International Journal of Geo-Information Article Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation Boxi Shen 1, Xiang Xu 2,* , Jun Li 1, Antonio Plaza 3 and Qunying Huang 4 1 Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
[email protected] (B.S.);
[email protected] (J.L.) 2 Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China 3 Hyperspectral Computing Laboratory Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura, 10003 Cáceres, Spain;
[email protected] 4 Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA;
[email protected] * Correspondence:
[email protected] Received: 9 October 2020; Accepted: 13 November 2020; Published: 15 November 2020 Abstract: Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs).