Ph.D. in Geospatial Analytics

The integrative Ph.D. in Geospatial Analytics* brings together departments from across NC State to train a new generation of interdisciplinary data scientists skilled in developing novel understanding of spatial phenomena and in applying new knowledge to grand challenges.

A photo of a man using a modeling tool at the North Carolina State University Center for Geospatial Analytics

Our one-of-a-kind program focuses on integrative thinking and experiential learning:

  • Multidisciplinary advising unites expertise from across complementary academic departments
  • Cross-cutting curriculum spans core classes in solution-driven analytics and discipline-specific electives
  • Professional experience enriches practical understanding through a 6- to 8-week internship with an external partner

Apply for a Ph.D. in Geospatial Analytics

A number of Ph.D. graduate assistantships with competitive salaries, benefits, and tuition support will be available each year through the Center for Geospatial Analytics. Students are encouraged to suggest prospective advisor(s) and describe shared research interests in their application’s personal statement. Application materials should be submitted by February 1 for Fall admission.

Admission Requirements

Minimum requirements include

  • Undergraduate GPA ≥ 3.0
  • Graduate GPA ≥ 3.0 (if entering with a Master’s degree)
  • GRE Scores (within last 5 years)
  • IBT TOEFL Score ≥ 80 overall (18 in each section) (International Applicants; the Office of International Services offers additional helpful information)

Required supporting documents include

  • Official NC State Graduate School application
  • Official transcripts from all colleges/universities attended
  • Personal statement
  • 3 letters of recommendation
  • Curriculum vitae
  • Writing sample

Application Instructions coming soon

If you have questions about the application process before instructions are available, please contact Dr. Ross Meentemeyer, Director of the Center for Geospatial Analytics and Director of the Graduate Program for the degree.

Degree Requirements

The Ph.D. program consists of

  • 72 credit hours beyond the Bachelor’s degree.  The core required courses comprise 21 credit hours (18 core courses + 3 credit professional experience; listed below). The remaining 51 credit hours are devoted to an individually tailored selection of electives and research.
  • an off-campus professional experience. In the summer following their first year in the program, students participate in an experiential learning activity within government (local, state, federal), industry, private and academic research institutions, or other organizations in the geospatial arena. Students consult with their advisors to identify specific opportunities that will enhance their doctoral program.
  • a written preliminary exam. The exam is required by the end of the 4th semester, followed by an oral exam consisting of the dissertation proposal defense, typically before the start of the 5th semester.
  • a written dissertation and final dissertation oral defense required to complete the degree.

Core Curriculum

The core curriculum includes the following courses; click course names to view descriptions:

 Demonstrates why sustainable solutions to grand challenges require geospatial analytics. Emphasis is given to the roles that place, spatial interaction, and multi-scale processes play in scientific understanding and communication. Grand challenges such as global environmental change and biological invasions, climate change and disaster management, urbanization and sustainable development, and interactions in the food-energy-water nexus are explored as case studies that benefit from spatial thinking and geospatial analytics.
 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 the 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.
 Experiential learning activity in the form of an off-campus professional experience within government (local, state, federal), industry, private, and academic research institutions, or other organizations in the geospatial arena. Students consult with their advisors to identify specific opportunities that will enhance their doctoral program.

If you have questions…

If you’d like to speak to someone about the Ph.D. program in Geospatial Analytics*, please contact Dr. Ross Meentemeyer, Director of the Center for Geospatial Analytics and Director of the Graduate Program for the degree.

* To launch Fall 2018, after final approval by SACS COC.