scholarly journals Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method

2020 ◽  
Vol 9 (6) ◽  
pp. 344
Author(s):  
Zhenghong Peng ◽  
Ru Wang ◽  
Lingbo Liu ◽  
Hao Wu
Keyword(s):  
Mobile Phone ◽  
Census Data ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Ordinary Least Squares ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Fine Scale ◽  
Voronoi Method ◽  
Population Mapping

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

scholarly journals Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method

2018 ◽  
Vol 10 (7) ◽  
pp. 2432 ◽  
Author(s):  
Lingbo Liu ◽  
Zhenghong Peng ◽  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu
Keyword(s):  
Land Use ◽  
Spatial Heterogeneity ◽  
Mobile Phone ◽  
Census Data ◽  
Base Station ◽  
Distance Decay ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Dasymetric Mapping ◽  
Spatial Feature

Dasymetric mapping of high-resolution population facilitates the exploration of urban spatial feature. While most relevant studies are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive mobile phone base stations. However, user population recorded by mobile phone base stations have no fixed spatial boundary, and base stations often disperse in extremely uneven spatial distribution, this study defines a distance-decay supply–demand relation between mobile phone user population of gridded base station and its surrounding land patches, and outlines a dasymetric mapping method integrating two-step floating catchment area method (2SFCAe) and land use regression (LUR). The results indicate that LUR-2SFCAe method shows a high fitness of regression, provides population mapping at a finer scale and helps identify urban centrality and employment subcenters with detailed worktime and non-worktime populations. The work involving studies of dasymetric mapping based on LUR-2SFCAe method and mobile phone data proves to be encouraging, sheds light on the relationship between mobile phone users and nearby land use, brings about an integrated exploration of 2SFCAe in LUR with distance-decay effect and enhances spatial heterogeneity.

scholarly journals Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyang Huang ◽  
Yongjian Yang ◽  
Yuanbo Xu ◽  
En Wang ◽  
Kangning Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Population Distribution ◽  
Base Stations ◽  
Human Origin ◽  
Safety Control ◽  
Fine Grained ◽  
Flow Prediction

The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

2020 ◽  
pp. 122-143
Author(s):  
Petr Kubíček ◽  
Milan Konečný ◽  
Zdeněk Stachoň ◽  
Jie Shen ◽  
Lukáš Herman ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Population Distribution ◽  
Temporal Scale ◽  
Mobile Phone Data ◽  
Distribution Modelling ◽  
Spatio Temporal

scholarly journals A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Zhengyi Cai ◽  
Dianhai Wang ◽  
Xiqun (Michael) Chen
Keyword(s):  
Mobile Phone ◽  
Assessment Tool ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Transit System ◽  
Transit Systems ◽  
The Public ◽  
Transit Accessibility ◽  
Transit Services ◽  
The Individual

Transit accessibility is an important measure on the service performance of transit systems. To assess whether the public transit service is well accessible for trips of specific origins, destinations, and origin-destination (OD) pairs, a novel measure, the Trip Coverage Index (TCI), is proposed in this paper. TCI considers both the transit trip coverage and spatial distribution of individual travel demands. Massive trips between cellular base stations are estimated by using over four-million mobile phone users. An easy-to-implement method is also developed to extract the transit information and driving routes for millions of requests. Then the trip coverage of each OD pair is calculated. For demonstrative purposes, TCI is applied to the transit network of Hangzhou, China. The results show that TCI represents the better transit trip coverage and provides a more powerful assessment tool of transit quality of service. Since the calculation is based on trips of all modes, but not only the transit trips, TCI offers an overall accessibility for the transit system performance. It enables decision makers to assess transit accessibility in a finer-grained manner on the individual trip level and can be well transformed to measure transit services of other cities.

scholarly journals Mobile Phone Indicators and Their Relation to the Socioeconomic Organisation of Cities

2019 ◽  
Vol 8 (1) ◽  
pp. 19 ◽  
Author(s):  
Clémentine Cottineau ◽  
Maarten Vanhoof
Keyword(s):  
Mobile Phone ◽  
Census Data ◽  
Social Science Research ◽  
Science Research ◽  
Computational Social Science ◽  
Mobile Phone Data ◽  
Spatial And Temporal Heterogeneity ◽  
Statistical Relationships ◽  
Socioeconomic Data ◽  
The City

Thanks to the use of geolocated big data in computational social science research, the spatial and temporal heterogeneity of human activities is increasingly being revealed. Paired with smaller and more traditional data, this opens new ways of understanding how people act and move, and how these movements crystallise into the structural patterns observed by censuses. In this article we explore the convergence between mobile phone data and more traditional socioeconomic data from the national census in French cities. We extract mobile phone indicators from six months worth of Call Detail Records (CDR) data, while census and administrative data are used to characterize the socioeconomic organisation of French cities. We address various definitions of cities and investigate how they impact the statistical relationships between mobile phone indicators, such as the number of calls or the entropy of visited cell towers, and measures of economic organisation based on census data, such as the level of deprivation, inequality and segregation. Our findings show that some mobile phone indicators relate significantly with different socioeconomic organisation of cities. However, we show that relations are sensitive to the way cities are defined and delineated. In several cases, changing the city delineation rule can change the significance and even the sign of the correlation. In general, cities delineated in a restricted way (central cores only) exhibit traces of human activity which are less related to their socioeconomic organisation than cities delineated as metropolitan areas and dispersed urban regions.

scholarly journals Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 38 ◽  
Author(s):  
Yi Shi ◽  
Junyan Yang ◽  
Peiyu Shen
Keyword(s):  
Land Use ◽  
Population Density ◽  
Mobile Phone ◽  
Spatial Correlation ◽  
Public Service ◽  
Urban Population ◽  
Census Data ◽  
Remote Sensing Data ◽  
Mobile Phone Data ◽  
Night Time

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

scholarly journals Dynamic population mapping using mobile phone data

2014 ◽  
Vol 111 (45) ◽  
pp. 15888-15893 ◽  
Author(s):  
Pierre Deville ◽  
Catherine Linard ◽  
Samuel Martin ◽  
Marius Gilbert ◽  
Forrest R. Stevens ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Dynamic Population ◽  
Population Mapping
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