Chapter 1
- Because GIS has been around for such a long time the tools and technology that use it have evolved significantly. The number of people that are familiar with GIS has also increased along with the usage.
- The most common uses of GIS seem like they could be used very broadly for a wide variety of fields. I would be interested in seeing an example of each of these uses, particularly the ‘finding what’s inside’ point because I am have a little difficulty imagining what this could be applied for. Maybe what species is inside a geographical region?
- GIS is described as being a process for observing patterns and relationships in features. It does so through the construction of maps or models.
- The way working with GIS is described sound very similar to the way conducting a scientific experiment is; Start with a question, choose methods, gather information, and observe and analyze the results.
- As I was reading I did have a question of the definition of a ‘parcel’ because it was brought up many times and used in a way that I was not familiar with. After a quick search I found that it was simply an area of land with clear boundaries, often split off from a larger chunk. This made the word make much more sense in the context of the book.
- I think the idea of continuous data/ phenomena showing similarities between areas rather than exact information is interesting. It’s seeing more of a relationship between areas that you may not be able to observe as easily without the visual, such as simply using a table.
- Summarizing data and mapping discrete features should use the vector model, and continuous numerical values should use the rester model.
- Categories and ranks are not continuous values because they are assigned one set, whole number. Contrarily, counts, amounts, and ratios are continuous values because they are not assigned a set number and can be anywhere within a range.
- The process to select features seemed a little technical at first, but this section of the chapter seemed to make it make sense and acted as a helpful guide.
Chapter 2
- The first few pages of chapter 2 seemed a little redundant. It established that mapping is important and locational information has many uses across a variety of fields, which is something that the reading we did for last week also elaborated on.
- It’s important to remember that when you’re making maps you should assign geographical coordinates to features and possibly category types as well.
- Mapping by subsets or mapping by whole features/categories can be beneficial in identifying patterns that may not have been observable using the opposite method (they both have valid uses).
- The connection between recommending a maximum of seven categories and the human ability to easily identify seven colors is pretty neat. Having a cut off to ensure clarity also seems like it could prove useful keep in mind for future projects (keeping in mind there seems to be a sweet spot in between having not enough categories and leaving out information and having too many that it gets confusing).
- You can group categories by either providing each category a detailed code and a general code or by creating a table with a detailed code and general code which can then be combined with the feature database table to be displayed using the general code. The second method make it easier to adjust the category groupings. The third method is to use a symbol for each general category, which can be reused if needed later.
- The issue with using symbols is that they are harder to separate than points using color, especially if the shapes are small. I would imagine that combing shapes and color variations may be an even more effective way of distinguishing features.
- ArcGIS provides basemaps that you can use that contains grayed-out reference buildings and landmarks for you to overlay your information on top of.
Chapter 3
- One of the first things I noted was how many examples there were in this chapter. I think it will be helpful to see all the different methods of mapping quantity – through contours, summaries of areas, and gradating colors, for example- that you can use choose from to most accurately and legibly display your information.
- If the areas you are summarizing vary in size you should use ratios (averages, proportions, or densities) rather than counts to be able to accurately observe patterns. This seems like important information, especially since skewed data can become a major problem and fuel misunderstanding of an issue or piece of information (can do more harm than good, despite the intentions).
- The concept of ranking seems intriguing to me, especially when it comes to things that are more subjective like the provided example of the scenic value of a river. What factors are used to determine the ranking? Though this may be helpful to some, what’s the determining factor of the rankings that makes it widely accepted as a ‘correct’ ranking?
- A lot of sections in this chapter appear to be ones taken almost directly from other chapters (ex. river ranking example, business example, and even several paragraphs explaining the relativity of ranks) so it was a little redundant at times.
- Natural breaks (classes are based on natural groupings of values), Quantile (Each class contains an equal number of features), Equal Interval (high and low values of each class have the same difference between them), and Standard Deviation (classes are based on their variation from the mean) are all ways to classify information. They each have their own strengths, weaknesses, and uses. This section of the book appears to be very helpful in determining which scheme to use based on a chart your data produces.
- Graduated symbols, graduated colors, charts, contours, and 3D perspective views are all map formats that you can choose from to effectively present your data. Similarly to the classifications, each map style has its own best uses, advantages, and disadvantages, but it mostly varies based on which type of information you would like to present.