Becker Week 2

Chapter 1- Mitchell

• • GIS grown enormously since 1999
    • New sources- lidar and drones
    • Shared more openly and widely
    • More people
  • ArcGIS Living Atlas of the World
  • GIS uses:
    • Mapping where things are
    • Mapping the most and least
    • Mapping density
    • Finding what’s inside
    • Finding what’s nearby
    • Mapping change

• GIS Analysis– process for looking at geographic patterns in your data and relationships between features

• Process For Analysis Steps:
  • Frame the Question
    • What info do you need
    • Question form- be specific
    • How will analysis be used, and by who?
  • Understand Your Data
    • Type of data and its features
    • Method you use could require you to obtain additional data (or change its format)
  • Choose a Method
    • Almost always multiple methods for obtaining needed information
    • Decide which method fits your question and research best
  • Process the Data
    • Once method is selected, use GIS to perform it
  • Look at the Results
    • Multiple ways of displaying results: maps, tables, charts
    • Decide what is new/valuable to add to your display
    • Decide whether your information is valid or useful; then adjust accordingly

Understanding Geographic Features

• • Types of Features:
    • Geographic features are discrete, continuous phenomena, or summarized by area
      • Discrete: location can be pinpointed
      • Continuous Phenomena: can be found or measured anywhere, blanket entire area being mapped

• Interpolation- GIS using sample points to assign values to the areas in between them

• • • • • Continuous data can also be areas enclosed by boundaries (everything within boundary must be same type)
      • Features Summarized by Area: counts or density of individual features within an area (ex: number of businesses in each zip code)
        • Data value applies to the whole area and not a specific place within it
        • Lots of data (especially demographic data) is sorted by area
        • GIS can be used to aggregate data that lies within similar areas
  • Ways of Representing Geographic Features
    • Vector
      • Each feature is a row in a table
      • Shapes defined by x,y locations
      • Features can be discrete locations or events, lines, or areas
        • Lines represented as series of coordinate pairs
        • Areas defined by borders and represented as polygons
      • Vector data analysis often times involves summarizing attributes in layer’s data table
    • Raster
      • Features represented as matrix of cells in continuous space
      • Each layer represents one attribute
      • Analysis is combining layers to create new layers
      • Cell size affects results of analysis/map quality
        • Cell size should be based on original map scale and minimum mapping unit
  • Discrete features and data summarized by area usually use vector model
  • Continuous categories either vector or raster
  • Continuous numeric values are raster

• Map projection– translate locations on the globe onto flat surface

• • Distort shape of features being displayed
• GIS systems usually already have their databases in the same coordinate system and projection

Understanding Geographic Attributes

• • Attribute Values
    • Categories
      • Groups of similar things that help you organize your data
      • Category values can be represented as numeric codes or as text (abbreviated)
    • Ranks
      • Put features in order from high to low
      • Used when direct measures are difficult or quantity represents multiple factors
    • Counts and Amounts
      • Total numbers
    • Ratios
      • Show relationship between two quantities
      • Created by dividing one quantity with another for each feature
      • Evens out differences between large and small areas

• Proportions- what part of a total each value is

• Density- distribution of features or values per unit area

Working With Data Tables

• Three common operations: selecting, calculating, summarizing
  • Selecting
    • Select features to work with subset or assign new attribute
    • In form of logical expression (code)
  • Calculating
    • Calculate attribute values to assign new values to features in the table
  • Summarizing
    • Could be mean, frequency, total, etc.

 

Chapter 2- Mitchell

Why Map Where Things Are?

• To see where/what an individual feature is
  • However looking at distribution of features allows patterns to form
• Can show you where to take action, or what areas meet your criteria

Deciding What To Map

• To look for geographic patterns, map features in a layer using different symbols
• Information Gained From Mapping
  • Where features are/aren’t
  • Locations of different features to look for patterns
• Make sure map is appropriate for audience

Preparing Your Data

• Make sure features being mapped have geographic coordinates
• GIS databases have coordinates assigned, incoming info must have coordinates (street address or latitude-longitude)
• When mapping by type, each feature must have an identifier for its type
• Many categories are hierarchical
• Sometimes a single code can indicate multiple types (major type and subtype)

Making Your Map

• • To create, tell GIS your features and their symbols

• Mapping a Single Type

• • • Draw all features using same symbol
    • Can use this to find differences in the feature to explore further

• What The GIS Does

• • • Stores location as pair of coordinates or set of coordinate pairs
    • Individual locations: GIS draws symbol for that specific point
    • GIS can also draw lines for line features, also draws area outlines

• Using a Subset

• • • Can map features in subset based on specific category values
    • Usually done for individual locations (linear features would often be incomplete)
    • For continuous data, you would be leaving out context of surrounding areas

• Mapping By Category

• • • Draw features by using different symbol for each category value

• What the GIS does

• • • • Stores category value for each feature in table
      • Separately stores specified symbols
  • Features might belong to multiple categories
  • Sometimes useful to make multiple maps with different categories to compare
  • Display no more than seven categories on one map (also affected by distribution of features and map scale)
  • Hard to distinguish categories on maps with small features
  • For large maps, multiple categories can distort trends and make it harder to find differences between categories
  • Conversely, important not to have too few categories on smaller maps (don’t leave out info)
  • If more than 7 categories, group the categories
  • The way categories are grouped can influence readers’ perception (categorize carefully!)
  • Ways to group categories: 
    • Assign each record two codes 
    • create table containing one record for each detailed code with its corresponding general code
    • Assign same symbol to various detailed categories that are apart of a general category
  • Symbols used to display data are important to display it properly
  • For areas/raster layers: display similar categories in different shades of same color
  • For linear features: use different widths/patterns (text labels could also be useful)
  • Maps more meaningful when they contain recognizable features, put reference features in light colors

• Reference feature– feature on map that makes the area identifiable

• Some GIS software provides base maps with reference features

Analyzing Geographic Patterns

• Play with map scale to try and find identifiable patterns in features
• Exceptions can reveal further causes/areas of further interest
• Use statistics to more scientifically find relationships between different features

 

Mitchell- Chapter 3

Why Map The Most And The Least

• Find places that meet criteria to take action or see relationships between places
• Mapping based on quantities adds additional info

What Do You Need To Map

• Discrete features
  • Locations, linear features usually represented by graduated symbols; area by shading
  • Continuous phenomena can be defined areas or surface of continuous values
    • Defined areas: graduated colors
    • Surfaces: graduated colors, contours, 3D perspective view
  • Data summarized by area displayed by shading each area
• Keep in mind whether or not you are exploring data or presenting your map

Understanding Quantities

• • Quantities: counts or amounts, ratios, ranks
    • Counts and Amounts

• Count- actual number of features on map

• Amount- total of value associated with each feature

• • • Can be mapped for discrete features or continuous phenomena
    • counts/amounts can skew data for areas with size variability
  • Ratios
    • Evens out differences for large/small areas (shows distribution of features)
    • Proportions often represented as percentages
    • Density good for showing distribution when large area size variability
    • Create ratios by adding new field to layer’s data
  • Ranks
    • Useful when direct measures are difficult
    • Assign ranks based on another feature attribute

Creating Classes

• • Counts, amounts, and ratios grouped into classes because each feature has a potentially different value
  • Valuable for maps used for public discussion
  • Mapping individual values = more accurate picture
  • If mapping ranks, assign one symbol to each rank
  • Can map ratios, counts, or amounts using individual values if:
    • No more than 11-12 unique values
    • <20 features

• Classes- group features with similar values by assigning same symbol

• • • Important to consciously define class ranges
  • Create classes manually if looking for features that fit specific criteria or comparing features to meaningful value
  • Use standard classification scheme if wanting to group similar values to find patterns
  • Four most common schemes

• Natural breaks- classes based on natural groupings of data values

• • • • GIS automatically determines high and low value for each class using math to test class breaks, maximizes difference between classes
      • Good for mapping not evenly distributed data values

• Quantile- each class contains equal number of features

• • • • GIS adds up number of features (plus puts them in order) then equally separates them into number of classes specified
      • Good for comparing areas of similar size, mapping evenly distributed data, emphasizing relative position of feature

• Equal interval- difference between high and low values is the same for every group

• • • • GIS subtracts lowest dataset from highest then divides that number by number of classes specified
      • Good for presenting info to nontechnical audience and mapping continuous data

• Standard deviation- placed in classes based on how they deviate from the mean

• • • • GIS finds mean, then calculates standard deviation and separates classes
      • Seeing what features are above/below mean value
      • Displaying data with lots of values around mean
  • Plot data values in chart to see their distribution
  • Easy to change class ranges in GIS so try different ones

• Outliers- few extremely high or low values

• To deal with them:
  • Put each in its own class
  • Group them together in a class
  • Group them with next closest class
  • Draw them using special symbol
• While exploring number of classes, start with more and work way down until desired clarity
• Rounding data when reasonable to make it palatable
• Give classes meaningful names

Making a Map

• • GIS options to show quantities
    • Graduated symbols
      • Which features: locations, lines, areas
      • Which values: counts/amounts, ratios, ranks
    • Graduated colors
      • Which features: areas, continuous phenomena
      • Which values: ratios, ranks
    • Charts
      • Which features: locations, areas
      • Which values: counts/amounts, ratios
    • Contours
      • Which features: continuous phenomena
      • Which values: amounts, ratios
    • 3D perspective views
      • Which features: continuous phenomena, locations, areas
      • Which values: counts/amounts, ratios

• Legend- displays subset of values and symbols to indicate relative value of individual features

• • Consciously choose colors to attract attention to what you want to
  • Charts on maps useful for quick study of patterns
    • Pie charts: show how much of total amount each category takes up
    • Bar charts: show relative amounts
  • Use contour lines to show rate of change in values across an area
    • Choose interval small enough to give surface definition but not too small that the lines are too close together
    • Label with value
    • Use bold line for every fifth interval
  • When using 3D perspective have viewer in proper spot so tall parts don’t block shorter ones

• Z-factor- value specified to increase the variation in the surface so differences are easier to see

Looking For Patterns

• Look at transition from highest to lowest values
• See distribution of values (clustered or spread out)
• Can summarize up but not down