CHAPTER 1
In Chapter 1 of The Ensri Guide to GIS Analysis, Mitchell introduces us to various common practices and techniques used to represent the geographic features of a GIS map, which I was not familiar with prior to reading of this chapter. Mitchell starts by introducing us to two common types of models that planners or researchers in general use: Vector and Raster models. Vector modeling uses points, lines, outlines, closed polygons, and other geometric objects to represent various geographic features of a map. For example, roads, rivers, and streams may be represented using outlines, while residential, commercial, and industrial buildings or the boundaries of these zones could use closed polygon shapes to represent these objects on a map. When thinking of how local law enforcement chooses to organize the various crime statistics in an area, categories, which Mitchell also introduces us to in this chapter, can be used to represent the various types of crime committed within an area using points; for example, types of crimes committed and the exact coordinates of the crime could include burglaries, traffic crimes, murder, etc. Mitchell in this chapter also introduces us to Raster models, which use a matrix of cells in a continuous space to represent, for example, the temperature across a State or the United States. When using a Raster model to represent large plots of land, Mitchell also expands on the use of categories with Raster models which assists with organizing to make better sense of data; for example, a geographer may choose to represent the different types of levels of a mountainous geographic area such as Alaska, and so, a geographer could choose to represent the level of each mountain using categories and then a raster model to show the variations of levels of mountains within the selected region.
CHAPTER 2
In Chapter 2 of The Esri Guide to Analysis, Mitchell further discusses the features that were being touched on in the previous chapter, while also touching on the application of those features in mapping, for example, various crime statistics such as burglary, traffic crimes, etc., or residential, commercial, and industrial zones in planning. Mitchell states that generally, when using categories to define the details of certain objects and features of scale within a map, he discusses that, with mapping vegetation as an example, it is difficult for viewers to distinguish the important aspects of the map if you have small contiguous features and large contiguous features if you are using a raster model. He provides a solution, suggesting that when mapping, to map each category equal weight to others. Mitchell also states that when using categories to map out specific zones for a municipality, in general, you should not use more than seven types of categories or else, visually, it could be difficult to comprehend the map and which details are important and needed and which details are better left out which my question to that would be when would it be appropriate to have seven or more categories when it would make a significant difference to the information that whoever is viewing the map?
CHAPTER 3
In Chapter 3 of The Esri Guide to Analysis, Mitchell introduces us to classes, charts, as well as classification schemes. When mapping quantities, Mitchell recommends assigning each individual value its own symbol or by grouping them in classes. When presenting quantities in a map, there is also a tradeoff between presenting values accurately and generalizing values to see the map. One example Mitchell provides is a map showing the poverty rate of counties or districts where each county or district is represented with a different shade which represents a different percentage range. When discussing how to get the classification scheme, I found this section of the chapter to be the most difficult and confusing since it covers several mathematical approaches to grouping schemes. The four schemes that Mitchell touches on in this chapter are natural breaks, quantile, equal interval, and standard deviation. From what I understand from the reading, Natural breaks isolate outliers in the highest and lowest class by emphasizing the jumps in values and somehow that translates to darker shades, and from the example, this somehow translates into a different shade of the map. Overall when touching on classification scheme, Mitchell doesn’t do a good job of elaborating on how the values from the chart translates into a different shade and I would likely reread the chapter or seek elsewhere for a better understanding of the statistical classification method behind how it translates into a visual map.