Chapter 1 gave a brief insight into what GIS is and how we can use it. One thing I find very interesting is that you can use GIS mapping for many different things, from crime in certain cities to deforestation or the loss of certain animals. To me, this is interesting because GIS seems like a science-type of thing, but in reality, it isn’t. Another interesting thing is that you can use GIS mapping for more precise things (like precise locations) or you can just use it for more broad stuff (like looking at a whole city).
Key Words:
Most common geographic analysis tasks:
- Mapping where things are
- Mapping the most and least
- Mapping density
- Finding what’s inside
- Finding what’s nearby
- Mapping change
GIS analysis: the process for looking at geographic patterns in your data and at relationships between features
Steps of GIS analysis:
- Frame the question
- Understand the data
- Choose a method
- Process the data
- Look at the results
Types of features:
- Discrete
- Continuous Phenomena
- Summarized by area
Ways to represent geographic features:
- Vector
- Raster
Types of attribute values:
- Categories: groups of similar things
- Ranks: put features in order, from high to low
- Counts: the actual number of features
- Amounts: any measurable quantity associated with a feature
- Ratios: show you the relationship between two quantities and are created by dividing one quantity by another for each feature
Three common operations on features and values:
- Selecting
- Calculating
- summarizing
Chapter 2 gave us a summary of why mapping is important in our world. When mapping is used, we are able to see where the main problems may be occurring and where people need to be focusing on to fix a problem. Another important thing is that there are many types of maps that can be used, which causes some to be better for showcasing certain topics than others. One cool thing that I realized in this chapter was that there are so many ways to show what data you are looking at. You can use dots, lines, or plots. There are so many different types. The only problem that I have is that it seems that some are more difficult to understand than others. For example, when they are showing the zoning code with all the different colors compared to the business map.
Chapter 3 helped give us an idea of how to pick which data is best for presenting. If you were focusing on something in a high-income area, you probably wouldn’t want to include low-income areas unless there is a similarity between the two in what you are searching. One thing that this chapter has shown me is that there is a decent amount of math that goes into these maps for each point of data. One thing that stuck out to me was mapping individual values. This helps with finding patterns in raw data and is also very useful when you are looking at something you aren’t used to. One final thing is that there are many advantages and disadvantages to the way people classify their schemes. So, no matter which one you use, there is always going to be a fault, and it will never be perfect
Key Words:
Counts: the actual number of features on a map
Amounts: the total of a value associated with each feature
Ratios: show the relationship between two quantities (averages, proportions, and densities)
Proportions: Show you what part of a whole each quantity represents
Densities: show you where features are concentrated
Ranks: put features in order, high to low
Classes: help features with similar values by assigning them with the same symbol
Natural Breaks: values within a class are likely to be similar and values between classes are different
Quantile: Each class has an equal number of features in it
Equal Interval: each class has an equal range of values
Standard deviation: defined by its distance from the mean value of all the features