Course info
CASA0013: Foundations of Spatial Data Science (21/22)
This module provides students with an introduction to programming (in
Python) through a mix discussion and coursework built around an applied
spatial data science question using real-world data. The module is intended
to complement Quantitative Methods and Geographic Information Systems by
showing how geographic and quantitative concepts are applied in a
computational context as part of a piece of spatial data science
analysis.However, particular attention will be given to a critical
reflection upon the nature of the data used: readings are intended to
develop a sound understanding of how real-world (geo)data are produced,
their potential insights and biases, as well as opportunities and
limitations. These seek to ground the specialist (geo)computational
component in a sound understanding of the fact that ‘the data do not
speak for themselves’ and of the role of the spatial data scientist in
selecting and developing evidence to support policy-making and practice.
Students should therefore be looking for ways to use this module to
integrate, reinforce, and generalise their understanding so to develop
sound judgement.Students will see how data cleaning and processing are part
of a larger ‘workflow’ involving Exploratory Data Analysis in both
non-spatial and spatial contexts, data transformation, visualisation, and
interpretation; and of how these, in turn, are situated within a wider
disciplinary ‘terrain’ entailing debates around the construction and
validity of different types of knowledge. It is hoped that students will
not only find ways to apply what they have learned here to support their
research and studies as part of the programme, but also to become familiar
with core tools employed by practicing geographic and spatial data
scientists in ways that further post-graduation employment opportunities.
Course contacts
Leader
JR
Tutor
HC
Course Administrator
SC
JD
MH
CT