A commuting spectrum analysis of the jobs–housing balance and self-containment of employment with mobile phone location big data

Zhou, Xingang; Yeh, Ago; Liu, Weifeng; Yue, Yang

doi:10.1177/2399808317707967

Cited by 30 publications

(32 citation statements)

References 28 publications

(32 reference statements)

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“…Mobile phone location and activities aggregated for transportation use typically only include data when network connections are present and specific activities are recorded, described as a "potential activity bias" (Diao et al 2016, p. 938). Additionally, big data from mobile phone locations can obscure trips in which the origin and destination are within the same cellular tower range (Zhou et al 2018). However, big data sources may fill gaps in the needed information, as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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Section: Literature Reviewmentioning

confidence: 99%

Mitigating Bias in Big Data for Transportation

Griffin

Mulhall

Simek

et al. 2020

J. Big Data Anal. Transp.

View full text Add to dashboard Cite

show abstract

“…They found that these were highly consistent with the survey results. Zhou et al [38] analyzed the relationship between the job-housing balance and self-containment of employment using mobile phone data; their study revealed that self-sufficiency was higher in the suburbs than in the center, which meant that the job-housing balance policy had a positive effect on self-containment of employment in the suburbs; however, it had a limited effect in the central area. Zhang et al [39] also analyzed the job-housing balance using mobile phone data and found that the commuting pattern of Shanghai was far from the extremes and that the relative balance of jobs with respect to housing was decent.…”

Section: Research Of Big Datamentioning

confidence: 99%

Evaluation of Plan Implementation in the Fast-Growing Chinese Mega-City: A Case of a Polycentric System in Hangzhou Core Built-Up Area

2020

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Previous evaluations of plan implementation focused on whether the materiality construction was in accordance with the plan. Without proper data, it is difficult to confirm whether the planning goals have been achieved. In this study, two types of big data have been used—full sample built-environment data and mobile-positioning big data—to evaluate the results of the implementation of the polycentric system in master planning in the Hangzhou core built-up area. Using the full sample built-environment data, the evaluation of materiality construction will be more objective and accurate. Using the mobile-positioning big data, the evaluation of the planning goals can be realized; this was almost impossible in the past. However, two aspects are considered: whether daily public activities, such as employment and recreation, have been dispersed from the old city and subsequently re-gathered in multiple centers outside the old city, and whether the polycentric system aids in optimizing the spatial relationship between residence and public activity. The following conclusions were drawn. In terms of actual materiality construction, the results showed minimal discrepancy from the plan. Fifteen city-level public centers have been constructed at principal, secondary, and sub-secondary levels. However, the polycentric system failed to achieve the expected effects of the planning goals. First, the public centers contributed in the gathering of public activities; however, the level of gathering at the newly built-up public centers was considerably lower than that at traditional public centers. Second, the public centers failed to encourage people to visit the nearest blocks for daily public activities, mainly because of the path dependence on the traditional centers in the process of multi-centralization and over-rapid expansion of the city. Owing to this, residents did not have sufficient time to adjust to the spatial relationship between the residence and daily public activities.

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“…Zhou et al [32] identified the residence and workplace of cellphone users from cellphone location data in Shenzhen. This study adopted a similar method, but with certain differences.…”

Section: Identifying Commuting Populationmentioning

confidence: 99%

The Influencing Factors of a Polycentric Employment System on Jobs-Housing Matching—A Case Study of Hangzhou, China

2019

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This paper discusses the relationship between the polycentric spatial structure and jobs-housing matching and its influencing factors in the urbanized area of Hangzhou City in China. First, 42 employment centers are identified while using the method of the threshold value of employment density and employment to residence ratio based on the places of employment and residence that were identified from cellphone signaling data. This study found that Hangzhou have remarkable features of a polycentric system. Second, we use the jobs-housing matching rate index to measure the jobs-housing matching characteristics of employment centers. We found that the polycentric structure does not guarantee jobs-housing matching of all centers, although nearly half of the employment centers had good jobs-housing matching and 14.29% do not achieve jobs-housing matching, which indicates the uncertainty of "co-location hypothesis" in China. Employment centers of different levels, industry types, and locations also display certain regularity in their jobs-housing matching level. Third, by using correlation analysis and stepwise multiple regression analysis, we found that, besides the polycentric factor, the spatial and industrial factors also exert a certain effect on jobs-housing matching, reasonable control of the employment center size, employment to resident ratio, and industrial diversity are the determinants that affect the jobs-housing matching level.

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A commuting spectrum analysis of the jobs–housing balance and self-containment of employment with mobile phone location big data

Cited by 30 publications

References 28 publications

Mitigating Bias in Big Data for Transportation

Mitigating Bias in Big Data for Transportation

Evaluation of Plan Implementation in the Fast-Growing Chinese Mega-City: A Case of a Polycentric System in Hangzhou Core Built-Up Area

The Influencing Factors of a Polycentric Employment System on Jobs-Housing Matching—A Case Study of Hangzhou, China

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