In this data rich world, we need to understand how things are organized on the Earth's surface. Those features are represented by spatial data and necessarily depend upon what surrounds them. Spatial statistics provide insights into explaining processes that create patterns in spatial data. In geographical information analysis, spatial statistics such as point pattern analysis, spatial autocorrelation, and spatial interpolation will analyze the spatial patterns, spatial processes, and spatial association that characterize spatial data. Understanding spatial analysis will help you realize what makes spatial data special and why spatial analysis reveals a truth about spatial data.
At a Glance
Details
Credits |
3.00
|
---|---|
Topics |
Applications in GIS
|
Technologies |
ArcGIS Pro
GeoDa
R
|
Syllabus | GEOG 586 Sample Syllabus |
Upcoming Offerings
Semester | Instructor |
---|---|
Fall 2, 2024 | Amy Burnicki, Ph.D. |
Fall 2, 2024 | Brandi Gaertner, Ph.D. |
Spring 1, 2025 | Fritz Kessler, Ph.D. |
Spring 1, 2025 | Yanling Lin, Ph.D. |
Spring 2, 2025 | Amy Burnicki, Ph.D. |
Summer 2025 | Marcela Suárez, Ph.D. |
Fall 1, 2025 | Fritz Kessler, Ph.D. |
Meet Our Faculty Instructors
Amy's research interests include investigating the perceptual and cognitive processes involved in map reading and applications of GIS to historical geography.