Mitchell Ch. 1,2,&3 readings
Chapter 1 seemed to talk a lot about the types of ways data can be used and how it is categorized. This included discrete features, which are data with precise location, and continuous phenomena, which are data that can not be pinpointed at one location and take up a range of areas like weather. Data can also be summarized by area, which is where counts or exact data is summarized by combining it based on different locations such as by households, towns, counties, etc. I was surprised to learn that there were many more options for mapping besides just x, y, and z coordinates. The x, y, and z coordinates are utilized in vector models to show precise locations while raster models use cells to show abnormal shapes of similar areas. These are two ways that geographic features can be represented in GIS. Additionally, the attribute values are beneficial for presenting data in different ways. Attributes included counts and amounts which showed the exact numbers of something. Ranks that would provide a numbered rank for things but not show the numeric difference between the ranks, just that they are in different ranks. Ratios show the average number of things per something, like the average number of pets per house. Lastly, categories allow for similar things to be categorized together such as rivers, streams, and waterways. For example, trying to show the exact number of animal shelters in a certain state would be better displayed using counts. Trying to show the average number of animals per shelter would be better displayed using ratios. For working with data in tables, how the data is selected is important. For example, to find a specific characteristic of something within a category, you would select the category and then add “and X <8” where x is the specific characteristic you want to look at. For looking for things that fall in either/or category, you would include “or” between the categories listed. Tables can also be used to calculate things such as rank or ratio or to summarize data.
Chapter 2, had a lot of good points about what information should be shown on the map and how to present it to make the purpose of the map clear. When mapping a single type, you would just plot all the data points using the same symbol, which can show the data distribution. You can break the data down into subsets to get more specific data to compare. An example of this may be instead of just stores, you can break them down into subsets of grocery stores, clothing stores, and gas stations. These more specific data points can help reveal distributions or patterns in the data that might reveal that 8/10 of the grocery stores are clustered between ⅖ of the towns on the map, making it more difficult for further away towns to get groceries. Different categories may be shown on a map to demonstrate where the data is found, however, the book warns that no more than seven categories should be used. I think that this is a rule because too much data can become very overwhelming and make it hard to see and read the data. If the map is hard to read or understand, the viewer is less likely to try to figure out what it is trying to show. Additionally, mapping by category can make it easier to read and understand the map and where the different data points are about different landmarks or roads. The overall conclusion of that chapter seemed to be that the amount of data listed on the map and how it was displayed, like what colors and shapes to use, depended on the purpose of the map, and that reference features are helpful to better view and understand the map.
Chapter 3 talks about mapping the most and least values as this can help find where certain things may be more popular or available like the number of bakeries within a state or where something is lacking like the number of dentists in different areas within a state. Again, the purpose of the map is important to keep in mind. Using a map to show a specific pattern would require fewer data to be displayed than trying to look for possible patterns that may be present. This chapter talks about the 4 different classes known as natural breaks, quantile, equal interval, and standard deviation. Natural breaks are grouped based on groups that have similar values. This is useful when the values are not evenly distributed but can make it difficult to compare to other maps. Quantile is where the data is grouped so that each group has an equal number of features. This is useful when the areas are approximately the same size and mapping data is evenly distributed but may make it so that data points seem more different from each other than they are. Equal intervals are grouped so that in each group the difference between the highest and lowest values in each group is the same. This type of class allows data to be displayed so it is easily understood but clustered data could lead to too many or no features in each class. Standard deviations are grouped based on how far from the mean the values deviate. This can be useful for easily identifying the values that stray above or below the average but doesn’t provide precise values for the features, just the difference of the actual value from the mean or average. I thought it was interesting that you can tell if you chose the right classification scheme based on if there is a significant change in the data when the number of classes is changed. The chapter talks about how to assign colors to classes and mentions that most people think of greater values in association with darker colors. This makes sense to me and I’ve seen this pattern in maps I’ve seen. Charts can also be used to display more info, quantities, and categories in different locations, but can make it more difficult for readers to interpret. Contour lines are lines commonly used to show changes in elevation or pressure on a map. When the lines are closer together, there is a higher rate of change, while lines further apart represent a lower rate of change.