Multi-Type Urban Crime Prediction
Authors: Xiangyu Zhao, Wenqi Fan, Hui Liu, Jiliang Tang4388-4396
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets validate the effectiveness of our framework. In this section, we conduct extensive experiments to evaluate the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | Xiangyu Zhao1, Wenqi Fan2 , Hui Liu3, Jiliang Tang3 1City University of Hong Kong, 2The Hong Kong Polytechnic University, 3Michigan State University |
| Pseudocode | No | The paper describes optimization steps using mathematical equations, but it does not present them as a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The data of K=7 types of crime is collected from 07/01/2012 to 06/30/2013 (T=365 days) in New York City. We respectively segment the city into disjointed 2km 2km grids (regions), and select N=100 regions with the most reported crimes. For the feature matrices, we collect multiple data resources that are related to crime: historical crime, stop-and-frisk, weather, Point of Interests (region function), human mobility and 311 public-service complaint data. No specific links or formal citations for public access are provided. |
| Dataset Splits | No | The paper mentions training and test samples, and uses cross-validation for parameter selection, but does not specify a distinct validation dataset split with proportions or counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using the ADMM technique and references related work, but it does not specify any software names with version numbers (e.g., Python version, specific libraries with versions). |
| Experiment Setup | Yes | We select parameters of the proposed framework such as α, β, γ, ρ and σ by cross-validation; (ii) For a fair comparison, we conduct parameter-tuning for baselines. More details about parameter selection will be discussed in following subsections. CCC achieves the best performance when α = 2 for 1-day prediction, while α = 3 for 7-day prediction. The performance achieves the peak when β = 1.25 for 1-day prediction and β = 1 for 7-day prediction and CCC approaches the best performance when γ = 0.5 for both 1-day and 7-day prediction. |