Chapter 1
I find it very intriguing how scientists are finding new ways to employ GIS aside from simply putting together maps alongside analyzing the space in and around those maps. Additionally, with the proliferation of remote sensing via drones and other contraptions, more spatial data in those harder to reach, ecologically sensitive, or other remote areas where new or different information can be recorded and applied in GIS software.
With all of the details concerning how types of data and phenomena exist in GIS, I do wonder about the process to collect, record, and input the spatial data in order for it to be fit to analyze within the software. Is there a generalized set of data for most areas that can be accessed freely with the proper software– such as the Delaware Data from the Delaware County Auditor and co? Does the same thing exist for more precise topography around the globe? Also, who is accountable for updating data in areas lacking government use of mapping systems for tax purposes, etc.?
Moving past that, there are the two types of representations for GIS features in vector and raster- which pique my interest in their differences. Vector models consist of XY coordinates, while raster models consist of expressions that somehow become continuous shapes. Each respective model takes up different amounts of shape and can be used for representing different types of data, but I wonder if there is another way to represent spatial data- especially since one model appears to be exponentially more complex than the other. Could there be any other way to represent continuous numeric data that would make doing so more accessible?
Chapter 2
One of the most critical aspects of mapping is that maps depict locations. Going a step further, is to use features within maps to analyze the various patterns within them in relation to locations and to each other. You could use these features to map out anything, really: bears, criminals, school zones, soybean fields, sewage leaks, etc. These features are given their own unique layer to be easily accessed and assessed.
From that point, features are used for various purposes. If I wanted to assess the yield of soybeans, I would collect data- or review already collected data, compile and input information, then review. Once I know the yield of soybeans, I could compare previous yields and report to the Ohio Soybean Council or to the farmers directly and let them know how their soybeans are doing. Maybe there is also a pattern between the manner in which the soybeans are cultivated, such as with no-till or with limited chemical use, then I could analyze that information and communicate accordingly. Another possibility is that there is a relationship between soybean yield and location, then, I could record the coordinates of soybean fields in a particular area. I do wonder if the Ohio Soybean Council uses extensive GIS to strategically plant their monoculture fields.
Shifting away from soybeans, GIS has a wide range of features that can be used to arrange, record, and track data. A major application of GIS mapping and analysis is for land-use and parcels, but there are many other possibilities- as established. GIS allows for easy assessment of distribution patterns from just taking a look at a zoomed-out map or through analyzing statistics for a statistically significant relationship.
Chapter 3
A highly important manner of GIS analysis is through mapping the most and least. An example of this would be mapping the amount of bubonic plague deaths per 10,000 people to detect hotspots where the bubonic plague is taking the most lives. There are three types of data: discrete, continuous, and data that is summarized by area. Discrete data represents bits of data including points of interest, lines, and areas. Meanwhile, continuous data represents entire areas or surfaces with continuous values- whereas discrete data is less encompassing. Summarized data represents shaded areas that are categorized- which can include discrete or continuous data.
The technicalities of GIS and map-making, in general, require an understanding of evaluating data and having the ability to apply that to a map. The many bits and pieces are the building blocks of GIS which allow users to visualize and express data. There are also many ways of quantitatively classifying data. Each means of classification, like the types of data, have their benefits and drawbacks that make them useful for different scenarios. Statistics play an important role in how many types of data within GIS are used and organized from standard deviation to outliers, and GIS has computing power to some extent for data classification. When creating a map, there are many options of the manner in which data is visually represented including symbols, colors, charts, contour lines, and 3D which, similarly to the ways of classifying data, have their pros and cons. The chapter provides a rudimentary guide for employing the various details that it discusses, but it is a bit difficult to retain every piece of information without something concrete to apply it to at the moment. There has been a lot of planning to shape GIS into what it is today.