Cailee Plunkett- Week 2

Chapter 1: Introducing GIS Analysis 

 

> GIS analysis is the process of finding geographic patterns in data and at the relationships between features.

 

Understanding Geographic Features  

 

Discrete Features / Data: The actual location can be pinpointed

 

Continuous Phenomena / Data: Can be found or measured anywhere (precipitation, temperature, etc.)

 

• The phenomena cover the entire area you are mapping, and there are no gaps.
• You can determine a value at any given location (precipitation in inches or temp. in degrees). 
• Usually starts as a series of sample points that are then used to assign values to the area between the points (interpolation). This can be used to show how a quantity, such as annual precipitation, varies from place to place. 
• Continuous data can also be represented by areas enclosed by boundaries (if everything inside the boundaries are the same type of something, such as the type of soil).

 

Features Summarized by Area: 

 

> Shows the density / counts of features within a boundary 

Examples: Number of businesses in a zip code, total length of streams in each watershed, number of households in each country

 

• The data value applies to the whole area, not a specific location in the area.

 

Two Ways of Representing Geographic Features

 

Vector Models: 

 

• Each feature is a row in a table 
• Feature shapes defined by x,y locations 
• Can be discrete locations, events, lines, areas

 

> Locations are represented as points with geographic coordinates

> Lines, such as streams, are represented by a series of coordinate pairs.

> Areas are represented by borders that are closed polygons.

 

Raster Models: 

 

• Features represented as a “matrix of cells in continuous space”
• Each layer represents an attribute 
• Analysis occurs by combining layers to create new layers with new cell values

 

> 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 

 

• Using too large of a cell size can cause info. to be lost 
• Using a cell size that’s too small takes up a lot of storage space and takes longer to process without adding precision to the map.

 

> Continuous categories can be represented by either the vector or raster models, but continuous numeric values are represented using the raster model.

 

Understanding Geographic Attributes: 

 

Attribute values include:

 

• Categories
• Ranks
• Counts: Actual number of features on a map 
• Amounts: Any measurable quantity associated with a feature, ex: number of employees at a business 
• Ratios

 

> Categories and ranks are non-continuous values. 

• There is a set number of values in the data, and multiple features may have the same value.

> Counts, amounts, and ratios are continuous values.

• Each feature may have a unique value anywhere in the range (between the highest and lowest values).

 

Chapter 2: Mapping Where Things Are

 

Preparing Data

 

> 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.

> If you are mapping features by type, you must assign each feature to a category. 

 

Making Your Map

 

Mapping a Single Type: 

 

> 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. 

 

> 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. 

 

Mapping by Category: 

 

> Using categories can help to understand how a place functions.

 

> Use different categories to reveal different patterns.

 

> 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.

 

Grouping Categories:

 

> Using fewer categories can make it easier for a broader audience to understand your map, but there will be less detailed information shown. 

 

> Patterns may be easier to see if you group many, similar categories together. 

 

> You must be explicit with what is included in each category to help others understand what your map is showing. 

 

There are multiple ways to group categories:

 

Option 1: 

 

– Assign each record in the database two codes. One for its detailed category and the other for its general category. 

 

Option 2: 

 

– Create a table that contains one record for each detailed code, with the corresponding general code. 

– Join the feature table with the new table, and use the general code to display features.

 

Option 3:

 

– When you make the map, assign the same symbol to the detailed categories that make up each general category.

 

Mapping Reference Features: 

 

> 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. 

 

> You may also want to reference features that are specific to your analysis so that you can observe geographic relationships. 

 

Chapter 3: Mapping the Most and Least 

 

Counts and Amounts:

 

• Use to map discrete features or continuous phenomena 

 

Ratios:

 

• The most common ratios are averages, proportions, and densities.
• Ratios are good for summarizing by area

 

> Create ratios by making a new field and adding it to the layer’s data table, and dividing the two fields containing the counts or amounts. 

 

Class Schemes:

 

> The most common schemes are natural breaks, quantile, equal interval, and standard deviation. 

 

Natural breaks: 

 

• Classes are based on natural groupings of data values
• Class breaks are set where there is a jump in values

> Finds patterns inherent in the data 

> Good for mapping data not evenly distributed 

 

Quantile: 

 

• Each class contains an equal number of features.

> Good for comparing areas that are similar in size, and for data that is evenly distributed

 

Equal interval:

 

• The difference between high and low values is the same for every class

> Easier to interpret since the range for each class is equal 

> Good for mapping continuous data 

Standard deviation:

 

• Features are placed in classes based on how much their values vary from the mean

> Good for seeing which features are above or below the average and for displaying data that has a normal distribution

 

Choosing a Map Type:

 

Graduated symbols:

 

• Use to map discrete locations, lines, or areas. 
• Used to show volumes or ranks for linear networks

 

Graduated colors:

 

• Use to map discrete areas, continuous phenomena, or data summarized by area

Example: percentage of population aged 18-29 (darker colors with higher values)

 

Charts:

 

• Use to map data summarized by area, or discrete locations or areas. 
• You can show patterns of categories and quantities at the same time 
• Can use pie charts or bar charts 

 

Contour lines:

 

• Use to show the rate of change in values in an area for spatially continuous phenomena 

 

3D perspective views:

• Use with continuous phenomena to help visualize the surface 

 

Chapter 4: Mapping Density 

 

> You can create a density map based on features summarized by defined area or by creating a density surface. 

Defined Area:

 

• You can map density graphically, using a dot map. You can also calculate a density value for each area. 
• Creates a shaded fill map or dot density map 
• Easier, but doesn’t pinpoint exact centers of density 

> Use if you already have data summarized by area or if you want to compare natural / administrative areas with defined borders

 

Density Surface:

 

• Usually created as a raster layer 
• Each cell in the layer gets a density value
• Creates a shaded density surface or contour map
• Requires more data processing, but gives a more precise view of centers of density

> Use if you want to see the concentration of point or line features

 

Mapping Density for Defined Areas: 

 

> You can map density for defined areas by graphically using a dot map or by calculating a density value for each area and shading each area based on this value.

 

Calculating a density value for defined areas:

 

• Calculate density based on the areal extent of each polygon 

> Add a new field to the feature data table to hold the density value. Then, assign density values by dividing the value you’re mapping by the area of the polygon. 

 

Calculating Density Values

 

Cell Size:

 

• Cell size determines how coarse or fine the patterns will appear 
• Cell size is the length of one of its sides 

> To calculate cell size: convert the density units from square kilometers to cell units (meters), then divide by the number of cells per density unit. This will give you the area of each cell. Then, take the square root of the cell area. 

 

Displaying a Density Surface:

 

> You can display a density surface with either graduated colors or contours

 

Graduated colors:

 

• Density surfaces are usually displayed using the shades of a single color
• Areas with higher density are typically shown with darker colors, since people tend to equate darker colors with “more.” 

 

Contours:

 

• Connect points of equal density value on the surface 
• Good for showing the rate of change across a surface (the closer the contours, the quicker the change).