- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest – PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people’s habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.
9. Conclusion and future work
In this work we have taken a fresh look at the concept of loca- tion. We have proposed a general framework for extracting, char- acterizing, and classifying the Points of Interest of each individual according to their relevance for her/him. We have also proposed suitable metrics and algorithms to describe the semantic values of locations and the commuting rules among them. Our key observations are as follows: • individuals are regularly drawn to a limited set of locations where they spend most of their time; • they also spend a significant amount of time in locations they 996 only visit once; • people commute between places based on temporal distance – not spatial distance – factors; • HOME and WORK are among the most frequently visited loca- tions, and, as such, the relevance R is a fundamental feature for their semantic identification. These observations hold true across different datasets with completely different properties. Based on above observations, we have derived a mobility framework where we are able to classify PoIs, the users and the way they move along PoIs, as well as the semantic meaning of PoIs. We have validated our framework with extensive experimen- tal work.