Abstract
Spatial statistics is an important field of data science with many applications in very different areas of study such as epidemiology, criminology, seismology, astronomy and econometrics, among others. In particular, spatial statistics has frequently been used to analyze traffic accidents datasets with explanatory and preventive objectives. Traditionally, these studies have employed spatial statistics techniques at some level of areal aggregation, usually related to administrative units. However, last decade has brought an increasing number of works on the spatial incidence and distribution of traffic accidents at the road level by means of the spatial structure known as a linear network. This change seems positive because it could provide deeper and more accurate investigations than previous studies that were based on areal spatial units. The interest in working at the road level renders some technical difficulties due to the high complexity of these structures, specially in terms of manipulation and rectification. The R Shiny app SpNetPrep, which is available online and via an R package named the same way, has the goal of providing certain functionalities that could be useful for a user which is interested in performing an spatial analysis over a road network structure.
Highlights
Spatial statistics studies have been commonly based on geographic structures made of polygons representing an administrative or political division of different order, depending on the size of the region being analyzed and on the specific interest of the researchers
Last years are bringing a higher number of spatial analysis that are defined over network structures, which allow a better understanding of some spatial point patterns of great interest
The SpNetPrep package (Briz-Redón 2018a, Briz-Redón 2018b) does not deal with statistics, but with the previous steps that can be required in order to perform a spatial statistical analysis of a point pattern that lies on a linear network representing a road structure
Summary
Spatial statistics studies have been commonly based on geographic structures made of polygons representing an administrative or political division of different order, depending on the size of the region being analyzed and on the specific interest of the researchers. The SpNetPrep package (Briz-Redón 2018a, Briz-Redón 2018b) does not deal with statistics, but with the previous steps that can be required in order to perform a spatial statistical analysis of a point pattern that lies on a linear network representing a road structure.
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