1,226 research outputs found
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
Gender Matters! Analyzing Global Cultural Gender Preferences for Venues Using Social Sensing
Gender differences is a phenomenon around the world actively researched by
social scientists. Traditionally, the data used to support such studies is
manually obtained, often through surveys with volunteers. However, due to their
inherent high costs because of manual steps, such traditional methods do not
quickly scale to large-size studies. We here investigate a particular aspect of
gender differences: preferences for venues. To that end we explore the use of
check-in data collected from Foursquare to estimate cultural gender preferences
for venues in the physical world. For that, we first demonstrate that by
analyzing the check-in data in various regions of the world we can find
significant differences in preferences for specific venues between gender
groups. Some of these significant differences reflect well-known cultural
patterns. Moreover, we also gathered evidence that our methodology offers
useful information about gender preference for venues in a given region in the
real world. This suggests that gender and venue preferences observed may not be
independent. Our results suggests that our proposed methodology could be a
promising tool to support studies on gender preferences for venues at different
spatial granularities around the world, being faster and cheaper than
traditional methods, besides quickly capturing changes in the real world
Government and Social Media: A Case Study of 31 Informational World Cities
Social media platforms are increasingly being used by governments to foster
user interaction. Particularly in cities with enhanced ICT infrastructures
(i.e., Informational World Cities) and high internet penetration rates, social
media platforms are valuable tools for reaching high numbers of citizens. This
empirical investigation of 31 Informational World Cities will provide an
overview of social media services used for governmental purposes, of their
popularity among governments, and of their usage intensity in broadcasting
information online.Comment: In Proceedings of the 47th Hawaii International Conference on System
Sciences (pp. 1715-1724). IEEE Computer Society, 201
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare
Food and drink are two of the most basic needs of human beings. However, as
society evolved, food and drink became also a strong cultural aspect, being
able to describe strong differences among people. Traditional methods used to
analyze cross-cultural differences are mainly based on surveys and, for this
reason, they are very difficult to represent a significant statistical sample
at a global scale. In this paper, we propose a new methodology to identify
cultural boundaries and similarities across populations at different scales
based on the analysis of Foursquare check-ins. This approach might be useful
not only for economic purposes, but also to support existing and novel
marketing and social applications. Our methodology consists of the following
steps. First, we map food and drink related check-ins extracted from Foursquare
into users' cultural preferences. Second, we identify particular individual
preferences, such as the taste for a certain type of food or drink, e.g., pizza
or sake, as well as temporal habits, such as the time and day of the week when
an individual goes to a restaurant or a bar. Third, we show how to analyze this
information to assess the cultural distance between two countries, cities or
even areas of a city. Fourth, we apply a simple clustering technique, using
this cultural distance measure, to draw cultural boundaries across countries,
cities and regions.Comment: 10 pages, 10 figures, 1 table. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and Social Media (ICWSM 2014
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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