Evaluating Spatial and Temporal Characteristics of Population Density Using Cellular Data

Lu, Danni; Li, Ye; Guo, Feng

doi:10.1109/tits.2021.3056434

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…In another interesting work, Jiang used mobile phone data to examine human spatial mobility patterns using Singapore as an example [25]. Lu et al employed a Kriging model to quantitatively assess the spatial autocorrelation of the population density based on mobile phone communication record data to construct an algorithm for identifying the traffic travel characteristics of mobile phone users [26]. Pintér et al used mobile network data to estimate home and work locations [27] and to analyse commuting in the Budapest metropolitan area [28].…”

Section: Introductionmentioning

confidence: 99%

Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning

et al. 2023

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Urban commuting characteristics have important implications for both the spatial planning and governance of cities. However, the traditional methods of surveying the characteristics of commuting are very time- and labour-intensive, with the results susceptible to subjective influences. In this work, taking the central city of Nanning as the research object, the commuting space of the population was constructed on the grid-block-subdistrict scale, and the distribution characteristics of the commuting space were systematically analysed. In addition, the influencing factors of the commuting volume were explored by combining multi-source and spatiotemporal data with a geodetector. From our analysis, it was demonstrated that the population density in the central city of Nanning showed a spatial distribution pattern of “decaying distance from the city centre”, with a weak agglomeration effect of large-scale commuters at the grid scale and a larger east-west than north-south commuter scale. At the block scale, large-scale commuters were more concentrated, and the commuting distances were shorter in areas with large commuter populations. At the subdistrict scale, the internal commuting population was also more than the cross-subdistrict commuting population, with more cross-subdistrict commuting flows and an uneven distribution of the flow sizes, with most commuters concentrating on two or three subdistricts for commuting. Various important factors that affect the size of the commuting population should be controlled, including the permanent population, residential distribution, medical facilities, recreational facilities, food services and workplace distribution; the interactions between the permanent population, the residential distribution and the house price factors have the strongest impact values. Our work provides valuable insights for the understanding of commuting patterns in cities and can be used as a scientific basis for urban spatial decision-making.

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