Week: Mapping

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Overview

In this course, the world of geospatial analytics is perhaps the most narrowly focused in purpose yet highly fragmented in term of methodology. Although maps can be used to tell complex and compelling stories, the workflow for generating maps themselves is relatively straightforward: create a basemap and add one or more informative layers. This is the same whether using advanced mapping software with drag-n-drop, no-code functionality (e.g. ArcGIS, Tableau or QGIS) or programming with popular mapping libraries (e.g. Leaflet.js, GeoPandas)

The world of geospatial analytics is vast. It ranges from no-code/low-code Tableau, ArcGIS and QGIS to unique visualizations realized through customized Python/JavaScript code building-upon geospatial resources like OpenMap. Although we'll introduce some of the breath of geospatial analysis, our coding will focus on two popular Python libraries for geospatial mapping: GeoPandas and GeoPy.

Applications

• [Monday]
  • Torn Apart Separado, Summary
  • Torn Apart Separado, Project
• [Wednesday]
  • Not Even the Past: Social Vunerability and the Legacy of Redlining
• [Friday]
  • Land Acquisition and Dispossession: Mapping the Homestead Act, 1863-1912

Coding Practice

• [Monday]
  • GeoPy Jupyter Notebook with Commentary (Read this Jupyter notebook for the big picture. We'll review the details and implement on Friday in lab)
  • Datafile for GeoPy Notebook: ithica-places.csv
  • Jupyter GeoPy Notebook
  • Part 1: Working with Geospatial Data in Python (NOTE: This datacamp is a bit less clear than most because it assumes a few key bits of prior knowledge. Don't get hung up on any unstated assumptions, work through it as best you can, and bring any questions to class. We'll explain the finer points in class.)
• [Wednesday]
  • Parts 2 & 3: Working with Geospatial Data in Python
• [Friday]
  • Part 4: Working with Geospatial Data in Python