A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data

2020 ◽  
Vol 35 (1) ◽  
pp. 116-134
Author(s):  
Zhensheng Wang ◽  
Yang Yue ◽  
Biao He ◽  
Ke Nie ◽  
Wei Tu ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Population Distribution ◽  
Mobile Phone Data ◽  
Temporal Model ◽  
Spatio Temporal ◽  
The Stability

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

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

2018 ◽  
Vol 12 (11) ◽  
pp. 1319-1340 ◽  
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 Mobile phone data in studying urban rhythms: Towards an analytical framework

2020 ◽  
Vol 28 (4) ◽  
pp. 248-258
Author(s):  
Martin Šveda ◽  
Michala Sládeková Madajová ◽  
Peter Barlík ◽  
František Križan ◽  
Pavel Šuška
Keyword(s):  
Mobile Phone ◽  
Urban Environment ◽  
Urban Space ◽  
Large Scale ◽  
Mobile Network ◽  
Analytical Framework ◽  
Mobile Phone Data ◽  
Work Cycle ◽  
Large Scale Dataset ◽  
Spatio Temporal

AbstractMobile phone data are considered one of the most promising information sources for monitoring and measuring the spatio-temporal activities of the population. Today, large-volume mobile phone datasets are widely applied to monitor the daily life of the urban population and to examine the structuring of the urban environment. In this paper, we discuss and develop a methodological procedure that uses such data to observe temporal differences of human presence in Bratislava, Slovakia. The study is based on a large-scale dataset of hourly records of signalling exchanges (VLR data) from all major mobile network operators in Slovakia. The records of the mobile network infrastructure are used as a suitable proxy variable for complex human activity at the city level, in the sense that they capture various kinds of spatial practices, and not only some specific activities (work cycle of a given locale, shopping, and similar events). Such an approach allows the classification of urban space using diurnal logs activity curves of mobile network cells. Six temporality types in Bratislava were identified, which may be designated as examples of an urban chronopolis. The results show the potential of the proposed method for measuring place temporality in cities and monitoring the urban environment with geo-referenced mobile phone data.

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.

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

scholarly journals Dynamic maps of people exposure to floods based on mobile phone data

2020 ◽  
Author(s):  
Matteo Balistrocchi ◽  
Rodolfo Metulini ◽  
Maurizio Carpita ◽  
Roberto Ranzi
Keyword(s):  
Mobile Phone ◽  
Flood Risk ◽  
Northern Italy ◽  
Flood Risk Management ◽  
Mobile Phone Data ◽  
Test Case ◽  
Time Patterns ◽  
Management Plans ◽  
Spatio Temporal ◽  
Flood Risk Management Plans

Abstract. Floods are acknowledged as one of the most serious threats to human lives and properties worldwide. To mitigate the flood risk, it is possible to act separately on its components: hazard, vulnerability, exposure. Emergency management plans can actually provide effective non-structural practices to decrease both people exposure and vulnerability. Crowding maps depending on characteristic time patterns, herein referred to as dynamic exposure maps, provide a valuable tool to enhance the flood risk management plans. In this paper, the suitability of mobile phone data to derive crowding maps is discussed. A test case is provided by a strongly urbanized area subject to frequent floodings located in the western outskirt of Brescia town (northern Italy). Characteristic exposure spatio-temporal patterns and their uncertainties were detected, with regard to land cover and calendar period. This novel methodology appears to be more reliable than crowdsourcing strategies, and has potentials to better address real-time rescues and reliefs supply.

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