A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

Andrienko, Gennady; Bak, Peter; Bremm, Sebastian; Keim, Daniel A.; Landesberger, Tatiana von; Pölitz, Christian; Schreck, Tobias

doi:10.1080/17489725.2010.532816

Cited by 29 publications

(19 citation statements)

References 27 publications

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“…Besides, visual analytics is an interactive and iterative process by definition [21]. The visual analysis of spatio-temporal data can follow two complementary logics [22], namely, focusing on a certain location or region over time, or focusing on a specific time or time interval and investigating space. The main benefit of explorative visual analytics is the possibility of viewing the data from multiple perspectives and different scales simultaneously, whilst never losing the overview.…”

Section: Related Workmentioning

confidence: 99%

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

Sagl

Loidl

Beinat

2012

IJGI

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Abstract:In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. More than 88 million movements between pairs of radio cells-so-called handovers-served as a proxy for more than two months of mobility within four urban test areas in Northern Italy. In contrast to previous work, our approach relies entirely on visualization and mapping techniques, implemented in several software applications. We purposefully avoid statistical or probabilistic modeling and, nonetheless, reveal characteristic and exceptional mobility patterns. The results show, for example, surprising similarities and symmetries amongst the total mobility and people flows between the test areas. Moreover, the exceptional patterns detected can be associated to real-world events such as soccer matches. We conclude that the visual analytics approach presented can shed new light on large-scale collective urban mobility behavior and thus helps to better understand the "pulse" of dynamic urban systems.

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Section: Related Workmentioning

confidence: 99%

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

Sagl

Loidl

Beinat

2012

IJGI

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“…While contextual partitioning can be accomplished using common clustering methods (e.g., [2,24]), few methods guarantee spatial contiguity of clusters. Such contiguity is critical for analyzing human movement to capture both spatial and geographic context of movements.…”

Section: Clustering Zip Codesmentioning

confidence: 99%

Inferring human mobility patterns from anonymized mobile communication usage

Tanahashi

Rowland

North

et al. 2012

Proceedings of the 10th International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM '12

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Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.

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“…It is not guaranteed that the subsets represent population samples with similar demographic and economical characteristics. Indeed, clustering days by feature vectors comprising counts of calls at each antenna, followed by assigning colors to clusters by similarity [8] clearly demonstrates the dissimilarity of patterns in consecutive two-weeks periods ( Figure 6). Additionally, this display also does not give any evidence of differences between week days and weekends.…”

Section: Assessing Daily Aggregates For Antennasmentioning

confidence: 99%

Multi-perspective Analysis of Mobile Phone Call Data Records: A Visual Analytics Approach

Andrienko

Fuchs

2015

Business Intelligence

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A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

Cited by 29 publications

References 27 publications

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

A Visual Analytics Approach for Extracting Spatio-Temporal Urban Mobility Information from Mobile Network Traffic

Inferring human mobility patterns from anonymized mobile communication usage

Multi-perspective Analysis of Mobile Phone Call Data Records: A Visual Analytics Approach

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