Chapter Four
Density mapping is a useful tool, especially for analyzing and tracking patterns in a data set. As Mitchell introduced this topic to the reader, I wondered which field of study uses density maps the most, as density maps could be useful in many types of measurements across multiple fields. As population density is a common measurement to map and analyze, I wonder what interdisciplinary work is performed by both geographers and other unrelated fields such as demographers, epidemiologists, other health fields, or economists. Additionally, I thought about what types of data would not benefit from density mapping, or the specificity needed for a density map based on what the target demographic of the map is. In the next section, Mitchell highlights that density by defined area is better for comparing data with defined areas, with density by density surface is better for seeing the patterns or lines of data in detail. Something I found interesting in this chapter is learning that the GIS software places dots randomly in areas of a density map, and clarifies that density maps do not directly correlate with features unless adjusted. Additionally, another interesting fact is that GIS doesn’t have a set distance unit, so users can use miles, kilometers, acres, etc.
Important vocabulary for this chapter:
- Density of features: locations, broad data to show areas of importance. Ex: locations of a chain grocery store
- Feature values: the values within a density of features, usually more specific. Ex: employees who work at the chain grocery store
- Density by defined area: calculated by dividing total number of features/total value of features by the area of the polygon containing the feature. Calculated with [op_density = total_pop / (area / 27878400)]
- Density by density surface: calculated by each cell in a raster layer in GIS getting a density value. Usually more specific, but more effort.
Chapter Five
In this chapter, Mitchell starts by highlighting the importance of monitoring and mapping the insides of an area, especially in emergency situations such as chemical spills or other disasters, either natural or human-influenced. To define a data analysis of an area, Mitchell emphasizes the importance of defining a single area or multiple areas. In a single area, one county, geographic area, radius around a central point, or another relatively small area is drawn in order to fully define and understand the data analysis. In a multiple area analysis, areas are drawn through geographic boundaries, around multiple counties, or through different habitats. In the next section, Mitchell highlights steps on how exactly to merge and analyze both discrete and continuous features, specifically floodplains and and use types in this example: Find out whether a particular parcel is inside the floodplain, get a list of all parcels inside the floodplain, count the number of parcels inside the floodplain, use a map of the land and its uses to get an exact reading of which parcels of land used for different purposes are affected by the preexisting floodplain. Thinking on this topic, I wonder how exactly cities were planned pre-GIS technology, or if they were planned at all, especially cities that face heavy precipitation and rely on floodplains to prevent flooding. Additionally, I wonder if GIS technology helps decide different factors of historic buildings, such as which buildings are weather or waterproofed, which historic buildings are moved altogether, and which buildings have to be torn down or otherwise modified.
Important vocabulary for this chapter:
- Discrete features: unique, identifiable features that can be counted quantitatively. Ex: locations, animal nests, rivers, or parcels of land
- Continuous features: features that cross set boundaries such as administrative boundaries. Ex: map of participation, soil types, or elevation.
Chapter Six
Chapter Six is dedicated to the importance of mapping nearby features, which initially makes me think back to the earlier chapters, where Mitchell specifically highlights the importance of using well-known landmarks in an area to make a map more legible to a larger audience. Closeness, being a relative scale, can be tricky to map. Mitchell suggests using highly traveled areas or other factors in order to map closeness, for example, mapping closeness by mapping between two cities that are commonly traveled between. Additionally, something nearby can be measured not only by distance, but also by cost, such as land types that cost the same to travel to, or two areas that cost relatively the same to travel to. From there, distance could either be calculated while taking the curvature of the Earth into account, which is called a geodesic model, or disregarding the curvature of the Earth, called the planar model.
Important vocabulary for this chapter:
- Planar model: A model and analysis of the Earth that does NOT take into account the curvature of the Earth, and instead reads the Earth as flat
- Geodesic model: A model and analysis of the Earth that DOES take the curvature of the Earth into account
- List: lists certain geographic IDs, such as addresses
- Count: total number of a certain data set, such as number of 911 calls in an area
- Summary: A total amount of something, or total amount by category, such as acres of land in a watershed, or number of acres of forests out of forests, hills, rivers, etc.
- Statistical summary: the average, minimum, mean, maximum, and standard deviation
- Inclusive rings: used to demonstrate how total amount increases as distance increases, using the radius around a point
- Distinct bands: overlapping inclusive bands, used to show amount in proportion to distance by two different distances.