Computer Science|Geographic Information Science|Geospatial Science|Information and Data Science

This module provides students with an introduction to programming through a mix discussion and coursework built around an applied spatial analysis problem...

Foundations of Spatial Data Science (CASA0013)

This module provides students with an introduction to programming through a mix discussion and coursework built around an applied spatial analysis problem 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 larger 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 an appreciation 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 of how a spatially-aware data science supports critical analysis and good 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.