Handbook of Spatial AnalysisTheory and Application with R
Insee Méthodes N°131 - October 2018

Insee - Eurostat - Directed by Vincent LOONIS - Coordinated by Marie-Pierre de BELLEFON
Dernière mise à jour le : 29/10/2018

The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) “acknowledged the critical importance of integrating geospatial information with statistics and socio-economic data and the development of a geospatial statistical framework”. There is a growing interest in spatial phenomena and, as a consequence, the need for geolocated data is also increasing. To this end, the French national institute of statistics and economic studies (Insee) has undertaken a reshaping of its geographical information system.

Integrating geographical and statistical data is one thing. Analysing such data is another one. To this end, Insee has coordinated, with the strong support of Eurostat and the European Forum for Geography and Statistics, the writing of a handbook of spatial analysis. The handbook draws up the list of analysis that can be carried out with spatial data and the pitfalls to avoid when using them.

The purpose of this handbook of spatial analysis is to answer the questions faced by data analysts in statistical institutes. What are the different usages of the geolocated data sources? In what cases should their spatial dimension be taken into account? How should spatial statistical and econometric methods be applied? Unlike existing manuals, this handbook has been expressly designed according to the issues specific to statistical institutes, such as spatial sampling, spatial econometrics, confidentiality or spatial smoothing.

The handbook is divided into four parts and fourteen chapters. The first three chapters match the stages one would follow to carry out a study with spatial data: describing the location of the observations, measuring spatial interactions and applying the appropriate model. Each chapter explains the theoretical foundations, gives practical applications based on data coming from public statistical institutes, and displays how to use the R statistical software to carry out the computations.


Table of contents (pdf, 27 Ko)

Part 1: Describing geolocalized data

Part 2: Measuring the importance of spatial effects

Part 3: Taking spatial effects into account

Part 4: Extensions


Index (pdf, 93 Ko)