Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn

Ahas, Rein; Aasa, Anto; Yuan, Yihong; Raubal, Martin; Smoreda, Zbigniew; Liu, Yingjun; Ziemlicki, Cezary; Tiru, Margus; Zook, Matthew

doi:10.1080/13658816.2015.1063151

Cited by 129 publications

(78 citation statements)

References 35 publications

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“…Using mobile telephone positioning data, researchers can analyze the diurnal rhythms of city life and its spatiotemporal differences [12]. Mobile telephone-based sensor data can be used for detecting dynamic urban activities in different time in different cities (Harbin, Paris, and Tallinn) [13]. Using GPS trajectory data from taxi drivers, Liu et al (2010) [14] revealed taxi drivers' spatial selection of routes and their operation behaviors.…”

Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

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Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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“…These data sets are a by-product generated for network management purposes and theoretically available at no cost for data analyses. In recent years, mobile phone tracking data have been widely used for human mobility studies (Gonzalez, Hidalgo, and Barabasi 2008;Ahas, Aasa, et al 2010;Song et al 2010;Ahas et al 2015;Xu et al 2015;Xu et al 2016) and transportation applications (Caceres et al 2012;Iqbal et al 2014;Pei et al 2014). Nevertheless, there has been little attention in the literature on using mobile phone tracking data for accessibility studies.…”

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confidence: 99%

Understanding the Impacts of Human Mobility on Accessibility Using Massive Mobile Phone Tracking Data

Chen

Wang

et al. 2018

Annals of the American Association of Geographers

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“…In addition depending on the exact data source there are obvious concerns of privacy and data may have to be aggregated. The most extreme form of this is when the data is not even available on the individual level but only aggregated on the level of the cells (Louail et al 2014;Ahas et al 2015). Even if the information is available on an individual level, if a fine spatial granularity is not the primary interest, aggregating into broader geographical regions can help reduce the uncertainty and noise in the data as well as simplify inference (Tanahashi et al 2012;Doyle et al 2014).…”

Section: Related Workmentioning

confidence: 99%

“…They can be used as an alternative to GPS to infer estimates of the temporally varying locations of significant fractions of the population of a given geographical area, such as a city, region or country (Ahas et al 2015;Trasarti et al 2015;Doyle et al 2014;Blondel, Decuyper, and Krings 2015). Similar to GPS-trajectories, CDR datasets are typically "data rich" but semantically very poor.…”

Section: Introduction and Aim Of This Workmentioning

confidence: 99%

Extracting regular mobility patterns from sparse CDR data without a priori assumptions

Burkhard

Ahas

Saluveer

et al. 2017

Journal of Location Based Services

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In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment. In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.

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Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn

Cited by 129 publications

References 35 publications

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Understanding the Impacts of Human Mobility on Accessibility Using Massive Mobile Phone Tracking Data

Extracting regular mobility patterns from sparse CDR data without a priori assumptions

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