Mulloy Week 2

Chapter 1:

This chapter reiterates the usefulness of spatial analysis and how employing to deepen understanding of an area can allow more accurate predictions. I feel that the step-by-step process that they provide is incredibly useful, not just for GIS (or so I presume) but also for any other method of data analysis. Often, trying to figure out what information is needed and how to interpret/gather it is the most difficult part, and it can be overwhelming when examining large amounts of data without fully understanding it. This part of the chapter is something I feel I will return to. 

Interpolation is the assigning of data to points that aren’t measured between points that are measured. Since measuring tools are only so efficient, and the landscapes that people are measuring are often rather large, they can only take so many measurements. This means that space between measurement points can vary greatly. In order to fill these gaps, they typically apply a continuous connection between points, even if they’re not continuous, because it’s generally accurate enough to be accepted. If more accurate data is needed, they can do more precise measurements and simply manually edit data.

A similar issue to inaccurate measurements is cell size in the Raster model. When measuring, it has to be done in a timely manner, but also be accurate enough, which is where compromises and interpolation come into play. Cells that make up the space in GIS can vary in size, and it quickly becomes a problem of balancing storage space and time to measure/render, and being precise enough. 

The vector model is different from the raster model in that it is based on coordinate points that are linked together to make lines and polygons.

The attributes that can be assigned to points/cells are Categories, Ranks, Counts, Amounts, and Ratios. Some of these seem slightly redundant, and I’m still not quite sure what the difference between “counts” and “amounts” are.

 

Chapter 2:

This chapter is primarily focused on mapping and how to assign data from a conceptual viewpoint, rather than practical; as in what to do to make your maps decipherable (via data values, map type, scale, color coordination, etc.) rather than what buttons to press. It also discusses what types of map may be more useful for certain applications.

The section about the different uses about mapping expands on the week 1 readings from Schuurman, and it really reinforces how versatile GIS is as a tool, and proves that it’s more than the sum of its parts.
I find it very interesting that 7 seems to be the sweet spot of categories on a map. I can imagine that that may cause issues when considering large scale maps with lots of varied categories, because detail would have to be sacrificed. Of course, that does explain why simply having more maps of different scales or categories split into groupings would be so useful in these situations. 

It appears that there never seems to be an “ideal” way to indicate points on a map. Even if it is the best in general, there is always the issue of accessibility for people with certain vision issues. I don’t have the greatest vision, and my eyes hurt when looking at maps with small symbols, as my brain has a hard time differentiating between them. So personally, I prefer colours to indicate different types of things on maps. However, color blind people would have a significantly harder time with that and so they would need to use symbols or some other indicator.

The end of this chapter is more about deciphering and interpreting maps based on what you can determine by simply looking at it. Often you can find quite a bit, and it can be used for simple things with fine accuracy, but of course more complex maps require complex calculations.

Chapter 3:

When I saw what this chapter was about, the first thought that popped into my head was using derivatives to determine local min/max. Of course that would only work for more advanced calculations and determining exact locations rather than general ones. For getting the general min/max, there are helpful tools through gis that allow you to just look at the map to determine. While I understand the point of making maps more presentable, I think that when presenting to certain audiences, one should share the map in multiple degrees of detail. Some detail is hidden with general maps, and some detail is hard to determine with too precise maps. Additionally sharing the maps with messier data do allow show your train of thought and how you came to what conclusions you came to. I believe this is especially important because it allows second opinions on your thought process, which can reinforce your conclusions or disprove them.

The relativity of the ranks is also something that I feel needs more than just a map to understand. Providing some explanation to the map when being presented would be much needed context. 

Mapping classes is a good way to immediately mark all data that falls within a certain group on the map. This is useful for examining similarities and differences between data points with certain qualities. The remainder of this chapter discusses varying features of GIS and how to implement them, along with their uses. I noticed that based on how the classes can be made and categorized, it seems rather easy to lie or warp conclusions. When making the classes, if the data groupings are not evenly distributed, (ex. 1-100 being 1-10, 11-30, 31-90, 91-100) it can be used to seriously warp the presentation of data. People would assume without looking at a legend that it would be gradual and evenly spaced.