The University of Maryland (UMD) and Nanjing Normal University (NNU) offer a pioneering cooperative Master of Science in Geospatial Information Science (MSGIS).

GIS technology plays a significant role in social development and environmental protection worldwide.  It allows the mapping and analysis of geospatial data to identify and address various environmental issues such as deforestation, pollution, and climate change.  GIS technology can help identify vulnerable communities and assess their exposure to environmental risks, enabling targeted mitigation and adaptation strategies.

GIS technology is also used for global environment protection, disaster response, urban planning, natural resource management, and wildlife conservation. It provides insights into areas of need for national and international development, helping to target aid effectively.  By providing valuable tools for decisionmaking, GIS technology can promote informed and sustainable decisions that benefit communities and the environment globally.

The MSGIS equips students to meet the challenges associated with the continuing evolution of geographic science while providing them with vibrant and fulfilling educational experiences. Graduates are empowered to apply geographic science and solutions to societal and environmental issues, both nationally and internationally. The program serves the educational interests of GIS communities by preparing its enrollees for challenging careers in relevant civilian sectors. 

UMD’s Department of Geographical Sciences (GEOG) is recognized nationally and internationally as a leader in geospatial research and education.   Similarly, the School of Geographical Sciences at NNU is recognized as a Top Five institution among its peers in China. The UMD-NNU cooperative program leverages the outstanding human, scholarly, and pedagogical resources of both institutions to create a unique learning environment for its students.

GEOG has outstanding faculty who contribute their knowledge and expertise by making fundamental contributions to the advancement of geographic science, conducting disciplinary and integrative research in the physical and social sciences that spans local to global scales, and emphasizing a geospatial perspective on our changing planet and its sustainability.

UMD’s Office of International and Executive Programs (OIEP) serves as liaison between UMD and NNU, ensures compliance with relevant UMD policies and procedures, and assists with program management and operations.

Candidates spend a year studying at NNU before transferring to the U.S. to complete a six-month residency at UMD prior to graduation.  UMD faculty visit NNU to teach some required classes, and all course instruction—whether given in China or the U.S.—is conducted in English.

At the end of the Capstone course there is a final presentation in the form of a "Poster Symposium" (the entire Department of Geographical Sciences—as well as alumni—are invited to participate).  The presentation serves as a portfolio of what each student has accomplished in the MSGIS program.

Research topics of the 2019-2020 MSGIS cohort:

  • Comparative Study of Spatial Statistical Methods: Taking Suzhou as an Example;
  • Web Map Service for Tours of Hengdian World Studios, China;
  • Web GIS Platform that Provides Tourist Information and Surrounding Environment for Tourists in Hefei, China;
  • Establish a Web-GIS Applications for Visualization and Analysis spatial pattern of residential area and accessibility of service facilities within 15-minute life circle in Shenzhen;
  • AKURA: Seek a Key You Really Adore: A site selection system for house buying based on web GIS technology;
  • Landslide Susceptibility Mapping based on Analytic Hierarchy Process (AHP): A Case Study from the Yibin, Southwest China;
  • GIS Map Network Application for Photography Sharing and Communication Website;
  • Potential Geographical Distribution of Desert Plants under Future Climatic Conditions in Xinjiang, China;
  • China's National Intangible Heritage and Tourism Web GIS Application;
  • Forest Fire Risk Prediction for Australia
  • Housing Price Analysis Model and Price Prediction Application in Taiyuan, China.

Research topics of the 2018-2019 cohort:

  • Spatiotemporal Analysis of Public Sentiment with Twitter : A Case Study in New York City, USA;
  • Developing a Habitat Suitability Model for Jaguar in Pantanal, Brazil;
  • Flood Risk Analysis in Louisiana Using Analytical Hierarchy Process (AHP);
  • Indoor & Outdoor 3D Model Construction and Navigation Planning Based on Crowdsourcing Data;
  • Demonstration of Wetland Water Regimes Mapping in Alaska From Optical and Radar Images;
  • Effect of Potato Field Fertilization on Greenhouse Gas Emissions and Yields in U.S. Eastern Seaboard Region;
  • Analyzing Spatiotemporal Hotspot Crime Patterns on Weekdays and Weekends;
  • The Changes in the Southern Amazon Basin Following Degradation and Deforestation From 2015 to 2019;
  • Spatial Analysis of Pedestrian Traffic Accidents in Washington, D.C. Based on GIS;
  • Economic Estimation of Countries Along the Belt and Road Region Based on Nighttime Lighting Remote Sensing Data;
  • Modeling and Mapping Forest Fire Risks in the Northwest of Brazil Using Remote Sensing and GIS;
  • Temporal and Spatial Analysis of Housing Prices: A Case Study of Nantong, China;
  • ONTRAVEL: Mobile Application Map Game Design For A New Way of Traveling;
  • Evaluating the Validity of Split-Window (SW) and Improved Mono-Window (IMW) Algorithm for Land Surface Temperature (LST) Retrieval from Landsat 8 Data;
  • Is There Any Relationship between Air Quality and Urban Area Change in Montgomery County, Maryland?;
  • Social App for Theater Lovers: An App Integrates Reading Performance Reviews with Getting Location on Map.

Research topics of the inaugural (2017-2018) cohort:

  • Space-Time Analysis and Spatial Modeling of Lyme Disease Cases in the USA, 2001-2015;
  • Choosing the Optimal Locations of Expansion for Bike-share System: A Case Study in Washington, D.C.;
  • Risk Assessment of Landslide Geological Hazard using AHP and Logistic Regression: A Case Study of California, USA.

 

Capitol GIS Graphic