May 2019
Beginner
170 pages
4h 9m
English
Content preview from Geospatial Data Science Quick Start Guide
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Spatial similarity and spatial weights
Here, we will use queen weights to construct the spatial similarity of this dataset with PySAL:
wq = weights.Queen.from_dataframe(final_result)wq.transform = 'r'
We then apply the weights to the crime counts variable and add the spatial similarity as a column to our final results GeoDataFrame:
y = final_result['CrimeCount']ylag = weights.lag_spatial(wq, y)final_result['ylag'] = ylag
Let's map out the choropleth of the crime data with a spatial similarity:
fig, ax = plt.subplots(figsize=(12,10))final_result.plot(column='ylag', scheme='Quantiles', k=5, cmap='YlGnBu', legend=True, ax=ax);plt.tight_layout()ax.set_axis_off()plt.savefig('choroplethmap-ylag.png')plt.title('Crimes Ylag Choropleth Map ')plt.show() ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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