{"id":29633628,"url":"https://github.com/ziqi-li/fastgwr","last_synced_at":"2025-07-21T14:05:59.706Z","repository":{"id":41446284,"uuid":"130419885","full_name":"Ziqi-Li/FastGWR","owner":"Ziqi-Li","description":"Fast Geographically Weighted Regression (FastGWR)","archived":false,"fork":false,"pushed_at":"2022-03-17T10:18:31.000Z","size":15270,"stargazers_count":57,"open_issues_count":2,"forks_count":18,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-07-14T19:56:26.232Z","etag":null,"topics":["gwr","spatial","spatial-statistics"],"latest_commit_sha":null,"homepage":"https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1521523","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ziqi-Li.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-04-20T22:38:16.000Z","updated_at":"2025-07-08T12:26:18.000Z","dependencies_parsed_at":"2022-08-01T00:48:03.844Z","dependency_job_id":null,"html_url":"https://github.com/Ziqi-Li/FastGWR","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/Ziqi-Li/FastGWR","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ziqi-Li%2FFastGWR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ziqi-Li%2FFastGWR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ziqi-Li%2FFastGWR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ziqi-Li%2FFastGWR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ziqi-Li","download_url":"https://codeload.github.com/Ziqi-Li/FastGWR/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ziqi-Li%2FFastGWR/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266315763,"owners_count":23909802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-21T11:47:31.412Z","response_time":64,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gwr","spatial","spatial-statistics"],"created_at":"2025-07-21T14:05:57.173Z","updated_at":"2025-07-21T14:05:59.692Z","avatar_url":"https://github.com/Ziqi-Li.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"![PyPI](https://img.shields.io/pypi/v/fastgwr)\n![GitHub](https://img.shields.io/github/license/Ziqi-Li/fastgwr)\n\n# FastGWR\nA command line tool for fast parallel computation of Geographically Weighted Regression models (GWR and MGWR).\n### New feature:\nMulti-scale GWR model added!\n\n### Installation:\n\nThe `fastgwr` program is dependent on `mpi4py` package and a working MPI implementation. The easiest way to install both dependencies is to use `conda`:\n\n```bash\n$ conda install mpi4py\n```\n\nBy installing `mpi4py`, `conda` will also install an MPI implementation based on your computer system (OpenMPI for Mac/Linux; MPICH/MS-MPI for Windows). Users may want to check whether the MPI implementation is successfully installed and is on your path by running the `mpiexec` command. Then the `fastgwr` program can be installed from PyPi:\n\n```bash\n$ pip install fastgwr\n```\n\nAfter sucessful installation, users can test the functionalities from the command line by running:\n\n```bash\n# Using zillow sample data for testing MGWR model fitting.\n$ fastgwr testgwr\n```\nor\n\n```bash\n# Using zillow sample data for testing MGWR model fitting.\n$ fastgwr testmgwr\n```\n\n\n## Examples\nExample call to the `fastgwr` to fit GWR model:\n\n```bash\n$ fastgwr run -np 4 -data input.csv\n```\n\nExample call to the `fastgwr` to fit MGWR model:\n\n```bash\n$ fastgwr run -np 4 -data input.csv -mgwr\n```\nwhere:\n\n```bash\n-np 4             Number of processors (e.g. 4).\n-data input.csv   Input data matrix. (e.g. input.csv)\n                  Can also be an URL (e.g. https://raw.github.com/\n                  Ziqi-Li/FastGWR/master/Zillow-test-dataset/zillow_1k.csv)\n-out results.csv  Output GWR results matrix including local parameter \n                  estimates, standard errors and local diagnostics.\n-adaptive/-fixed  Adaptive Bisquare kernel (defualt) or Fixed Gaussian kernel.\n-bw 1000          Pre-defined bandwidth parameter. If missing, it will\n                  search (golden-section) for the optimal bandwidth and use\n                  that to fit the GWR model.\n-minbw 45         Lower bound in golden-section search. (e.g. 45)\n-mgwr             Fitting an MGWR model.\n-chunks           Number of chunks for MGWR computation (set to a larger \n                  number to reduce memory footprint).\n-estonly          Allowing MGWR to output parameter estimation only.\n```\n\nThe input needs to be prepared in this order:\n\n|   | X-coord | y-coord | y    | X1  | X2  | X3  | Xk  |\n|---|---------|---------|------|-----|-----|-----|-----|\n|   | ...     | ...     | ...  | ... | ... | ... | ... |\n|   | ...     | ...     | ...  | ... | ... | ... | ... |\n|   |         |         |      |     |     |     |     |\n\n```\nwhere:\nX-coord: X coordinate of the location point\nY-coord: Y coordinate of the location point\ny: dependent variable\nX1...Xk: independent variables\n```\nSee the example Zillow datasets in the repository.\n\n## Results Validation\n\nThe results are validated against the [mgwr](https://github.com/pysal/mgwr), which can be seen in the [notebooks here](https://github.com/Ziqi-Li/FastGWR/tree/master/validation%20notebook).\n\n\n## Citations\n\nThis program is developed based on these two papers:\n\n[FastGWR](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1521523)\n\nLi, Z., Fotheringham, A. S., Li, W., Oshan, T. (2019). Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographic Information Science. doi: 10.1080/13658816.2018.1521523.\n\n[FastMGWR](https://www.tandfonline.com/doi/abs/10.1080/13658816.2020.1720692)\n\nLi, Z., \u0026 Fotheringham, A. S. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), 1378-1397.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fziqi-li%2Ffastgwr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fziqi-li%2Ffastgwr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fziqi-li%2Ffastgwr/lists"}