green-15min-city

Greenness attributes for routes and visualization

This readme contains all information to the generating route greens attributes and generating visualisation maps for both central green routes and 15min city

Setup input Greenspaces inputdata

Urban green space data used within the scope of this project is based on existing research and methods by Christina Ludwig.

The source code to produce the urban greenness polygons can be found here: https://github.com/redfrexx/green_index

Behind the following link you find geopackages for Mannheim and Barcelona preprocessed:

heibox link

make sure to put them in the following path project_folder/data/greenness

Setup Python

Python is used with the version 3.8 on a Mac OS. JupyterLab with version 3.4.4 was used for running Jupiter notebook based visualisations. The main libraries comprise:

"gdal","geopandas","pandas","numpy", "shapely" ,"folium" ,"branca"

Run the analysis for extracting greenness attributes for each route of a given city

In the srcpython folder there is a script located:

assign_streets_greenness.py

Provide the following parameters in this script:

-datadir- location of the directory which contains the centrality geopackages for example heibox link

EPSG- UTM EPSG for a given city for example EPSG = 32631 for Barcelona
centrality_threshold - This is used to filter the important or high centrality routes to reduce the workload/size of generated files for example 20, 10 or 5
greenness_gpkg_file - Specify location of the greenness geopackage with calculated green polygons for a city for example heibox link
greenraster - Specify output location of raster that would be generated from the greenness_gpkg_file with spatial resolution 10m
buffersize- specify in m the buffer size for which greenness w.r.t the street segments should be calculated. Default = 25m

1)The script has a function rasterzonalstats that would loop over all the files in the centrality folder and calculate for each file the greenness of the routes/streets based on a buffersize of 25m. 2)Greenness attributes such as mean, median, 25percentile, 75percentile, greenarea_sqm, greenness area coverage w.r.t the buffer area i.e gnar_cov%, mean * gnar_cov% i.e. scaledmean 3)We use scaledmean as a robust indicator for greenness of routes/segments 4)A new folder greencentrality is created in the same directory that contains centrality folder with the new geopackages with same name as original geopackage and with an added ‘_green’. heibox link

Converting geopackages into geojson format for plotting with folium

In folder jupyternotebooks_vis the notebook shp2geojson.ipynb converts the geopackages in greencentrality folder or 15minjson folder to geojson formats.

Provide the following parameters in this script:

-datadir- location of the directory greencentrality or 15mincity that contains the geopackages with greenness attributes calculated for road segments or 15mincity attributes respectively

1) outputdir- Output directory greencentralityjson is generated that contains all the geojson converted files. for example heibox link

Centrality and Greenness Visualisation

In folder jupyternotebooks_vis the notebook routesvis.ipynb generates an html file that is plotted

Provide the following parameters in this script:

greenroutes - Specify the location of the geojson file with greenness attributes in greencentralityjson folder outputfile- - Specify the location of the output html file citycentriodLat - Specify the location of the centroid Latitude for the city visualisation citycentriodLong - Specify the location of the centroid Longitude for the city visualisation

1) Two colormaps are generated for Centrality and Greenness respectively. 2) Base Map is selected as ‘cartodbpositron’ , ‘openstreetmap’ and ‘ESRI Satellite’ for convenience. One could switch between the different basemaps for background reference. 3) For overall 15min City score we use the parameter Centrality_rel i.e. normalized Centrality for visualisation and tooltip allows one to hover over, highlight and see values as popup for each road segment of interest. Higher Centrality is represented by darker colours (towards red)

4) Similarly, for Route Greenness, we use the parameter scaled mean i.e. mean * gnar_cov% (greenness area coverage in %). For visualisation, the tooltip allows one to hover over, highlight and see values as popup for each road segment of interest. Higher Greenness is represented by darker colours (towards green)

Final output as html files is located in data/vis folder and could be opened with any web browser.

15 minute city Visualisation

In folder jupyternotebooks_vis the notebook routesvis.ipynb generates an html file that is plotted

Provide the following parameters in this script:

pts15mincity - Specify the location of the geojson file in 15minjson folder outputfile- - Specify the location of the output html file citycentriodLat - Specify the location of the centroid Latitude for the city visualisation citycentriodLong - Specify the location of the centroid Longitude for the city visualisation

1) A colormap are generated for the 15min city score. 2) Base Map is selected as ‘cartodbpositron’, ‘openstreetmap’ and ‘ESRI Satellite’ for convenience. One could switch between the different basemaps for background reference. 3) For Centrality we use the parameter index_score_adult i.e the total score (max=9 and min = 0) for visualisation and tooltip allows one to hover over, highlight and see the overall and individual breakup scores for each sub-topic as popup for each point of interest. Higher 15 minute readiness score is represented by blue colour while the lower score by red. 4) Different maps are generated for Adults, Children and Elderly

Final output as html files is located in data/vis folder and could be opened with any web browser.