Chapter 1:
Rather than jumping straight into the intricacies of mapping and data analysis, Chapter 1 stresses the importance of understanding and clearly defining the topic at hand. I believe that this is an effective way to introduce new students to the basics of GIS, as it eases you into it. One major takeaway I noted is that Mitchell highlights how GIS analysis conducted with intent tends to begin with asking the right questions related to the information you need, with more specific questions helping guide your analysis. I had not realized this prior, as I just saw GIS as simply plotting data. I realized we can use GIS to address important problems by asking pressing questions that are specific to the area of interest. It’s also important to note that Mitchell presents GIS systems as accessible, inviting us to ask our own questions and come to our own conclusions and discoveries. This is an exciting revelation for me.
Chapter 1 also introduced me to the basics, like understanding and identifying geographic features as how they are presented within a GIS. My takeaway is that there are many different ways to visualize data as features, depending on what kind of data it is and the purpose of creating a visual for said data. For example, vector vs raster modeling. Vector modeling represents features as points and lines often using coordinate-based data, making it good for plotting things like roads, boundaries, and buildings. Raster modeling represents the features as a grid of cells using continuous data, which is good for plotting things like elevation, temperature, weather patterns, and land types. I found it interesting how these two forms of modeling could technically be used interchangeably for the same purpose, but they are used for whatever they are visually better suited for.
I feel that explaining how we can all use GIS as an effective tool is the premise and the author’s main goal after reading chapter 1. It introduces GIS analysis as a system that is capable of examining and visualizing geographic data to understand specific spatial patterns, relationships, and trends in a way that I found understandable. It takes this information and directly ties it to visual features within a GIS mapping system. Question: Would reading printed park maps be considered a form of GIS analysis? Where is the line drawn for maps being purely for visualization or analysis?
Chapter 2:
Chapter 2 starts off by building on the foundation that Chapter 1 created by highlighting the importance of visualizing data using mapping. Honestly, I found this redundant. The benefits of mapping data already seemed clear to me. For example, Mitchell discusses how visualising data on maps can help us look for patterns in the distribution of the features, and make decisions based on these patterns. I thought that was obvious, but I appreciate that Mitchell is making things very understandable for new students like myself. Some things that I was unfamiliar with that chapter 2 discusses is deciding what to map, and preparing my data for said map. I learned that when deciding what to map, you need to consider the information you want to analyze and how the map containing this information will be used. This made me realize that it’s important to take into consideration the specific audience the map will be presented to. For example, a highly detailed and overly complicated map is ineffective if it was intended to be made for the purpose of sharing basic information with the general public. As for preparing data, I was highly unfamiliar with the topic. I learned that a crucial first step is to assign geographic coordinates to the feature you wish to plot. Another important thing I learnt is that you need to assign category values to features if there are differing features, or features sorted by type. The category value is a code/tag that identifies the feature type. I often see these categorizations of features when looking at maps, but I’m now realizing that this feature identification can be used for various applications, such as distinguishing areas for city planning. Chapter 2 does a good job at answering basic questions about mapping and how to create a map, as well as explaining how to proficiently analyze these maps. Question: How can we effectively and critically evaluate data sources to identify biases/untrustworthy information before incorporating it into our GIS analysis?
Chapter 3:
When I first read the title of chapter 3, it being called “Mapping the Most and Least”, I was confused. Unlike the previous titles, what it was trying to convey wasn’t immediately clear to me. However, Mitchell explained it in an understandable fashion. Mapping the most and least means to identify where values relating to your data/criteria are highest or lowest to analyze certain aspects about the data, most often through patterns. If I were to think of an example, I would say that analyzing a low income area for care facilities scarcity is an example of analyzing where values are the least. This is something that I had not previously considered, as I was focused on the idea of mapping where things might be located, not where things might be missing or lacking. It showed me how presenting the quantities of data in different ways is an important thing to consider when deciding what I want the purpose of my map to be. This is just one method of GIS analysis that is presented in chapter 3. Other methods are given.
For example, there are different types of quantities that you can use. According to Mitchell, being aware of the quantity type your mapping can help with deciding how to present your data in the best way. Once the type is determined, a decision must be made about how to represent it on a map. This can be done either through grouping the values into classes or by assigning each value its own individual symbol. I learnt that each choice has its merits, and I now know how to apply them to my own maps. For example, grouping the values into classes is useful for maps with a large range of values to present the data in an easily readable manner. Showing overall patterns is favored over exact data using this type. This would be good for maps about concentration levels of rain or air pollution. On the other hand, assigning each value its own individual symbol is useful for maps where precision matters, and exact values are important due to the specificity of the data. This would be good for maps geared towards recording specific sampling sites, or showing how specific geographic locations may present differently from each other. Question: What if the data is in a middle ground where it isn’t clear if I should present my data with simplicity or complexity?