Chapter one is a beautiful introduction to the geographic information system basics, as well as the basics of mapping. Before reading this, while I had a general idea of what GIS is and what it is used for, it was largely undefined and nowhere near the proper scope of this tool. Mapping and geospatial analysis are more relevant to my line of study than I previously realized, by a large margin. I’m already thinking of a multitude of ways that this software can help me in my future research endeavors. Besides an introduction to mapping as a whole, this chapter also introduced a lot of concepts that will become important later down the road. Of these, I think the most important concept is the process of performing an analysis. It reminds me a lot of the general scientific method, which makes sense as it is a way to perform scientific research, however it is more specific than the usual method. With mapping, the main purpose is to get an idea about large amounts of geospatial data through visual media, which can allow conclusions to be drawn more easily than by just looking at the raw data, so the method for performing analysis reflects this notion. I also find the clarification on things such as geographic features and geographic attributes to be highly useful. Being able to put information into neat categories is something I prefer when learning, so having these broader categories, such as discrete features versus continuous features, or the difference between sorting by counts and amounts is very nice. I will likely be referring back to this chapter a lot in the future, as this class continues.
Chapter two is focused on the nuances of mapping itself. It focuses on teaching the reader how to effectively make a map, and how to ensure that it is easily readable and understandable. This is an important thing to consider, as mapping is a form of data analysis, and if done incorrectly, could cause results to be inconclusive or skewed. A good chunk of the chapter is focused on how to properly group visual data into categories, and what using different styles of map can do to a reader’s understanding of a map. It goes over many types of maps, such as single-type maps, which are maps where only one category or feature is visualized, such as all crime in an area, or all roads. These maps can be useful for getting basic data about the distribution of the category, but are not highly detailed, meaning more complex conclusions cannot be drawn from them. This chapter also begins to introduce factors of how GIS creates maps, such as how it uses a coordinate system to assign locations on a map, or how it uses tables of data sets to form results. One big part of this chapter is a discussion of how much detail should be included in a map. To put it simply, it depends a lot on what one is trying to show with the map, and what would be too complex for the reader to understand. For example, if a map is too complex, it can draw away from the conclusion one wants, such as including too many vegetation categories and being unable to distinguish the broad interactions. This is also important when it comes to map scaling, as using too much or too little of a location can make it difficult to understand the distribution of results. Not to mention, the scale can affect how many categories one should use for visualizing data, as at smaller scales general data is less useful.
Chapter three is all about how mapping changes when using numerical values as opposed to general categories. Specifically, it focuses on how to show the variance between amounts in the data as an appropriate visual. This is more difficult than mapping by category, as there are more factors to consider. For one thing, the numbers need to be split into categories in order to show any data, but deciding what kind of split is highly dependent on the data being used. There are four main ways to split data, all of which have their own positives and negatives. These are defined as natural breaks, which are when the data is grouped based on natural groupings in the values, quantile, which is when the data is grouped so that each class has an equal amount of features, equal interval, which is when the data is split evenly across the entire value set, and standard deviation, which is when the data is organized based on how many standard deviations it is away from the mean. The main concern with most of these is skewing the data, either through outliers causing the distribution to appear different, or ensuring that data is not grouped too much. Regardless, there are cases where each of these types is the most useful, such as equal interval being very useful for mapping continuous data, or standard deviation being useful for displaying what features are apart from the average. This chapter also goes in-depth about the five styles of mapping quantitative data, and what situations they are most useful in. This is a highly important chapter, and I will likely be using the skills described in it a great deal.
