Once we have the data we want to map, we can create a Kepler Map. def count_incidents_neighborhood(data, neighb): # spatial join and group by to get count of incidents in each poneighbourhood joined = gpd.sjoin(gdf, neighb, op=”within”) grouped = oupby(‘ntaname’).size() df = grouped.to_frame().reset_index() df.columns = merged = rge(df, on=’ntaname’, how=’outer’) merged.fillna(0,inplace=True) merged = merged.astype(int) return merged merged_gdf = count_incidents_neighborhood(gdf, Neighborhood) I have a created a function that does that: spatial join, group by and then return a Geodataframe. I download the neighbourhood file and perform a simple spatial join to count out how many incidents are within each neighbourhood. Let us say we are interested in aggregating the data into neighbourhoods. The beauty of this is that you get the Kepler’s functionality without leaving Jupyter notebook. Time-series animation Process your data, visualise and Never leave Jupyter notebook

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