Study on Shenyang urban employment spatial characteristics based on cell phone signaling data

Pengfei Li

doi:10.1117/12.3007448

30 August 2023 Study on Shenyang urban employment spatial characteristics based on cell phone signaling data

Pengfei Li

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 1279713 (2023) https://doi.org/10.1117/12.3007448
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

The study of urban employment spatial characteristics using static statistical data often has limitations in terms of time and spatial scales, making it difficult to obtain accurate results. This paper used Shen Yang city as a case, space-time behavior information of urban residents was analyzed deeply based on cell phone signaling data. The spatial distribution of employment and job-housing connection data was extracted by establishing data cleaning rules of place of residence and employment identification. Use the concept of employment centers proposed by McMillen (2001) as the theoretical basis, we established of an analytical model. Employment centers was identified with the tecnologies of nuclear density, spatial autocorrelation, natural discontinuous classification and contour line, and we analyzed the spatial distribution characteristics of the employment centers. We have also constructing the hierarchy system of employment centers, through measuring the service efficiency of the employment centers from employment scale and function connection. Based on the above research, we have explored the spatial structure of urban employment, and revealed the internal mechanism of urban operation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengfei Li "Study on Shenyang urban employment spatial characteristics based on cell phone signaling data", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 1279713 (30 August 2023); https://doi.org/10.1117/12.3007448

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