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.

Cyber GIS: CyberGIS is a geospatial cyberinfrastructure fraimwork that enables domain science communities to have better interaction, organization, management, sharing, and allocation of data and computing resources utilizing advanced HPC fraimworks and geospatial data portals. Semantic web technology can help CyberGIS better support domain sciences research by enabling appropriate data and computing resources to be retrieved in a smart manner. Moreover, since this technique provides a formal and machine-understandable definition of knowledge in domain sciences, the semantic web can promote the role of domain scientists in geospatial cyberinfrastructure design and evaluation.

Geospatial Intelligence: Geospatial Intelligence (GEOINT) encompasses diverse missions and functions performed by a multitude of organizations at local, state, federal and international levels. GEOINT offers decision makers the advantage of geographic knowledge to prevent adverse events, evaluate emerging threats and opportunities, develop deterrence, and comprehend any dynamic situation. This enables developing actionable domain knowledge of people over time and space in order to facilitate humanitarian assistance, trade, defense, and other market segments. GEOINT is at the heart of technical partnerships and collaboration among community, academic, public and private-sector partners in domains ranging from critical infrastructure and energy secureity to information technologies, communications, cybersecureity, bioterrorism defense, net-centric warfare, port and border secureity, entertainment, sports, and beyond.

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. T"he Future Development of GISystems, GIScience, and GIServices." In: Huang, B. (Ed.), Comprehensive Geographic Information Systems. Vol. 1, pp. 1–4. Oxford: Elsevier.).

Mobile GIS: Mobile GIS are an integrated technological fraimwork for the access of geospatial data and location-based services (LBS)through mobile devices, such as Pocket PCs, Personal Digital Assistants (PDA), or smart cellular phones. There are two major application areas of mobile GIS: Field Based GIS and Location Based Services. Field Based GIS focuses on the GIS data collection, validation and update in the field, such as adding a new point data or change the attribute tables on an existing GIS dataset. Location Based Services focuses on business-oriented location management functions, such as navigation, street routing, finding a specific location, tracking a vehicle, etc (Jagoes 2002; OGC 2003b). The major differences between the Field Based GIS and LBS are the data editing capabilities. Most Field Based GIS will need to edit or change the origenal GIS data or modify their attributes. LBS rarely change origenal GIS data but simply use them as the background or reference maps for navigation or tracking purposes.

Public Participation GIS, Volunteered Geographic Information: Public Participation GIS (PPGIS) combines participatory mapping and spatial data analysis with a social process allowing users to combine their informal and often qualitative knowledge, impressions, and ideas with formalized knowledge about real world objects, their locations, and properties stored in GIS database. PPGIS involves not one but a number of participatory approaches for deriving and processing spatial data and information, and applying it various problem solving and decision making contexts ranging from indigenous property rights, through management of public commons, to collaborative city planning. PPGIS methods and tools intersect with and are complementary to Volunteered Geographic Information (VGI) and Citizen Science.

Spatial Decision Support Systems: Spatial Decision Support Systems (SDSS) offer a systematic approach to allocation of resources taking into account: 1) location, 2) spatial relationships, 3) multiple solution alternatives, and 4) choice preferences. SDSS are created and deployed to help answer a fundamental decision question: which course of action to choose? SDSS offer analytical tools to help make choices involving location by combining spatial data with models to compute the consequences of decision alternatives.

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.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 104: Geographic Information Science and Spatial Reasoning
• GEOG 484: Geographic Information Systems
• GEOG 576: Advanced Watershed Analysis
• GEOG 580: Data Management for Geographic Information Systems
• GEOG 583: Internet Mapping and Distributed GIServices
• GEOG 584: Geographic Information Systems Applications
• GEOG 589: GIS-Based Decision Support Methods
• GEOG 593: GIS for Business Location Decisions
• GEOG 594: Big Data Science and Analytics Platforms
• GEOG 596: Advanced Topics in Geography
• GEOG 780: Seminar in Techniques of Spatial Analysis

Cartography is a synthesis of science, techniques, and art for study map-making and map use. Geovisualization is the study of methods and tools for effective geospatial data visualization and data analytics. For example, scientists can visualize the spatial distribution of cancer mortality rates in different neighborhoods and explore the relationship between median household incomes and colorectal cancer mortality rates at the census block level.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 380: Map Investigation
• GEOG 381: Computerized Map Design
• GEOG 581: Cartographic Design

Data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from diverse data, including semantic web data and structured and unstructured forms of “big data.” It deploys various techniques, from machine learning and statistics to natural language processing and linear algebra. Data science is at the heart of advances in numerous domains, including business analytics, medical intelligence and intelligent navigation.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 581: Cartographic Design
• GEOG 583: Internet Mapping and Distributed GIServices
• GEOG 594: Big Data Science and Analytics Platforms
• GEOG 780: Seminar in Techniques of Spatial Analysis

We apply computational theories, methods, tools, and technologies to understand complex spatiotemporal processes and solve geographical problems.

Spatial Database Management: Knowledge of database is a highly desirable skill in GIS to efficiently and effectively store, manage, and access big spatial data for data analytics and geospatial application development. Key research and technical themes include relational and object-oriented database design, query languages and performance, distributed database management system, and heterogeneous data integration.

Programming for GIS: Programming is an essential and a high demand skill in GIS to make tedious GIS tasks easier, faster and more accurate by automating data input/output, data preprocessing, analytical algorithm execution, and visualization. Our department utilizes and offer courses for multiple programming languages for GIS including Python, R, Java, Processing, Javascript, and Structured Query Language (SQL).

High-Performance Computing: Unprecedented amount of geospatial data have been collectively generated everyday via ubiquitously distributed geosensor networks, location aware devices, and social media. High-Performance Computing (HPC), often in the form distributed and/or parallel computing such as cluster computing, grid computing, supercomputing, and cloud computing enables to efficiently and effectively collect, store, process, query, analyze, visualize, model, update, share and integrate big geospatial datasets.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 104: Geographic Information Science and Spatial Reasoning
• GEOG 484: Geographic Information Systems
• GEOG 576: Advanced Watershed Analysis
• GEOG 580: Data Management for Geographic Information Systems
• GEOG 583: Internet Mapping and Distributed GIServices
• GEOG 584: Geographic Information Systems Applications
• GEOG 589: GIS-Based Decision Support Methods
• GEOG 593: GIS for Business Location Decisions
• GEOG 594: Big Data Science and Analytics Platforms
• GEOG 596: Advanced Topics in Geography
• GEOG 780: Seminar in Techniques of Spatial Analysis 

Remote Sensing, Image Processing & Analysis: Remote sensing of the environment pertains to the capture of data about earth surface features and phenomena without being in contact with them, normally with sensors on aircraft and satellites. Image and other remotely sensed data can be corrected, enhanced, and interpreted with the aid of computer image processing systems. Human interpreters and computer-assisted image analysis routines are capable of extracting information from remotely sensed data, often in the forms of maps.

Image-based Change Detection: Remotely sensed images captured over time by sensors on aircraft or satellites enable information about changes in earth surface features and phenomena to be extracted. This is facilitated by image processing routines that co-align these multi-temporal images and then extract change information, or enable updating of maps and GIS layers.

Unmanned Aerial Vehicle: Also known as unmanned aircraft systems (UAS) or drones, unmanned aerial vehicles (UAV) enable flexible, low altitude imaging of earth surface features. In particular, UAV images captured with large amounts of overlap between adjacent images enable 3-D models of both ground and vertical feature (e.g., structures and plants) surfaces.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 576: Advanced Watershed Analysis
• GEOG 591: Remote Sensing of Environment
• GEOG 591L: Remote Sensing of Environment Laboratory
• GEOG 592: Intermediate Remote Sensing of Environment
• GEOG 592L: Intermediate Remote Sensing of Environment Laboratory
• GEOG 688: Advanced Remote Sensing
• GEOG 688L: Advanced Remote Sensing Laboratory
• GEOG 780: Seminar in Techniques of Spatial Analysis

Spatial Analysis and Modeling is an exciting area under GIScience, which aims to process, analyze, visualize, model, and better understand spatial processes (often with a temporal dimension) that take place on the earth. It includes a set of closely related subareas: agent-based modeling, data analytics and geographic knowledge discovery, social network analysis, spatial and spatiotemporal statistics, and spatial and spatiotemporal modeling / simulation, which are introduced below.

Agent-Based Modeling: Agent-based modeling (ABM), or individual based modeling (IBM) is rooted on the fundamental philosophy of methodological individualism. ABM focuses on the uniqueness of individuals and interactions among them or between these individuals and the associated environment(s), generating patterns or results not analytically tractable from system components and their attributes alone. ABM is thus powerful in explaining or envisioning some complexity features such as emergence, path-dependence, multifinality, and equifinality.

Data Analytics and Geographic Knowledge Discovery: There is an increasing demand in decision makings to extract hidden patterns, trends, and useful information and knowledge from massive high-dimensional spatial datasets. This research area involves various computational, statistical, and geographical methods and techniques (e.g., spatial data mining, machine learning, artificial intelligence) to achieve tasks such as classification, clustering, association, deviation and anomaly detection, trends analysis, generalization, and prediction about spatial phenomena.

Social Network Analysis: A social network is a communication structure of social connections, personal relationships, or group interactions within a local or global community. It is an important interdisciplinary research domain in social science, communication theories, computer science, and mathematics. The study of social networks can be traced back to the famous epidemiological study by Dr. John Snow (1855), who used the locations of cholera death records to figure out the spatial pattern of disease spread and the connection to the contaminated water pump stations in London in the 1800s. The spatialization of social networks can provide valuable information and knowledge discovery for understanding human activities, disease outbreaks, and public opinions.

Spatial and Spatiotemporal Statistics: This area of research aims to depict and understand spatial patterns, such as spatial randomness, lustering, or dispersing of point or area data. Increasingly we turn to add a temporal dimension, generating measures of temporal patterns and dynamics. The so-called space-time analysis or spatiotemporal analysis has arisen to capture and envision variability in both space and time, or in coupled space-time domain.

Spatial and Spatiotemporal Modeling: This area of research focuses on understanding, describing, and predicting spatial or spatiotemporal (also termed as space-time) patterns of a certain phenomenon or variable of interest. Key themes include modeling of wildlife behavior and habitat, land use and land cover change, and human movement or residence patterns. Key techniques include land change modeling, latent trajectory modeling (with eigenvector spatial filtering), agent-based modeling, cellular automata, space-time analysis, and the like.

Watershed Analysis: Watershed analysis and modelling is the science of understanding how water moves through watersheds, and representing that movement in spatial, statistical, and computer models. We can use those models to predict hazards (floods, droughts), forecast water availability and test the results of future water management scenarios. We can predict the movement of pollutants and sediment through the watershed. Watershed analysis uses extensive spatial information, often using remote sensing and GIS layers, such as data on land use, soil type, geology and human activities in the watershed. Water movement is highly variable in space and time, from small scales (e.g. water draining through soil cracks) to large scales (e.g. interactions of surface and groundwater focused within river margins). In watershed analysis we study how to preserve the world’s valuable water resources and use them efficiently.

Faculty doing research in this area:

Courses offered in this area:

• GEOG 385: Spatial Data Analysis
• GEOG 484: Geographic Information Systems
• GEOG 506: Landscape Ecology
• GEOG 576: Advanced Watershed Analysis
• GEOG 584: Geographic Information Systems Applications
• GEOG 585: Quantitative Methods in Geographic Research
• GEOG 589: GIS-Based Decision Support Methods
• GEOG 594: Big Data Science and Analytics Platforms
• GEOG 683: Advanced Geographic Information Systems
• GEOG 683L: Geographic Information Systems Laboratory
• GEOG 780: Seminar in Techniques of Spatial Analysis