Clustering in Geo-Social Networks

The rapid growth of Geo-Social Networks (GeoSNs) provides a new and rich form of data. Users of GeoSNs can capture their geographic locations and share them with other users via an operation named checkin. Thus, GeoSNs can track the connections (and the time of these connections) of geographic data to their users. In addition, the users are organized in a social network, which can be extended to a heterogeneous network if the connections to places via checkins are also considered. The goal of this paper is to analyze the opportunities in clustering this rich form of data. We first present a model for clustering geographic locations, based on GeoSN data. Then, we discuss how this model can be extended to consider temporal information from checkins. Finally, we study how the accuracy of community detection approaches can be improved by taking into account the checkins of users in a GeoSN.published_or_final_versio

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

SEGMENTATION TECHNIQUES BASED ON CLUSTERING FOR THE ANALYSIS OF MOBILITY DATA

La Tesi riguarda l'analisi e applicazione di metodi di segmentazione per il partizionamento delle traiettorie spaziali in sotto-traiettorie semanticamente significative, e il loro utilizzo per l'analisi del comportamento di oggetti in movimento. Le traiettorie spaziali sono dati strutturati complessi costituiti da sequenze ordinate di punti spazio-temporali che campionano il movimento continuo di un oggetto in uno spazio di riferimento. Le tecniche di segmentazione sono essenziali per l'analisi delle traiettorie spaziali. In generale, l'attivit\ue0 di segmentazione divide una sequenza di punti dati in una serie di sottosequenze disgiunte basate su criteri di omogeneit\ue0. La Tesi si focalizza, in particolare, sulle tecniche di segmentazione basate su \u201cdensity based clustering\u201d. A differenza dei processi di clustering tradizionali, che sono applicati ad \u201cinsiemi\u201d di punti, le tecniche di segmentazione basate su clustering partizionano \u201csequenze\u201d in una serie di \u201cclusters\u201d temporalmente separati. Possibili applicazioni includono l'analisi del movimento di individui in ambito urbano e lo studio del comportamento di animali. Alcune tecniche di segmentazione basate su \u201ccluster\u201d sono descritte in letteratura, tuttavia nessuna di queste soluzioni permette di gestire in modo efficace i punti non strutturati (noise). Inoltre, le metodologie adottate per validare queste tecniche soffrono di gravi limitazioni, ad esempio le verifiche sperimentale utilizzano dati molto semplici che non riflettono la complessit\ue0 del movimento reale, come pure non permettono di effettuare un confronto con ground truth. Questa Tesi si focalizza su una recente tecnica per la segmentazione basata su cluster con noise, chiamata SeqScan, proposta in un lavoro precedente. In particolare, la ricerca ha affrontato i seguenti problemi: i) definizione di un framework rigoroso per l' analisi delle propriet\ue0 del modello di segmentazione; ii) validazione del metodo attraverso un'ampia sperimentazione che prevede il confronto con la ground truth; iii) estensione dell'approccio per consentire la individuazione di gatherings. Il gathering \ue9 un gruppo di oggetti mobili che condividono la stessa zona, per un certo periodo di tempo con la possibilit\ue0 di assenze occasionali; iv) sviluppo di una piattaforma software che integra i diversi algoritmi ed ulteriori strumenti a supporto dell'analisi dei dati di mobilit\ue0.The Thesis focuses on segmentation methods for the partitioning of spatial trajectories in semantically meaningful sub-trajectories and their application to the analysis of mobility behavior. Spatial trajectories are complex structured data consisting of sequences of temporally ordered spatio-temporal points sampling the continuous movement of an object in a reference space. Spatial trajectories can reveal behavioral information about individuals and groups of individuals, and that motivates the concern for data analysis techniques. Segmentation techniques are key for the analysis of spatial trajectories. In general, the segmentation task partitions a sequence of data points in a series of disjoint sub-sequences based on some homogeneity criteria. The Thesis focuses, in particular, on the use of clustering methods for the segmentation of spatial trajectories. Unlike the traditional clustering task, which is applied to sets of data points, the goal of this class of techniques is to partition sequential data in temporally separated clusters. Such techniques can be utilized for example to detect the sequences of places or regions visited by moving objects. While a number of techniques for the cluster-based segmentation are proposed in literature, none of them is really robust again noise, while the methodologies put in place to validate those techniques suffer from severe limitations, e.g., simple datasets, no comparison with ground truth. This Thesis focuses on a recent cluster-based segmentation method, called SeqScan, proposed in previous work. This technique promises to be robust against noise, nonetheless the approach is empirical and lacks a formal and theoretical framework. The contribution of this research is twofold. First it provides analytical support to SeqScan, defining a rigorous framework for the analysis of the properties of the model. The method is validated through an extensive experimentation conducted in an interdisciplinary setting and contrasting the segmentation with ground truth. The second contribution is the proposal of a technique for the discovery of a collective pattern, called gathering. The gathering pattern describes a situation in which a significant number of moving objects share the same region, for enough time periods with possibility of occasional absences, e.g. a concert, an exhibition. The technique is built on SeqScan. A platform, called MigrO, has been finally developed, including not only the algorithms but also a variety of tools facilitating data analysis