Course information provided by the Courses of Study 2021-2022.

The course will introduce students to a wide array of spatial data analytical techniques and will be organized as follows: 1) Students will use the common Python packages to retrieve, clean, and manage spatial data and integrate them into spatial analyses. Topics may include the basic Python syntax and functions, web scraping zillow data, spatial data cleaning and management using Pandas and Geopandas, and geoprocessing using ArcPy package.  2) Students will analyze and interpret spatial data to answer urban related research questions using a variety of software platforms. Topics may include exploratory spatial data analysis, spatial autocorrelation, point pattern analysis, spatial interpolation techniques and Geostatistics, spatial regression (including geographically weighted regression), as well as spatial lag and spatial error models.

When Offered Spring.

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Syllabi: none
  •   Regular Academic Session.  Combined with: CRP 4680CRP 5680DESIGN 5680

  • 3 Credits Graded

  • 17302 DESIGN 4680   LEC 001

    • MW Sibley Hall 305
    • Jan 24 - May 10, 2022
    • Schmidt, S

  • Instruction Mode: In Person