Probabilistic Graphical Models Applied to Spatial Analysis in R: Cell Phone Thefts in Bogotá


  • Danna Lesley Cruz Reyes Departamento de Estadística, Universidade Federal de Minas Gerais, Minas Gerais, Brazil. Escuela de Medicina y Ciencias de la Salud (EMCS), Universidad del Rosario, Bogotá, Colombia.


Conditional Auto-Regressive, big data, R programming


Recent technological advances allow large-scale collection, storage and processing information. As a consequence textbf big data has become more important nowadays, since the increase in information has given rise
to large and complex data sets that can be potentially exploited to find solutions to relevant problems. This
work aims to explain how statistical methods can analyze these large and complex data sets, specifically spatial
data. A spatial dependency analysis is carried out by means of a graph that characterizes the spatial structure
and a widely used approach known as Conditional Auto-Regressive (CAR). These models are useful for obtaining multivariate joint distributions of a random vector based on uni-variate conditional specifications. These
conditional specifications are based on the Markov properties. Hence, that the conditional distribution of a
component of the random vector depends only on a set of neighbors defined by the graph. CAR models are
particular cases of random Markov fields. Finally, it is explained how to carry out these analyzes in R language
programming including the handling of graphs and the packages used. Finally, the parameters estimation in
R is carried out following the Bayesian methodology to data corresponding to stolen cell phones in BogotáColombia.




How to Cite

Cruz Reyes, D. L. . (2020). Probabilistic Graphical Models Applied to Spatial Analysis in R: Cell Phone Thefts in Bogotá. Tecciencia, 15(29), 9–22. Retrieved from