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Extracting Semantics of Individual Places from Movement Data by Analyzing Temporal Patterns of Visits
Data reflecting movements of people, such as GPS or GSM tracks, can be a source of information about mobility behaviors and activities of people. Such information is required for various kinds of spatial planning in the public and business sectors. Movement data by themselves are semantically poor. Meaningful information can be derived by means of interactive visual analysis performed by a human expert; however, this is only possible for data about a small number of people. We suggest an approach that allows scaling to large datasets reflecting movements of numerous people. It includes extracting stops, clustering them for identifying personal places of interest (POIs), and creating temporal signatures of the POIs characterizing the temporal distribution of the stops with respect to the daily and weekly time cycles and the time line. The analyst can give meanings to selected POIs based on their temporal signatures (i.e., classify them as home, work, etc.), and then POIs with similar signatures can be classified automatically. We demonstrate the possibilities for interactive visual semantic analysis by example of GSM, GPS, and Twitter data. GPS data allow inferring richer semantic information, but temporal signatures alone may be insufficient for interpreting short stops. Twitter data are similar to GSM data but additionally contain message texts, which can help in place interpretation. We plan to develop an intelligent system that learns how to classify personal places and trips while a human analyst visually analyzes and semantically annotates selected subsets of movement data
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Extracting Personal Behavioral Patterns from Geo-Referenced Tweets
This paper presents an exploratory study of the potential of geo-referenced Twitter data for extracting knowledge about significant personal places, behaviors and potential interests of people. The study was done analysing two months’ worth of tweets from residents of the greater Seattle area
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Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces
Mobility diaries of a large number of people are needed for assessing transportation infrastructure and for spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the internet, or posting georeferenced contents (texts, photos, or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work, or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries while preserving the privacy of individuals whose data are analysed. We have devised a visual analytics approach and a set of supporting tools making such privacy-preserving analysis possible. The approach was tested in two case studies with publicly available data: simulated tracks from the VAST Challenge 2014 and real traces built from georeferenced Twitter posts
On the properties of human mobility
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
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
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
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
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
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
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-Graduação em CiĂŞncia da Computação, FlorianĂłpolis, 2015.Ter o conhecimento sobre o perfil dos habitantes de uma cidade ou paĂs tem grande valor para administrações pĂşblicas e empresas. Conhecer o perfil de uma população pode auxiliar o trabalho de planejadores urbanos, administradores de transporte pĂşblico, serviços governamentais ou empresas de diferentes maneiras como, por exemplo, decidir onde Ă© interessante instalar uma nova loja ou personalizar anĂşncios para um determinado pĂşblico. A forma mais comum utilizada na análise de informações demográficas de uma população Ă© atravĂ©s da segmentação da mesma em perfis sĂłcio-demográficos, como idade, ocupação, estado civil ou renda mensal. Atualmente, para que essas informações sejam descobertas e analisadas, os dados sĂŁo coletados atravĂ©s de entrevistas realizadas de casa em casa, periodicamente, em diversos paĂses. No entanto, este tipo de abordagem possui algumas desvantagens: 1) os dados nĂŁo sĂŁo atualizados e precisos, pois sĂŁo coletados em um intervalo de 5 - 10 anos; 2) a coleta Ă© muito custosa e cobre apenas uma parcela da população por um curto perĂodo de tempo, apesar de ser estatisticamente significante; 3) nĂŁo caracteriza as atividades completas do indivĂduo, apenas o perĂodo de 1 dia de atividades, fornecidas atravĂ©s da entrevista realizada. Atualmente, Ă© possĂvel 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 estĂŁo disponĂveis como: dados de telefone celular, redes sociais, dados de GPS, etc. Nesta dissertação, Ă© proposto um mĂ©todo para a extração de perfis sĂłcio-demográficos a partir de trajetĂłrias de objetos mĂłveis, e apresenta as seguintes contribuições: (i) proposta de um modelo de perfil geral para representar o perfil sĂłcio-demográfico de pessoas, como trabalhador, estudante, desempregado, etc; (ii) proposta de um modelo para representar o histĂłrico de movimentação diária dos indivĂduos; (iii) proposta de funções de similaridade para fazer o casamento entre histĂłrico e modelo de perfil e; (iv) um algoritmo chamado T-Profiles que realiza a comparação entre modelo de perfil e modelo de histĂłrico, com o intuito de inferir o perfil sĂłcio-demográfico de um objeto mĂłvel a partir de sua trajetĂłria. O algoritmo T-Profiles Ă© validado utilizando dados reais de trajetĂłrias, obtendo em torno de 90% de precisĂŁo.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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