Forecasting the rise and fall of volatile point-of-interests

Volatile Point-of-Interests (vPOIs) refer to those small businesses which appear and disappear quickly in cities. How to maintain and incubate small business in the urban area is a big concern for both business owners and government administrators. Therefore, the prediction task for the rise and fall of vPOIs is valuable for both shopkeepers and administrators by supporting a variety of applications in urban economics. In this paper, we propose a framework, named FRFP, to predict the prosperity of vPOIs over time. Specifically, due to the data sparsity and skewness of the individual vPOIs, we first aggregate vPOIs prosperities at focal areas w.r.t. each vPOI category. Then we develop the dynamic-continuous CRF (DC-CRF) model to integrate the association between input and output as well as the correlations between outputs from temporal, spatial and contextual perspectives. Finally, we conduct empirical experiments on real-world data from Google Maps and NYC OpenData. The evaluation results demonstrate that our proposed approach outperforms baseline algorithms with considerable margins. In addition, we explore the predictability of different explanatory variables and provide actionable insights for both shopkeepers and urban planners.