This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course opens with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis. In the final session the focus turns, with the help of series of official and user written commands specifically developed for the visualization of spatial data in Stata, to the main mapping techniques implemented for the visual analysis of spatial data in Stata.
In common with TStat’s training philosophy, each session of the courses is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained) and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced using applied case studies, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques.
At the end of the course, participants are expected to be able to autonomously implement (with the help of the Stata routine templates specifically developed for the course) the appropriate methods, given both the nature of their spatial data and the analysis in hand, within their own research context.
This course of particular interest for criminologists, social psychologists, sociologists, economists, epidemiologists and political scientists seeking to acquire the requisite tools required for the exploration and visualisation of spatial data in Stata.
A knowledge of basic statistics (distributions of variables, position indices, dispersion indices) and the statistical software Stata is advisable.
SESSION I: SPATIAL DATA
General characteristics of spatial data
Types of spatial objects
Spatial coordinate systems
Maps and shapefiles
The transformation of spatial databases
SESSION II: SPATIAL PROXIMITY
Spatial proximity matrices
Spatial lagsSpatial autocorrelation
SESSION III: VISUAL ANALYSIS OF SPATIAL DATA
Visual analytics and data science
Graduated symbol maps
Anthamatten, P. (2021) How to Make Maps: An Introduction to Theory and Practice of Cartography. Abingdon: Routledge.
Lambert, N. & Zanin, C. (2020) Practical Handbook of Thematic Cartography: Principles, Methods, and Applications. Boca Raton, FL: CRC Press.
We are currently putting the finishing touches to our 2023 training calendar. We therefore ask that you re-visit our website periodically or contact us at email@example.com should the dates for the course which you are interesting in following not yet be published. You will then be contacted via email as soon as the dates are available.
This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course opens with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis.