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I would first make a join of the two tables crime and population. Then I would add another column where I calculate the crime case per population, simply divide the crime case column by the population coloumn. Works if the numbers refer to the exact same areas. Then you can visualize this column in a choropleth map and immediately see where the crime is ...


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Start by adding a new field to determine whether "time of occurrence" is "day" or "night" to simplify subsequent analysis. The simplest approach -- and always a good start -- would be to symbolize daytime/nighttime incidents differently, and visually examine the data. Does it look like there is a visible difference, are there any patterns? A statistical ...


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Given your initial thoughts I'd take a look at scipy.cluster.hierarchy to start with (or the equivalent in scikit-learn) to build the clusters based on distance. For instance, given a numpy.array of coordinates - coords you could build a cluster based on distance like so: import numpy as np import scipy.cluster.hierarchy import collections labels = ...


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scikit-learn has an extensive clustering library with many different methods available. As a bonus scikit-learn is one of the best documented Python libraries I've seen. When working with 3d point clouds I've had a lot of success with DBSCAN for instance. Alternately as @Fezter suggests above, scipy offers two different methods of clustring: k-means ...



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