Resources#

Book(s)#

You can consult the following book for most of the content presented in this course. We will provide more specific resources for each lecture throughout the semester.

McClain, B. (2022). Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence. United States: O’Reilly Media, Incorporated. (Available online. You can sign in using your Clark credentials to freely access the book.)

Canvas#

You can access the materials on Clark University Canvas page.

Slack#

We will be using Slack to facilitate communication during the semester. If you are new to Slack, checkout this page to learn about it.

You will be invited to join the Slack channel for this course with your Clark email address (check your spam folder to make sure you don’t miss the email). If you have not received the invitation yet, reach out to the course instructor.

Software#

Python Resources#

If you need a refresher on introduction to Python, check out the following resources:

Datasets and Projects#

Use this section to learn about existing open-access datasets and projects. These can be helpful in defining your final projects.

Datasets#

  1. Source Cooperative [link]

  2. Microsoft Planetary Computer Data Catalog [link]

  3. Registry of Open Data on AWS [link]

  4. World Terrestrial Ecosystems(WTE) 2015 and 2050 [link][blog]

  5. Public Data on Google Cloud [link]

  6. Umbra Open Data Program [link]

  7. Maxar Open Data Program [link]

  8. Planet Education and Research Data [link]

  9. Google Earth Engine Data Catalog [link]

  10. Global River Widths from Landsat (GRWL) Database [link]

  11. Open Topography [link]

  12. Nigeria Geodata [link]

  13. A multi-year crop field boundary labels for Africa [link]

  14. Indiana Statewide Digital Aerial Imagery Catalog [link][Tutorial]

  15. U.S. Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS) [link]

  16. US Structures from Oak Ridge National Laboratory [link]

Projects#

  1. Understanding at risk crops in 2050 [link]

  2. World Bank’s Open Night Lights [link]

  3. A Guidebook on Mapping Poverty through Data Integration and Artificial Intelligence [link]

  4. Cookiecutter Data Science [link]

  5. Analyze NLCD annual time series [link]

  6. Climate change threatens the world’s olive legacy: How GIS can help understand crops at risk by 2050 [link]