Week 6

Geospatial Visualization

What can we say about the people in an area - http://www.esri.com/data/esri_data/ziptapestry

Photograph of the Cortical Homunculus from the Natural History Museum in London (Wikipedia)

Visual contrasts established by manipulating perceptual qualities

the following are retinal variables - perceived immediately and effortlessly - fundamental units of visual communication

• Size
• Value (saturation)
• Orientation
• Texture
• Shape
• PositVerdana;ion
• Hue

Information represented in a visual display is characterized by

• Number of dimensions (things being measured)
• Length of each dimension (number of possible values in each dimension)
• Scale of measurement for each dimension
  • nominal (categorical) (associative, selective) - distinct categories should be obvious (e.g. representations of census data on religion, or gender, or martial status or hair color or whether you call a carbonated soft drink pop / soda / coke)
    
  • ordered - determine relative ordering - which one is 'more' than the other should be obvious (e.g. representations of questionnaire answers with low, medium, high options)
    
  • quantitative - determine amount of difference between ordered values - how much 'more' should be obvious (e.g. representations of census data on age in years or income in dollars)
  • interval - ordering of the data plus explicit numerical differentiation between those categories (e.g. temperature values) with an arbitrary zero point - e.g. SAT scores, temperature in F & C
  • getting 2x an SAT score doesn't mean you are twice as smart
  • 20 degrees C is not twice as hot as 10 degrees C
  • ratio - ordering of the data plus explicit numerical differentiation between those categories (e.g. temperature values) with a non-arbitrary zero point (e.g. temperatures in degree kelvin)

so lets do an example. The data from Project 1 included the following 19 dimensions. Take a few minutes, break into small groups and decide on the length and scale of each of these dimensions.

State
County
Year
Days with AQI
Good Days
Moderate Days
Unhealthy for Sensitive Groups Days
Unhealthy Days
Very Unhealthy Days
Hazardous Days
Max AQI
90th Percentile AQI
Median AQI
Days CO
Days NO2
Days Ozone
Days SO2
Days PM2.5
Days PM10

Nominal - User interested in categorizing

In associative perception the viewer ignores variation in one visible dimension in reading the remainder of the display

An associative variable does not affect the visibility of other dimensions (e.g. we can recognize hue regardless of orientation.) A variable is dissociative if visibility is significantly reduced for some values along that dimension (e.g. its hard to determine hue of a very thin line or small dot)

Hue, Orientation, Texture, Shape, Position are associative

Size and Value are dissociative - they dominate perception and disrupt processing of other correlated dimensions
  

In selective perception viewer attempts to isolate all instances of a given category and perceptually group them into a single image. The task is to ignore everything but the target value on the dimension of interest - to see at a glance where all the targets are within the display
  

All the variables except shape are selective

  

In ordered perception the viewer must determine the relative ordering of values along a perceptual dimension. Given any two visual elements, a natural ordering must be clearly apparent so the element representing 'more' of the corresponding quality is immediately obvious

Position, size, and value are ordered

In quantitative perception the viewer must determine the amount of difference between two ordered values. The user does not need to refer to an index or key - the relative magnitudes must be immediately apparent

Position and size are quantitative

Visual variables differ substantially in length:

• Shape is longest - almost infinite variety but can be hard to tell apart if they are too similar
  
• Position in 2D space is limited by display size and resolution, but very fine grained
• Size and color 10-15
• Value and texture support less than 10
• Orientation is shortest - confusion arises if more than 4 levels are attempted

Back to Colour Blindness

lets run it on one of the 'obvious' examples above ... its not that obvious if you are colour blind.

Nature of Geographic Phenomena:

Spatial Dimension

• 0d - point phenomena located in 2d or 3d space (e.g. data collected at weather monitoring stations)
• 1d - linear phenomena (e.g. the path an AUV or a drone takes while taking measurements)
• 2d - areal phenomena (e.g. data collected on the surface of a lake)
• 2.5d - volumetric phenomena - each x, y position has a single z value associated with it (e.g. the maximum depth at any point in the lake)
• 3d - volumetric phenomena - each x, y, z position has a value associated with it (e.g. the ph values collected at various points and depths in the lake)

Type of visualization used depends both on the nature of the underlying phenomenon and the purpose of the map

We previously looked at a variety of weather maps where the data was collected from a set of discrete weather stations but formed a very smooth continuous map. We looked at  the pop / soda / coke map where the data was collected from discrete individuals but formed a less smooth and less continuous map. We looked at the map for gas prices which showed abrupt changes on political boundaries. We looked at the map of target stores opening that had very discrete points.

• orientation - direction/orientation of the marks/symbol
  
• shape - different shapes are used
  
• arrangement - different arrangement of marks making up the symbol
  
• hue - different colors are used but careful choices need to be made here so there is NO sense of 'less' to 'more' in the different hues

• spacing (texture) - smaller spacing between marks suggest higher value
• size - larger symbol or larger marks making up the symbol suggests a higher value
  
  
• perspective height - higher elevation suggests a higher value (cant be used for 3D phenomena because all 3 dimensions are already in use)
  
  
• color (hue) - what is the dominant wavelength (red, green, blue, etc but careful choices need to be made here so there IS a sense of 'less' to 'more' in the progression on hues) 
  
• color (lightness) - how light or dark the color is (light green, dark green)
• color (saturation) - how far is the intensity is from grey (bright red, muted red)

So, lets try an in class activity to make this more clear, and for that we will take a look at mapping the data for the winning countries of the Eurovision Song Contest from 1956 to the present - https://en.wikipedia.org/wiki/Eurovision_Song_Contest

Here are the totals:

and there is a current map of Europe available here:

• commonly used to portray data collected for units such as counties or states
• regions are shaded / colored based on the phenomena
  
• good for when values change abruptly at unit boundaries but hides variation within units, and the boundaries may be artificial in relation to the phenomena.

• good when data collected was from a smooth continuous phenomenon
• regions are shaded / colored based on the phenomena
  
• interpolating set of isolines between sample points of known values

• scale symbols in proportion to the magnitude of the data
• symbol might be a true point (located at a data collection point) or a conceptual point (at the center of a unit)

• one dot is set equal to a certain amount of the phenomenon
• dots should be placed where the phenomena occurs (much higher level of accuracy than other maps)

and a 2019 solution to the problem by making each US congressional district the same size and trying to keep the states in the correct shape and relative position to each other, though the locations of the districts themselves within each state are only correct relative to each other.

and a nice interactive processing example for US zip codes: http://benfry.com/zipdecode

Interpolation

• (nearest neighbor)
• linear
• quadratic
• etc
  

shepards method is one way to perform that interpolation - http://en.wikipedia.org/wiki/Inverse_distance_weighting

Here is a nice interactive map showing some of the information at various monitoring sites: http://www.wunderground.com/wundermap/

Coming Next Time

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