This module provides an introduction into the theory, methods and tools of spatial analysis essential for your career as a Spatial Data Scientist. Unlike traditional GIS or Geocomputation courses, it has a specific focus on the principles, properties and parameters that are part of spatial analysis and how to understand and apply these effectively within geographic and data science-oriented research.

The first half of the module provides a detailed introduction into spatial concepts required to accurately and effectively analyse spatial data, such as scale and geography, spatial dependency and autocorrelation, and spatial heterogeneity and spatial regression models. The second half then focuses on the applications of different spatial analysis techniques within current data science research, including, but not limited to, cluster analysis, geodemographic classification, geostatistics and network analysis.

The sessions in this module will equip students with an understanding of the principles underlying the analysis of spatial data and how these principles are currently being applied within spatial data science.

By the end of the module, the student should:

Possess a good understanding of the principles underlying the analysis of spatial data in general and spatial statistics in particular.

Be able to account for the key properties of spatial data within their analysis techniques and highlight issues of uncertainty with the results of these analyses.

Be able to examine, analyse and simulate a range of spatial patterns and processes. Be able to use geostatistical tools to analyze and interpolate spatial patterns

Explain how different spatial analysis techniques are currently being used within spatial data science, from cluster analysis, network analysis to geodemographic classification.

The module aims to:

Equip students with an understanding of the principles underlying the conception, representation/measurement and analysis of spatial phenomena
	Present an overview of the core techniques of spatial analysis, and the software and tools that are integral to their effective deployment in advanced spatial analysis.
	Provide an introduction to the principles underlying the analysis of spatial data in general and spatial statistics in particular.
	Demonstrate how to utilise GIS and spatial programming software (R-Studio) effectively to conduct reproducible spatial data analysis.
	Illustrate the different applications of spatial analysis within spatial data science research.
The course will usually consist of approximately 10 lectures and 10 practical sessions. 