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

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.

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

Sustainability ◽

10.3390/su10072432 ◽

2018 ◽

Vol 10 (7) ◽

pp. 2432 ◽

Cited By ~ 8

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.

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

Sustainability ◽

10.3390/su12125018 ◽

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.

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Mobile Phone Data Based Urban Functional Area Land Use Classification Algorithm

CICTP 2017 ◽

10.1061/9780784480915.049 ◽

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

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The time varying network of urban space uses in Milan

Applied Network Science ◽

10.1007/s41109-019-0245-x ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 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.

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Resembling Population Density Distribution with Massive Mobile Phone Data

Data Science Journal ◽

10.5334/dsj-2018-024 ◽

2018 ◽

Vol 17 ◽

Cited By ~ 2

Author(s):

Teerayut Horanont ◽

Thananut Phiboonbanakit ◽

Santi Phithakkitnukoon

Keyword(s):

Population Density ◽

Density Distribution ◽

Mobile Phone ◽

Mobile Phone Data

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The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182212160 ◽

2021 ◽

Vol 18 (22) ◽

pp. 12160

Author(s):

Yisheng Peng ◽

Jiahui Liu ◽

Tianyao Zhang ◽

Xiangyang Li

Keyword(s):

Land Use ◽

Population Density ◽

Spatial Structure ◽

Density Distribution ◽

Rapid Growth ◽

Urban Population ◽

Spillover Effects ◽

Urban Spatial Structure ◽

Direct Effects ◽

Spatial Spillover

Urban population density distribution contributes towards a deeper understanding of peoples’ activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of “rapid growth-gentle decline-rapid growth-rapid decline”. Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

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Using remote sensing data in population density estimation

10.25743/sdm.2021.34.56.066 ◽

2021 ◽

Author(s):

E.G. Shvetsov ◽

N.M. Tchebakova ◽

E.I. Parfenova

Keyword(s):

Remote Sensing ◽

Spatial Distribution ◽

Population Density ◽

Vegetation Index ◽

Census Data ◽

Remote Sensing Data ◽

Satellite System ◽

Population Census ◽

Krasnoyarsk Krai ◽

Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

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