SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
Authors: BANG AN, Xun Zhou, YONGJIAN ZHONG, Tianbao Yang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on three real-world datasets demonstrate that Spatial Rank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-the-art methods in terms of NDCG by up to 12.7%. |
| Researcher Affiliation | Academia | Bang An Department of Business Analytics University of Iowa Iowa City, IA 52242 bang-an@uiowa.edu Xun Zhou Department of Business Analytics University of Iowa Iowa City, IA 52242 xun-zhou@uiowa.edu Yongjian Zhong Department of Computer Science University of Iowa Iowa City, IA 52242 yongjian-zhong@uiowa.edu Tianbao Yang Department of Computer Science and Engineering Texas A&M University College Station, TX 77843 tianbao-yang@tamu.edu |
| Pseudocode | Yes | Algorithm 1: Spatial Rank Training |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or explicitly state that the code is publicly available. |
| Open Datasets | Yes | We perform comprehensive experiments on three real-world traffic accident and crime datasets from Chicago2 and the State of Iowa3. 2https://data.cityofchicago.org/Transportation/Traffic-Crashes-Crashes/85ca-t3if 3https://icat.iowadot.gov/ |
| Dataset Splits | Yes | In the Chicago dataset, we collect data from the year 2019 to the year 2021. The first 18 months of this period are used as the training set, and the last 6 months of 2020 are used as the validating set. The year 2021 is used as a testing set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |