Chapter 1:
- GIS has been growing enormously and the use of it is also increasing. It started as a database but now has many more applications. The first step of GIS is examining geographical patterns and the relationship between their features. This can be done by making a map of these patterns. The next step is to formulate a question to better understand what information you need, the more specific the better. You still may not have all the information you need after this which is why choosing the correct method for your analysis is important. Then comes the GIS to process the data. Finally, the last step is to display the results as a table, map, graph, etc.. Being able to see your processed data is important as it allows for patterns to be noticed more easily than looking at raw data.
- There are a couple of different features in GIS that affect the analysis process.
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- Discrete Features: When there are discrete locations or lines the actual location can be pinpointed.
- Continuous Phenomena: Two examples of this are temperature and precipitation. Continuous phenomena can determine a value at any given location.
- Interpolation: A process in which GIS assigns values to the area between the points, using the data points.
- Summarized Data: Data representing the counts or density of individual features within area boundaries.
- Map Projections: Translates locations on the globe onto the flat surface of your map. The map projections distort the features being displayed on the map and this can be a concern if you are mapping larger areas.
- Categories: A process that lets you organize your data by grouping similar things.
- Ranks: This puts features in order from high to low and is used when direct measurements are difficult or there is a combination of factors.
- Counts and Amounts: Shows you the total numbers, and the number of features on a map.
- Ratios: Show you the relationship between two quantities. These are created by dividing one quantity by another for each feature. This is used to even out the difference between large and small areas.
Chapter 2:
- This chapter is set up similarly to the first chapter in which it explains the step-by-step details about figuring out what to map and how to use it. It also focuses solely on what is on the map and the presentation of it. To properly use a map one must figure out what map is appropriate for the issue addressed. You have to think about from the perspective of someone who knows nothing about the data, what would they need to see on the map to properly interpret the data. Just like in the last chapter with making categories, these features that were categorized need to have their code of identification. Codes can indicate the major type and subtype of each feature.
- Originally I had no idea how to start making these maps but I understand a bit better that each process is step by step and not all at once. Such as in making a single map type, you add features by drawing symbols on the map. Mapping by category can show patterns of that specific data.
- There seem to be a lot of different ways to present the data on the map such as mapping by category as stated before. Displaying the features by type allows you to use different categories to display different patterns instead of just using category information. However, with any feature, you do not want to display too much on a map as it can make patterns difficult to follow. To fix this problem you can always group the categories.
- I kept reading about symbols and wasn’t sure if it was as direct as it seems but it is. Choosing a symbol is as simple as picking one, but it can also help show the pattern of the data. Symbols usually use a combination of shape and color.
Chapter 3:
- The start of the chapter seems to be a small refresher to the last chapter about what you need to map. Once again it is important to remember who is going to be seeing the map, as you may be able to present the data differently depending on the person. In the past chapters there was a lot of discussion about mapping categories but mapping individual data is just as important. While it may take more effort it does create a more accurate representation of the data.
- Classes: Groups features with similar values by assigning them the same symbol and allows you to see features with similar values. This does change how the map looks.
- Natural Breaks: This is done by using classes based on natural grouping data values, separating them from highest to lowest.
- Quantile: Block groups with similar values are forced into adjacent classes. The block groups at the high end are put into one class.
- Equal interval: The difference between high and low is the same for every class. In this example, it allows for the blocks with the highest median income to be identified.
- Standard deviation: In this case, the classes are based on how much their values vary from the mean.
- Natural Breaks: Values within a class are likely to be similar and values between classes are different. Due to the natural break finding groupings and patterns inherent in the data.
- There are multiple formats to make a map such as graduated symbols, graduated colors, charts, contours, and 3D perspective views. Understanding which features you are using is important to making the map. If I were to have discrete locations or lines I would use graduated symbols to show value ranges, charts to show both categories and quantities, or a 3D view to show relative magnitude. The chart starting on 154 will probably be useful later down the course.