Scaling DBSCAN-like algorithms for event detection systems in Twitter

The increasing use of mobile social networks has lately transformed news media. Real-world events are nowadays reported in social networks much faster than in traditional channels. As a result, the autonomous detection of events from networks like Twitter has gained lot of interest in both research and media groups. DBSCAN-like algorithms constitute a well-known clustering approach to retrospective event detection. However, scaling such algorithms to geographically large regions and temporarily long periods present two major shortcomings. First, detecting real-world events from the vast amount of tweets cannot be performed anymore in a single machine. Second, the tweeting activity varies a lot within these broad space-time regions limiting the use of global parameters. Against this background, we propose to scale DBSCAN-like event detection techniques by parallelizing and distributing them through a novel density-aware MapReduce scheme. The proposed scheme partitions tweet data as per its spatial and temporal features and tailors local DBSCAN parameters to local tweet densities. We implement the scheme in Apache Spark and evaluate its performance in a dataset composed of geo-located tweets in the Iberian peninsula during the course of several football matches. The results pointed out to the benefits of our proposal against other state-of-the-art techniques in terms of speed-up and detection accuracy.Peer ReviewedPostprint (author's final draft

‘Good relations’ among neighbours and workmates? The everyday encounters of Accession 8 migrants and established communities in urban England

Drawing on data generated in a recently completed qualitative study in a northern, English city, this paper explores the everyday social encounters of Accession 8 (A8) migrants who entered the UK following the expansion of the European Union in 2004. A number of options from permanent residence in another Member State on the one hand, to more fleeting circulatory and multiple short-term moves on the other, now exist for these new European citizens. The relatively short-term and temporary residence of some A8 migrants calls into question the focus of much UK government policy, which emphasises the need for migrants to integrate into diverse yet cohesive communities. Against this backdrop, the aim of this paper is two-fold. First, it considers the somewhat different character of A8 migration (encompassing a spectrum from permanency to temporariness) and what this means for routine experiences of mixing between new migrants and established host communities. Second, the paper explores such interactions in terms of ‘everyday encounters’ in both neighbourhood and work spaces and asks whether such spatio-temporal practices and experiences enhance or inhibit the building of ‘good relations’ in a multicultural city

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.Agent-Based Social Simulation, Experiments, Ontologies, Replication, Semantic Grid

"So go downtown": simulating pedestrian movement in town centres

Pedestrian movement models have been developed since the 1970s. A review of the literature shows that such models have been developed to explain and predict macro, meso, and micro movement patterns. However, recent developments in modelling techniques, and especially advances in agent-based simulation, open up the possibility of developing integrative and complex models which use existing models as 'building blocks'. In this paper we describe such integrative, modular approach to simulating pedestrian movement behaviour. The STREETS model, developed by using Swarm and GIS, is an agent-based model that focuses on the simulation of the behavioural aspects of pedestrian movement. The modular structure of the simulation is described in detail. This is followed by a discussion of the lessons learned from the development of STREETS, especially the advantages of adopting a modular approach and other aspects of using the agent-based paradigm for modelling

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach

Teaching geography with literary mapping: A didactic experiment

The relationship between maps and literature has long been debated from both narrative and geographical perspectives. At the core of this contribution are so-called reader generated mappings, mapping practices performed after the reading of a literary text. The aim of this article is to suggest possible didactic directions for teaching geography through geo-visualisations based on the reading of literary texts. In particular, this research draws from the results of a literary mapping workshop attended by students during an introductory human geography course at the University of Padua (Italy). Focusing on one of the literary mappings performed by the students, namely the mapping of a short story written by the Italian writer Mario Rigoni Stern, a deductive process is used to understand the possible future potentialities of literary mapping in didactics. Analysing the students\u2019 literary maps, this article aims to direct attention to literary mapping practices as constellations of learning moments to exploit. The reading of the text, the envisioning and creation of the map are here explored as the steps of a complex practice capable of visually developing geographical knowledge

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment