Chapter 4:
This chapter deals with mapping density, which allows you to see the concentration of certain features, rather than individual data points for each feature, which can make observing trends in distribution easier. Generally, density is displayed using a gradient of colors, with different shades representing different concentrations of the feature in question. Alternatively, dot density mapping can be used, where each dot represents a certain quantity of a feature in a general area, rather than the location of any one specific feature. Since density is calculated by taking the total number of a feature in some area, and dividing it by the area of the region it is found, it can be useful in showing things like population densities across counties, even if the size of the counties vary. Another factor in making density maps is cell size and search radius. As cell size and search radius increase, patterns become more generalized, making trends easier to pick out, but if the radius becomes too large, the pattern may become too general and no longer accurately represent the data. When calculating the cell values for the density map, there is also the option to use a weighted average, rather than a simple averaging of all the points within the search radius of the cell, and by using a weighted average, an easier to interpret, albeit more general map is produced. Rather than using a gradient of colors to represent the different density values, contour lines can be used to represent regions of equal density, with areas having more rapidly changing density having a higher concentration of lines close together. Often, using two methods in conjunction, such as a dot map overlaid on top of a gradient map will most accurately represent the data, allowing you to visualize both general trends in the data, as well as specific data points that would be lost if only a gradient map was used.
Chapter 5:
Mapping what’s inside an area is a useful tool for making determinations about actions that should be taken and to find trends or make comparisons between areas. Finding what is inside an area usually begins with determining whether the data you are looking at is inside a single area, or within several disconnected areas, along with whether the features are discrete, like store locations, or continuous, like soil type or rainfall amounts. Depending on the research you are conducting, you can also make decisions about whether to include features that are partially within your area, or within a certain distance of the feature you are focusing on. Multiple methods exist for finding what’s inside an area, those being drawing the area and the features, selecting the features that are within the area, and finally by overlaying the area and its features on top of each other, then calculating the stats for the areas where they overlap. When overlaying discrete features like house locations with your area, you are able to create summaries regarding quantities, densities and any other data you have available for these points. Meanwhile, if you are working with already summarized data, or continuous data like rainfall amounts, you must make sure that your summarized data falls completely within the area you are researching, since you cannot subdivide already summarized data further. Additionally, when overlaying areas on top of each other, sometimes slivers may occur, where small areas of overlap are formed due to boundary mismatches. In order to determine which areas are, or are not slivers, there are multiple methods that can be used, including comparing the potential sliver size to the smallest area input, since areas smaller than that value may not be accurate, or by comparing the sliver dimensions to the accuracy of your collected data, and removing areas smaller than this threshold.
Chapter 6:
GIS can be used to find out what is within a distance, travel range along roads, or travel range in terms of time, of a feature or region. Defining distance by straight line distance is often used when determining area of influence, such as all properties within 1 mile of a power station, while using a cost, such as travel time or distance, can be more useful when finding precisely how many of something are within some distance along roads, such as all bus stations within 3 minutes of walking from a store. By creating a buffer around objects, you can find which features are within a distance of the object, and by selecting multiple objects, you can find which features are near a set of objects, like which houses are within a quarter mile of a fire hydrant. Similarly, by computing statistics for multiple distance ranges around a single or set of objects, you can find differences in the ranges of features effected at each distance, such as houses within 3 vs 5 vs 10 minutes of a fire station. Another way to visualize distance data is by using a distance surface, which superimposes a gradient onto the map to help show how distance or cost changes as you get farther away from your object. By selecting multiple objects, you can even highlight the regions that fall within or outside a distance range for both objects, like houses in a city within 4 minutes of two or more fire stations. Measuring distance by cost, be that travel time or distance, allows you to set specified time and distances costs for each road segment, turn, and other factors along the path, allowing you to accurately estimate boundaries based on travel factors. Cost distances can also be calculated for surfaces or continuous features like terrain, allowing assessment to be made, for example, for the maximum distance a road could be built through a hilly region, or all forested areas within some cost distance of a house in the wilderness.