51,843 research outputs found
Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks
Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.Comment: ASE/IEEE SocialCom 201
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition
Driven by the wave of urbanization in recent decades, the research topic
about migrant behavior analysis draws great attention from both academia and
the government. Nevertheless, subject to the cost of data collection and the
lack of modeling methods, most of existing studies use only questionnaire
surveys with sparse samples and non-individual level statistical data to
achieve coarse-grained studies of migrant behaviors. In this paper, a partially
supervised cross-domain deep learning model named CD-CNN is proposed for
migrant/native recognition using mobile phone signaling data as behavioral
features and questionnaire survey data as incomplete labels. Specifically,
CD-CNN features in decomposing the mobile data into location domain and
communication domain, and adopts a joint learning framework that combines two
convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN
employs a three-step algorithm for training, in which the co-training step is
of great value to partially supervised cross-domain learning. Comparative
experiments on the city Wuxi demonstrate the high predictive power of CD-CNN.
Two interesting applications further highlight the ability of CD-CNN for
in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc
Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany
The ongoing growth of cities due to better job opportunities is leading to increased labour-relatedcommuter flows in several countries. On the one hand, an increasing number of people commuteand move to the cities, but on the other hand, the labour market indicates higher unemployment ratesin urban areas than in the surrounding areas. We investigate this phenomenon on regional level byan alternative definition of unemployment rates in which commuting behaviour is integrated. Wecombine data from the Labour Force Survey (LFS) with dynamic mobile network data by small areamodels for the federal state North Rhine-Westphalia in Germany. From a methodical perspective, weuse a transformed Fay-Herriot model with bias correction for the estimation of unemployment ratesand propose a parametric bootstrap for the Mean Squared Error (MSE) estimation that includes thebias correction. The performance of the proposed methodology is evaluated in a case study based onofficial data and in model-based simulations. The results in the application show that unemploymentrates (adjusted by commuters) in German cities are lower than traditional official unemployment ratesindicate
Radio frequency optimization of a Global System for Mobile (GSM) network
Includes bibliographical references
Interplay between telecommunications and face-to-face interactions - a study using mobile phone data
In this study we analyze one year of anonymized telecommunications data for
over one million customers from a large European cellphone operator, and we
investigate the relationship between people's calls and their physical
location. We discover that more than 90% of users who have called each other
have also shared the same space (cell tower), even if they live far apart.
Moreover, we find that close to 70% of users who call each other frequently (at
least once per month on average) have shared the same space at the same time -
an instance that we call co-location. Co-locations appear indicative of
coordination calls, which occur just before face-to-face meetings. Their number
is highly predictable based on the amount of calls between two users and the
distance between their home locations - suggesting a new way to quantify the
interplay between telecommunications and face-to-face interactions
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