Crime Prediction

10 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

San Francisco Crime Classification

PranotiDesai/San-Francisco-Crime-Classification 13 Jul 2016

San Francisco Crime Classification is an online competition administered by Kaggle Inc.

AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction

YeasirRayhanPrince/aist 16 Dec 2020

Accuracy and interpretability are two essential properties for a crime prediction model.

Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction

echoyi/MAPSED 5 Oct 2021

We additionally introduce a novel Frobenius norm-based contrastive learning objective to improve latent representational generalization. Empirically, we validate MAPSED on two publicly accessible urban crime datasets for spatiotemporal sparse event prediction, where MAPSED outperforms both classical and state-of-the-art deep learning models.

Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions

sftekin/high-res-crime-forecasting 29 Nov 2021

Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions.

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

akaxlh/ST-SHN IJCAI 2021

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.

Multi-Graph Fusion Networks for Urban Region Embedding

wushangbin/mgfn 24 Jan 2022

Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

lzh-ys1998/sthsl 18 Apr 2022

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.

Explainable Spatio-Temporal Graph Neural Networks

hkuds/stexplainer 26 Oct 2023

Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.

Spatio-Temporal Meta Contrastive Learning

hkuds/cl4st 26 Oct 2023

Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios.