GEOG 213/313: Advanced Geospatial Analytics with Python#

Overview#

Geospatial analytics is being revolutionized by the increasing availability of multi-modal observations from various sensors, new analytical methods including machine learning techniques, and the shift to using cloud infrastructures. Along with these drivers, the software landscape for geospatial analytics has changed during the last decade. While there are still several commercial software providers, there is a growing ecosystem of open-source software and toolboxes for geospatial analysis. Python is one of main programming languages in this landscape. Python is a general-purpose language which can be used for a wide range of tasks including web development and data manipulation in addition to data analytics. These features along with the large developer community who maintains and expands various Python packages, have increased popularity and usability of Python for geospatial applications.

This course is a follow-on to Intro Python Programming (IDCE 302) offered as part of the Geographic Information Science, MS program (MSGIS) at Clark University. The course is designed to fill the gap for an advanced Python programming course (200/300) at Clark with a focus on geospatial data analytics. Students who take this course will be introduced to the principles of open-source software for science, and how to develop reproducible workflows in Python. They will also learn to access geospatial data on various portals (with an emphasis on cloud data stores) using Python. The key focus of the course will be on geospatial data analytics and data visualization for the rest of the semester.

The intended audiences for the course are PhD students in Geography, MSGIS students, and majors in Geography, GES, ESS, and Data Science.

Learning Goals#

  • Develop reproducible scientific code;

  • Gain comprehensive understanding of geospatial Python packages;

  • Access and work with geospatial data in Python;

  • Transform, merge, and manipulate geospatial data in Python;

  • Visualize geospatial data in Python;

  • Scalable and parallel computations in Python;