Chlebowski – Week 2

Chapter 1:

This chapter really sets the table for the idealization and tenants of how you need to have when in the mindset of making a map, both physically and mentally. These two ideas are different from each other but also very important, because as it is made known by this section, simply having data to map is not all that is needed to start on the creation of said map.  Firstly, there are different types of physical data that can be used (vector and raster), which both can be used to represent most if not all types of feature types. Being comprised of X and Y coordinates, vector data is most usefully utilized in discrete features and area bound data, while raster data is most commonly utilized with continuous numeric data like elevation maps. Despite this chapter being very wordy with its introductions of the many types of features and approaches that can be done in mapping, I found it very interesting to look through, especially with the colored illustrations to compliment the text. It seems very elementary but showing how two different approaches at showing data can be done both in words as well as with pictures (especially when the one way of mapping something is very clunky or scattered) to compare them and see which is more advantageous for the end goal of the map was a great idea. I thought the final sections about attribute values was an apt summary of how you can play around with your data to make it accessible to the viewers. Many of these reminded me of the population project in GEOG 112, where we were given big census data and allowed to play around with it as much as we wanted, but eventually made to chop it down into digestible, readable data for the viewers. Utilizing ratios and counts/amounts is perfect for this, especially when there is so much raw data that you do not absolutely want to use every last piece of.

Chapter 2:

The beginning of this chapter jumps into deciding what to map, and from this answer, how to map such idea. While this is a rather personalized question to ask, it is quite important as it is really easy to map too many things and confuse the meaning of the map. I learned this from experience, especially when you are given a bunch of types of data from a specific area and want to provide your reader with as much of it as possible. It is here where you have to keep your specialized reason for mapping in mind, as the point and execution of your map will be most clear if you keep as many extracurriculars out of the map interface as possible; you would not want to confuse your reader from your main point. The part about categorical mapping was pretty neat, especially the part where it expressed that in general you should not exceed seven categories of data, since after seven it is apparently difficult to distinguish one from another. This is probably due in part to (1) there are only so many colors that can look totally different from each other with a background color and a separate color for boundaries and (2) seven may just be the arbitrary limit of a map having too much going on in terms of categories. Scale is also very important, which I learned from the GEOG 112 project, when working with categories of data. Having smaller scaled sections of data with many categories can be tricky, as sections that are similar in numeric value but in adjacent quadrants can look the same when they are not, especially if you are using a 2-color scale to distinguish all of the categories. Using the full range of colors in a small area of boundaries, I have also learned, can be less advantageous to the reader, as it may require more looks at the legend to distinguish the different categories as opposed to a 2-3 color scale.

Chapter 3:

The start of the chapter brought up an interesting point about how maps are made with different purposes. For example, if you are making a map to explore relationships between two things, you would limit the use of information that is not relevant and try to display the data in multiple different ways to show how deep the relationship is. On the other hand, if you are making a map to explore the results of a finding, utilizing all of the data in the result is most likely the best approach to show the full extent of what was tested and what was found. It talks later in the chapter about the different methods of splitting up or classifying categories via natural breaks, equal interval, and standard deviation. The first two I have heard about and used but I was unfamiliar with standard deviation being used in this way, as “each class is defined by its distance from the mean value of all the features”. Displaying data based on its distance from the mean seems very specialized, as I cannot recall any times where I have seen this done in graphic form. When in the making maps portion, they mentioned the use of graduated symbols when measuring volumes or numeric values in area. I always thought that graduated symbols were a strange choice when expressing the size of numeric values, since the size of a specific shape can be hard to quantify in my opinion. While having to constantly check a legend or key to determine what size relates to what, it can be hard to determine such size when there are too many categories of size, especially if the area in question has a smaller scale.

Chapter 4:

One of my favorite techniques used in density maps are the contour lines. I think that showing the rate of change across a surface of the highest densities is a really cool approach and one that is unique to many of the other methods that they mentioned like dot diagrams, color gradients, etc. A use for them that I am aware of is with isobars in determining the rate of change in pressure across a weather map. In this scenario, determining the rate of change is very important as high rates of change in pressure can allow storms and bad weather to permeate in these areas, making it very crucial to be able to map and identify these areas. Additionally with the methods of displaying density information, I think dot density maps are quite neat in its simplicity and straightforward idea in showing what places has higher density values, but I do think they can make a map a fair bit cluttered at times. The book mentions that you can try to avoid this by making the size of the dot small enough so that it obstructs as little of the boundary lines as possible, but  I still feel that even if the dots are small, they can still make the boundary lines confusing to follow, especially in spaces where the scale is small or if there are small areas of high dense areas near each other, like in a density map of a U.S. state’s counties. Counties with very high density will have a hard time being distinguished from one another from an eye that is not familiar with their placement on a map. Also, I think that density surfaces are really great tools to show density while preserving boundary line integrity, but the type of very precise locational data that would be needed to accomplish this makes the actual creation of such maps really specialized for very exact data. Map density area is a lot more generalized and can be done with more simple data, but it requires much less data processing, and it is gives decent ideas of where densities are located in a larger area of land, which might just be what a person is looking for as opposed to the exact regions of density increase within boundary lines.