Urban events prediction via convolutional neural networks and Instagram data

Abstract

In today’s world, it is crucial to be proactive and be prepared for events which are not happening yet. Thus, there is no surprise that in the field of social media analysis the research agenda has moved from the development of event detection methods to a brand new area - event prediction models. This research field is extremely important for all sorts of applications, from natural disasters preparation and criminal activity prevention to urban management and development of smart cities. However, even the leading models have an important disadvantage: they are based on prior knowledge about events being expected. So forecasting systems based on such models are heavily limited by a list of events that can be predicted and all events of other types will be out of systems’ scope. In this work, we try to address this issue and propose a deep learning model, which is able to predict an area of the future event in the urban environment. This model is able to predict the future state of the city - a level of users activity in the location-based social network Instagram - with the average deviation from the ground truth of 1%, and achieves 69% recall when solving the events prediction problem.