Introduction to spatial data with R: spatial econometrics

Governments and statistical agencies often make available area-level data on a number of topics (mortality, population, socio-economic variables, etc.) for different administrative regions. Visualization of spatial data is important, as it can provide hints on the relationships between the different study variables. However, a serious statistical analysis is required in order to assess and confirm any possible relationships. Furthermore, understanding the particularities and subtleties of spatial modeling is important in any serious statistical analysis. In particular, assessing spatial autocorrelation is the first step when analyzing area-level data. Also, building spatial regression models can highlight important dependencies between the variable of interest and other covariates. Some of these models were initially developed in the fields of disease mapping and spatial econometrics, which has helped spatial statistics to grow as a discipline as well as providing a number of important statistical models. Most of these statistical methods are available in R statistical software, which has become an important tool for the visualization and analysis of spatial data. For this reason, several examples on disease mapping and spatial econometrics will be developed with R statistical software to illustrate the analysis of spatial data in practice.Fundación BBVAN