Chapter 1:
I enjoyed reading this chapter because it helped me fully grasp just how GIS is able to turn data into information that can be understood in a visual format. It also helped me understand the importance of GIS Analysis as a whole, which looks for geographic patterns in data and relationships between features.
I was also able to grasp the different kinds of data GIS is able to present and process, such as continuous phenomena (values that vary across a space, such as precipitation or temperature). This chapter was extremely helpful for me to understand the 2 ways GIS represents features; vector (features are represented in a row in a table by an x,y coordinate, where features like streams are represented as a series of coordinate pairs) and raster (features are represented by a matrix of cells in continuous space where each layer represents an attribute and layers can be combined to make new ones).
Of the 3 chapters in this week’s reading, I think this chapter was the best in helping me prepare for the work we will be doing with the software later in the semester. This chapter had a lot of information about the different kinds of attribute values:
- Categories: Group of similar things
- Ranks: Put features in order
- Counts + amounts: Show measurable quantities associated with certain features
- Ratios: Show the relationships between quantities
- Continuous + noncontinuous values
While reading the chapter, I did start to wonder if any new kinds of attribute values would arise as modern technology develops and improves, and if this would affect the efficiency of GIS and its ability to present and analyze information.
Chapter 2:
As covered in Chapter 1, GIS works by storing the exact location of each feature as a coordinate. The symbols used to represent these coordinates can come in different forms based on the size of the map being used or the context in which the information is being presented. For example, mapping subsets of features is more common for individual locations, but mapping subsets of continuous data (eg. temperature) leaves the data without context.
Chapter 2 of Mitchell also covered the different types of pattern analysis that are used based on different situations, as well as the steps involved in map analysis. These include;
- Assigning geographic coordinates in terms of longitude and latitude.
- Assigning category values (each feature on the map having a code that identifies its type)
For the most part, Chapter 2 covered actual data presentation, and how minor details like the colors used to represent distributions of data points should be considered when a map is being compiled and designed. For example, when using categories to represent the different kinds of trees in a forest, using no more than 7 categories is considered ideal, because it would make it easier for the average viewer to understand. This is especially true for smaller maps, due to the fact that too many categories could be overwhelming or make the map itself more difficult to perceive. Using color categories with a legend is advised, but other methods such as text labels can be used in conjunction with them.
The chapter also goes over processes, such as how to actually create categories in GIS software using a table, which will be extremely helpful when it comes to working with actual raw data later in the semester.
And finally, the chapter covers how to find and analyze patterns in geographic data, as well as how exactly these patterns can come to be. This section of the textbooks focuses more on the theory aspect of GIS, which I personally found interesting as someone who is interested in IT as a whole. It made me think about how map representation could eventually change and evolve over time as technology improves over the coming decades.
Chapter 3:
Chapter 3 of Mitchell explains the importance of how geographic information is mapped, and focuses on the concept of “most and least”. People map most and least in order to find places that meet certain criteria, or to find relationships between places. In order to map most and least, features must be mapped based on a quantity associated with each (eg. a catalog company searching for zip codes with many young families with relatively high incomes).
Geographic mapping based on quantity adds an additional level of information, increasing its value. However, this also increases the amount of software that must be processed, and this can take more time than having less detailed information. With GIS, you can map quantities associated with discrete features (individual locations, linear features or areas), continuous phenomena (defined areas/surface of continuous values) or data summarized by area.
Quantities can be categorized by ratios (eg. 0-10%, 11-30%). They can also be categorized by;
- Averages: mainly used in comparing places that have few places to those that have many
- Proportions: used to show which part of a whole each quantity represents, usually presented as percentages
- Densities: show where certain features are concentrated or ranks
- Ranks: put features in order, mainly high concentration to no concentration
The chapter also covers different classification schemes, such as natural breaks, quantities, equal intervals, and standard deviation. When choosing a classification scheme, we need to know how the data values are distributed across their range. GIS can help find this through the use of tables and charts. For example, if data values are unevenly distributed, it is advisable to use natural breaks.
Overall, this chapter focuses on the more data focused aspect of GIS, which I found interesting because it made me think of how experience in mathematical or IT fields would make using and understanding GIS much easier for anyone who wants a career in GIS. It made me wonder what other fields or concepts may be related to GIS that haven’t been fully explored in the textbook, such as economics or architecture.