Detecting and Analyzing Mobility Hotspots using Surface Networks

TL;DR

This paper introduces a novel topological and graph-based method to identify and analyze mobility hotspots from large-scale surface data, exemplified by taxi data in Shanghai, revealing patterns of human activity and mobility complexity.

Contribution

It develops a new approach combining kernel density estimation and topological algorithms to extract and analyze mobility hotspots and their topological features from large mobile object datasets.

Findings

Increased hotspot complexity during late morning, afternoon, and evening.

Spike in hotspot connectivity in the morning related to commuting.

Results align with known human activity patterns.

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 paper 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.