geoai-lab/PyGRF
An improved Python Geographical Random Forest model
This tool helps researchers and practitioners analyze data where location plays a key role, such as in public health or disaster response. You input your spatially referenced data with various attributes and geographical coordinates. The output is more accurate predictions and insights into how different factors locally influence outcomes across different areas, benefiting data scientists or GIS analysts working with spatial information.
No commits in the last 6 months. Available on PyPI.
Use this if you need to make more precise predictions and understand local influences within your geographically distributed data, especially when traditional models fall short due to spatial variations.
Not ideal if your data lacks geographical coordinates or if spatial relationships are not a critical factor in your analysis.
Stars
60
Forks
5
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Aug 19, 2025
Commits (30d)
0
Dependencies
6
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