2026-10-06T08:30:52Z https://api.figshare.com/v2/oai

oai:figshare.com:article/31951983 2026-09-18T11:55:31Z category_25789 item_type_3 month_year_09_2026

Data and Code Zer0 Star (20545655) Computational modelling and simulation in earth sciences Geographically weighted models Spatial statistics methods co-location analysis This repository serves as the accompanying resource for a manuscript submited to International Journal of Geographical Information Science: An explainable multi-level statistical method of cross-population spatial co-location. It provides the data, source code, and supporting documentation associated with the manuscript. And all provided code is compatible with Python 3.11 and 3.12.The Data directory contains all datasets used in the study. The CaseStudy folder includes the 2023 Philadelphia crime event data and urban facility POI data used in the empirical analysis, provided as CSV files, together with the corresponding spatial neighborhood information stored in TXT files. The Synthetic folder contains the two synthetic datasets, Dataset1 and Dataset2, used in the simulation experiments, as well as their corresponding spatial neighborhood information. The Philadelphia folder contains the study area boundary, road network, and water area boundary used for map visualization.The Code directory contains the source code used in the study. The proposed_method folder provides the core implementation of the proposed framework, including the Global Statistical Indicator (GSI), Geographically Weighted Global Statistical Indicator (GWGSI), Local Statistical Indicator (LSI), and Geographically Weighted Local Statistical Indicator (GWLSI). The Synthetic folder contains the Python implementations of the GSI and co-location quotient (CLQ) used in the synthetic experiments, together with the MATLAB script used to reproduce Figure 5. All Python implementations are provided as callable functions for reuse.For reproducibility, two Jupyter notebooks are also included in Code directory: Synthetic.ipynb and CaseStudy.ipynb. Synthetic.ipynb provides the complete executable workflow for reproducing the synthetic experiments and the results reported in Tables 1–3. CaseStudy.ipynb provides the complete workflow for reproducing the case study based on the 2023 Philadelphia crime and urban facility POI data. It reproduces the analyses and numerical results reported in Tables 4–7 and generates the GWLSI results required to reproduce Figures 6–8.Note: Users are recommended to read Instruction.docx before running the code. This document provides an overview of the repository structure, software requirements, and step-by-step instructions for reproducing the figures, tables, figures, and quantitative results reported in the manuscript. 2026-04-08T07:37:52Z Dataset Dataset 10.6084/m9.figshare.31951983.v6 https://figshare.com/articles/dataset/Data_and_Code/31951983 CC BY 4.0