Geo-Social Co-location Mining

Abstract

Modern technology to capture geo-spatial information produces a huge flood of geo-spatial and geo-spatio-temporal data with a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discover new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very sparse check-in data, and thus very sparse trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient methods for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.

From Data to Rhizomes: Applying a Geographical Concept to Understand the Mobility of Tourists from Geo-Located Tweets

“…To the best of our knowledge, itemset mining has been rarely used to study how people live space. We found only [42], where the authors adopted itemset mining to address the problem of geo-social co-location. Suppose that people that are found in the same place in a given time slice are described by features such as the university they are studying in, the course and so on; the itemset-mining approach is used to find out the most frequent associations of personal features that characterize people that frequently are in the same places.…”

Seed-Driven Geo-Social Data Extraction

“…Despite the growing interest in geo-social related topics, the existing related work does not focus specifically on optimizing the data extraction process. Most of the existing research uses either publicly available datasets [4,8,10,11,21,22,22,32,33,36,[38][39][40][42][43][44], crawl using the default settings of the API [3,6,13,17,24,30,35,37,41] or do both [25,27]. The (sparsely described) crawling methods used in these papers can be categorized as either user-based crawling or locationbased crawling.…”