Deep multi-task learning for air quality prediction

© 2018, Springer Nature Switzerland AG. Predicting the concentration of air pollution particles has been an important task of urban computing. Accurately measuring and estimating makes the citizen and governments can behave with suitable decisions. In order to predict the concentration of several air pollutants at multiple monitoring stations throughout the city region, we proposed a novel deep multi-task learning framework based on residual Gated Recurrent Unit (GRU). The experimental results on the real world data from London region substantiate that the proposed deep model has manifest superiority than shallow models and outperforms 9 baselines

Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively

Learning to rank spatio-temporal event hotspots

Background Crime, traffic accidents, terrorist attacks, and other space-time random events are unevenly distributed in space and time. In the case of crime, hotspot and other proactive policing programs aim to focus limited resources at the highest risk crime and social harm hotspots in a city. A crucial step in the implementation of these strategies is the construction of scoring models used to rank spatial hotspots. While these methods are evaluated by area normalized Recall@k (called the predictive accuracy index), models are typically trained via maximum likelihood or rules of thumb that may not prioritize model accuracy in the top k hotspots. Furthermore, current algorithms are defined on fixed grids that fail to capture risk patterns occurring in neighborhoods and on road networks with complex geometries. Results We introduce CrimeRank, a learning to rank boosting algorithm for determining a crime hotspot map that directly optimizes the percentage of crime captured by the top ranked hotspots. The method employs a floating grid combined with a greedy hotspot selection algorithm for accurately capturing spatial risk in complex geometries. We illustrate the performance using crime and traffic incident data provided by the Indianapolis Metropolitan Police Department, IED attacks in Iraq, and data from the 2017 NIJ Real-time crime forecasting challenge. Conclusion Our learning to rank strategy was the top performing solution (PAI metric) in the 2017 challenge. We show that CrimeRank achieves even greater gains when the competition rules are relaxed by removing the constraint that grid cells be a regular tessellation