O’Neill Week 2

Chapter One: The first chapter begins by sharing a few interesting advancements in Geographic Information Systems in recent years, including the fact that spatial data is more abundant and accessible than ever before. Spatial data scientists are discovering that they can use GIS for far more than just making maps and analyzing geographic phenomena. They can use it to address many of the world’s problems, which interests me because I’m not and don’t plan on being a geographer and it’s comforting to know that I can apply the skills I learn in this course to my field(s) of interest. 

The chapter then moves on to more practical facts about GIS analysis, including what it is: the process of collecting and interpreting spatial data to inform decision-making. It draws on many types of data, such as satellite imagery or sociodemographic statistics (among many, many other things). One thing it discusses is data interpolation. GIS uses interpolation to predict values from a series of sample points to represent continuous data more accurately. 

The chapter also talks about the types of attribute values. The book reads, “Each geographic feature has one or more attributes that identify what the feature is, describe it, or represent some magnitude associated with the feature.” An attribute value is just an amount or description that relates to an attribute, and they come in the following forms: categories, ranks, counts, amounts, and ratios. The book goes deeper into what each of these forms means and what they are used to represent. Pretty cool, reminds me of when I took AP Computer Science and Statistics when we talked about the different forms of data.

Chapter 2: Chapter 2 touches on the “whys” and “whats” of GIS, as well as on some technical details about GIS. Why map where things are in the first place? Mapping things out gives us insight and information about communities and areas that we would not have otherwise. By looking at the distribution of features on a map, you can pick up on patterns that will help you better understand the area you’re mapping. For example, Planned Parenthood could use GIS to map out where the most low-income people having unplanned pregnancies are in a city to learn what the best location could be for establishing a clinic.

The chapter also provides an explanation of how the GIS uses geographic coordinates to display features. It’s fascinating how the software translates location information into visual representations on a map. The distinction between mapping a single type and mapping by category was also enlightening. Mapping by category allows us to see how different types of features are distributed and whether they tend to occur in the same places. The chapter also highlights the importance of including reference features, such as roads or boundaries, to provide context and make the map more meaningful to the audience.

 

Chapter 3: This chapter explores how to map quantities to identify areas that meet specific criteria or to understand relationships between places. I found the distinction between mapping locations and mapping quantities to be important. Mapping locations shows where things are, while mapping quantities shows how much is at each location. I appreciated the breakdown of different types of quantities: counts, amounts, ratios, and ranks. Understanding the nuances of each type is essential for choosing the appropriate mapping method. The discussion on continuous and noncontinuous values also helped clarify how to group values for presentation. Classifying continuous values into discrete categories allows us to visualize patterns more easily.

The section on creating classes was particularly informative. The different classification schemes—natural breaks, quantile, equal interval, and standard deviation—each have their strengths and weaknesses. Choosing the right scheme depends on the distribution of the data and the message you want to convey. I also found the discussion on dealing with outliers to be relevant. Outliers can significantly skew the data and affect the map’s patterns. The suggestions for handling outliers, such as putting them in their own class or grouping them, provide practical solutions for dealing with this issue. The section on choosing symbols for graduated symbols and graduated colors provided valuable guidance for creating visually effective maps representing the underlying data. The distinction between using color alone and using a combination of color, width, and pattern to distinguish categories is very helpful.