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Introduction to Spatial Data Science

Program(s): Undergraduate Courses

Spatial data science is an evolving field that can be thought of as a collection of concepts and methods drawn from both statistics/spatial statistics and computer science/geocomputation. These techniques deal with accessing, transforming, manipulating, visualizing, exploring and reasoning about data where the locational component is important (spatial data). The course introduces the types of spatial data relevant in social science inquiry and reviews a range of methods to explore these data. The course will focus on data gathered for aggregate units, like census tracts or counties (e.g., unemployment rates, disease rates by area, crime rates), but also introduce data measured at spatially located sampling points (e.g., air quality monitoring stations and urban sensors) as well as observations at the point level (e.g., locations of crimes, commercial establishments, traffic accidents). Specific topics covered include the special nature of spatial data, geovisualization and visual analytics, spatial autocorrelation analysis, cluster detection and regionalization. An important aspect of the course is to learn and apply open source geospatial software tools, primarily GeoDa, but also R.

Course Overview

Current Grade / Education Level

Undergrad / Grad


Undergraduate Courses

Start Date

June 22

End Date

July 24

Class Details

Primary Instructor


Academic Interest

Social Sciences (e.g., history, sociology)

Class Specifics

Course Code

SOCI 20253 91

Class Day(s)

Mon Tues Wed Thurs

Class Duration (CST)


12:00 P.M.


Session I

Course Length

5 weeks