Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data

Gao, Qi-Li; Li, Qing Quan; Yue, Yang; Zhou, Yan; Chen, Zhi Peng; Kong, Hui

doi:10.1016/j.compenvurbsys.2018.02.006

Cited by 38 publications

(24 citation statements)

References 42 publications

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“…[3]. Works continue this research line including discussing spatial distribution [4], spatial interaction [5] and place attractiveness [6]. However, because of the lack of individual characteristics, these aspects were…”

Section: Introductionmentioning

confidence: 99%

Unfolding the City: Spatial Preference Based on Individual Demographic Characteristics

Wang

Lü

2020

IEEE Access

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A city is shaped not only by its assembled infrastructures but also by the people living inside it. People with a wide range of individual demographic characteristics visit places at different weights and with different preferences, sculpting a city into a space of diverse spatial preferences. Due to the challenges in the collection and analysis of data considering multi-dimensional demographic characteristics, the daily movement of characterized individuals has been less studied. Therefore, we aim to develop a visual analytics system to better explore spatial preference based on individual demographic characteristics. We take a step forward and perform an online survey to collect individual profiles and movements from volunteers on a large-scale. Then, we design a visual analytic system to investigate the spatial preferences of groups by specifying demographic characteristics. To facilitate the identification of characterized groups, individuals are embedded in t-SNE projection for an abstract overview, and vivid graphics are drawn by a data-driven profile method for detailed examination. A 2.5D spatial visualization is proposed to maintain a compact multivariable analysis by relaxing the z-axis to encode information, such as visiting frequency, demands, and traveling distance. Together with a cross-filter and flexible 2.5D interactions, the effectiveness and usability of the system are demonstrated well by a study conducted in Shenzhen. INDEX TERMS Visual analytics, spatial preference, individual demographic characteristics, data collection.

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Section: Introductionmentioning

confidence: 99%

Unfolding the City: Spatial Preference Based on Individual Demographic Characteristics

Wang

Lü

2020

IEEE Access

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“…A growing body of research regards the study of multi-modal transportation from the perspective of passengers' travel behavior using urban big data [4][5][6]. Passengers' travel behavior, however, is complex and entails travel modes preference and interactions between different transit modes (taxi, bus, and metro) [7].…”

Section: Introductionmentioning

confidence: 99%

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

Huang

Liu

Zhao

et al. 2019

IJGI

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Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services.

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“…Data cleaning and O-D pair extraction are prerequisites for analysis of urban structures and human mobility patterns based on spatiotemporal LBS data. Previous cleaning methods were mainly intended for data that contained complete attribute or uniform GPS information, such as phone signaling data [2,3], taxi GPS data [4,5] and smart card data of bus or subway stations [6][7][8], whose O-D information can be extracted directly by using the locations of smart card transactions and taxi pick-up and drop-off points. Under the initiative of low-carbon transportation, cheap and convenient public bicycle rental systems have become one of the most popular modes of travel for urban residents.…”

Section: Introductionmentioning

confidence: 99%

Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

Dai

Peng

et al. 2019

Sustainability

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Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week of shared e-bike GPS data in the city of Tengzhou (Shandong Province), sparse characteristics of discontinuities and nonuniformities of the GPS trajectory and a lack of riding status are observed. Based on the characteristics and the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segment from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. A week of shared e-bike GPS data in Tengzhou is scrubbed and, by the sampling method, the extraction accuracy of 91% is verified. We provide preliminary cleaning rules for sparse trajectory shared e-bike data for the first time, which are highly reliable and suitable for data mining from other forms of sparse GPS trajectory data.

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Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data

Cited by 38 publications

References 42 publications

Unfolding the City: Spatial Preference Based on Individual Demographic Characteristics

Unfolding the City: Spatial Preference Based on Individual Demographic Characteristics

Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

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