78,689 research outputs found
Using user-generated content to explore the temporal heterogeneity in tourist mobility
In tourism studies, new means of data collection are opening up opportunities for disclosing hidden mobility patterns. This paper aims to analyze and model the tourist flow networks for different lengths of trip on urban scale, using user generated content (UGC) data collated from an open tourism web service. The textual UGC data, with high spatial and temporal resolution, is utilized to construct three tourist flow networks in response to length of trips. Social network analysis and a revised spatial interaction model are deployed for exploring the temporal heterogeneity in the tourist movements. This empirical study from Nanjing city has further confirmed the power law of distance decay in intra-urban tourist mobility. Furthermore, the research reveals temporal variations with length of trip. The paper highlights the role of time in the tourism study through incorporating a temporal dimension into the analyses and taking advantage of the availability of new data
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
Towards new methods for mobility data gathering: content, sources, incentives
Over the past decade, huge amounts of work has been done in mobile and opportunistic networking research. Unfortunately, much of this has had little impact as the results have not been applicable to reality, due to incorrect assumptions and models used in the design and evaluation of the systems.
In this paper, we outline some of the problems of the assumptions of early research in the field, and provide a survey of some initial work that has started to take place to alleviate this through more realistic modelling and measurements of real systems. We do note that there is still much work to be done in this area, and then go on to identify some important properties of the network that must be studied further. We identify the types of data that are important to measure, and also give some guidelines on finding existing and potentially new sources for such data and incentivizing the holders of the data to share it
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
The availability of massive data-sets describing human mobility offers the
possibility to design simulation tools to monitor and improve the resilience of
transport systems in response to traumatic events such as natural and man-made
disasters (e.g. floods terroristic attacks, etc...). In this perspective, we
propose ACHILLES, an application to model people's movements in a given
transport system mode through a multiplex network representation based on
mobility data. ACHILLES is a web-based application which provides an
easy-to-use interface to explore the mobility fluxes and the connectivity of
every urban zone in a city, as well as to visualize changes in the transport
system resulting from the addition or removal of transport modes, urban zones,
and single stops. Notably, our application allows the user to assess the
overall resilience of the transport network by identifying its weakest node,
i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To
demonstrate the impact of ACHILLES for humanitarian aid we consider its
application to a real-world scenario by exploring human mobility in Singapore
in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic
Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility flows at different spatial scales is challenged by
the heterogeneity of individual trajectories and the multi-scale nature of
transportation networks. As vast amounts of digital traces of human behaviour
become available, an opportunity arises to improve mobility models by
integrating into them proxy data on mobility collected by a variety of digital
platforms and location-aware services. Here we propose a hybrid model of human
mobility that integrates a large-scale publicly available dataset from a
popular photo-sharing system with the classical gravity model, under a stacked
regression procedure. We validate the performance and generalizability of our
approach using two ground-truth datasets on air travel and daily commuting in
the United States: using two different cross-validation schemes we show that
the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure
Delay Tolerant Networking over the Metropolitan Public Transportation
We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case
The Effect of Pok\'emon Go on The Pulse of the City: A Natural Experiment
Pok\'emon Go, a location-based game that uses augmented reality techniques,
received unprecedented media coverage due to claims that it allowed for greater
access to public spaces, increasing the number of people out on the streets,
and generally improving health, social, and security indices. However, the true
impact of Pok\'emon Go on people's mobility patterns in a city is still largely
unknown. In this paper, we perform a natural experiment using data from mobile
phone networks to evaluate the effect of Pok\'emon Go on the pulse of a big
city: Santiago, capital of Chile. We found significant effects of the game on
the floating population of Santiago compared to movement prior to the game's
release in August 2016: in the following week, up to 13.8\% more people spent
time outside at certain times of the day, even if they do not seem to go out of
their usual way. These effects were found by performing regressions using count
models over the states of the cellphone network during each day under study.
The models used controlled for land use, daily patterns, and points of interest
in the city.
Our results indicate that, on business days, there are more people on the
street at commuting times, meaning that people did not change their daily
routines but slightly adapted them to play the game. Conversely, on Saturday
and Sunday night, people indeed went out to play, but favored places close to
where they live.
Even if the statistical effects of the game do not reflect the massive change
in mobility behavior portrayed by the media, at least in terms of expanse, they
do show how "the street" may become a new place of leisure. This change should
have an impact on long-term infrastructure investment by city officials, and on
the drafting of public policies aimed at stimulating pedestrian traffic.Comment: 23 pages, 7 figures. Published at EPJ Data Scienc
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