Stratton- Week 2

Chapter 1

Chapter one gives a basic overview and an introduction of what GIS is, and how it is used. GIS is a process that looks at geographic patterns in data and relationships between features. This chapter lays out the steps to starting the GIS analysis process; frame the question, understand your data, choose a method, process the data, and look at the results. There are three types of features, discrete features, continuous phenomena and summarized by area. Discrete features are locations that can be directly pinpointed. Continuous phenomena is measured anywhere and covers entire areas, like temperature or rain. Lastly, features summarized by area represent numbers or density of an entire area. There are two ways to represent these three features, vector (typically used with discrete and data summarized by area) and raster (usually used with continuous phenomena). The chapter then goes over briefly how map projections distort shapes and measurements, and advises that small areas can ignore the distortion but it’s more of a concern when mapping larger areas like states or countries. There are five geographic attributes that help describe features, categories, ranks, counts, amounts, and ratios. Categories are groups of similar things that organize and make sense of the data, like categorizing roads as freeways or highways. Ranks are putting features in order, from high to low when measures are difficult or a combination of factors. Counts are numbers of features on a map and amounts represent any measurable amount of things associated with a feature. Ratios represent the relationship of two quantities by dividing one by the other. Lastly the chapter overviews how to use data tables that hold attribute values and summary statistics. There are three common operations, selecting, calculating, and summarizing. 

 

Chapter 2

Chapter two goes into detail about making a map using GIS, and what they could be 

used for. Mapping features can show patterns in the distribution of those features, and start to find the causes of those patterns. The chapter describes the process of making a map, starting with patterns from the data you collect and mapping those features with symbols. You have to think about the audience that will be viewing the map, adding reference locations to give context to the analysis or to make it more recognizable, and how the map will be presented, changing information presented based on size scale. Another thing it reminds you to do is make sure there are geographic coordinates assigned to the features you’re mapping. To map a single type of feature you would use the same symbol, and the GIS stores the locations of the features as a pair of coordinates that define its shape, so it can draw the features with the symbols you choose. To map by category, you would draw the features with a different symbol for the different category values, and the GIS stores each value for the features. You can also use many different categories to show other patterns in the data sets, but using more than 7 categories can make them much more difficult to see. The larger the area, the smaller number of categories would be beneficial and vice versa. Grouping a large number of categories can make it easier to see the patterns as well, as long as you’re specific about what the categories include. When choosing symbols, choose based on the type of feature. Individual locations would use a single marker in a color or shape for each category and linear features would get different variations of lines, and shaded or raster layers get different shades of the same color. 

 

Chapter 3

Chapter three describes mapping most and least values. You would use mapping most and least when you want to go beyond just mapping locations of features and give your audience more information about your data. You would map patterns of these features that have similar values, and the quantities associated with the three types of features discussed in chapter one, discrete, continuous phenomena, and data summarized by area. You assign symbols to these features based on their attribute (also discussed in chapter one, counts, amounts, ratios, and ranks), which contains a quantity. Counts and amounts show you total numbers, and lets you see values of the features. This is only a good method for discrete features and continuous phenomena because it would skew the patterns for summarizing by area. Ratios even out the differences between large and small areas, areas with many or few features, to give a more accurate distribution of said features. It’s very useful when using the summarizing by area type of feature. Ratios could be; averages, for comparing a place that has few features against one with many, proportions, showing part of a whole quantity and what it represents, and densities, for showing where features are concentrated. Ranks are useful when direct measures are difficult or for combinations of factors. The chapter also overviews how to create classes when using counts, amount and ratios, because each feature has a different value. There are four common standard classification schemes for grouping classes, natural breaks, quantile, equal interval, and standard deviation. Natural breaks, also known as Jenks, are based on natural groupings in your data and breaks where values jump. Similar values go in the same class. Quantile classes include an equal number of features in each class. Equal interval classes show the difference between the high and low values as the same for each class. And lastly, standard deviation classes are based on how much their values vary from the mean. The chapter then goes over how to compare each class, and the advantages and disadvantages for each one.