Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics

Sensors

Günther Sagl²  Thomas Blaschke¹  Euro Beinat¹ 

[1] Centre for Geoinformatics, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria; E-Mails:;Doctoral College Geographic Information Science, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria

关键词: ubiquitous sensing;    collective sensing;    environmental monitoring;    context awareness;    sensor data;    human-environmental interaction;    spatio-temporal dynamics;    urban dynamics;    maximal information coefficient;    geographic information science;   

DOI  :  10.3390/s120709800

来源: mdpi

【 摘 要 】

Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.