GES Elective Courses (12 Credits)
GES 668: Building Spatial Databases
In this course, students will learn how to find, understand, and work with spatial data for research and practice. This course leverages open-source tools, online educational resources, and real-world data from urban environments to help students build a methodological framework for academic and professional work with spatial data now and in the future. The course explores the process of building and maintaining data sets about local places and prepares students to navigate critical issues including open data licensing, location accuracy, data “cleaning”, and privacy considerations. Assignments and readings will introduce students to the range of practical uses for spatial data in planning, public policy, and advocacy around housing, health, transportation, the environment, and more. Students in the course will learn to work with common file formats (e.g. GeoJSON, GeoPackage) and web services (e.g. FeatureServer, APIs) and how to read, write, document, and share data using GIS applications (QGIS), web applications (Mapbox or ArcGIS Online), and programming languages (R or Python). This course does not require any prior experience with desktop GIS software or R programming. Assignments will require students to use R, RStudio, and QGIS during the course. Optional resources on working with spatial data using ArcGIS Online or Python will be provided where feasible. Students will be required to use GitHub in order to share completed assignments and develop their professional portfolios.
GES 680: Introduction to Satellite Image Analysis
The course provides an introductory overview of remote sensing analysis. As one of several spatial technologies available to geospatial practitioners, remote sensing often complements and integrates with other geomatic disciplines. The emphasis of this course is on satellite imagery (single-band, multispectral, hyperspectral, radar, and lidar) originating from airborne and spaceborne platforms. The course consists of three sections. In section one, students will conduct a brief survey of allied geomatic disciplines (GIS, GPS, CAD, Surveying, etc.) and learn about several types of remotely sensed imagery (aerial and satellite platforms and sensors). Section two covers image analysis and transformations, including the integration of remotely sensed imagery with other types of data in a GIS. The final section covers image visualization and presentation methods, discussing how to apply remotely sensed data for different applications. A basic understanding of remote sensing and image raster principles is assumed as a prerequisite for this course, such as topics covered in an introductory GIS class.
GES 700: Special Topics
This course is provided to allow flexibility in offering graduate-level work in Geography and Environmental Systems not found elsewhere in the course offerings. The topic will be announced prior to the semester when it will be offered. Instructor and topics will rotate each semester. Check with your Program Director for acceptable subtopics.
GES 762: GIS Project Capstone
This capstone course demonstrates a student’s ability to apply the knowledge and skills attained during their tenure in GIS program. This is a semi-independent course that has students working with industry-relevant clients and undergoing the entire process of developing a real-world solution to meet that client’s needs. These solutions can be in the form of applied research or technological development but will be geared toward the development of some aspect related to an enterprise-wide Geographic Information System. The project will be done in conjunction with a private, government, or academic partner who will be responsible for providing the requirements, data, and system access needed to develop a functional and stable solution in their GIS production environment. Students will produce documentation that demonstrates the design of the GIS, the method for its construction, and instructions for its operation. Guidance from the instructor will interplay with feedback from the client to ensure the student’s success
GES 771: Advanced Spatial Data Management
This course covers the key web technologies used for publishing and orchestrating web services for geospatial data. The course provides a formal methodology for designing geospatial distributed information systems. Topics include distributed computing, OGC Web Services (WMS, WFS, WCS, CSW, SOS, WPS), workflows, clients, and visualization. Students will configure and develop tools to publish data on the web and clients for visualization of the data, using open source (e.g GeoServer, OpenLayers, Leaflet, Open Street Map, Google Maps API) and commercial (ArcGIS) software.
GES 775: Advanced Web-GIS Development
The course will review the Python coding basics and NumPy, Pandas to brush on the big data toolkit. It then digs into machine learning and social media toolkit. To transition this course to GIS oriented, it switches the topic to Python GIS toolkit and we will spend time learning mpl_toolkit and BaseMap. Geopandas is a powerful python geospatial tool. Its Geoseries and GeoDataFrame enable convenient data analysis and spatial analysis. Geopandas are unique that they can have multiple geometry columns. You can set its active geometry to display different features. In the same geometry column, it can store different geometry types. Its geometry attributes and methods make spatial analysis straightforward. It inherits all the big data analysis power from pandas. Therefore, all pandas’ capabilities are available in Geopandas. Once we have our focus on GIS, we then move to ArcGIS. It introduces the concept of geoprocessing and how to use Python to automate the process. Students will explore available system tools in ArcToolbox and learn model and ModelBuilder. They will also learn how to build a model and how to create a model tool. One focus of the class is ArcPy site package. Students will work with the embedded ArcGIS Python window to write and execute scripts. They will also use Eclipse with PyDev plug-in. Students will write scripts using tools from ArcPy to perform various tasks, such as managing maps and layers, performing data editing, executing geoprocessing tools, and creating custom geoprocessing tools. They will develop Python scripts in Eclipse. Once the Python scripts are developed, the students will learn how to convert it to a script tool so it will be available for other Geoprocessing models. At the end of the class, the students will be able to implement a geoprocessing solution for a complex geospatial problem.
GES 776: Application of Remote Sensing
This course provides an introduction to remote sensing with emphasis on the basics of imagery acquisition technologies, and data processing techniques. Participants in this course will gain hands-on experience with image interpretation, digital image processing, and digital image classification. The course is specifically designed for adult professionals and is offered as a hybrid learning course where information is presented either as an in-class lecture or online. Students will complete 10 lessons with corresponding hands-on assignments, 2 quizzes, a two-part midterm exam, and a final project. Those who successfully complete the course will be able to recognize and understand different space and aerial remote sensing sensors, locate and download available space and aerial imagery, and apply different image processing techniques using ArcGIS.
GES 777: Public & Crowdsourced Spatial Data
The generation of crowdsourced data is being produced in unprecedented quantities and is used in numerous ways from research to navigating daily life. This course introduces students to topics ranging from the conceptual to the applied with particular attention to public and crowdsourced data, their utility in drawing insights about place and space, and ethical and legal concerns with data and data gathering processes. Students will first learn the behavioral aspects of how geographic data are sensed, surveilled, volunteered, or gathered from crowdsourcing platforms and covers the principles of spatial thinking that lead to our understanding of geographic patterns such data. Crowdsourcing platforms are discussed and presented to students so that they understand the sources of data, followed by in-depth analyses of data from some of the platforms as a segue into conducting analysis of these data. Students will engage in assignments and projects that assess the quality of crowdsourced data, gaining a comprehensive knowledge-base of the statistical properties and limitations when using data naturally generated and limitations when using data gathered through purpose-driven initiatives. Students will learn to use the crowdsourcing platforms (Neighborland, CityAtlas, Twitter, Instagram, Place Pulse,
SeeClickFix, Amazon Mechanical Turk, Google AdWords, GrubHub, Yelp, eBird, iNaturalist) to conduct geographical analysis to identify trends and estimate changes to space and place.
GES 778: Digital Cartography & Visualization
GES 779: Spatial Regression & Advanced Statistics
Regression techniques are used in every social and physical science discipline, and these disciplines often attempt to measure spatial relationships with geographic data. As such, this course introduces the regression framework in the statistical analysis of data to account for geography and measure spatial relationships. The course first presents the basics of regression, followed by the coverage of three regression techniques used in geographical analysis. The regression techniques covered are (i) spatial lag and error, (ii) geographically weighted, and (iii) multi-level (hierarchical) regression. The theoretical, conceptual, and practical foundations for each of these techniques are presented, providing students with the knowledge and skills necessary to conduct robust analyses. The concepts of spatial dependence and spatial heterogeneity are specifically covered in how they underpin the measurement of spatial effects in the regression framework. The course material is presented from a methodological approach that incorporates and extends the material covered in GES 774: Statistics for Geographers. Students are introduced to the GeoDa and R Studio programs to conduct their analyses