Chapter 1: Introducing GIS Analysis\u00a0<\/b><\/p>\n
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> GIS analysis is the process of finding geographic patterns in data and at the relationships between features.<\/span><\/p>\n <\/p>\n Understanding Geographic Features<\/span>\u00a0\u00a0<\/span><\/p>\n <\/p>\n Discrete Features \/ Data: The actual location can be pinpointed<\/span><\/p>\n <\/p>\n Continuous Phenomena \/ Data: Can be found or measured anywhere (precipitation, temperature, etc.)<\/span><\/p>\n <\/p>\n <\/p>\n Features Summarized by Area:\u00a0<\/span><\/p>\n <\/p>\n > Shows the density \/ counts of features within a boundary\u00a0<\/span><\/p>\n Examples: Number of businesses in a zip code, total length of streams in each watershed, number of households in each country<\/span><\/p>\n <\/p>\n <\/p>\n Two Ways of Representing Geographic Features<\/span><\/p>\n <\/p>\n Vector Models:\u00a0<\/span><\/p>\n <\/p>\n <\/p>\n > Locations are represented as points with geographic coordinates<\/span><\/p>\n > Lines, such as streams, are represented by a series of coordinate pairs.<\/span><\/p>\n > Areas are represented by borders that are closed polygons.<\/span><\/p>\n <\/p>\n Raster Models:\u00a0<\/span><\/p>\n <\/p>\n <\/p>\n > Cell size affects how the map looks as well as the results of the analysis, and should be based on the original map scale and minimum mapping unit\u00a0<\/span><\/p>\n <\/p>\n <\/p>\n > Continuous categories can be represented by either the vector or raster models, but continuous numeric values are represented using the raster model.<\/span><\/p>\n <\/p>\n Understanding Geographic Attributes:\u00a0<\/span><\/p>\n <\/p>\n Attribute values include:<\/span><\/p>\n <\/p>\n <\/p>\n > Categories and ranks are <\/span>non-continuous<\/span> values.\u00a0<\/span><\/p>\n > Counts, amounts, and ratios are <\/span>continuous <\/span>values.<\/span><\/p>\n <\/p>\n Chapter 2: Mapping Where Things Are<\/b><\/p>\n <\/p>\n Preparing Data<\/span><\/p>\n <\/p>\n > Before you begin mapping, you need to make sure that you have geographic coordinates assigned. If the data is already in a GIS database, coordinates will already be assigned. If not, you will have to manually enter them.<\/span><\/p>\n > If you are mapping features by type, you must assign each feature to a category.\u00a0<\/span><\/p>\n <\/p>\n Making Your Map<\/span><\/p>\n <\/p>\n Mapping a Single Type:\u00a0<\/span><\/p>\n <\/p>\n > Draw all features using the same symbol to map features as a single type. This can suggest differences in the feature that may need to be explored further.\u00a0<\/span><\/p>\n <\/p>\n > You can also map features in a data layer or subset based on a category value that you create. For example, instead of mapping all crimes, you could map only burglaries.\u00a0<\/span><\/p>\n <\/p>\n Mapping by Category:\u00a0<\/span><\/p>\n <\/p>\n > Using categories can help to understand how a place functions.<\/span><\/p>\n <\/p>\n > Use different categories to reveal different patterns.<\/span><\/p>\n <\/p>\n > If you are displaying several categories on the same map, use no more than seven categories at a time. Most people can distinguish up to seven patterns on a map, so using more can become confusing or difficult to see.<\/span><\/p>\n <\/p>\n Grouping Categories:<\/span><\/p>\n <\/p>\n > Using fewer categories can make it easier for a broader audience to understand your map, but there will be less detailed information shown.\u00a0<\/span><\/p>\n <\/p>\n > Patterns may be easier to see if you group many, similar categories together.\u00a0<\/span><\/p>\n <\/p>\n > You must be explicit with what is included in each category to help others understand what your map is showing.\u00a0<\/span><\/p>\n <\/p>\n There are multiple ways to group categories:<\/span><\/p>\n <\/p>\n Option 1:\u00a0<\/span><\/p>\n <\/p>\n – Assign each record in the database two codes. One for its detailed category and the other for its general category.\u00a0<\/span><\/p>\n <\/p>\n Option 2:\u00a0<\/span><\/p>\n <\/p>\n – Create a table that contains one record for each detailed code, with the corresponding general code.\u00a0<\/span><\/p>\n – Join the feature table with the new table, and use the general code to display features.<\/span><\/p>\n <\/p>\n Option 3:<\/span><\/p>\n <\/p>\n – When you make the map, assign the same symbol to the detailed categories that make up each general category.<\/span><\/p>\n <\/p>\n Mapping Reference Features:\u00a0<\/span><\/p>\n <\/p>\n > You may want to add recognizable landmarks to your map to make it more meaningful, especially to those who may not be familiar with the area they are observing.\u00a0<\/span><\/p>\n <\/p>\n > You may also want to reference features that are specific to your analysis so that you can observe geographic relationships.\u00a0<\/span><\/p>\n <\/p>\n Chapter 3: Mapping the Most and Least\u00a0<\/b><\/p>\n <\/p>\n Counts and Amounts:<\/span><\/p>\n <\/p>\n <\/p>\n Ratios:<\/span><\/p>\n <\/p>\n <\/p>\n > Create ratios by making a new field and adding it to the layer\u2019s data table, and dividing the two fields containing the counts or amounts.\u00a0<\/span><\/p>\n <\/p>\n Class Schemes:<\/span><\/p>\n <\/p>\n > The most common schemes are <\/span>natural breaks<\/span>, <\/span>quantile<\/span>, <\/span>equal interval<\/span>, and <\/span>standard deviation<\/span>.\u00a0<\/span><\/p>\n <\/p>\n Natural breaks:\u00a0<\/span><\/p>\n <\/p>\n > Finds patterns inherent in the data\u00a0<\/span><\/p>\n > Good for mapping data not evenly distributed\u00a0<\/span><\/p>\n <\/p>\n Quantile:\u00a0<\/span><\/p>\n <\/p>\n > Good for comparing areas that are similar in size, and for data that is evenly distributed<\/span><\/p>\n <\/p>\n Equal interval:<\/span><\/p>\n <\/p>\n > Easier to interpret since the range for each class is equal\u00a0<\/span><\/p>\n > Good for mapping continuous data\u00a0<\/span><\/p>\n <\/p>\n > Good for seeing which features are above or below the average and for displaying data that has a normal distribution<\/span><\/p>\n <\/p>\n Choosing a Map Type:<\/span><\/p>\n <\/p>\n Graduated symbols:<\/span><\/p>\n <\/p>\n <\/p>\n Graduated colors:<\/span><\/p>\n <\/p>\n Example: percentage of population aged 18-29 (darker colors with higher values)<\/span><\/p>\n <\/p>\n Charts:<\/span><\/p>\n <\/p>\n <\/p>\n Contour lines:<\/span><\/p>\n <\/p>\n <\/p>\n 3D perspective views:<\/span><\/p>\n <\/p>\n Chapter 4: Mapping Density\u00a0<\/b><\/p>\n <\/p>\n > You can create a density map based on features summarized by <\/span>defined area <\/span>or by creating a <\/span>density surface.\u00a0<\/span><\/p>\n <\/p>\n\n
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\n<\/span>Standard deviation:<\/span><\/p>\n\n
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\n<\/span>Defined Area:<\/span><\/p>\n\n