Chapter 1
This first chapter was mostly about the logistics of GIS. This included the different types of things that GIS maps can show, including where things are, density, change, and others. However, it cannot be useful if you do not know what question you are trying to answer with data and how you plan to represent your data, what data you actually need to gather and how precise it needs to be, and which features you plan to represent in your map.
This chapter also went into the different ways geographic features can be mapped out through different systems, like vector and raster models, and discrete, continuous, and features summarized by area within those models.
Discrete Features:
- Can show a pinpointed location and has no “grey area”
- Things like physical barriers or legal boundaries could be shown this way.
Continuous Features:
- Shows features that occur within the entire selected area
- Value shown can be determined at any location within the “selected area”
- Can either be the entire map, or an area closed by a boundary within a map
- Values are grouped together simply by being more similar to each other than not
Features Summarized by Area:
- Shows density of features in an area
- Would apply to the entire area
- Value shown is an average rather than several pinpointed values shown at their respective locations.
I am still unsure about exactly how vector models work. From what I have gathered, they are points placed manually by coordinate rather than by GIS like with raster models, but I am not sure what exactly they are outside of knowing the difference between them and raster models.
In addition to the basics of map features, information on a map can be shown through categories, ranks (both non-continuous), counts, amounts, and ratios (all continuous). Which of these that are used can completely determine what type of analysis is possible using the map created.
Another concept discussed was how to work with the visual data by selecting, calculating, or summarizing it through the GIS software.
Chapter 2
Chapter 2 spent a lot of time exploring how to actually set a map up to reflect what it is intended to show. It described different ways of using patterns to show correlation. Categories can be split up simply or split several times so that one location can be part of multiple different categories to make its label more specific. They can also be completely separated into 2 maps if they get too cluttered on one. They say the maximum number of categories on one map should be 7. It is also important to note that while more information can get into more specifics than less, it is much easier for a general audience to notice trends on a simpler map. It is then good to find a happy medium where the map can be just specific enough without making the information difficult to decipher. When putting attention into these things, trends on your map can show where attention or action is needed in an area.
This chapter also went a bit into how the GIS handles different inputs of data to make them visually make sense. When you place a point, it can store that value as an (x,y) coordinate, and it can assign points to multiple subsets, as I mentioned in the paragraph prior, to allow information about locations more specific. This can be done using multiple symbols, like using colors for one category and shapes for the other.
Colors and shapes can also be strategically used to make your map more visually friendly. Colors that are close together can help to show subcategories that are similar to one another, but if there are too many categories, it can be hard to see the differences between colors that are very close to one another. In order to figure out what color system is best, sometimes it is helpful to understand the type of place you are mapping.
Chapter 3
This last chapter went into maps using different types of quantitative data, as most data in GIS tends to not be qualitative. It compared the effects that raw numbers, ratios, proportions, densities, and ranks had on maps, as well as continuous and categorical classes. Most of the time, with continuous measurements, the GIS software would sort them into classes using one of 5 methods. Which one of these that is chosen to be used, as a common theme, completely depends on what you are doing with your map and what you want it to show.
The GIS can sort continuous data with:
Natural Breaks: creating categories based on natural breaks in the data
-good for unevenly distributed data, because it goes by clusters rather than values alone
-hard to compare with other maps because it is a case-by-case system
Quantile: equal number of features for each class
-good for comparing areas of the same size and mapping evenly distributed values
-can determine position of features among others
-some values can become a skew for classes and recognizing patterns
Equal Interval: equal range of values in every class
-every class is an equal ratio to one another
-good for continuous data
-there may be classes that don’t have any features at all in them because of the sort of “disregard” for the actual numbers
Standard Deviation:
-good for seeing features relative to an average, but doesn’t show the actual numbers, just their relativity to this number
-outliers can really skew the overall picture of the data
You could also create classes manually, but I think this has the possibility of creating bias in your data if you were expecting a specific result. However, this method could also easily cater to getting rid of outlier effects on data.
This chapter also went a bit into specific ways that concentration of data can be shown, including graduated symbols and colors, charts, contours, and 3D views. It also described situations where all of these would be best, but as always, it depends on your data and the picture you are trying to create for a desired audience. The goal is to find patterns where concentration is.