Chapter 4-
Chapter 4 highlights why density mapping is such an important GIS tool. Simply plotting locations shows where things exist, but density mapping shows where they’re actually concentrated and where they thin out. Looking at values per unit area makes spatial patterns much easier to notice and compare across a region.
One thing the chapter makes clear is that there isn’t just one way to map density. In some cases, using predefined boundaries like counties or census tracts works best. This approach calculates density within each area and usually shows up as shaded polygons. It’s helpful for comparing regions, but it can also be limiting since internal variation gets hidden by those boundaries.
Another option is density surface mapping, which creates a continuous surface instead of sticking to fixed borders. Density values are calculated for each raster cell based on nearby features within a chosen distance. These maps are better at showing gradual changes and identifying hotspots, which makes them feel more realistic. At the same time, they take more processing power and require more careful decisions.
Chapter 5-
Chapter 5 is all about answering the question of what is actually inside something else. Instead of just mapping layers and looking at them separately, this chapter explains how GIS can be used to figure out which features fall within certain boundaries. It feels like one of the most useful parts of GIS because it connects maps directly to real questions.
A big idea in this chapter is spatial overlay. This is when different data layers are stacked on top of each other to see how they interact. Depending on the tool you use, such as intersect or union, you end up with different results and keep different pieces of information.
The chapter also talks a lot about containment, which is figuring out whether points, lines, or polygons fall inside another feature. It sounds simple, but it becomes really powerful when applied to real situations. Things like counting how many schools are within a certain area or identifying neighborhoods located in an environmental risk zone feel very doable using GIS.
Something that stood out to me is how careful you have to be with your data. If layers do not line up correctly or boundaries are inaccurate, the results can easily be off. The chapter makes it clear that GIS is not automatic or perfect and that users still need to think critically. Overall, Chapter 5 makes GIS feel more relevant and useful.
Chapter 6-
Chapter 6 focuses on figuring out what is nearby and why distance matters in GIS. Instead of just asking what is inside certain boundaries, this chapter looks at how close things are to each other and how that closeness can affect analysis. This feels useful because so many questions depend on distance, like access to services or exposure to certain conditions.
The chapter talks a lot about proximity analysis, which is used to measure distance between features. One common method is creating buffers around points, lines, or areas to see what falls within a certain distance. Buffers make it easier to answer questions like which schools are within a mile of a park or which homes are close to a major road. I liked how this made distance feel more concrete instead of abstract.
Another important idea in this chapter is choosing how distance is measured. Distance can be straight line or based on actual travel paths like roads or sidewalks. The chapter points out that this choice can change results a lot, which made me realize how important it is to think about what “nearby” really means in each situation.