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Novel Visualization Suite for Multiscale Geographically Weighted Regression (MGWR)

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MGWRVisualizer 🕸️🗺

Netlify Status

Objective

Learning Multiscale Geographically Weighted Regression (MGWR) can prove difficult and unaccesible. This website + clients aim to address this problem by creating easy-to-understand interactive visualizations of MGWR properties.

Deployed Test Site

Project Status

  • Python client - ✔️
    • visualize with local server ✔️
    • save results to upload to web client ✔️
  • Web client - 🟨 functional
    • pre-built datasets to visualize - ❌
    • upload dataset to visualize - ❌

Current Features

  • Spatial viz of covariate bandwith
  • Spatial viz of attribute bandwith
  • Dynamic chart of signifigant parameters at 95th confidence
  • Model results
  • Diagnostic information

Work in Progress

  • viz model fit based on AIC versus bandwith

Python Client

The Python client can do 2 main things

  • Run a local mgwrvisualizer webclient server
  • Export formatted MGWR result objects for later visualization

Install

pip install mgwrvisualizer

Usage

See examples for full example Jupyter notebooks

Running local server

from mgwrvisualizer import MGWRVisualizer

data_df = pd.DatFrame ...  # non-spatial dataframe
geodata_df = gpd.GeoDataFrame ...  # spatial dataframe
mgwr_results = MGWR(coords, _y, _X, mgwr_selector).fit()

viz = MGWRVisualizer(mgwr_results, data_df, geodata_df, merge_key="AreaKey")

viz.run()  # run server

>> browser opens to local server

Saving results

You can save a formatted version of the MGWR model results. You can then upload this file to the webclient for later visualization (in-progress)

from mgwrvisualizer import MGWRVisualizer

data_df = pd.DatFrame ...  # non-spatial dataframe
geodata_df = gpd.GeoDataFrame ...  # spatial dataframe
mgwr_results = MGWR(coords, _y, _X, mgwr_selector).fit()

viz = MGWRVisualizer(mgwr_results, data_df, geodata_df, merge_key="AreaKey")

viz.save_results("mgwr-results-file.json") 

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Novel Visualization Suite for Multiscale Geographically Weighted Regression (MGWR)

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