JR007
Helper II
Helper IISmart Cities of Tomorrow Notebook
The Notebook contains Data Cleaning , EDA and Advanced Analysis for the Smart Cities of Tomorrow Notebook contest - MS Fabric Nov 2025. https%3A%2F%2Fgithub.com%2FJJTech357%2FCities-of-Tomorrow
odeddrorHelper II
Helper IINice work
In section 4 Creating 4 New Key Grouped Features for Urban Spatial Data
How did you calculate Calculated based on Land use type, building density, road connectivity, public transport access?
The mean for example (I combined the three together but I got wrong result) ?
Thanks,
Oded Dror
JR007Add up the fields and take the average of them. Make sure it is normalised between 0 and 1 for ease of comparison with other scores.
# Raw (unscaled) score: simple average of components df_urban_planning["urban_spatial_raw"] = spatial_components.mean(axis=1) # Normalised score [0, 1] df_urban_planning["urban_spatial_score"] = min_max_normalize( df_urban_planning["urban_spatial_raw"] )- odeddror
Thanks