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

Kubíček, Petr; Konečný, Milan; Stachoň, Zdeněk; Shen, Jie; Heřman, Lukáš; Řezník, Tomáš; Staněk, Karel; Štampach, Radim; Leitgeb, Šimon

doi:10.1080/17538947.2018.1548654

Cited by 30 publications

(21 citation statements)

References 41 publications

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“…As a central focus of many study fields, including time and space geography, urban functionalities, and human mobility, big data analysis is a vast research field that was initially studied using statistical data from surveys, interviews, travel diaries, questionnaires, and other manual collections of datasets [27][28][29]. Statistical data collection may not be an efficient way to determine patterns in said fields and related studies; therefore, data from mobile devices, smart cards, global positioning system (GPS) navigators, and location-based and online applications containing users' activities with geo-locations are widely used, and have been found to be more efficient for such studies in recent years [24,[30][31][32][33][34]. With the advancement of mobile technologies and widespread use of mobile devices, it is easy to track users' locations from their devices and activities.…”

Section: Related Workmentioning

confidence: 99%

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Khan

Wan

2020

IJGI

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The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

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Section: Related Workmentioning

confidence: 99%

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Khan

Wan

2020

IJGI

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“…Visual analytics of spatiotemporal changes can bring new insights into the consequences of human decisions in many areas. We may identify several application domains, starting from smart mobility policy [6,34,35], through the monitoring of machinery and sensor data in agriculture [36,37], the monitoring of population movement and distribution [38,39], and noise mapping [40], to spatiotemporal visualizations of crime scenes [41,42]. The main focus of visual analytics is, therefore, placed on interactivity through utilizing the concept of multiple coordinated views [43,44] and dynamic queries to emphasize the impact of changes in various phenomena.…”

Section: Description Of the Investigated Toolsmentioning

confidence: 99%

User Evaluation of Map-Based Visual Analytic Tools

Popelka

Heřman

Řezník

et al. 2019

IJGI

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Big data have also become a big challenge for cartographers, as the majority of big data may be localized. The use of visual analytics tools, as well as comprising interactive maps, stimulates inter-disciplinary actors to explore new ideas and decision-making methods. This paper deals with the evaluation of three map-based visual analytics tools by means of the eye-tracking method. The conceptual part of the paper begins with an analysis of the state-of-the-art and ends with the design of proof-of-concept experiments. The verification part consists of the design, composition, and realization of the conducted eye-tracking experiment, in which three map-based visual analytics tools were tested in terms of user-friendliness. A set of recommendations on GUI (graphical user interface) design and interactive functionality for map makers is formulated on the basis of the discovered errors and shortcomings in the assessed stimuli. The results of the verification were used as inputs for improving the three tested map-based visual analytics tools and might serve as a best practice for map-based visual analytics tools in general, as well as for improving the policy making cycle as elaborated by the European project PoliVisu (Policy Development based on Advanced Geospatial Data Analytics and Visualization).

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“…Extensive studies focused on different aspects of human presence estimation based on mobile phone data have been presented, particularly from Europe and Asia (Ahas et al 2010;Batista e Silva et al 2013;Cao et al 2017;Järv et al 2017;Kang et al 2012). Kubíček et al (2018) proposed analysis of human presence using data from mobile operators. The analysis is based on a dataset describing the estimated human presence (EHP) with two values-visitors and transiting persons-depending on the overall time spent within a specific mobile cell.…”

Section: The Benefits Of Digital Earth For Risk Assessment-using Dynamentioning

confidence: 99%

“…The third and fourth analysis quantifies the EHP for working days and weekends. Using of EHP proposes a greater number of necessary water tanks in administrative units, and their optimal locations change according population fluctuations" (Kubíček et al 2018).…”

Section: The Benefits Of Digital Earth For Risk Assessment-using Dynamentioning

confidence: 99%

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Digital Earth for Disaster Mitigation

Konečný

Bandrova

Kubíček

et al. 2019

Manual of Digital Earth

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This chapter describes the state-of-the-art of the potential of Digital Earth for progressively better solutions for disaster mitigation. The chapter illustrates the use of strong Digital Earth tools for data sharing and important potential for users, such as 2D or multi-D visualizations. Milestones of developments in early warning, disaster risk management and disaster risk reduction concepts are highlighted as a continuous movement between sustainable development and original concepts of disaster risk reduction. Improved solutions have been based on new research directions formulated in Sustainable Development Goals tasks and by expanding the possibilities of new effective solutions via newly organized data ecosystems generated by the United Nations Global Geospatial Information Management, the Group on Earth Observations and the Group on Earth Observations System of Systems, Copernicus and, more recently, the Digital Belt and Road initiative. The new trends in spatial big data are emphasized; the most important for disaster risk reduction are the basic theses of the U.N. Conference in Sendai. This chapter describes three aspects: innovative Digital Earth development, national and local disaster risk assessment and the benefits arising from the use of maps and dynamic data, and analyses of the contributions of cartography to disaster risk reduction.

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

Cited by 30 publications

References 41 publications

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

User Evaluation of Map-Based Visual Analytic Tools

Digital Earth for Disaster Mitigation

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