Buroker Week 3

Chapter 5:

Mapping what’s inside can be used to decide if action needs to be taken. For example in times of emergencies, maps can be used to show what areas are at risk. You can also use multiple maps to compare what is “inside” of different areas. Using an area boundary allows you to select features that you will be mapping and therefore create the “stuff” that you are mapping inside. Understanding your data is once again an important part of the process of mapping what’s inside. You must know whether you are mapping what’s inside a single area or several areas, because this will affect how to best map the data. If it’s a single area you can easily monitor activity or summarize information within that area. If it’s multiple areas, you can see how much of a specific thing is specific areas and compare them. There are also three different ways to find what’s inside an area, drawing areas and features, selecting the features inside the area, and overlaying the areas and features. Drawing areas and features allows you to find out whether features exist within the area or not, but only give you surface level information (you can’t get information about the features inside an area). Selecting the features inside the area results in a list or summary of features inside an area, but doesn’t separate information by area (you only get a list of features inside all areas combined together). Overlaying the areas and features allows you to find out which features are inside each area, and summarizes how many or how much is in each area. This gets the most expansive information, and solves the issues from the other two methods, but takes the most time and effort processing. The choosing a method section on page 148 will be useful for choosing a method if I ever have to do this in the future. The section on selecting features inside an area shows a bunch of example maps that look to me like they show the data very well and are “good” maps. The map and section on page 177 about overlaying areas with continuous values is really cool. I like the way that the GIS is able to combine elevation surface and a watershed layer and show how the elevation and watershed mesh together.

Chapter 6:

This chapter highlights why it can be important to map what’s nearby. Traveling range is an important component of doing this, and is defined by distance, time, and cost. Understanding what’s within the traveling range of an area can help you better understand how that area can be used and serve important purposes. The first step in the process of mapping what’s nearby is figuring out how to define and measure “near”. Making a definition like this feels important because “near” can mean a lot of things and a baseline definition would make things a lot easier. This nearness can be defined by either a set measurable distance, or by travel to and from a specific feature. You can find what’s nearby using straight-line distance,  or by measure cost over a network or over a surface. Using straight-line distance is the simplest and in my opinion most intuitive. Cost over a network and over a surface seemingly gets more complicated and involves more thinking/understanding. Useful ways to choose a method are found on page 191 and involve thinking about if you can define an area of influence, need a quick estimate of travel time, are measuring travel over fixed infrastructure, or are measuring overland travel. I think the way that you can use straight-line distance around a specific feature to find distance is really cool. I liked the example map of the selected parcels surrounding or within 100 feet of the road. I also think creating a buffer feature could be really useful and is something I’d like to practice doing. Once you have point-to-point information, you can create a map that color-codes locations by distance from the source (and closest to the source), make a spider diagram, or map source features using graduated point symbols. A spider diagram is when the GIS draws a line between each location and the nearest source. You can do this with multiple different sources and create a map that resembles a multicolored spider-web, comparing and representing the different patterns between source features.

Chapter 7:

Mapping change feels like a different thing than what we’ve been reading about because it’s a future phenomenon. This can be useful because it allows people to anticipate future conditions. You’re able to map expected conditions by looking at historical conditions and to eventually anticipate future needs. In order to best map change, you need to understand the types of features you are mapping. Features that move can be mapped using discrete features. These features can be tracked as they move through space. They include features you can map paths for (like hurricanes, a vehicle or animal), linear features (like a changing stream channel), or an area feature (like a fire boundary or oil spill). You can map change in three ways, time series, single tracking map, or map the differences in values between two times or dates. Time series show movement or change in character and have a strong visual impact. Tracking maps show movement and better show subtle change. Mapping change shows changes in character and shows the actual difference in amounts or values. If you generate multiple maps over different dates, it can be important to correctly decide the number of maps to show. By showing fewer maps, farther apart in time you can make the change in values easier to show, but less nuanced. Showing a bunch of maps with dates more closely together in time, you can reveal more detailed patterns about the change. Also, it can be helpful to include tables and charts that summarize data along with your maps. A tracking map is a map where the movement of individual features is mapped using a series of contiguous points. You can add a line connected points to emphasize the path the feature followed or even map the points at equal intervals to see how far the feature moved in a set time. Mapping continuous categories or classes is more complicated than mapping other features because it involves combining two layers, for both date and time. I get a little bit confused when it starts talking about raster data and areal extent. I feel like I’m going to have to do some more reading and investigating to understand this.