Abstract
Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years.
Highlights
Hot spot detection is a spatial analysis method aimed to detect regions on a map, called hot spots, within which a high concentration of events characterizing a specific phenomenon is localized
In [30], the HR-EFCM algorithm is applied to detect hot spots in disease analysis; the results show that the reliability of a hot spot is linearly dependent on the standard deviation of the values of the membership degrees of the data points to the corresponding fuzzy cluster
The incidence of hot spots in a selected area is measured by calculating an index called the hot spot strength, and by evaluating its reliability using a method based on the De
Summary
Hot spot detection is a spatial analysis method aimed to detect regions on a map, called hot spots, within which a high concentration of events characterizing a specific phenomenon is localized. In addition to analyzing the evolution of the phenomenon in a selected area for each time frame, our method evaluates with what intensity this area has been affected by the phenomenon in a given period of time; we measure this intensity by calculating the hot spot strength index. This measure is essential in an application context to understand with what intensity a certain phenomenon is spreading over an area of investigation;.
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