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.

中文翻译：

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预测新场所的时间活动模式。

估算新开业场所的收入和业务需求至关重要，因为这些早期阶段通常涉及关键决策，例如第一轮人员配备和资源分配。传统上，这种估算是通过粗粒度的措施进行的，例如观察本地场所或类似地点的场所（例如，同一城市另一个车站周围的咖啡店）的数量。来自个人日常携带的设备和服务的众包数据的出现开辟了对位置和场所的时间访问模式进行更好的预测的可能性。在本文中，我们使用来自以位置为中心的平台Foursquare的移动性数据，将场所类别视为城市活动的代理，并分析它们随着时间的流逝如何流行。ķ-最近邻指标收集城市地区之间的相似性，以预测城市社区中新场所的每周受欢迎程度动态。我们还将进一步展示如何通过使用地点和时间相似性作为特征，预测新场馆开业后一个月后的受欢迎程度。为了评估我们的方法，我们重点关注伦敦。我们显示，该城市时间上相似的区域可以成功地用作新场所访问模式预测的输入，与随机选择病房作为预测任务的训练集相比，改善了41％。我们将这些在时间上相似的区域和地点的概念应用于与新场馆有关的实时预测，并表明这些功能可以有效地用于预测场馆的未来趋势。