A Visual Analysis of Spatial Data – Mapping in Stata

This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course begins 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 moves, 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.


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.


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.

This course is of particular interest to 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.


  1. General characteristics of spatial data
  2. Types of spatial objects
  3. Spatial coordinate systems
  4. Maps and shapefiles
  5. The transformation of spatial databases



  1. Spatial distance
  2. Spatial proximity matrices
  3. Spatial lags
  4. Spatial autocorrelation



  1. Visual analytics and data science
  2. Thematic maps
  3. Dot maps
  4. Graduated symbol maps
  5. Diagram maps
  6. Choropleth Maps
  7. Isarithmic Maps
  8. Multivariate 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 adding the finishing touches to our 2024 training calendar. We therefore ask you to check our website regularly or contact us at should the dates for the course you are interested in not be published yet. 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.