22,992 research outputs found

    Hot Routes: Developing a New Technique for the Spatial Analysis of Crime

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    The use of hotspot mapping techniques such as KDE to represent the geographical spread of linear events can be problematic. Network-constrained data (for example transport-related crime) require a different approach to visualize concentration. We propose a methodology called Hot Routes, which measures the risk distribution of crime along a linear network by calculating the rate of crimes per section of road. This method has been designed for everyday crime analysts, and requires only a Geographical Information System (GIS), and suitable data to calculate. A demonstration is provided using crime data collected from London bus routes

    Event detection in location-based social networks

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    With the advent of social networks and the rise of mobile technologies, users have become ubiquitous sensors capable of monitoring various real-world events in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the White House. However, the deluge of user-generated data on these networks requires intelligent systems capable of identifying and characterizing such events in a comprehensive manner. The data mining community coined the term, event detection , to refer to the task of uncovering emerging patterns in data streams . Nonetheless, most data mining techniques do not reproduce the underlying data generation process, hampering to self-adapt in fast-changing scenarios. Because of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of event detection in Twitter : a data mining technique called Tweet-SCAN and a machine learning technique called Warble. We assess and compare both techniques in a dataset of tweets geo-located in the city of Barcelona during its annual festivities. Last but not least, we present the algorithmic changes and data processing frameworks to scale up the proposed techniques to big data workloads.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware (TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft

    THE VISUALIZATION AND ANALYSIS OF URBAN FACILITY POIS USING NETWORK KERNEL DENSITY ESTIMATION CONSTRAINED BY MULTI-FACTORS

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    The urban facility, one of the most important service providers is usuallyrepresented by sets of points in GIS applications using POI (Point of Interest) modelassociated with certain human social activities. The knowledge about distributionintensity and pattern of facility POIs is of great significance in spatial analysis,including urban planning, business location choosing and social recommendations.Kernel Density Estimation (KDE), an efficient spatial statistics tool for facilitatingthe processes above, plays an important role in spatial density evaluation, becauseKDE method considers the decay impact of services and allows the enrichment ofthe information from a very simple input scatter plot to a smooth output densitysurface. However, the traditional KDE is mainly based on the Euclidean distance,ignoring the fact that in urban street network the service function of POI is carriedout over a network-constrained structure, rather than in a Euclidean continuousspace. Aiming at this question, this study proposes a computational method of KDEon a network and adopts a new visualization method by using 3-D “wall” surface.Some real conditional factors are also taken into account in this study, such astraffic capacity, road direction and facility difference. In practical works theproposed method is implemented in real POI data in Shenzhen city, China to depictthe distribution characteristic of services under impacts of multi-factors
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