CrimeGraph

In the CrimeGraph project, I will explore training a graph deep learning model to forecast the probability of crime events in the City of Baltimore, using data provided by SpotCrime.

I plan to adopt the Gated Localised Diffusion (GLDnet) architecture described in the 2020 paper Graph Deep Learning Model for Network-based Predictive Hotspot Mapping of Sparse Spatio-Temporal Events.

Models with this design can learn from the spatial patterns of crime events by representing intersecting city streets as a graph of nodes and edges.

• Street Graph Feature Engineering
• Event Timestep Feature Engineering
• Graph Localized Diffusion Network
• Model training and turning

Source Code

Available on GitHub.