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ISPRS Int. J. Geo-Inf. 2017, 6(5), 151; doi:10.3390/ijgi6050151

Spatio-Temporal Behavior Analysis and Pheromone-Based Fusion Model for Big Trace Data

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Urban Design, Wuhan University, Wuhan 430079, China
3
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 7 March 2017 / Revised: 11 April 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
View Full-Text   |   Download PDF [6103 KB, uploaded 18 May 2017]   |  

Abstract

People leave traces of movements that might affect the behavior of others both online in cyberspace and offline in real space. Previous studies, however, have used only questionnaires, network data, or GPS data to study spatio-temporal behaviors, ignoring the relationship between online and offline activities, and overlooking the influence of previous activities on future behaviors. We propose a Pheromone-based Fusion Model, viewing human behaviors as similar to insect foraging behaviors to model spatio-temporal recreational activity patterns, on and offline. In our model, website data were combined with GPS data to evaluate the attractiveness of destinations over time using twenty-nine landscapes in Beijing, China; big website data and GPS trajectories were gathered from 181 users for 57 months. The datasets were portioned into two periods. Online and offline recreational pheromones were calculated from the first period, and the visitation rates were extracted from the second period. These data were subsequently applied in a regression analysis to determine unknown parameters and estimate the attractiveness of destinations. The proposed method was compared with two other approaches that use either GPS data or online data alone, in order to verify effectiveness. The results show that the proposed method can estimate future behaviors, based on real world and online past actions. View Full-Text
Keywords: spatio-temporal analysis; GPS trajectory; recreational behavior; big data; pheromone spatio-temporal analysis; GPS trajectory; recreational behavior; big data; pheromone
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MDPI and ACS Style

Tang, L.; Zou, Q.; Zhang, X.; Ren, C.; Li, Q. Spatio-Temporal Behavior Analysis and Pheromone-Based Fusion Model for Big Trace Data. ISPRS Int. J. Geo-Inf. 2017, 6, 151.

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