Abbey S- Week 2

Chapter 1: Introducing GIS Analysis

Map uses:

• Where things are
• Most and least
• Density
• What’s inside
• What’s nearby
• Change

GIS can be simple, like a basic map, or a more complex figure with multiple layers (like an onion)

Geographic features can be discrete, continuous phenomena, or summarized by area

• It’s important to understand what you’re mapping!
• Discrete features can be pinpointed
  • Businesses represented by number of employees
• Continuous phenomena are always present, and we map the changes (e.g. temperature)
  • Interpolation– values that are assigned to areas in between points
  • Non continuous data can be continuous if showing variation across a given location
  • Centroid– center points
• Summarized data is used for density/ counts of individual points in a certain area
  • Would this mean the individual points would be discrete features, and the presence of a boundary is what makes it summarized data?

Geographic features can be represented using vector or raster

• Vector-
  • Feature is a row on the table
  • Shapes are defined by x and y
  • Lines= coordinate pairs
  • Shapes= closed polygons
• Raster-
  • Features are a matrix of cells in continuous space
  • 1 layer= 1 attribute
  • Sizing is important! Too large and info will be lost, too small and it takes longer to process

Geographic attributes:

• Categories
  • Groups of similar things 
  • Represented by numeric codes or text abbreviations
• Ranks
  • Order from highest to lowest
  • Used to compare features that are harder to quantify
• Counts
  • Number of features on a map
• Amounts
  • Measurable quantity associated with a feature
  • So a count would be the number of circles on a map, and an amount would be the number that each circle represents?
• Ratios
  • Shows relationship between two quantities
  • Dividing one quantity by another
    • Proportions- part of a total value
    • Density- distribution of feature per unit area

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Chapter 2: Mapping Where Things Are

Maps show where action is needed. It is important to know what features need to be present and how to display them.

Creating a map:

• It needs to be relevant for your audience
  • Avoid unnecessary details
  • Smaller maps need to be concise and only show important aspects, while larger maps are able to provide more detail
• Features need to be geographically assigned!
• Categories help identify groups of features
  • Types and subtypes
  • If only one type, all features should be the same shape
• Subsets used for individual locations
• Context is important!

Categories can reveal patterns

• Can provide an understanding of how a place functions
• Most people can distinguish up to seven colors/ patterns on a map
  • I thought this was really interesting! Because of this, it is strongly encouraged that there are no more than seven categories displayed at once
  • The spacing of the categories also plays a role
    • I assumed that categories would be more easily distinguishable if they were spread out, but Mitchell says the opposite.
  • You may have to lose some information in the process of making it easy to read
    • How would you determine what information is needed the least? How often does this happen?

Grouping categories can change the viewer’s perception of the information

• Assign detailed and general category codes
• Create a table with detailed and general category codes side by side
• Assign the detailed categories a symbol that represents the general category

Symbols

• Size needs to be “just right”- large enough to be distinguishable, small enough as to not obscure
• Use different widths to distinguish lines
  • Patterns as well ( dashed lines, dotted lines, etc.)

Geographic Patterns

• Clustered distribution- Features more likely to be found near other features
• Uniform distribution- Features less likely to be found near other features
• Random distribution- Features likely to be found at any given location

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Chapter 3: Mapping the Most and Least

Add a layer of information that can be useful instead of just a location on a map (What is the elephant population at this point, and why is it higher than the population at a different point?)

Can map all 3 types of quantities (discrete features, continuous phenomena, summarized area)

Continuous phenomena can be portrayed as a gradient of colors-

• More saturated= most
• Least saturated= least
• Red= =high
• Blue= low
• etc

As I am going through the chapters, I noticed that Mitchell will repeat the same definitions for words previously mentioned. Some people may find that annoying but I appreciate the repetitiveness as it helps me remember what certain words mean.

Creating Classes

Simplicity is key when it comes to comparing values

• Balance between portraying accurate data values and generalizing data enough to show a pattern
• Map example:
  • Shading each area with a unique shade based on its data value can muddle the image (too many colors!)
  • It is much easier to create classes that each contain a range of the values (less color good)

Mapping individual values can be overwhelming for viewers. The data may be more accurate but not necessarily better.

Standard Classification Pyramid Schemes

• Natural Breaks
  • Based on natural groupings of values
  • Use if data is unevenly distributed
• Quantile
  • Each class has same number of features
  • Use if data is evenly distributed and you want to show relative difference between features
• Equal Interval
  • Difference between high and low values is same for every class
  • Use if data is evenly distributed and you want to highlight the difference between features
• Standard Deviation
  • Placed based on how far they deviate from the mean
  • Use if data is evenly distributed and you want to highlight the difference between features

How does one decide between equal interval and standard deviation?

Map type is dependent on type of features-

• Discrete locations/ lines
  • Graduated symbols
  • Charts
  • 3D view
• Discrete areas
  • Graduated colors
  • Charts
  • 3D view
• Spatially continuous phenomena
  • Graduated colors
  • Contours
  • 3D view

In conclusion, you can’t go wrong with a 3D view

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Chapter 4: Mapping Density

Shows you where the highest concentration of features are

More useful for mapping patterns

Looking at the images I can already tell that it is much easier to comprehend a gradient of density instead of a bunch of lines or dots

Two ways to map density-

• Density map
  • Dot maps
  • Calculate density value to create a shaded map
• Density surface
  • Raster layer
  • Requires more effort
• Trade offs
  • Use density map if you have data summarized by area, but beware if you want exact centers of density
  • Use density surface if you have individual features and are prepared for more data processing

When creating a shaded map, make sure to limit the amount of colors/ shades used

Dot maps

• Allows for more detail
• Dots represent values in each area
• Dots that are larger represent more values, and will therefore be more spread out
• Make sure the boundary is larger than the dotted area

Density Surface

• This is where I start to get lost
• Cell size determines coarseness of the pattern
  • Smoother= more data processing
• To calculate cell size:
  • Convert density units to cell units
  • Divide by the number of cells
  • Take the square root to get one side of the cell
• When the cell size is too big, it starts to resemble a shaded map
• Usually shades of a single color are used
  • Exception is standard deviation, where one color equals above mean, and another color equals below mean
• Contour lines connects points
  • Lines closer together= rapid change
  • Lines farther apart= slower change