… Having built an understanding of similarities in the temporal … to predict the stationary temporal
profile of a new venue. We … temporally similar wards to the ward in which the new venue is …

… with the most temporally similar pro le of venues with the … temporal pro le of random wards.
Looking within the same ward as the new venue, we look at the temporal pro le of all venues …

… We also show the model is capable of forecasting the popularity of a new venue one month
… In this chapter, we use a longitudinal dataset describing urban mobility and activity patterns …

… of venues to predict their popularity, represented by Google “Popular Times” data; secondly,
quantifying whether Google “Popular Times” data are a good measure of venue popularity …

… of time series forecasting algorithms for predicting the venue attendance levels. To this
end, we evaluate daily forecasts by predicting the attendance of each of the venues for the …

… the activity in shops could not be well explained or predicted … that allow the exploration of
temporal patterns of activity. By … the conversion of discrete locations into functional spaces and …

… predict the potential number of visitors for specific venues based on the analysis of mobility
patterns … The ability to accurately predict the number of visitors to a venue allows authorities …

… of new places across the city, tracking where new places tend to be created. While the influence
of a strong urban hierarchy is prevalent, with more new places being … on existing places. …

… As we are interested in predicting locations that people will revisit as part of their routine
patterns, … We ‘replayed’ the users’ travel activities and used each abovementioned method for …

… of human mobility and activity patterns for the … to predict tourism venues with the help of
supervised methods from our previously used dataset from Weibo. These venues are predicted …