Spatial Data Management

Spatial data are key ingredients for spatial analysis. In many GIS projects, you will need to find the spatial data suitable for your project needs, and manage them in an effective and efficient manner. Without suitable and well-managed spatial data, a GIS cannot exert its full power. In this course, we will delve into the world of spatial data models and explore various techniques for efficiently retrieving and managing spatial data. Throughout the semester, we will cover a range of topics that are central to spatial data management. One key aspect we will focus on is geospatial big data, discussing its characteristics, challenges, and potential applications. We will also explore Google Earth Engine, a powerful cloud-based platform for analyzing geospatial data at scale. Another important component of the course will be structured query language (SQL), which is widely used for managing and manipulating spatial data. You will learn how to leverage SQL to query, analyze, and manipulate spatial databases effectively.

Class Email: ut_geog414@live.utk.edu 

 Dr. Qiusheng Wu an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. He is also an Amazon Visiting Academic and a Google Developer Expert (GDE) for Earth Engine. His research interests include geospatial data science, remote sensing, and environmental modeling. Dr. Wu is particularly interested in utilizing big geospatial data and cloud computing (e.g., Google Earth Engine, Microsoft Planetary Computer, Amazon Web Services) to study environmental change, especially surface water and wetland inundation dynamics. His research has been funded by NASA, USDA, and the Department of Defense. Dr. Wu is an advocate of open science and reproducible research, having developed and published various open-source packages for advanced geospatial analysis and data visualization, such as geemap, leafmap, lidar, and segment-geospatial. Check out his open-source projects at opengeos.