2,630 research outputs found

    On the properties of human mobility

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    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

    From movement tracks through events to places : extracting and characterizing significant places from mobility data

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    Best VAST 2011 paperInternational audienceWe propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

    Trajectory Data Analysis in Support of Understanding Movement Patterns: A Data Mining Approach

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    Recent developments in wireless technology, mobility and networking infrastructures increased the amounts of data being captured every second. Data captured from the digital traces of moving objects and devices is called trajectory data. With the increasing volume of spatiotemporal trajectories, constructive and meaningful knowledge needs to be extracted. In this paper, a conceptual framework is proposed to apply data mining techniques on trajectories and semantically enrich the extracted patterns. A design science research approach is followed, where the framework is tested and evaluated using a prototypical instantiation, built to support decisions in the context of the Egyptian tourism industry. By applying association rule mining, the revealed time-stamped frequently visited regions of interest (ROI) patterns show that specific semantic annotations are required at early stages in the process and on lower levels of detail, refuting the presumption of cross-application usable patterns

    Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

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    The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.Comment: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, 2013, Pages 793-80

    The Effect of Recency to Human Mobility

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    In recent years, we have seen scientists attempt to model and explain human dynamics and, in particular, human movement. Many aspects of our complex life are affected by human movements such as disease spread and epidemics modeling, city planning, wireless network development, and disaster relief, to name a few. Given the myriad of applications it is clear that a complete understanding of how people move in space can lead to huge benefits to our society. In most of the recent works, scientists have focused on the idea that people movements are biased towards frequently-visited locations. According to them, human movement is based on an exploration/exploitation dichotomy in which individuals choose new locations (exploration) or return to frequently-visited locations (exploitation). In this work, we focus on the concept of recency. We propose a model in which exploitation in human movement also considers recently-visited locations and not solely frequently-visited locations. We test our hypothesis against different empirical data of human mobility and show that our proposed model is able to better explain the human trajectories in these datasets

    Trajectory data mining: A review of methods and applications

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    The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used

    T-profiles: a method for inferring socio-demographic profiles from trajectories

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    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Ter o conhecimento sobre o perfil dos habitantes de uma cidade ou país tem grande valor para administrações públicas e empresas. Conhecer o perfil de uma população pode auxiliar o trabalho de planejadores urbanos, administradores de transporte público, serviços governamentais ou empresas de diferentes maneiras como, por exemplo, decidir onde é interessante instalar uma nova loja ou personalizar anúncios para um determinado público. A forma mais comum utilizada na análise de informações demográficas de uma população é através da segmentação da mesma em perfis sócio-demográficos, como idade, ocupação, estado civil ou renda mensal. Atualmente, para que essas informações sejam descobertas e analisadas, os dados são coletados através de entrevistas realizadas de casa em casa, periodicamente, em diversos países. No entanto, este tipo de abordagem possui algumas desvantagens: 1) os dados não são atualizados e precisos, pois são coletados em um intervalo de 5 - 10 anos; 2) a coleta é muito custosa e cobre apenas uma parcela da população por um curto período de tempo, apesar de ser estatisticamente significante; 3) não caracteriza as atividades completas do indivíduo, apenas o período de 1 dia de atividades, fornecidas através da entrevista realizada. Atualmente, é possível inferir muito conhecimento a partir do comportamento das pessoas analisando seu movimento do dia-a-dia, uma vez que grandes quantidades de dados de movimento estão disponíveis como: dados de telefone celular, redes sociais, dados de GPS, etc. Nesta dissertação, é proposto um método para a extração de perfis sócio-demográficos a partir de trajetórias de objetos móveis, e apresenta as seguintes contribuições: (i) proposta de um modelo de perfil geral para representar o perfil sócio-demográfico de pessoas, como trabalhador, estudante, desempregado, etc; (ii) proposta de um modelo para representar o histórico de movimentação diária dos indivíduos; (iii) proposta de funções de similaridade para fazer o casamento entre histórico e modelo de perfil e; (iv) um algoritmo chamado T-Profiles que realiza a comparação entre modelo de perfil e modelo de histórico, com o intuito de inferir o perfil sócio-demográfico de um objeto móvel a partir de sua trajetória. O algoritmo T-Profiles é validado utilizando dados reais de trajetórias, obtendo em torno de 90% de precisão.Abstract : The knowledge about people living in a city or country has great value for the public administration as well as for enterprises. To know the population profile may help the job of smart city planners, public transportation administrators, government services or companies in many different ways, such as to decide if and where to install a new store or to personalize an advertisement, for example. The usual approach for population demographic analysis is to segment the population in socio-demographic profiles, such as age, occupation, marital status or income. Most attempts to discover and measure the population profiles is through human surveys, and the most well-known example is the socio-demographic census with diary activities, done periodically in many countries. However, the main drawbacks of the census data is that they: 1) are not up to date since they are usually collected every 5 - 10 years; 2) are expensive to collect, and cover only a small - although statistically significant - part of the population for a short period of time; 3) do not collect the actual movement of the individuals, but only the activity performed during one day and which is mentioned by the user during the interview. We believe that nowadays we can infer much knowledge and the real behavior about people from their every day movement. In this thesis we propose a method to extract socio-demographic profiles from trajectories of moving objects, and make the following contributions: (i) we propose a general profile model to represent socio-demographic profiles of people such as worker, student, unemployed, etc; (ii) we propose a moving object history model to represent the daily movement of the object, and (iii) we propose similarity functions and an algorithm called T-Profiles for matching the profile model and the history model in order to infer the socio-demographic profile of a moving object from his/her trajectories. We validate T-Profiles with real trajectory data obtaining about 90% of precision
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