Spatial Data Science | Autumn 2017 | University of Chicago

University of Chicago

Explore the fundamental concepts and techniques of spatial data analysis with hands-on experience using cutting-edge software and programming languages.

University CoursesPythonR

Introduction

Spatial Data Science is a course taught by Luc Anselin at the University of Chicago in Autumn 2017. The course covers the fundamental concepts and techniques of spatial data analysis, focusing on the unique challenges and opportunities presented by spatial data.

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Highlights

Covers the essential tools and methods for spatial data analysis, including spatial regression, spatial clustering, and spatial visualization.
Emphasizes the importance of spatial context and the need to account for spatial dependence and heterogeneity in data analysis.
Provides hands-on experience with real-world spatial data and applications using cutting-edge software and programming languages.

Recommendation

This course is highly recommended for students and professionals interested in geospatial analysis, urban planning, environmental studies, public health, and other fields that involve the study of spatial phenomena. The course provides a solid foundation in spatial data science and equips learners with the skills to tackle complex spatial problems.

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