Chapter 4: Mapping Density
The chapter starts off by explaining why it is important to map density in a map. The primary reason being to show patterns rather than showing locations of features. This is especially helpful when you have a large abundance of features on a map, and it is hard to discern the concentration of said features. There are a few ways to map density, so in order to find the way that fits your goals, you need to assess a few things. These would be whether you have dots and lines or summarized data. Dots and lines benefit from a density surface. Another thing to keep in mind is whether you are mapping features, or feature values. A map of the density of businesses could look completely different then a map of the density of employees. Areas with high amounts of businesses could only have one worker each, while areas with low amounts of businesses could have a high amount of employees. The two ways of mapping density are either by using density surface, which is continuous, or by defined area, which is segmented/separated. It is also important to keep in mind the scale at which you are mapping. If you have a large map with hundreds of tiny dots, it can be overwhelming and hard to read. If you group/combine some dots, it can make your map more accessible. You could have a similar problem with density surfaces. If you make your cells too small, your gradient will become too smooth, and finding distinguishable areas will be impossible. On the other end of the spectrum, if your cell size is too big, you lose clarity and information in your gradient. It is also possible to combined the two methods, by rending density as a density surface, and then placing your map with boundaries, i.e. county lines, over that map to see a continuous gradient and how it is spread through each county.
Chapter 5: Finding What’s Inside
Shockingly, this chapter describes why you might need to find what features are inside an area in GIS. There are a multitude of reasons, including crime analytics (finding where crimes are located to identify hotspots), how many roads are in a county or a park, and assessing flood damage. Looking at your data, you may only need to find what is inside one area, such as one state, county, park, or zipcode, or multiple areas. The multiple areas could be adjacent or disjunct. The features you are looking for could also be either discrete or continuous, Which could be land parcels or soil types. GIS is also helpful for gathering different types of data. By overlaying continuous values over discrete land parcels, such as smoke plumes over a city, you could either get a list of parcels affected, the number of parcels within the smoke, find out what each land parcel does, and more. There are three methods to “finding what’s inside”, those being, Drawing Areas and Features, Selecting the features inside the area, and Overlaying the areas and features. Drawing can help you easily find which discrete features are inside or outside an area. Selecting is good for grouping areas together and finding what is within a given distance of a feature. Overlaying features are good for seeing how much of a discrete feature is in a certain area, and what type of feature it may be.
Chapter 6: Finding What’s Nearby
Mapping what is nearby a feature can be helpful in many ways. Figuring out the time it would take to get from your house to the store, or monitoring logging near a river or property line. However, what is near to a feature could be defined in different ways. It could be a set distance, like mapping every tree of a certain species that is within a mile of a river. It could also be travel to or from a feature, like a fire truck driving to a fire.The units of measurement could also be different than just distance. Time, money and effort are also units of measurement when measuring what is nearby to a feature. It is also important to decide whether you want to factor in the curvature of the Earth when measuring distance or not. You can find what’s nearby in three ways: Straight line distance, Distance or cost over a network, and cost over a surface. I have already talked about the first two, so I will just describe cost over a surface. This approach is really only good for finding the cost of traveling long distances, as it uses a raster surface to show how much it costs to move away from a feature across the map. You can also use GIS to just select features within a distance. By inputting a distance from a source, it will highlight every feature within that distance, and give you either a list, count, or summary of those highlighted features without setting a boundary. Although, when doing this with multiple sources, you must label each feature for every source you place in order to know which is near which. GIS also has a street network built in, so you do not have to put in any data when measuring distances or costs over a network.