospatial modeling context.

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data (~70% of all digital data), and the emergence of geosocial media and location sensing technologies has transformed the field in recent years. This course reviews the current state of the art in spatial, temporal and spatiotemporal data mining and looks at real-world applications ranging from geosocial networks to climate change impacts. Course introduces various spatial and temporal pattern families and teaches how to incorporate spatial relationships and constraints into data mining approaches like clustering, classification, anomalies and colocations.

Methods, algorithms and tools for geospatial modeling and predicting spatio-temporal dimensions of environmental systems. The course covers the physical, biological, and social processes that drive dynamics of landscape change. Deterministic, stochastic, and multi-agent simulations are explained, with emphasis on coupling empirical and process based models, techniques for model calibration and validation and sensitivity analysis. Applications to real-world problems are explored, such as modeling multi-scale flow and mass transport, spread of wildfire, biological invasions and urbanization.

Principles of visualization design and scripting for geospatial visualization. This course provides a systematic framework of visualization design principles based on the human visual system and explores open-source geospatial data visualization tools. Topics include challenges and techniques for visualizing large multivariate dataset, spatio-temporal data and landscape changes over time. Students have the opportunity to work with broad range of visualization technologies, including frontiers in immersive visualization, tangible interaction with geospatial data and eye tracking.

No, the Ph.D. in Geospatial Analytics is a traditional full-time on-campus program.

Yes. We accept unofficial transcripts with your application. Official transcripts will be requested if you are admitted to the program.

No, we welcome applications from students with strong computational skills from diverse backgrounds, including computer science, data science, environmental science, ecology, engineering, and more.

No, students may enroll without a master’s degree. Successful applicants, however, will have had previous academic research experience.

Application fee waivers are offered only for domestic students who have participated in specific research programs (i.e. McNair Scholars).

Incoming doctoral students receive a tuition waiver, health insurance benefits, and a $30,000 stipend.

While you are encouraged to connect with faculty who share your interests prior to applying (the application will ask you to name a preferred advisor), students can be admitted on program funding without a specific advisor/position.

Students in the Geospatial Analytics doctoral program work on a diverse range of data science frontiers intersecting multiple disciplines, with funding available from the Ph.D. program as well as from external grants secured by faculty. Assistantships are each fully funded for four years. Below are a sample of the opportunities that were available in previous years. For more details about each opportunity, and to learn about past projects, visit our Graduate Assistantships page.

• Landscape Connectivity Dynamics in Surface Water Networks — Join the Geospatial Analysis for Environmental Change Lab to investigate climate and land-use change effects on landscape connectivity dynamics.
• Seasonality from Space — Join the Spatial Ecosystem Analytics Lab on a NASA-funded project investigating satellite data fusion and time series analysis.
• Winter Weather — Join the Environment Analytics group to study the complex interactions within snow storms and wintery mix storms.
• Modeling Forest and Water Resources under Changing Conditions — Join the Watershed Ecology lab group and combine various data sources to create projections of future landscape conditions.
• Modeling Agricultural and Water Resource Dynamics — Join the Biosystems Analytics Lab to study the effects of global and local change on fresh and estuarine water quality, land-sea connectivity and agroecosystem productivity.
• Surface Water Dynamics from Space — Join the Geospatial Analysis for Environmental Change Lab to investigate hydroclimatic drivers of surface water extent dynamics and advance quantification of water extent and volume.
• Remote Sensing Forest Gap Dynamics — Join the Applied Remote Sensing and Analysis lab group to examine the role and influence of forest gaps in relation to localized large-scale disturbances.

Funding is available for additional projects, and in all cases students are encouraged to develop research questions and methods that suit their interests and career goals.