Week 7

Geospatial Visualization



Lets start with some perceptual issues that affect all types of visualization.

Visual contrasts established by manipulating perceptual qualities

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

retinal
        variables

Information represented in a visual display is characterized by


Nominal
- User interested in categorizing


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


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


Visual variables differ substantially in length:




here are some more examples from our textbook:











Back to Colour Blindness

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




This chapter from Thematic Cartography and Geovisualization, 3rd ed. by Slocum, McMaster, Kessler, and Howard gives a nice introduction on mapping data to symbols.

A lot of data today can represented geographically, as the popularity of Google maps / earth can attest to, so its a nice place to start looking at details.


Nature of Geographic Phenomena:

Spatial Dimension


Discrete vs Continuous and Abrupt vs Smooth Phenomena
    discrete - occur at distinct locations (and have a space between them)
    continuous - occur throughout a region of interest

    abrupt - can change suddenly
    smooth - change gradually

Discrete-Continuous / Abrupt-Smooth Phenomena
Figure 5.1 from Thematic Cartography showing phenomena and appropriate ways of representing them


Distinction between data that has been collected to represent a phenomenon and the phenomenon being mapped
    ie we are typically collecting data at discrete sites (weather stations, well sites) or aggregating over small regions (counties, states) where the actual phenomena being modeled is continuous. Other times we are collecting discrete data on a discrete phenomena.

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



Levels of Measurement:

qualitative
quantitative


Visual Variables:


visual variables for qualitative should reflect only a nominal level of measurement - i.e. there shouldn't be a sense that one value is 'more' than another, just that they are different.


Visual Variables for Qualitative Phenomena
Figure 5.4 from Thematic Cartography

so for example as I look at the areal visualization I don't (and I shouldn't) get a sense of which area is higher or lower, or has more cows, etc.

Why are 2.5D representations not recommended for qualitative phenomena?

note that only different hues are used for qualitative data - not saturation or lightness which have an obvious ordering

much of this work was done at a time when a line plotter was the tool to make drawings like this, giving very high resolution vector black and white drawing capability. Today those maps still exist but more work is done on bitmapped displays with lower resolution but a greater use of colour.



visual variables for quantitative should reflect ordinal, interval, or ratio level of measurement

Visual Variables for
      Quantitative Phenomena

so for example as I look at the areal visualization I do get a sense of which area is higher or lower.



here is an example of different ways of using colour to map life expectancy in the US which is quantitative. Which is more readable?

the first is from mapoftheunitedstates.org



the second is from www.measureofamerica.org




Here is a more appropriate use of a wide variety of colors showing data from facebook on the favorite American Football teams for various counties.





There are also pictographic symbols
Pictographic Representation

Here its pretty easy to make rough comparisons - its tricky to make exact comparisons as its hard to avoid the lie factor we talked about last week. Even avoiding that, we have to be careful about the conclusions drawn from an image like this. The states have very different populations (as we talked about last week comparing Wyoming to California) and its not directly correlated to the size of the state.

One issue directly related to the size of the states is the overlap of the icons in the northeast making it hard to make any sense of the data there.



Comparison of choropleth, proportional symbol, isopleth, and dot mapping:

choropleth

isopleth (contour map)

proportional symbol

dot mapping

Thematic Mapping Techniques
Figure 5.10 from Thematic Cartography

and again lets go back to our favorite question, how would you enhance these visualizations if they were software-based?


Selecting visual variables for choropleth maps

Visual Variables
Colour Visual Variables
Figure 5.11 from Thematic Cartography



Nominal
Ordinal
Numerical
Spacing
P
Mc
Mc
Size
P M
M
Perspective Height
P
Ma
Gb
Orientation
G
P
P
Shape
G
P
P
Arrangement
G
P
P
Lightness
P
G
M
Hue
G
Gd
Md
Saturation
P
M
M

                                                       P = Poor       M = Marginally Effective      G = Good
 
a - Since height differences are suggestive of numerical differences, use with caution for ordinal data
b - Hidden enumeration units and lack of a north orientation are problems
c - Not aesthetically pleasing
d - The particular hue selection must be carefully ordered, such as yellow, orange, red

Here is a good example using different colour maps for data on high-school graduation percentages in the US



Here are some other ways of displaying data from Information Graphics -  A Comprehensive Illustrated Reference



Here is another New York times example - this time for the 2009 Afghanistan election
http://www.nytimes.com/interactive/2009/09/21/world/asia/0921-afghan-election-analysis.html


and back to Information Graphics


Here is an image showing crime statistics compared to the average over time for the US from CommonGIS via the Thematic Cartography and Geovisualization book



Here are some interesting examples from the US Environmental Protection Agency: http://www.epa.gov/airtrends/2011/

and here are some other nice more general examples:
http://mapscroll.blogspot.com/

and some H1N1 flu data mapped at:
http://flutracker.rhizalabs.com/

NY Times - Mapping America
http://projects.nytimes.com/census/2010/explorer?ref=us

and here are some variations of the typical red/blue election map for the 2008 presidential election
http://www-personal.umich.edu/~mejn/election/2008/

and a different way to view election data using dots by John Nelson
http://uxblog.idvsolutions.com/2012/11/election-2012.html


and a nice interactive processing example: http://benfry.com/zipdecode/

here is a nice map of cell phone strength
http://webcoveragemap.rootmetrics.com/us

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



When looking at much larger regions of the planet the issues are a bit more complex. A 'flat' map can be a good way to see data from all over the planet simultaneously, but it does add in distortions as the earth is sphere-ish, and trying to represent a sphere, or even a portion of it, on a rectangular plane will generate errors. Other tools like Google Earth can be used to map data onto a spherical Earth model, but then there are issues of only being able to see part of the planet at one time.


First some definitions:

the Earth rotates about its axis of rotation which passes through the North and South Poles (and note the North Pole is nowhere near the North Magnetic Pole)

We can place a plane halfway between the North Pole and the South Pole and perpendicular to that axis. Where that plane intersects the surface of the Earth we have the Equator allowing us to split the planet into the Northern Hemisphere and the Southern Hemisphere.

Any point on the Earth's surface can be given by its Latitude and Longitude. They are measured in degrees, minutes, and seconds. Each degree is divided into 60 minutes ' and each minute into 60 seconds ". Any position on the surface of the Earth can be given by these two angles.

Lines of latitude (parallels) are parallel to each other and the equator. The North Pole is 90 degrees North or +90. The equator is 0. The South Pole is 90 degrees South or -90 degrees.

Lines of Longitude (meridians) run from pole to pole so they are not parallel to each other. Where is equator makes a nice 0 point for latitude there is no obvious 0 point for longitude so the Prime Meridian is declared to run through the Royal Observatory in Greenwich England. On the opposite side of the planet from the Prime Meridian the longitude is 180 degrees west, or +180 degrees  and 180 degrees east, or - 180 degrees, and is mostly where the International Dateline is chosen to exist. The US is west of the prime meridian.

We are at 41 degrees, 52 minutes, 13 seconds North and 87 degrees, 38 minutes, and 51 seconds West here in Chicago

of course there is more information on Wikipedia: http://en.wikipedia.org/wiki/Graticule

another common system in use is UTM http://en.wikipedia.org/wiki/UTM_coordinates
and a big map of them here: http://upload.wikimedia.org/wikipedia/commons/e/ed/Utm-zones.jpg

Data related to the planet also comes referenced in multiple ways. Some data will be in feet / meters / miles / kilometers from a known 0,0 point, some will be given as Latitude, Longitude, some in UTM coordinates. All of them may need to be combined  to integrate the data.

issues of  different map projections - http://en.wikipedia.org/wiki/Map_projection
and a nice applet http://www.btinternet.com/~se16/js/mapproj.htm

Here is a nice example from FlowingData showing the true size of Africa - http://flowingdata.com/2010/10/18/true-size-of-africa/

and a nice xkcd explanation

We also often need to deal with data points above / below the surface of the Earth - e.g. earthquake hypocenters



Often deal with continuous data represented by discrete sampling

A familiar example is a weather map showing the current temperature across the state or country, but the data is only sampled at certain scattered stations which is then interpolated. You can click on the map to gain access to the data files and to see how the data is interpolated across the state. Here are the sites in Illinois.

http://www.sws.uiuc.edu/warm/icnstationmap.asp
Illinois
        Weather Stations

Interpolation

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/



Today Google maps and Google earth is a nice common platform to distribute geospatial information about the earth. http://www.google.com/gadgets/directory?synd=earth&cat=featured&preview=on

Here is a map from the LA Times that was updated regularly during the Los Angeles 'Station Fire' in August 09 to show where the fire was believed to be, where it seemed to be headed, and where important places in the news were located. Its not an overly professional job but it works really well to give current information about a fast changing news story.



Project Vulcan has a nice tool that makes use of Google earth to look at CO2 production
http://www.youtube.com/watch?v=Iu-s9IHPGmM


During the LA 'Station Fire' this map was used to give hourly air quality reports showing how the affect of the fires reached far beyond their immediate area.


WorldProcessor has some rather nice visualizations using the globe as a backdrop - some more literal than others - http://worldprocessor.com/catalog/world/

A related issue is how those fancy maps are put together. When you use Google maps / earth you may notice different map providers at different zoom levels. Some of the images are taken by satellite, some by airplanes. They all need to be geo-referenced and then stitched together to form those seamless images.

These large images are then typically broken down into regular sized images (say 512 x 512 pixels) making them easy to cache and move in and out of memory. Image pyramids are also generated, e.g. a 3 x 3 matrix of 512 x 512 images are combined into a single 512 x 512 image at a higher level allowing fast interactive browsing at multiple scales.


Coming Next Time

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last revision 10/10/2014 - added in link to esri tapestry