In Chapter 1 of the Esri Guide to GIS Analysis, primarily the fundamentals of GIS are explained. GIS analysis is looking at geographic patterns within specific data and looking at the connection relationships. The steps to GIS analysis include asking a specific question, choosing the method that works for the data you are trying to discover, processing the data, and reading the results. Similar to GIS analysis I’ve participated in biological studies where it is better to get a more specific question when doing an experiment to get specific data. When reading the results at the end, there are specific types of features to look out for on the map. Those include discrete, continuous phenomena, or summarized by area. To me I would think discrete would not mean any specific location, but in fact that is quite the opposite. To me, I feel the most common feature is the features summarized by area. I also feel they are the easiest to read because of the clear area boundaries that they have. The two models that represent features are vector and raster models. I prefer the vector models because I prefer having hard boundaries when reading a map in most instances.
In Chapter 2 it features the actual mapping process. It emphasizes the need to carefully select the amount and type of information included in a map, depending on its intended purpose and audience. For example, urban planners may require a map with categorized road types to inform their decision-making, while a tourist map of a park should prioritize simpler information to aid navigation. Including too many categories or too little can either overwhelm the user or make the map difficult to use. The chapter also covers various methods for analyzing geographic distributions, such as finding the “center” of a cluster of features, which can be defined using different statistical measures like mean center, median center, or central feature. These centers help understand patterns like crime distributions or the most central locations in a set of data points. For example, a crime analyst may use GIS to track changes in crime patterns by comparing the center of auto thefts during different times of day. A key takeaway is that outliers can skew the results of these calculations, especially when there are fewer data points. Additionally, the chapter discusses how GIS maps rely on coordinate systems and data tables to assign locations and generate visualizations. The complexity of a map should align with its objective, balancing enough detail to convey meaningful patterns without overwhelming the viewer. Proper map scaling and categorization are essential for clarity, as too much detail or too broad a focus can obscure the main message the map is meant to communicate.
Chapter 3 of The Esri Guide to GIS Analysis, Volume 1 focuses on mapping quantities to reveal patterns and relationships between features. The key idea is that mapping the most and the least of something helps identify areas that meet specific criteria or require more resources. The type of data being mapped—whether counts, amounts, ratios, or ranks—determines how it should be represented. Once the data is classified, the map can use different symbols or group values into classes to make the patterns easier to visualize. To map quantities effectively, a standard classification scheme such as natural breaks (Jenks), quantile, equal interval, or standard deviation is used to group similar values. This helps identify patterns like clusters or trends in the data. Visualizing the data with bar charts can also aid in selecting the right classification scheme. Several mapping techniques are discussed in the chapter based on the type of data and features being mapped. Graduated symbols are ideal for mapping discrete locations, lines, or areas, while graduated colors are better suited for discrete areas or continuous phenomena. Charts are used to map data summarized by area, and contour lines show the rate of change in values across a spatial area. For visualizing continuous data, 3D perspectives are employed, where the viewer’s position and other factors like the z-factor are manipulated to provide a detailed view of the surface. The chapter stresses the importance of selecting the right map type and classification method based on the data’s characteristics and the map’s purpose. A well-designed map will clearly highlight where the highest and lowest values are, providing valuable insights into the distribution of the data.