Analysis of the Spatial Distribution of Electricity Theft: Case Study for Delimiting the Inspection Area

Inhalt:
This paper presents a geospatial study on electricity theft in an area of the city of Florian´opolis in the state of Santa Catarina (SC), Brazil. The main objective is to identify areas with a high density of irregular cases and associate them with an importance index. Interactive maps are generated to help the electricity distributor better plan inspections and anti-fraud policies. Firstly, the null hypothesis study is carried out to check whether the data pattern presents clustering, for this the nearest neighbor method is applied, which ruled out the null hypothesis for the data used. Once the clustering pattern is confirmed, the spatial weight matrix is created to study spatial autocorrelation by applying Global Moran’s I and Local Moran’s I. Moran scatterplot is used to evaluate the degree of fit, identify outliers and leverage points, and local pockets of stationarity. The Local Moran index is used to determine the location of the clusters and the relationship between the points, which can be used to create the interactive map of the study case. The Google Street View layer is then added to the interactive map to enable visualization of points within the clusters with the most important index values.