We began by plotting the distribution of each of the original crime types in our dataset. As we can see below, larceny had the highest amount of offenses:

Next we plotted the distributions of the number of streetlights within an 80m radius by crime types. We observe many outliers, and high variability among the crime types. There is particularly high variability in drug crimes and larceny crimes.

Below we see the distribution of average property values ( in $ millions) within three blocks from each crime by crime type. We can see very high variability for of larceny. Drugs crimes also have a high variability.

Next we observe the distribution of distance from the nearest police station by crime type. Interestingly, we observe that drug-related crimes have a lower average distance to the nearest police station than other crime types.

We do not see much separation in distances to the nearest public school, private school, library, or community center based on crime type:

Below we see the distribution of distances from the nearest college or university. Interestingly, larceny had the lowest average distance:

The plot below shows the crime counts split up by day and night hours. Night consisted of hours between 9pm and 4am. We can see that larceny and drug-related crimes occurred proportionally more frequently during the day than at night.

Original Categories: Models >> Results

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