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. 2024 May 24;19(5):e0299726.
doi: 10.1371/journal.pone.0299726. eCollection 2024.

Identification and classification of urban employment centers based on big data: A case study of Beijing

Liang Wang  1   2 , He Cui  2
Affiliations

Identification and classification of urban employment centers based on big data: A case study of Beijing

Liang Wang et al. PLoS One. .

Abstract

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Research area of the study.
Reprinted from [https://www.resdc.cn/, accessed on 10 May 2021], original copyright 2021.
Fig 2
Fig 2. Technology roadmap.
Fig 3
Fig 3. Heat map of employment distribution in Beijing.
Reprinted from [https://www.resdc.cn/, accessed on 10 May 2021], original copyright 2021.
Fig 4
Fig 4. Check with the resident population data of the seventh population census.
Fig 5
Fig 5. Contour lines of employment place.
Reprinted from [https://www.resdc.cn/, accessed on 10 May 2021], original copyright 2021.
Fig 6
Fig 6. Employment center.
Reprinted from [https://www.resdc.cn/, accessed on 10 May 2021], original copyright 2021.
Fig 7
Fig 7. Radar map of total registered capital of different industry types(TOP10) in the employment centers.
Fig 8
Fig 8. Employment center categories.
See this image and copyright information in PMC

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