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are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive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 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 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.

Mobile Phone Data Based Urban Functional Area Land Use Classification Algorithm

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Jiyuan Tan ◽  
Luxi Dong ◽  
Yanwei Wang ◽  
Yibin Huang ◽  
Li Li ◽  
...  
Keyword(s):  
Land Use ◽  
Mobile Phone ◽  
Classification Algorithm ◽  
Mobile Phone Data ◽  
Land Use Classification ◽  
Functional Area

scholarly journals The time varying network of urban space uses in Milan

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Alba Bernini ◽  
Amadou Lamine Toure ◽  
Renato Casagrandi
Keyword(s):  
Land Use ◽  
Mobile Phone ◽  
Urban Space ◽  
Temporal Analysis ◽  
Mobile Phone Data ◽  
Natural Environments ◽  
Urban Structure ◽  
Land Uses ◽  
Population Mobility ◽  
The City

AbstractIn a metropolis, people movements design intricate patterns that change on very short temporal scales. Population mobility obviously is not random, but driven by the land uses of the city. Such an urban ecosystem can interestingly be explored by integrating the spatial analysis of land uses (through ecological indicators commonly used to characterize natural environments) with the temporal analysis of human mobility (reconstructed from anonymized mobile phone data). Considering the city of Milan (Italy) as a case study, here we aimed to identify the complex relations occurring between the land-use composition of its neighborhoods and the spatio-temporal patterns of occupation made by citizens. We generated two spatially explicit networks, one static and the other temporal, based on the analysis of land uses and mobile phone data, respectively. The comparison between the results of community detection performed on both networks revealed that neighborhoods that are similar in terms of land-use composition are not necessarily characterized by analogous temporal fluctuations of human activities. In particular, the historical concentric urban structure of Milan is still under play. Our big data driven approach to characterize urban diversity provides outcomes that could be important (i) to better understand how and when urban spaces are actually used, and (ii) to allow policy makers improving strategic development plans that account for the needs of metropolis-like permanently changing cities.

Residents’ Traveling Track and Analysis Methods Based on Mobile Phone Data

2014 ◽  
Vol 926-930 ◽  
pp. 2730-2734 ◽  
Author(s):  
Pan Li ◽  
Ye Wen Gao ◽  
Ju Wei Wu ◽  
Xu Li ◽  
Bing Bing Wu
Keyword(s):  
Mobile Phone ◽  
Base Station ◽  
Urban Transport ◽  
Mobile Phone Data ◽  
Analysis Method ◽  
Station Data ◽  
National Economic ◽  
Gis Data ◽  
Mobile Phone Base Station ◽  
National Economic Development

To avoid traffic congestion’s becoming the obstruct of social and national economic development is the final goal that professionals in transportation field make great efforts to pursue. At the same time, with the increasing popularity of mobile phones, we can get a lot of phone base station data to identify the residents’ travelling track. Thus we can analyze the residents’ travelling behavior and get residents’ travelling patterns and mechanism. Also, residents’ travelling could be induced and guided in order that the condition of urban transport can be improved. Based on the above background, this paper is mainly based on mobile phone base station data and GIS data analysis method research on the urban transportation of residents’ travelling track.

scholarly journals Uncovering the Relationship between Human Connectivity Dynamics and Land Use

2020 ◽  
Vol 9 (3) ◽  
pp. 140 ◽  
Author(s):  
Olivera Novović ◽  
Sanja Brdar ◽  
Minučer Mesaroš ◽  
Vladimir Crnojević ◽  
Apostolos N. Papadopoulos
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Community Structure ◽  
Dynamic Change ◽  
Base Station ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Data Set ◽  
Human Dynamics ◽  
The City

CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

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 Land use inference from mobility mobile phone data and household travel surveys

2020 ◽  
Vol 47 ◽  
pp. 417-424
Author(s):  
Noelia Cáceres ◽  
Francisco G. Benítez ◽  
Luis M. Romero
Keyword(s):  
Land Use ◽  
Mobile Phone ◽  
Mobile Phone Data ◽  
Household Travel ◽  
Household Travel Surveys

scholarly journals Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model

2020 ◽  
Vol 12 (12) ◽  
pp. 5018
Author(s):  
Yanyan Chen ◽  
Hanqiang Qian ◽  
Yang Wang
Keyword(s):  
Land Use ◽  
Population Density ◽  
Regression Model ◽  
Mobile Phone ◽  
Geographically Weighted Regression ◽  
Mobile Phone Data ◽  
Weighted Regression ◽  
Working Population ◽  
Development Status ◽  
Geographically Weighted Regression Model

Evaluation of urban planning and development is becoming more and more important due to the large-scale urbanization of the world. With the application of mobile phone data, people can analyze the development status of cities from more perspectives. By using the mobile phone data of Beijing, the working population density in different regions was identified. Taking the working population density in Beijing as the research object and combining the land use of the city, the development status of Beijing was evaluated. A geographically weighted regression model (GWR) was used to analyze the difference in the impact of land use on the working population between different regions. By establishing a correlation model between the working population and land use, not only can the city’s development status be evaluated, but it can also help city managers and planners to make decisions to promote better development of Beijing.

Estimation of Originating-Destination Trips in Yangon by Using Big Data Source

2018 ◽  
Vol 13 (1) ◽  
pp. 6-13 ◽  
Author(s):  
Thein Aye Zin ◽  
Kyaing ◽  
Ko Ko Lwin ◽  
Yoshihide Sekimoto ◽  
◽  
...  
Keyword(s):  
Mobile Phone ◽  
Coming Out ◽  
Time Window ◽  
Time Frame ◽  
Mobile User ◽  
Base Station ◽  
Public Health Care ◽  
Mobile Phone Data ◽  
Infrastructure Development ◽  
Data Generation

The ubiquitous massive mobile phone data generation presents new opportunities to determine the requirements of transportation, disaster management and public health care systems. Currently, data from mobile phone records can help in identifying the location of the users while they are making trips. Generally, this estimation is achieved using traditional data collection methods; however, these methods are difficult to apply in developing countries with rapidly growing cities owing to the high population and limitation in conducting a survey. Call detail records (CDRs) are used as base data because they are valuable data sources and can reduce the cost and time limitations. The aim of this study is to estimate origin-destination (OD) trips from each zone by using the CDRs. The OD trips are estimated by using the CDRs of one week taken from Myanmar Post and Telecommunication mobile operator for over 1.9 million users per day in Yangon, the economic center of Myanmar. The OD trips are estimated from CDRs based on the location of the base station in a limited time window and time frame. If the same mobile users is observed in two different the ones within the time limit, it is assumed that the mobile user is coming out from the first zone and the trips represents an originating trip. This trip would be the destination trip for zone where the mobile user enters. In this study, the originating (outgoing) and destination trips (incoming) from each township on a weekday and weekend are determined. These data are useful for infrastructure development and urban transportation planning.

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