Chapter 1:
GIS is a “process for looking at geographic patterns in your data and at relationships between featuresâ. When utilizing GIS lots of information has to be taken into account, and depending on what information is present leads to different routes to form your map or data. Next, choose a method for processing the data and analyzing the results. Features can be classified as discrete or continuous, and they can also be summarized by area. Discrete features (such as specific locations) can be pinpointed, while continuous phenomena (like temperature) can be measured at any location since every spot has a temperature. Continuous data is typically derived from a set of discrete points, and summarized data represents counts or densities of features within defined areas, like the number of households in a county. Geographic features can be represented as vectors or rasters. Vector features are represented as rows in a table with defined x and y coordinates, making them ideal for discrete data. In contrast, rasters use a grid of cells to represent features in continuous space, making them suitable for continuous numeric values. While rasters can depict continuous categories, they can also combine discrete features with other layers. Categories help organize and make sense of your data, while ranks create a relative order among features. Counts indicate the total number of features visible on a map, while amounts refer to any measurable quantity associated with those features. Ratios show the relationship between two quantities by dividing one by the other for each feature, and proportions indicate what portion of a total each value represents. Finally, densities provide insights into how features or values are distributed per unit area.
Chapter 2:
The beginning of the chapter stresses the significance of analyzing patterns among various features on a map. It illustrates how police utilize GIS to monitor crime and decide where to deploy patrols. The selection of features to display and their representation is guided by the information required and the map’s intended purpose. Before creating your map, itâs crucial to assign geographic coordinates to the features you want to include, a process mainly handled by the GIS. An optional step prior to mapping involves assigning category attributes with values to each feature. For single-type features, you can represent them all with the same symbol. The chapter notes that GIS records each feature’s location as pairs of geographic coordinates or sets of coordinates that define its shape, whether itâs a line or an area. Using a subset of features can help reveal patterns that might be obscured when mapping all at once. Mapping by category, with distinct symbols for each, can improve your understanding of how a location functions. Additionally, organizing features by type can uncover different patterns, as features may belong to multiple categories. Sometimes, creating separate maps for each category is beneficial if features are too close together, making them hard to distinguish. When mapping multiple categories, itâs best to limit the number to seven on a single map. The number of categories that can be effectively displayed may also vary based on the map’s scale and the features involved. If you have more than seven categories, consider grouping them to enhance pattern visibility. Including recognizable landmarksâsuch as major roads, political boundaries, towns or cities, and significant riversâcan greatly assist viewers in interpreting the map.
Chapter 3:
Mapping both the most and least frequent locations for a subject helps reveal the relationships between different areas, enabling better data analysis. If you only map the most common places for a feature, you miss out on valuable control data for comparison. For example, a map showing just âOhioâ as is lacks context and is essentially pointless. In the section âWhat type of feature are you mapping?â the chapter reiterates points made in Chapter 1, discussing three types of features: discrete features, continuous phenomena, and data summarized by area. It also emphasizes the importance of considering your audience to ensure that the data represented is relevant to them. Quantities play a crucial role in mapping both the most and least frequent features. These can include counts, amounts, ratios, or ranks. While the chapter revisits counts and amounts, it also offers useful tips, noting that these metrics apply across all three mapping types. Presenting quantities in different ways can enhance understanding; for example, showing exact locations can be more effective than using generalized areas like area codes. Proportions can illustrate how much a particular area contributes to the whole, similar to political maps that depict voter turnout by county. Creating classes for specific features is also a valuable strategy, making it easier to differentiate between them. Using distinct symbols is an effective way to achieve this. In some cases, classifying by percentages can highlight densely populated areas versus less populated ones. However, be mindful of outliers in the data that may skew these percentages. Using numerical classes allows for more precise breaks and enhances the clarity of your mapping.