@dra2020/compactness

compactness

This is a library of routines to calculate classic measures of compactness -- Reock, Polsby–Popper, Convex Hull, and Schwartzberg -- as well as the other SmartFeatures in Kaufman, King, and Komisarchik's "know it when you see it" (KIWYSI) compactness model that replicates how people assess compactness (paper, supplement).

NOTE: This functionality has been folded into the dra2020/dra-analytics repo along with other analytics used in Dave's Redistricting.

Exports

There are 7 features in the simplified KIWYSI PCA model:

• The first 4 measures below are computed using geometric properties of a shape that have been extracted previously, and
• The last 3 take the polygon directly

Extracting the geometric properties of district shapes allows their compactness to be computed later when the full shapes themselves are no longer available. Use the poly package to extract the geometric properties. The properties themselves can either use geodesic (curved earth) or cartesian (flat earth) calculations.

The final routine -- scoreShapes -- scores each shape in a GeoJSON feature collection using the simplified KIWYSI compactness model.

calcReock (REOCK)

export declare function calcReock(area: number, diameter: number): number;

Reock is the primary measure of the dispersion of district shapes, calculated as “the area of the distric to the area of the minimum spanning circle that can enclose the district.”

R = A / A(Minimum Bounding Circle)
R = A / (π * (D / 2)^2)
R = 4A / πD^2

where A is the area of the district and D is the diameter of the minimum bounding circle.

calcPolsbyPopper (POLSBYPOPPER)

export declare function calcPolsbyPopper(area: number, perimeter: number): number;

Polsby-Popper is the primary measure of the indendentation of district shapes, calculated as the “the ratio of the area of the district to the area of a circle whose circumference is equal to the perimeter of the district.”

PP = A / A(C)

where C is that circle. In other words:

P = 2πRc and A(C) = π (P / 2π)^2

where P is the perimeter of the district and Rc is the radius of the circle.

Hence, the measure simplifies to:

PP = 4π * (A / P^2)

calcConvexHullFeature (Hull(D))

export declare function calcConvexHullFeature(area: number, chArea: number): number;

Convex Hull is a secondary measure of the dispersion of district shapes, calculated as “the ratio of the district area to the area of the minimum convex bounding polygon(also known as a convex hull) enclosing the district.”

CH = A / A(Convex Hull)

where a convex hull is the minimum perimeter that encloses all points in a shape, basically the shortest unstretched rubber band that fits around the shape.

Note: This is not the convex hull, but rather a metric based on it.

calcSchwartzberg (SCHWARTZBERG)

export declare function calcSchwartzberg(area: number, perimeter: number): number;

Schwartzberg is a secondary measure of the degree of indentation of district shapes, calculated as “the ratio of the perimeter of the district to the circumference of a circle whose area is equal to the area of the district.”

Azavea defines Schwartzberg as:

S = 1 / (P / C)

where P is the perimeter of the district and C is the circumference of the circle. The radius of the circle is:

Rc = SQRT(A / π)

So, the circumference of the circle is:

C = 2πRc or C = 2π * SQRT(A / π)

Hence:

S = 1 (P / 2π * SQRT(A / π))
S = (2π * SQRT(A / π)) / P

This feature matches the verbal description of P / C(feature_helpers.R). So, use P/C, not C/P as Azavea describes.

calcYSymmetry (Y-SYMMETRY)

export declare function calcYSymmetry(poly: any): number;

The area of a district overlapping with its reflection around a vertical line going through the centroid, divided by the area of the district. Values range [1–2].

calcXSymmetry (X-SYMMETRY)

export declare function calcXSymmetry(poly: any): number;

The same as Y-SYMMETRY except reflect the district around a horizontal line going through the centroid.

calcBoundingBox (BOUNDING-BOX)

export declare function calcBoundingBox(poly: any): number;

Here this is defined as the ratio of the area of the district to the area of the minimum bounding box of the district. It's not a simple bounding box!

scoreShapes

export declare function scoreShapes(shapes: GeoJSON.FeatureCollection, pca: T.PCAModel, options?: Poly.PolyOptions): number[];

Takes a GeoJSON feature collection of shapes and returns an array of 1–100 KIWYSI compactness scores. For each shape, it:

• Calculates the 7 features above
• Then applies the simplified KIWYSI PCA model, and
• Produces a 1–100 KIWYSI compactness score

Note: These are ranks where small is better.

Versions

• 1.0.6 -- First public version

Repo contents

There are three packages that are part of this repo:

1. src: building compactness.bundle.js, the production code
2. cli: building cli.cs, a command line utility to run compactness
3. test: automated test code run by jest

Developing in this repo

npm install install all dependencies

npm run build build all bundles

npm run test run automated jest tests

Build status for master branch