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points v. polygons

lindbrook 2019-05-12

There are two types of “expected” pump neighborhood area plots. The first uses points(); the second uses polygon().

plot(neighborhoodWalking(vestry = TRUE, case.set = "expected"),
  type = "area.points")

plot(neighborhoodWalking(vestry = TRUE, case.set = "expected"),
  type = "area.polygons")

why two?

The reason for having two types is that while the points() based approach is computationally faster, the polygon() approach has vector graphics on its side. In certain applications, when you zoom in you’ll see the granularity of the points() based approach:

streetNameLocator("marshall street", zoom = TRUE)
addNeighborhoodCases(observed = FALSE, pch = 15, point.size = 1.25)

streetNameLocator("marshall street", zoom = TRUE)
addNeighborhoodWalking()

how to compute neighborhood area plots

For both types, I use “expected” data to compute the expected neighborhood area. Using sp::spsample() and sp::polygon(), I place 20K regularly-spaced points across the face of the map (in the “real world”, points are approximately 6 meters apart.).[1]

sp::spsample(sp::Polygon(map.frame[, c("x", "y")]), n = 20000, type = "regular")

For each simulated case, I compute the closet water pump using Euclidean or walking distances. The details are in simulateFatalities(), which is located in simulateFatalities.R.

This function classifies the “expected” or simulated cases by their pump neighborhood. By coloring the points by “their” pump, the different pump neighborhoods emerge.

how to compute polygon vertices

To use polygon(), we need the vertices. To my knowledge, this is not a simple, straightforward task.

Using the case of pump the Broad Street pump (#7) and walking distance as the measure of proximity, I illustrate how I approach the problem. By my calculation, 1,709 of the simulated cases fall in the Broad Street pump neighborhood:

While the convex hull may be the first thing that comes to mind, it’s not a good general solution. This is because the convex hull creates a polygon based on the most outlying points. As a result, when there are concavities, points outside the neighborhood will fall within the resulting polygon:

To find the vertices of the pump neighborhood polygon, I’d argue that what we want is to use the points along the perimeter to serve as the vertices of the neighborhood’s polygon. To identify these points, I select the point that do not have neighbors at all of the four cardinal directions (i.e., North, South, East and West). See peripheryCases().

The final task is to connect the dots in the “right” order (no overlapping edges). Essentially, we want to add pearls to a string to form the polygon.

String of pearls via the traveling salesman problem.

My current working solution is travelingSalesman(). This functions uses the ‘TSP’ package and its implementation of repetitive nearest neighbors to compute the string of pearls.

neighborhood <- neighborhoodWalking(-6, case.set = "expected", vestry = TRUE)
plot(neighborhood, type = "area.polygons")

why 20K observations?

Of equal, if not greater importance is the density of simulated cases. I found that the algorithm can fail by getting stuck in dead ends or by skipping over points. As is often the case, more data helps. As a tradeoff between computational speed and functional robustness, I ended up using 20K simulated cases.

Expected areas v. expected roads.

plot(neighborhoodWalking(case.set = "expected"))

While easier to “read”, the area plots are potentially misleading. Conceptually, the problem is that they take the focus away from roads and puts it on regions, which may not be meaningful because they can represent locations where there are no roads or residences. Computationally, the problem is that the shape of a neighborhood will be sensitive to how we determines a case’s street address. Different implementations can produce different results.

notes

  1. Because the map frame is not rectangular, the actual number of points is 19,993.
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