Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China

Abstract

Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones.

Monitoring travel patterns in German city regions with the help of mobile phone network data

“…For example, spatial structure and commuting dynamics have been studied on the basis of hotspot clustering derived from mobile phone network data (Louail et al 2014). One article authored by Yang et al (2018) proposes a workflow for characterizing communities according to commuting patterns using mobile phone location data collected by a telecommunications company. Having ascertained the work and home locations of the mobile phone users from the location records, they constructed a directed and weighted commuting flow network, using it to identify thirteen communities (functional regions) within the study area of Shenzhen, China.…”

Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

“…Currently, it encompasses 10 administrative districts, which are further divided into downtown areas, suburbs, and rural areas according to the economic development ( Figure 1). Shenzhen covers an area of approximately 2000 km 2 and has a population of more than 15 million, making it the most densely populated among Chinese cities [15,38]. The unique socioeconomic and demographic status of Shenzhen makes it an interesting area for the study of urban bus networks.…”

“…Complex network theory has been developed to investigate the network characteristics of the connections and interactions between elements in the last few decades [8,9]. More recently, this theory has been widely applied by researchers to urban transport-related studies for exploring urban polycentric spatial structures [10][11][12] or commuting structures [13][14][15], revealing human mobility patterns [16,17], identifying critical locations or the backbone of urban streets [18][19][20][21], and evaluating the vulnerability of urban metro networks [22][23][24]. Therefore, complex network analysis can be powerful for examining the underlying characteristics of systems that represent spatial interaction activities such as public transport systems.…”

Where Urban Youth Work and Live: A Data-Driven Approach to Identify Urban Functional Areas at a Fine Scale

“…In recent years, with the widespread use of positioning devices such as civilian GPS (Global Positioning System) technology on mobile terminals and the development and popularization of location-based services and mobile social networks, a large number of trajectory or geotag data are accumulating in daily life and are serving different types of applications [8][9][10]. These data sources mainly include mobile phones [11][12][13][14][15][16], taxis [17][18][19][20][21], public or shared bicycles [22][23][24][25], transit smart cards [26][27][28][29], and so on. They convey human mobility and activity information and provide content and methodological innovation to the research of the spatial-temporal behavior of people in urban areas [8,30].…”