Applied GIS

Applied GIS focuses on methods and techniques of gaining insight and intelligence from spatio-temporal data in order to manage natural resources, man-made infrastructure, and support a variety of decision making processes. Courses in Applied GIS offered by the department emphasize an analytical approach where the end product is useful not only for day-to-day operations but also for more strategic decisions involving allocation of resources.

Business Intelligence: Contemporary Business Intelligence that recognize location and its spatial context as intrinsic components of business decision making takes a data-centric approach that is focused on locational analytics. Methods of location analytics include spatial analysis with GIS, statistical analysis, and spatial optimization. They are useful in delivering solutions for problems such as site selection, territory design, trade and service area selection, supply-demand location/allocation, and geodemographics-based marketing.

Geospatial Security Intelligence:

Hazards: Using advance GIS tools or techniques, we characterize and envision the spatial or spatiotemporal patterns of various hazards, such as wildfires, diseases, landslides, and earthquake epicenters. The results are instrumental to improved hazard planning (e.g., preparedness, envisioning), management (e.g., response, mitigation), and recovery (trajectory representation, simulation).

Human & Urban Dynamics: Human dynamics is a transdisciplinary research field focusing on the understanding of dynamic patterns, relationships, narratives, changes, and transitions of human activities, behaviors, and communications. Many scientific research projects (in the fields of public health, GIScience, civil engineering, and computer science) are trying to study human dynamics and human behaviors. One main goal of human dynamics is to develop effective intervention methods to modify or change human behaviors and to resolve public health problems (such as obesity, disease outbreaks, and smoking behaviors) or transportation problem (traffic jams and vehicle incidents). Several innovative data collection methods can be applied to study human dynamics. For example, researchers can use computer vision algorithms to analyze Google Street Views and to estimate the built environment index and neighborhood social status. Combined CCTVs in urban areas and street traffic cameras can be used to analyze the usage of bike lanes and biking behaviors in different communities/neighborhoods. The frequency of geotagged social media check-ins can be used to estimate dynamic changes of population density for supporting disaster evacuation decision support systems (cited from Tsou, M-H., 2018. The Future Development of GISystems, GIScience, and GIServices. In: Huang, B. (Ed.), Comprehensive Geographic Information Systems. Vol. 1, pp. 1–4. Oxford: Elsevier.).

Watershed Science: Watershed science relates to the natural and human controls on watershed processes, including water quality, quantity, erosion, and stream channel morphology. GIS and remote sensing are used extensively in watershed science to map land cover, organize geospatial information, and generate models of watersheds.


multilevel model of meme diffusion chart

Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks Leave geography site

This Interdisciplinary Behavioral and Social Science Research (IBSS) project will study human dynamics across social media and social networks and focus on the modeling of information diffusion over both time and space, and the connection between online activities and real world human behaviors. The research team will study diffusion patterns of human messages, activities, and communications by using both computational methods (e.g., social network analysis, geographic information systems, and machine-learning) and traditional social scientific approaches (e.g., qualitative analysis, inferential statistics, and behavior analysis). New communication theories, new knowledge discovery tools, and new computational models will be developed and validated by the interdisciplinary research team. This project will enable the convergence of spatial science, social media, communication, computer science, and social behavioral analysis, and facilitate the transformation of behavioral and social science research to computational applications and modeling (simulation, prediction, and analytics) using both quantitative and qualitative methods. Two scenarios (public response to disaster warnings/alerts, and political and electoral referenda of controversial social topics at state or national level) will be used to validate and improve a new communication theory regarding memes, or reproducible messages.

smart dashboard for flu outbreak

Integrated Stage-based Evacuation with Social Perception Analysis and Dynamic Population Estimation Leave geography site

The research will help emergency response agencies better understand public perceptions and needs during disaster events, and create more effective evacuation plans for local communities. This project will integrate multiple data sources—including social media, census survey, geographic information systems (GIS) data layers, volunteer suggestions, and remote sensing data—to develop an integrated wildfire evacuation decision support system (IWEDSS) for the County of San Diego as a demonstration prototype system. IWEDSS will consist of four core modules: dynamic population estimation, stage-based robust evacuation models, social perception analysis, and a web-based geospatial analytics platform. It will offer scientifically-based and data-driven analytic tools for evacuation planers, resource managers, and decision makers to support efficient and effective decision-making activities that can reduce the evacuation time and potential number of injuries and deaths. The research team will collaborate with staff from the Office of Emergency Services (OES) of San Diego County, the San Diego/Imperial Counties Chapter of the American Red Cross, and 2-1-1 San Diego to develop IWEDSS together.



  • Theory and techniques in watershed analysis. Use of GIS and statistical programming for analyses of geomorphology, hydrology, and water quality data.

  • Spatial analysis methods in GIS, to include terrain, raster, and network analysis. Feature distributions and patterns. GIS data processing techniques to include spatial interpolation, geocoding, and dynamic segmentation. Designing and executing analytical procedures.

  • Integration of Geographic Information Systems (GIS) with discrete and continuous multiple criteria decision making (MCDM) methods. Applications of MCDM in land use planning, site selection, and resource management spatial decision problems.

  • Geographic Information Systems (GIS) and location analysis methods to include modeling and spatial analysis. Applications of GIS and location analysis in business site selection, market segmentation, retail marketing, and service area analysis.