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A platform for research: civil engineering, architecture and urbanism
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
Highlights We propose a gated localised diffusion network for predictive hotspot mapping of sparse spatio-temporal events on networks. The temporal spread of events is modelled by a gated network. The spatial spread of events on networks is modelled by a localised diffusion network. A weighted loss function is employed to train the model to tackle the sparsity problem. An empirical study using crime data in the South Chicago, USA validates the effectiveness of the proposed model.
Abstract 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.
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
Highlights We propose a gated localised diffusion network for predictive hotspot mapping of sparse spatio-temporal events on networks. The temporal spread of events is modelled by a gated network. The spatial spread of events on networks is modelled by a localised diffusion network. A weighted loss function is employed to train the model to tackle the sparsity problem. An empirical study using crime data in the South Chicago, USA validates the effectiveness of the proposed model.
Abstract 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.
Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events
Zhang, Yang (author) / Cheng, Tao (author)
2019-09-03
Article (Journal)
Electronic Resource
English
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