Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

Xiaoqian Cheng,Weibing Du,Linjuan Xu,Leiku Yang,Chengming Li

doi:10.3390/ijgi9120742

Abstract

Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies.

Highlights

Human activities generate diverse and sophisticated functional areas, but may change the existing planning of functional areas [1]
Understanding the relationship between human activities and functional areas is key to identifying urban functional areas
Trajectories imply rich interactive information of human-region, which is the basis for urban functional area identification

Summary

Introduction

Human activities generate diverse and sophisticated functional areas, but may change the existing planning of functional areas [1]. Understanding the relationship between human activities and urban regions is key to functional area identification [2]. In the big data era, various trajectory data can be obtained because of the popularity of location-aware devices and smart sensors in a city. These trajectory data convey the underlying information on people and cities, and imply the interactive information of people and the urban environment [3,4]. Exploring the inner relationship and identifying functional areas based on trajectory data are helpful for city planners and administrators to comprehend urban dynamics and evaluate urban environments timely and rapidly

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Conclusion