Note: Not all courses are offered every semester, and new courses may be added at any time. Check the schedule of classes, for the latest offerings.
Required Core Courses (18 credits):
GES 666: Just Maps – Critical & Ethical Aspects of Mapping
This course employs a variety of mapping tools, in conjunction with R-Studio, to develop a student’s ability to critically approach cartographic production. A methodological approach will be taken that ranges from the selection and preparation of data to the choice of map representation in the final visualization product. From their own viewpoint, students will systematically analyze, interrogate, and reflect on how each stage of the cartographic process impacts the final product, as well as alternative viewpoints by their audiences. Assignments in this course will use urban-based data to make social science-related maps that are for a wide array of audiences. The course will also foster a student’s ability to assess other maps they encounter from a variety of sources. As part of the map production process, this course will illustrate the principles of graphical excellence so that students gain the ability to produce superior visual products. Students are introduced to the latest version of ArcGIS or ArcGIS Pro, as well as maintaining a GitHub page for portfolio development during their tenure.
GES 671: Launching Spatial Databases
This course provides the fundamentals of relational databases, including data modeling, database design, database implementation and database security. It focuses on managing and working with geospatial data using the StructurThis course provides the fundamentals of relational databases, including data modeling, database design, database implementation, and database security. It focuses on managing and working with geospatial data using the Structure Query Language (SQL). Students will also learn how data in databases can be exposed on the Web. Students will have hands-on experience with different tools: Microsoft Access, PostgreSQL, PostGIS, QGIS and GeoServer. Students are introduced to challenging labs to solve complex problems helping them improve their geospatial thinking, database skills, and tools proficiency.
GES 673: Processing Geographic Data
This course has three primary objectives. The first objective is for students to learn the mechanics of several geo-processing tools so that they can (i) describe how the tools work, (ii) determine which is the most appropriate to be used in manipulating or creating data, and (iii) conceptualize the final state of the data once the tools is used. The second objective is to develop skills in selecting a set of geo-processing tools to develop a methodological process that (i) modifies data to create a standard data set for use by a broad range of analysts, and (ii) creates new data to pursue the answering of analytical questions. As such, students will derive a geo-processing method and combine several tools in a logical order to create, clean, and finalize a data set or analytical use. Several GIS operation theories will be presented so that students can identify how a GIS operates, stores, manipulates, and outputs geographic data. The third objective is to demonstrate how to document a geo-processing method for the purpose of (i) allowing others to replicate the method and (ii) provide transparency for data quality assessment.
GES 675: Web-GIS Development
This is a python development and data science course for students who pursue application development skills. It covers the python development language basics and python toolkits for big data analytics. The students will learn python data type, function, class, module, and packages. After mastering the basics, they will learn to use the Eclipse IDE tool to develop and debug complicated code to solve real-world problems. They also practice logic and code flow to be able to write efficient procedures. File I/O and database access are the two most common development tasks. Students will write code to read, copy, update and delete files. They will also write code to create tables, retrieve data and update records in a database. Once the students make the breakthrough in coding applications, they have the skills to tackle big data. This course focuses on data science. Data manipulation and visualization are thoroughly discussed. Python toolkit Numpy, Pandas, and Matplotlib are explained in-depth. Real-world data science examples are analyzed and python code to solve these problems is provided step by step. The solution leads the students to visualize how it relates to GIS. To prepare the students for advanced topics, the class introduces mpl_toolkits.basemap, a toolkit for GIS developers. After the class, the students shall be able to develop applications to solve complex big data problems using python and its toolkits.
GES 678: GIS Project Leadership & Management
This graduate-level course focuses on the study and application of structured analysis and design methods throughout the GIS life cycle. The course stresses standard approaches for gathering requirements, modeling, analyzing, and designing geographic information systems. The course employs the case method of instruction.
GES 774: Statistics for Geographers
The objective of this class is for students to learn how to analyze geographic data using several spatial statistical techniques, grounded in geography principles. Students are given a foundation in basic spatial statistics techniques, including an understanding of how the techniques work in conjunction with the geographic concepts that underpin each. The emphasis in this course is on interpreting and describing analysis results, and less on the statistical mechanics. Students will become familiar with the most common tools used for spatial statistics and gain a detailed understanding of how each technique works. Specifically, students will use several spatial tools in the ArcGIS 10.6 Spatial Statistics Toolbox, CrimeStat 4.0, and Open GeoDa 1.14.
Elective GIS Courses (12 Credits) Choose 4
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
Looking for more info?