Chapter One
Mitchell begins by framing the process of GIS analysis similar to that of the scientific method: First with asking a question, then researching and compiling and presenting data in a digestible format based on the type of data presented. I find this similarity very interesting, yet explainable, as GIS analysis and GIS as a software is based heavily in the environmental sciences, especially geography. Additionally, Mitchell highlights multiple careers that could benefit from or currently use GIS technology, even if they are not associated with the environmental sciences. Careers such as criminology, law (Mitchell highlights how GIS data could be included in court cases), and development all use or could benefit from GIS technology or analysis. In the next section, I learned of discrete features on maps, which include color-coding, shapes, etc. to highlight and present a certain feature of the map, such as streams. Continuous phenomena, another new term to me, are phenomena that can be measured anywhere, such as precipitation. Features summarized by area are used to show density of something in an area, such as population density. All of these are reliant on either of two ways to represent geographic features: Vector models, which rely on the x,y coordinates of a feature to shape out the features. The other, raster models, use cells to shade in and represent a feature, and layer on one another to represent intersecting features. A disadvantage of raster models is that larger cells lead to a loss in detail, so some may choose to make the pixel size of the cell smaller for more detail. Additionally, since map projections are distorted by the curvature of the Earth, mapping larger areas such as states, countries, or continents means that this distortion needs to be taken into account, while with mapping smaller areas, this distortion is negligible.
Chapter Two
When deciding what to map, Mitchell highlights two questions that should be taken into account for GIS analysis. First, what information do you need from the analysis? This will largely differ on a case to case basis, as Mitchell highlights with the examples of police mapping crime rates or a retailer mapping their audience and where they live in order to place the most effective advertisements. Second, how will you use the map? Audience should be heavily considered, especially for factors such as the amount of detail for the map, labels, whether to include reference locations such as streets and landmarks, size of the final planned map, etc. I find these thorough steps in analysis interesting from a professional standpoint, as the process of deciding what to map varies heavily between tasks and audience, in comparison to other forms of measuring data like composition analysis in soil science or raw data in the aforementioned case of crime rates. Additionally, this section highlights both the scientific and communication skills required for GIS analysis, as researching and understanding your audience is incredibly important to conduct a thorough GIS analysis, as Mitchell notes. In the next section of Mitchell’s introduction to GIS and GIS analysis, we learn about preparing data to be used in mapping. I find it interesting that some of the data is hierarchical, and how hierarchies are assumed in the data. Additionally, in the following section, the rule of seven categories to best show data points is very interesting to me, as I originally thought that far more categories would be feasible given that categories are not similar colors. However, thinking back to GIS-made maps that I’ve used for research and for simplifying my thoughts, most maps were not as complex as the zoning map Mitchell uses as an example, and grouped some categories together, even if the audience was scholars and researchers.
Chapter Three
Mitchell starts this chapter reflecting on terms from the first chapter: discrete features, continuous phenomena, or data summarized by area. With additional knowledge from the previous chapters, the context of data types and different forms of quantification are mentioned, allowing the reader to understand these previously learned concepts in context. Following this tie-in to previous chapters, the reader is presented with different ways to present map data in a graduated format; with symbols, shading, etc. in order to analyze patterns in the data in addition to mapping discrete features. This section helped me better understand the content of the chapters in a visual way, highlighting the intersection between discrete features and data analysis in Chapter 2. A new concept in this chapter is types of quantities: counts and amounts, ratios, and ranks. Counts and amounts is the raw data mapped directly, such as the number of restaurants in an area, or the population sightings for a species. Given that the data is placed as points on the map, I assume that counts and amounts quantities only work on smaller amounts of data unless the points of gradation are grouped together in areas of high concentration. Ratios and ranks are less direct forms of quantitative data, ratios being based off of the data in relation to another factor, such as the ratio of schools to children, while ranks put the data into gradual tiers of severity, assigning a shade to each level. Thinking back to maps I have seen both inside and outside of an academic context, I have seen all of these forms of data presentation on maps. Interestingly, I have noticed that in an academic setting, I have seen quantitative data presented more through ratios and ranks, while when presenting data to the general public, counts and amounts are more widely used.