Chapter 4.
This chapter covers mapping density and the process of deciding how to do so. The primary use of density mapping is to determine patterns and groups, rather than analyze individual data points. Density is mapped by finding the amount of something within a consistent unit of space. It could be features or feature values, which will often present different results and patterns.Â
Thereâs a few different methods to mapping density.
One of the ways of mapping density is mapping by defined areas. Typically, you could map features on a surface using dots, where each dot represents 1 feature. In certain scenarios, however, this can become too cluttered to discern some important patterns. Instead, the dots could be defined by certain areas (legal borders, zip codes, etc.) and then randomly redistributed throughout their area, which gives a good sense of which areas are more densely packed with this feature. This can also be done by adding each feature in an area and assigning ranges of number of features to a colour, then give each area their respective colour based on this range.Â
Another way is density surface maps. This is a method using raster cells instead of other defined areas like what would be with vector boundaries. Density surface takes each raster and measures how many features fall within a certain radius of that raster, then assign a value (and typically colour) to it based on the amount. That radius is called the âSearch Radius.â Often, the search radius is weighted so that closer points to the cell have a higher value than further ones. Iâm assuming that it uses a Gaussian function for the weighting, or at least an approximation of a Gaussian function. This is inherently similar to the Kuwahara filter, which is a noise-reduction filter. Itâs used to discern patterns out of impossible-to-read, noisy scannings of peopleâs heart muscles by dividing an image into cells and giving each cell a weighted search radius.
This chapter further expands on the ideas surrounding cell size in order to properly show patterns without generalizing to becoming choppy, while still not taking much storage space.Â
Iâm not entirely sure why the numbers for the class ranges are chosen for the natural breaks and quantile methods. It seems exponential, but also partly just random and I don’t fully understand the choices.
Chapter 5
 Chapter 5 is all about mapping areas. This can be used to determine where is the best place to place something, or to see what is possibly close enough to something to affect it. These can be in a radius around a point, in a radius surrounding a line, in a manually created bordered area (such as property line/zip code).
There are two main types of values to assign to features: Discrete and Continuous. Discrete are things that are unique and separated clearly by a point or border. Continuous are features that are not separated like this, but instead have a continuous path from one data point to another in all places. This is things like density or elevation maps. Knowing what features are all within a certain area, such as other areas or single points, creates a better understanding of the area and allows determining how the features may affect the area.Â
When dealing with features that have a distance or area that are within an area, mappers can choose to only select features that are fully inside the area, partially inside, or just only consider the parts of the features inside the area. There are different applications for each, I would assume, such as streams in the area of dangerous chemical spill. It would be useful to map teh entirety of streams that fall at all within the area of the spill, because likely they would be affected downstream since the water may carry the contaminants. The overlay method would be very useful for legal things like property lines, since its not important to know the rest of the are, just where it overlaps.Â
You are also able to find the features of features/areas within marked areas, and find values, frequencies, densities, averages, or summaries.
I feel as if the shaded areas outside the highlighted areas would be especially difficult to see, and I have a hard time discerning which is which colour.
I feel as though there would not be many uses for taking the average elevation of watersheds in a scenario like the one shown where they overlay a continuous elevation graph over watershed boundaries. There seems to be greatly varied elevation within the watershed boundaries, with lower land on one side and very high land on the other. I suppose that over a very large map that contains many many watersheds could be more useful as to generalize elevation.Â
Chapter 6
This chapter covers how to find and analyze things within a certain distance or time of a location. Determining time or distance is considerably more complex when itâs regarding how something like a person or animal would actually get to that location. People can only drive on roads, and there arenât always direct roads to a location. Additionally, stops and traffic lights could influence how long it takes to get to somewhere. Steep elevation or dense forest could influence how deer may travel across land, and direct paths may not be short or possible.
The straight line distance system seems like it works fine for many situations, especially ones where short time/distance is not immediately important.
The âbufferâ for straight line distances can be placed around lines or shapes in addition to just points. Additionally, you can have multiple lengths of buffer lines for different classes of points/lines/shapes, and they can be combined where they overlap.
Earthâs curvature causes maps to become distorted at large scales, and so this is something that has to be taken into account when working with large maps and distances.
Itâs also possible to determine the exact distance between two features (usually a feature and a source) within a selected range. With multiple sources, one can find whichever source is the closest to the point you want to determine, and then finding the distance to that and classifying it based on that one. The distance from the further ones will not affect itâs distance to one.
The algorithm used to determine distance across networks was considerably more simple than I thought it would be.Â
Using cost layer, you can determine the cost to travel or expand to that location, for things like roads.