  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.
