Hot Spot Analysis: Improving a Local Indicator of Spatial Association for Application in Traffic Safety

Abstract

Abstract. As in most European countries, traffic safety has become top priority in the National Safety Plan in Belgium. The first phase in every safety analysis concerns the identification of the hazardous locations. In this respect, a local indicator of spatial association (Moran's I) is improved and applied to determine hot spots locations on highways in Limburg, a province in Belgium. However, the analysis is complicated by the fact that accident data have a very specific nature: they form a Poisson random process rather than a Gaussian random process and they are prone to sparseness. Therefore, the well-established indicator needs some adaptations and simulations are required to determine the underlying distribution of Moran's I. This paper emphasizes the importance of using a correct distribution and indicates what could go wrong otherwise e.g. at policy level.

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

“…x are the values of variable x (vegetation indexes) at location i and location j, respectively. x is the average value of x, n is the total number of locations, and ij w represents the spatial weight [4,44,81,82].…”

Hot Spots and Spatial Autocorrelation in Certified Organic Operations in the United States

“…The studies that used geographic proximity employed various mathematical methods to test for and identify spatial autocorrelation, which measures the degree to which observations depend on the characteristics of neighbors (LeSage 1998, Anselin 1999). The Moran's I and local Moran's I (LMI) test statistics, which can both be used to test the null hypothesis of no spatial autocorrelation, are commonly used (e.g., Lian et al 2009, Banasick, Lin, and Hanham 2009, Moons, Brijs, and Wets 2008, Zhang and Lin 2008, Eades and Brown 2006, Richards, Hamilton, and Patterson 2010, Hatzenbuehler, Gillespie, and O'Neil 2012, Schmidtner et al 2012). In a study of organic agriculture, Schmidtner et al (2012), for example, used the LMI to identify hot spots of organic farming in integrated counties in Germany.…”

A frequency distribution approach to hotspot identification

“…The most commonly used spatially local methods are kernel estimation (Fortin and Dale 2005) and local measures of spatial autocorrelation, like Moran's I i and Getis' G i (Nelson and Boots 2008). Kernel estimators have been widely used in ecological studies, mainly for home range detection (e.g., Horne and Garton 2006), while Getis' G i * and Moran's I i have been used especially in socio-economic studies (e.g., Anselin et al 2005;Moons et al 2008).…”