Chlebowski – Week 3

Chapter 5:

This chapter starts with explaining how to classify specific areas of interest as well as the types of data inside these distinct boundaries. The book gives three types of methods for mapping such phenomena: drawing the area and features, selecting the features inside the area, and overlaying the areas and features. Each are used when specific reasons for mapping or types of data are present. For example, you would want to overlay the areas and features if you wanted a display of all the types of features in many different areas as well as if you had a single area but are dealing with displaying continuous data values. Then, the types of ways that these data or features inside the areas can be displayed was discussed. It talked about how counts, frequencies, and numeric statistics like the means and medians of data can be used to classify how much data is in each area of discussion. Within their explanation of how overlapping data can be displayed, I found it very clever how they were able to overlap a color scale of workers in specific areas with a floodplain by using a transparent type of blue on top of the different shades of orange and red. When they mentioned the two different types of methods of overlaying areas on areas (vector and raster) I was and still am a bit confused on what causes slivers in the vector method. It defines them as small areas where the boarders get slightly offset when overlaying areas, but I am confused how this occurs, as I would assume that the only way this might occur is if the data itself is accurate only within a specific area, and thus if two areas have boundaries that are close enough to each other to make a zone that is ambiguous, that would be a sliver, maybe. Finally, I really like the idea of using histograms to supplement multiple types of data in a single area, which makes comparing the different types of data a lot easier than just having the key to show which values are what.

Chapter 6:

Measuring how close something is by cost is very unique; I did not really think that this was a valid way of measurement, but it does have its uses. Travel cost is a real concern for many people to get from one place to another, but it can vary from person to person (because cars and milage and traffic and stuff). Also, the distinct bands comparison method for distances is really neat too, having ring values within a specific ring arc distance to display the count of specific data is a cool way of displaying it. Personally, I really do not think of distance areas in terms of within, let’s say, 1000-2000 feet in a ring, so this type of display is really interesting. Cost over a surface is one method of finding what is nearby that they explained, and I think that it has some really unique applications, especially with terrain. You would have to need extremely specific raster data of how certain terrain is easier or harder to traverse, indicating that harder terrain will be less cost effective to traverse than the ladder. Spider diagrams are quite the useful tool in determining the relationships and rough distances of two objects from two or more source points in a spiderweb-like formation. Setting travel parameters when using cost as a measurement is shown to be quite tricky, as there are many external factors and assumptions that are needed be made to determine cost quantities. When dealing with time, estimated traffic, turns, and speed limits must be made very precisely and made into a formula to determine how fast or slow a specific road route is. These types of assumptions are also made when creating cost layers, which can give impressions on the ease or cost of moving or building on specific land is by the specific qualities of the land being surveyed.

Chapter 7:

Mapping change is something that I did in GEOG 122 (whooo) which is super awesome for the cross-ciriculumativity! Mapping areas or things that do not change in location is what I am familiar with (being the changing populations numbers of counties in a state by decade), but mapping moving data like the path and speed, or size of a hurricane is a whole different ballgame than from what I am used to.  The time patterns that are commonly used when displaying change are trends, before and after, and cycles. Time can also be summarized by grouping events that happened in timely proximity to each other together. These can be displayed in cyclic patterns like in the use of many different versions of the same area, denoting the time differences, and also with discrete data by using point locations and different colors to describe the different times of day/year that the events happened at that location. The three specific ways that mapping change can be done is by a time series, tracking map, and measuring change. Tracking maps are really neat as they show the spread of movement of data from an initial start area to newer area boundaries denoted by time. Additionally, measuring change by denoting the amount, percentage, or rate of change is something that I remember doing very briefly in Human Impacts on the Environment. We did color compositions of land data and had to denote changed land with specific colors kind of like the map in the book concerning the change in forest cover after a hurricane, except our map was full of color and every area was assigned a specific label in the color composition. A more similar representation to what we did was like the book example of land cover change in 1914 vs 1988, where the whole map is covered in data categories.