fraire week 3

Chapter 4
Two ways to map density: by defined areas or by density surface.

defined areas: you can show and calculate density for the defined area. You can use a dot map.

density surface: created in GIS raster layers. Simple calculations are not as easy to read as weighted calculation in terms of rings. You can use graduated colors or contours to map density surfaces. Be aware of how many class you use, between 3-15 is the sweet spot, more or less gets confusing and loses data. Also note the colors you choose for the gradient and what appeals to the eye more (dark or light color gradient indicates high density).

Be cautious of how much info we need or don’t need, it’s a fine line between too much and too little info to not lose the obvious patterns in densities.
I remember calculating cell size conversions in remote sensing, it took such a long time. I think I left for lunch, used the restroom, got Rowley coffee and it still wasn’t done. I think I was converting points to tangible pixels with units but it’s crazy how much power and time it takes for these things sometimes.

This chapter was pretty short and covered a lot of things I knew how to do technically, but gave me more info on the use and reason behind these techniques. I liked comparing the dot and contour maps, I think it would be cool to do something with those in a project.

Chapter 5
to find out what’s inside, first build you area of study, and if its one or many.
I recall searching for feature attributes in remote sensing to narrow down a price range for potential house buyers. I also remember trying to import a boundary layer (shapefile) of Brazil and it kept not working. The datums were the same but it was not wanting to place itself properly. It took me 2-3 days to figure out how to do it.

Drawing areas/features: find whether features are in area or not. good for single area.
Selecting features in area: get a list of features in area, good for single area.
Overlaying areas/features: which features are in which areas and how many/how much in that area. good for multiple areas.

Most common summaries: count and frequency.
count: the total number of features inside the area, such as the number of businesses in a neighborhood.
frequency: the number of features with a given value, or within a range of values, inside the area, displayed as a table.

These slivers are very annoying. I remember making data points on a top layer that was slivered and when I flushed it out those data points were nulled because they didn’t fall in the area. I had to go back and move the points in just a hair to get them to be present.

The vector method provides a more precise measure of areal extent but requires more processing and postprocessing to remove slivers and to calculate the amount of each category in each area.

when choosing overlay to remove slivers: the raster method is more efficient because it automatically calculates the areal extent for you, but it can be less accurate, depending on the cell size you use. also prevents the problem of slivers. It is often faster because the computation that the GIS must do is simpler.
single area with one category: bar chart, or pie graph; multiple areas with one category: bar chart; multiple areas with multiple categories: histogram, cluster, or stacked bar chart, with few areas/categories you can use pie chart too

Chapter 6

I didn’t consider time or effort a cost in distance before this chapter.

Planar: calculating distance assuming the surface of the earth is flat

geodesic: taking into account the curvature of the earth when calculating distance

Inclusive bands: tells you the total number within bands as distance increases

distinct bands: lets you compare distance to other characteristics like how much someone 1000m away spends on groceries compared to 2000m.

I like the chapter setups where it introduces a concept, tells you its pros and cons, and also tells you how GIS does it as a function/what you need to do it, etc. Its helpful to have consistency. 

These few chapters have covered a lot of what was in our exercises for remote sensing. I had to do parcel selection within a given boundary to find homes for homebuyers that met their specifications.  I was reminded of this when it discussed selection within boundaries. I’m glad that a lot this is getting explained now. I would get pretty confused doing raster calculator calculations and not understanding what the numbers and symbols I entered meant. It is plugging in data into the calculator as a word problem too, the worst kind of math.

The spider diagram is cool, I like it. The graduated symbols map seems harder to read, the graduation of triangle size is hard to distinguish (for me)

The calculation of these distance seems like a really useful tool. I have worked with this concept a little bit but not to the extent that they went into in this chapter. I learned more about what Arc is doing behind the scenes in my random clicking and it makes things more comprehensive for me. I am more aware of why I’m doing something as opposed to just following directions to get it done.


1 thought on “fraire week 3”

  1. getting rowley coffee. lots of stuff here. you have had lots of raster concepts and applications. this class is more vector (points, lines, areas, and associated data) and really different (processes much faster – no one has time to get me coffee).

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