Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu, Zhang; Ma, Ting; Du, Yunyan; Pei, Tao; Yi, Jiawei; Peng, Hui

doi:10.1111/tgis.12323

Cited by 59 publications

(46 citation statements)

References 58 publications

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“…Finally, a number of studies employ some form of modelling or simulation. These include agent-based simulation (Walker and Barros, 2012;Kashiyama et al, 2017;Crols and Malleson, 2019), cellular automaton models (Khakpour and Rød, 2016), and neural networks (Liu et al, 2018;Chen et al, 2018).…”

Section: Results and Discussion: Estimating Temporary Populationsmentioning

confidence: 99%

Estimating temporary populations: a systematic review of the empirical literature

Panczak

Charles‐Edwards

Corcoran

2020

Humanit Soc Sci Commun

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The estimation of temporary populations is a well-established field, but despite growing interest they are yet to form part of the standard suite of official population statistics. This systematic review seeks to review the empirical literature on temporary population estimation and identify the contemporary “state of the art”. We identify a total of 96 studies that attempt to estimate or describe a method of estimation. Our findings reveal strong growth in the number of studies in recent decades that in part has been driven by the rise in both the type and availability of new sources of information, including mobile phone data. What emerges from this systematic review is the lack of any “gold standard” data source or methodology for temporary population estimation. The review points to a number of important challenges that remain for estimating temporary populations, both conceptually and practically. What remains is the need for clear definitions along with identification of appropriate data and methods that are able to robustly capture and measure the diverse array of spatial behaviours that drive temporary population dynamics. To our knowledge, this is the first review on this topic that brings together literature from various disciplines and collates methods used for estimation.

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Section: Results and Discussion: Estimating Temporary Populationsmentioning

confidence: 99%

Estimating temporary populations: a systematic review of the empirical literature

Panczak

Charles‐Edwards

Corcoran

2020

Humanit Soc Sci Commun

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“…In recent years, with the emergence of different kinds of mobile devices, large amounts of OD flow data have emerged, including mobile phone signal data [9], GPS trajectories [10],…”

Section: Introductionmentioning

confidence: 99%

Identifying Flow Clusters Based on Density Domain Decomposition

Pei

Shu

2020

IEEE Access

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Flow clustering is one of the most important data mining methods for the analysis of origin-destination (OD) flow data, and it may reveal the underlying mechanisms responsible for the spatial distributions and temporal dynamics of geographical phenomena. Existing flow clustering approaches are based mainly on the extension of traditional clustering methods to points by redefining basic concepts or some spatial association indictors of flows and the implementation of classic clustering processes, such as aggregating, collecting or searching. However, current techniques still suffer from two main problems: poor identification accuracy and complicated parameter selection processes. To resolve these problems, a new clustering method is proposed in this study for arbitrarily shaped flow clusters based on the density domain decomposition of flows. Simulation experiments based on our method and existing methods show that our method outperforms the three most commonly used methods in terms of the overall identification rate and almost all F1 measures, and it does not require any manual adjustments during the parameter selection process. Finally, a case study is conducted on taxi trip data from Beijing. Several flow clusters are identified to represent different types of residents' travel behaviors, including daily commuting, return travel, tourism and behaviors on special days. INDEX TERMS Origin-destination (OD) flow, flow space, flow clustering, density domain decomposition, point process.

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“…Mapping population dynamics is of great significance for city and transport planning [1,2,3], public safety warning [4,5,6], disaster impact assessments [7,8,9], and epidemic modeling [10,11,12]. The analysis of mobile phone data is frequently used to map the spatial and temporal situation of users [13,14]. However, a more fine-grained temporal resolution of dynamic population distributions is still a challenge when studying human activities [13].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Zhang

Rui

Poslad

et al. 2019

Sensors

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Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

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Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Cited by 59 publications

References 58 publications

Estimating temporary populations: a systematic review of the empirical literature

Estimating temporary populations: a systematic review of the empirical literature

Identifying Flow Clusters Based on Density Domain Decomposition

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

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