Detecting and Analyzing Mobility Hotspots using Surface Networks

Abstract

Capabilities for collecting and storing data on mobile objects have increased dramatically over the past few decades. A persistent difficulty is summarizing large collections of mobile objects. This article develops methods for extracting and analyzing hotspots or locations with relatively high levels of mobility activity. We use kernel density estimation (KDE) to convert a large collection of mobile objects into a smooth, continuous surface. We then develop a topological algorithm to extract critical geometric features of the surface; these include critical points (peaks, pits and passes) and critical lines (ridgelines and course-lines). We connect the peaks and corresponding ridgelines to produce a surface network that summarizes the topological structure of the surface. We apply graph theoretic indices to analytically characterize the surface and its changes over time. To illustrate our approach, we apply the techniques to taxi cab data collected in Shanghai, China. We find increases in the complexity of the hotspot spatial distribution during normal activity hours in the late morning, afternoon and evening and a spike in the connectivity of the hotspot spatial distribution in the morning as taxis concentrate on servicing travel to work. These results match with scientific and anecdotal knowledge about human activity patterns in the study area.

Understanding the spatial distribution of crime in hot crime areas

“…Several methods have been proposed to identify hot crime areas in current literature (Chainey et al, 2008;Ratcliffe, 2010;Hu et al, 2014). These approaches include spatial ellipses, thematic mapping of administrative units, grid thematic mapping, and kernel density estimation (KDE) (Chainey et al, 2008;Ratcliffe, 2010).…”

“…Among all the existing methods, KDE is the most popular one for its ability to discover different shapes of hot areas. In a KDE surface, the areas with high values over the threshold are identified as hot areas (Hu et al, 2014).…”

“…Bandwidth selection is a critical step in kernel density estimation due to its crucial impact on risk surface (Hu et al, 2014). The cross-validation method is adopted for the bandwidth selection in the STKDE2 method (Hu et al, 2018).…”

On the topology of topography: a review

“…Orlandini, Moretti, and Gavioli (2014) for analytical representation and 495 Mower (2009) for visualization have re-examined the role of slope lines as used in the original work. Hu, Miller, and Li (2014) have shown that surface network theory has use in the hotspot analysis of mobile objects, such as vehicles in traffic or potentially diffusion of 500 disease in a populace. Wolf (2014) has examined the use of the surface network in nanotechnology.…”

An Entropy-Based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data

“…Taxi cab trajectory data recorded in April 2015 are used to present the average taxi mobility trends and evaluate travel time predictability. Commonly seen mobility research often focuses on the mobility patterns of independent individuals (person or vehicle) based on such as mobile phone data [22,30,31] and GPS data [22,32,33]. Differently, travel time predictability emphasizes the expected success rate of travel time prediction based on a given travel time series from individual or statistics values of travel time, and the statistical trends of travel times from multiple vehicles may present traffic patterns more effectively than individual mobility which may be affected by unpredictable driving behaviors.…”