11,465 research outputs found
Predicting the temporal activity patterns of new venues
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners
Predicting the temporal activity patterns of new venues.
Estimating revenue and business demand of a newly opened venue is paramount
as these early stages often involve critical decisions such as first rounds of staffing
and resource allocation. Traditionally, this estimation has been performed through
coarse-grained measures such as observing numbers in local venues or venues at
similar places (e.g., coffee shops around another station in the same city). The
advent of crowdsourced data from devices and services carried by individuals on a
daily basis has opened up the possibility of performing better predictions of
temporal visitation patterns for locations and venues. In this paper, using mobility
data from Foursquare, a location-centric platform, we treat venue categories as
proxies for urban activities and analyze how they become popular over time. The
main contribution of this work is a prediction framework able to use characteristic
temporal signatures of places together with k-nearest neighbor metrics capturing
similarities among urban regions, to forecast weekly popularity dynamics of a new
venue establishment in a city neighborhood. We further show how we are able to
forecast the popularity of the new venue after one month following its opening by
using locality and temporal similarity as features. For the evaluation of our
approach we focus on London. We show that temporally similar areas of the city
can be successfully used as inputs of predictions of the visit patterns of new
venues, with an improvement of 41% compared to a random selection of wards as
a training set for the prediction task. We apply these concepts of temporally
similar areas and locality to the real-time predictions related to new venues and
show that these features can effectively be used to predict the future trends of a
venue. Our findings have the potential to impact the design of location-based
technologies and decisions made by new business owners
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
Navigating MazeMap: indoor human mobility, spatio-logical ties and future potential
Global navigation systems and location-based services have found their way
into our daily lives. Recently, indoor positioning techniques have also been
proposed, and there are several live or trial systems already operating. In
this paper, we present insights from MazeMap, the first live indoor/outdoor
positioning and navigation system deployed at a large university campus in
Norway. Our main contribution is a measurement case study; we show the spatial
and temporal distribution of MazeMap geo-location and wayfinding requests,
construct the aggregated human mobility map of the campus and find strong
logical ties between different locations. On one hand, our findings are
specific to the venue; on the other hand, the nature of available data and
insights coupled with our discussion on potential usage scenarios for indoor
positioning and location-based services predict a successful future for these
systems and applications.Comment: 6 pages, accepted at PerMoby Workshop at IEEE PerCom 201
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
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