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title tags authors affiliations date bibliography
A course on Geographic Data Science
Geographic Data Science
GIS
Data Science
geopandas
pysal
name orcid affiliation
Dani Arribas-Bel
0000-0002-6274-1619
1, 2
name index
Geographic Data Science Lab
1
name index
The University of Liverpool
2
06 August 2018
paper.bib

Summary

This paper presents a computational learning module on Geographic Data Science (GDS). This resource is part of a larger set, that also includes a series of lecture slides, that has been used for three conscutive academic years to teach the course "Geographic Data Science" at the University of Liverpool.

Statement of need

This module is needed because both the contents are relevant, and the available set of options to teach them leaves clear gaps. A large proportion of the data society generates nowadays, from smartphone GPS logs to credit card transactions to web activity, is directly or indirecly geographic. This means that every observation can be associated with a particular set of map coordinates, analysed spatially, and acted upon taking into account the implications of its spatial location. This approach can bring a new set of perspectives to several questions. At the same time, traditional resources for teaching the handling, visualisation, and analysis of geographic data are based on a paradigm that emphasises graphical interfaces and "point-and-click" software packages. This approach is entirely valid but, in many contexts, it limits the flexiblity with which the analyst can effectively move from data to insights.

Learning objectives

This module has been designed so, upon completion, students are able to:

  • Demonstrate advanced GDS concepts and be able to use the tools programmatically to import, manipulate and analyse spatial data in different formats.
  • Understand the motivation and inner workings of the main methodological approcahes of GDS, both analytical and visual.
  • Critically evaluate the suitability of a specific technique, what it can offer and how it can help answer questions of interest.
  • Apply a number of spatial analysis techniques in Python and explain how to interpret the results, in a process of turning data into information.
  • When faced with a new data-set, work independently using GDS tools programmatically to extract valuable insight.

Content

The module is structured along

Instructional design

Experience of use

ENVS3/563

As building block

References