Validation for government unit associated incident density as department jurisdiction

[meilinger]

In order to prove out the ability to use government units for department borders, we need to determine the fit of existing government units to known-good (eg. verified boundaries).

IPython notebook: For all departments that have a verified boundary, find all government units that intersect with the department headquarters (buffered by an amount). For each of those government units, sum the # of intersecting geocoded incidents and divide by area of the government unit. Government units with highest incident/area density will be ranked highest as candidates for department jurisdiction boundaries and should be compared against the existing validated jurisdictional boundary for our test set (% difference in area, % difference in centroid). Ideally, the most-dense candidate government unit will match the validated jurisdiction exactly if we hope to make this process programmatic.

If this test proves mostly-accurate, then we can continue to flesh out a system to more easily assign government units as boundaries by humans (and/or programmatically).

@cweinschenk @garnertb this was what I was thinking.

[cweinschenk]

The next logical step then would be to tie stations that exist inside those boundaries to the departments.

[meilinger]

For transparency, the next step with this is to cull out outlier incidents and perform a % inside vs outside of government units that are "close" to the department headquarters (or close to the median centroid of geocoded incidents associated w/ the department). Ideally, the govt unit w/ the highest percentage of contained incidents would be geospatial object chosen as the boundary for the department.

Prove this out using existing verified boundaries litmus test for the calculus.