Mitchell Chapter 4
Chapter 4, Mapping Density, shows the mapmaker where the targeted feature is concentrated. Density itself is a ratio, measuring counts (OR amounts) per unit area. Density can be valuable when working with boundaries creating areas of different sizes, like counties or census tracts. Two distinct areas might have the same number of features, like businesses or population, but their difference in size is what determines their densities.
Mapping density is a good way to summarize discrete data. You can plot density graphically as discrete data to get a “bird’s eye view” of feature distribution, then code each area on the map based on the number of features per unit area. This is helpful for understanding overall trends but does not show specific densities within each area boundary. I don’t think this method of mapping is particularly useful for planning; it may be helpful for general trends but not much else. In my opinion, an alternative that seems more ideal is the creation of a density surface with a raster layer. This creates an appearance of continuous shading that transcends boundary lines. Additionally, mapping by features and mapping by feature values can show trends differently. Mapping by features tells you where things are, but feature values (like number of employees) can show trends within the density of the feature.
One thing I didn’t really understand in this chapter was “you often display the dots based on smaller areas but draw the boundaries of larger areas.” In that case, are the dots are not 100% accurately transposed onto the area boundaries? I suppose it doesn’t have to be perfect because the purpose of density maps is just for noticing general trends, not worrying about exact locations.
Finally, this chapter circles back onto topics discussed in previous chapters, like determining the best cell size, ways to separate graduated colors, and contours. I bet that the best method of determining graduated colors depends on each individual map, but in the book’s examples, the natural breaks method seems the most effective.
Mitchell Chapter 5
Chapter 5, Finding What’s Inside, describes ways to look at what is happening inside of a certain area. This area can be on the map boundary already, like a census tract or county, or it can be a natural feature like a watershed, state park, or protected area superimposed onto a layer of preexisting map boundaries.
Density, as discussed in Chapter 4, is a frequent example of “finding what’s inside.” I found it really cool that the GIS can clip out the target area on a map to simplify our view of the continuous data inside of those boundaries, especially when those areas are disjunct. AND it can calculate amounts of land use/type within these specific areas? Sick!
There are three ways to show what mapped boundaries are inside a particular area. You can 1.) superimpose the target area on top of the map’s preexisting features, 2.) highlight parcels with any portion inside of the target area, or 3.) view the target area alone divided into the parcels that make it up. The entire target area is full, and no mapped boundaries beyond the target area are shown. As with most map-related topics, there are merits and drawbacks to each style of mapping here. Drawing the target area on top is a good basic visual, highlighting all included parcels shows a potentially larger scope of effect from the target area, and overlaying the features within the area can help summarize characteristics within the area.
This chapter gives several methods for drawing the target area on top of the map of parcels; the best method of which once again depends on how specific you want your map to be. I like comparing the different methods of color and shading, but all of this study of maps has led me to realize that in many cases, the simpler the map, the better. Using fewer colors and focusing on specific areas typically gives enough surface-level information for a general audience. Then, when more specialized information is needed, conclusions from the more general maps can be used to create the most relevant specific maps. Additionally, GIS software itself can take some of the manual labor out of category-making. One example that seems particularly useful is when one feature on one map splits itself between two features on another layer—the GIS will create two subcategories to split that feature in two.
Mitchell Chapter 6
Chapter 6, Finding What’s Nearby, seeks to help the mapper answer questions like, “What areas will a facility serve?” and “What should the facility expect in terms of service volume?” These questions are affected by “costs” such as distance and time, or literal monetary quantities like gas mileage.
There are three main ways to define analysis of finding what’s nearby: using straight-line distances, finding the distance or cost over a network, or measuring the cost over a surface. As with any other type of map analysis with multiple options, there are times and places for each method.
A straight-line method finds any features within a certain radius of the center. This method provides a quick, simple estimate of features within a spatial constraint, and is often used when determining buffer areas. One type of straight-line mapping that I found particularly interesting was the spider mapping method. This involves drawing straight lines from the center to features within the designated radius. These maps show if there is any skew in location of likely consumers, or if some consumers are in radii of multiple centers and can incite competition. However, this method fails to consider geographical obstacles. A feature may be within the specified distance of one center, but when travel costs are accounted for, another center may be in a more efficient location.
These instances can be mapped by a method considering distance or cost over a network. This type of analysis is typically more considerate of real-world application, and considers the impedance value, or cost to travel from the center to surrounding locations. Some locations may be nearer than others but have higher impedance values, and a cost over a network method takes this into account. An example I found fascinating was taking different kinds of road turns and junctions into account when planning travel costs in terms of time. For example, a turn at a stop sign takes less time than one at a traffic light. A feature may be outside of a straight-line distance radius but have a lower travel cost than another feature within that radius.
Finally, mapping cost over surface is most commonly used for travel over terrain. It’s sort of a mix of the previous two methods: there’s not really an established infrastructure, but geographical land features are accounted for in travel costs. This method uses a raster layer to display continuous data, and the shading can illustrate differences in rates of change across terrain, showing where travel cost increases rapidly or slowly.