Mitchell 1
Spatial analysis and data is more abundant than ever and growing in acceptance as it experiences more advances and is shared more openly and widely. It is also growing in uses and accessibility and will continue to grow in the coming decades. GIS analysis is a process of identifying geographic patterns in data that range in complexity. In order to effectively perform an analysis you need to: frame the question by figuring out what information you need; understanding your data; choosing a method that works for your data collection and intended use of the results; process the data; and look at the results.
The types of geographic features we’re working with affect all steps of the analysis process. Geographic features are discrete, continuous phenomena, or summarized by area. For discrete locations and lines, the actual location can be pinpointed. At any given spot, the feature is either present or not, examples include streams and parcels. Continuous phenomena can be found or measured anywhere and blanket the entire area you’re mapping. A value can be determined at any given location and includes precipitation and temperature. Summarized data represents the counts or density of individual features within area boundaries. Examples of features summarized by area include the number of businesses in each zip code, the total length of streams in each watershed, or the number of households in each county. The data value applies to the entire area, but not to any specific location within it.
Geographic features can be represented in a GIS using two models of the world: vector and raster. With the vector model, each feature is a row in a table, and feature shapes are defined by x,y locations in space. Features can be discrete locations or events, lines, or areas. Locations are represented as points having a pair of geographic coordinates. When you analyze vector data, much of your analysis involves summarizing the attributes in the layer’s data table. With the raster model, features are represented as a matrix of cells in continuous space. Each layer represents one attribute and most analysis occurs by combining the layers to create new layers with new cell values.
Mitchell 2
Patterns help understand the area you’re mapping, you can use a map to identify individual features or to look for patterns in the distribution of features. Looking at the location of features lets you explore causes for patterns. It is important to understand the purpose of your analysis and your audience to know what information is relevant and what to include. Every feature on a map needs geographic coordinates, if the data is coming from a GIS database it will already have coordinates assigned. Many categories are hierarchical, with major types divided into subtypes.
When making a map, you can map all features in a layer as a single type or show them by category values. Mapping features as a single type might reveal differences between them. The GIS stores the location of each feature as a pair of geographic coordinates or, if the location is a line or area, as a set of coordinate pairs that define its shape. You can map features by category, by drawing features using a different symbol for each category value. Mapping features by category can provide an understanding of how a place functions. When mapping different categories you can use different symbols or different maps to express the different categories. If you’re showing several categories on a single map, you’ll want to display no more than seven categories. Because most people can distinguish up to seven colors or patterns on a map, displaying more categories than this makes the patterns difficult to see. The distribution of features and the scale of the map also affect the number of categories you can display. The way you group the categories can change the way readers perceive the information.
Your map will be more meaningful for people if you display recognizable landmarks, such as major roads or highways, administrative or political boundaries, locations of towns or cities, or major rivers. You may also want to map reference features specific to your analysis so that you can look at geographic relationships.
Mitchell 3
People map where the most and least are to find places that meet their criteria and take action, or to see the relationships between places. Mapping features based on quantities adds an additional level of information beyond simply mapping the locations of features. Knowing the type of features you’re mapping, as well as the purpose of your map, will help you decide how to best present the quantities to see the patterns on your map.
You can map quantities associated with discrete features, continuous phenomena, or data summarized by area. Discrete features can be individual locations, linear features, or areas. Locations and linear features are usually represented with graduated symbols, while areas are often shaded to represent quantities. Continuous phenomena can be defined areas or a surface of continuous values. Areas are displayed using graduated colors while surfaces are displayed using graduated colors, contours, or a 3D perspective view. Data summarized by area is usually displayed by shading each area based on its value or using charts to show the amount of each category in each area.
To map the most and least you assign symbols to features based on an attribute that contains a quantity. Quantities can be counts or amounts, ratios, or ranks. Knowing the type of quantities you’re mapping will help you decide the best way to present the data. Ratios show you the relationship between two quantities, and are created by dividing one quantity by another, for each feature. Using ratios evens out differences between large and small areas, or areas with many features and those with few, so the map more accurately shows the distribution of features. Because of this, ratios are particularly useful when summarizing by area. The most common ratios are averages, proportions, and densities. Averages are good for comparing places that have few features with those that have many.