Chapter 4
This chapter explains map density and how it can help with seeing concentrated patterns on given areas. Methods such as using a uniform areal unit can allow the distribution to be seen clearly. When it comes to deciding on what to map, it is very useful to think about the features being mapped and the information needed in order to create the map. There is also a difference between mapping the density of features and mapping feature values, such as the difference between mapping the locations of a business versus the density of its employees. Very different data sets.
There are two ways to map density: either based on features summarized by a defined area or by creating a density surface. Mapping by a defined area is recommended if you already have data summarized by area or lines that indicate this. It usually includes the use of dot density maps, helpful for representing the density of individual things such as people, trees, crimes, etc. It also explains further about the different layers for density values and different approaches when it comes to how detailed you may want your map. Its also noted as relatively easier than mapping by density surface. For mapping a density surface, it’s usually made in GIS as a raster layer. Meaning each cell gets a specific density value instead of being grouped into one. This approach is very helpful to use if you have many individual locations or sample points. This precision does require more effort as a result.
I also learned that some GIS software, such as ArcGIS, lets you calculate density on the fly, or do things like summarize features or feature values for each polygon to make it easier. When reading about how to create a density surface, I did get a little lost, honestly. Especially on cells and their size, plus converting density into their units. However, their different sizes and their important roles in mapping is really interesting. Theres a good balance between big and small to get the smoothest results.
Chapter 5
This chapter is mainly about whats inside the map and monitoring it. By doing this, it can be known when action needs to be taken in certain areas or not. For example, how close a crime was committed to a school, and therefore requiring harsher consequences. It is important to define one’s analysis as well, and there are several methods to fit into different types of data sets. Consider how many areas there are, and what type of features are inside the areas. This will help determine finding whats inside a single or several areas. It defines what is found inside a single area, and it allows you to monitor activity or summarize info about the area. For example, the number of calls to 911 within a 1.5-mile radius of a fire station. As for multiple areas, you can compare the data. Such as zipcodes being contiguous (borders touching).
Another aspect mentioned is if the features inside are discrete or continuous. Discrete features would be defined as unique and identifiable. They can beput in a list or summarized numerically, such as crimes, pipelines, streams, etc. Continuous features are a seamless category, and don’t have an easily defined amount; they are more of a summarization. Things such as vegetation or elevation range. They have continuous numeric values that vary across a surface. There are even more different methods for features, such as: Drawing areas and features, quick and easy, but visual only. Selecting features in the area is good for a list or summary, but only for info on single areas. Overlaying the areas and features is very good for displaying what’s within several areas and summarizing by area, just takes more processing.
Chapter 6
For the last chapter, on finding what’s nearby, it explains how you can use mapping to see what’s within a set distance of a feature. It emphasises that it allows you to monitor activity in an area. Such as finding the traveling range of a feature, which can be done by distance, time, or cost. When finding things nearby, you have to decide how you want to measure the closeness of a location or feature, and what info you need to find a method. Methods could be straight-line distance, measure distance or cost over a network, or measure cost over a surface. Straight-line distance should be if you’re defining an area of influence or want a quick estimate of travel range. It is simple, a rough approximation. Cost or distance over a network should be if you’re measuring travel over a fixed infrastructure to or from a source. It is more precise; it just needs more accurate network layering. Cost over a surface should be if you’re measuring overland travel. It gives an area within travel range, allows several combined layers, and just requires some data preparation.
Key terminology, such as network layersare also introduced. Network layers are a geometric network composed of edges, junctions, and turns. Junctions are the points where edges meet, and turns are used to specify the cost to travel through a junction. The GIS can tell where edges are connected. When creating things like this, you can also use buffers. Buffers are used to define a boundary and find what’s inside it. To make a buffer, you have to specify the source feature and the buffer distance. The GIS draws a line around the feature at the specified distance. The line can be kept as either a permanent boundary or temporarily. More important details like knowing if it is a flat plane or follows the curvature of the Earth, whether its necessary to have a list, count, or summary, etc. There are many repetitive concepts near the end of the chapter, which do help with remembering their functions, but it is difficult to separate the new;y obtained info from the old.
Theres so much information in these chapters I have no idea how to keep it down to 300 words tbh