Open jGaboardi opened 1 week ago
Hello, to clarify my issue. Here is my project. I have a geographical aera divided into multiple cities (for each city I have a polygon geometry). Each city is characterised by a metric A (for instance number of clients) and metric B (number of prospects). I have a number of N salesmen. I try to create N contiguous region/cluster such as each cluster/salesman has a number of client similar and the a number of prospect similar too. I might have others objectives in the future.
After some researches I found that the methods "Max-p-regions" or "Skater" could resolve my problem. I tried to understand the demonstration made with Spatial ‘K’luster Analysis by Tree Edge Removal: Clustering Airbnb Spots in Chicago in spopt documentation.
At the end of this demonstration, differents number of clusters are tested. For each tests, the number of Airbnb spots by created cluster is computed. However I thought the distribution of number of Airbnb spots intercluster (column "num_spots" would be balanced. Here is my misunderstanding.
I used Skater on my project and try to minimize these two metrics while having contiguous regions without any success:
'client_std' = gdf.groupby(region_id)['number_clients'].sum().std() 'prospect_std' = gdf.groupby(region_id)['number_prospects'].sum().std()
with gdf, my geodataframe.
May I have misunderstood the use of the model ? Maybe I have to find out the right combination of parameters (floor, trace, center,...) ?
Thank you very much for your help.