Chapter 4
Chapter Four is all about mapping density, in other words, looking at how particular criteria are disturbed. Density doesn’t just show us locations of the criteria we are searching, but instead helps us see concentration and relationships among the data. We are able to find per unit area with density. This helps in many different fields and areas. There are two ways to approach density maps. The first way looks at density in defined areas. Existing boundaries are used to calculate how many of a particular criteria fall in a particular square mile. Maps of this nature are displayed with shaded areas. The other method is density surface mapping. Continuous areas can be used for this instead of predefined boundaries. The raster type layering is used for these maps. This helps show patterns as well. When using density mapping you first have to decide if you are looking at how many, called raw counts, unit area amounts, called normalized values, or density gradients, called interpolated surfaces. If you want a simple distribution, use raw counts. If you want to make comparisons, use normalized values, and if you want to see patterns, use interpolated surfaces. I like that density mapping is more than just seeing dots on a map. We can see where things occur the most and when they thin out. We can also easily see outliers. This all helps to add context to points. For another class I have selected a project idea about hellbenders and the effect the PFAs have on them. I would use density mapping to determine where populations of hellbenders are highest and lowest. I would do the same for PFA levels. These layers together would help me draw relationships and make educated guesses about the effects of PFAs on the hellbender population. My point is, this can be used to make sorts of comparisons and relationships.
Chapter 5
Chapter Five is focused on isolating the relevant data needed, instead of looking at the big picture. GIS helps isolate the features and boundaries that the researcher is interested in looking into and filters out the rest. There are three methods to use to do this. The first method is drawing areas and features by using either the boundaries already existing in the software or creating new ones to look at the area of interest. This method is limited however because manual drawing can be less precise. The second method is selecting features within an area. This has more precision. The program selects features inside specific boundaries. The last method is using overlaying areas and features where layers are stacked so that datasets intersect. This chapter helps pull some of the broad concepts of earlier chapters together and focuses on methods using spatial reasoning. This chapter helped to understand more of the “science” behind mapping. I like that in each chapter there are multiple methods to do things and the book helps outline the reasoning behind choosing one over the other. This brings back the point from earlier chapters, where the researcher needs to know what they are looking for and how they want to convey that message, in order to choose the correct method. This chapter also reviews the idea of discrete vs continuous data. This is important to remember when selecting methodology, and I like that the chapter did a reminder of that.
Chapter Six
Chapter six focuses on the concept of proximity. This means finding what is nearby a particular place or feature. Proximity is important in real-world applications. Distance plays a role in assessing risks, distribution of goods, and access to different things. For example, if you are planning a park, you may need to map how close the park is to a school or other parks in order to determine if a park is needed or if there are likely to be people to go to a park. You also wouldn’t want to put a park next to a prison. So looking at proximity to different features is really important. Just like in previous chapters, this chapter mentions that it is important to define what you are looking for, before you begin. The proximity question starts with defining. You need to define what nearby means, what metrics are you using? One way to find nearby is to use the straight line distance. This is measurement of a direct like between two features. This is simple, but doesn’t account for obstacles in the way. My mom would call this “straight as the crow flies” measurement. We have all been told that something is just a mile down the road, but it took 10 minutes to drive there due to stop lights and cross walks, and having to keep the car on the road. None of those things are accounted for in the straight-line method. A second method is measuring distance or cost over a network. Network distance is more meaningful when a straight path can’t be taken. This accounts for the time and distance along actual paths. This is what you get when you put your destination into Google Maps. Lastly, there is the calculated cost across a geographic surface. This takes barriers into account. Environmental analysis uses this method. It is important to account for the method needed for what you are analyzing. If we think back to the park example, if there is a river running through town and a railroad track, there are barriers to kids accessing the park. You can’t just pick a point and assume that everyone has access that is inside that circle of proximity.