Standardized GVI

Calculate standardized GVI from GVI point data / original road network data / boundary data. The process uses geovoronoi package in order to achieve less-biased estimation of GVI at zonal level.

Prepare a virtual environment

First, create a virtual environment and activate it.

python3 -m venv ~/.sGVI
source ~/.sGVI/bin/activate

Due to dependency of rtree, install spatialindex via homebrew.

brew install spatialindex

Once spatialindex is installed, install all the other libraries, using requirements.txt.

# clone sGVI repository and install remaining dependencies using pip
git clone https://github.com/yusukekumakoshi/standardizedGVI.git
cd standardizedGVI
pip3 install -r requirements.txt

Work flow

Input files are the followings:

• GVI point data
• Original road network data
• Boundary data

Original road network data must be the same network as that you used to calculate GVI in Treepedia.

Boundary data must be shapefile of polygons (any form is accepted).

After setting the paths to those files in sGVI.py, run the following:

python3 code/sGVI.py

Notes

• The parameter crs_common in sGVI.py is the CRS on which the process is done. Default is epsg:4326 (WGS84).

Reference

Kumakoshi, Y., Chan, S. Y., Koizumi, H., Li, X., & Yoshimura, Y. (2020). Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation. Sustainability, 12(18), 7434.