My final project

I did my final project on Shaun White and his Competitive History.
http://mason.gmu.edu/~dlader/shaundanfinaldayschool.swf

Map exhibit in Baltimore (the extra credit write-up)

Hi everyone,
So overall, the map exhibit was o.k. I was expecting a little more out of it. First off, I loved the Christian map drawings which showed the world revolving around the sun. I loved it because of the appearance even though the idea was a little ridiculous, it was still really interesting to look at. Another thing I was really impressed with was the map showing Charles Lindbergh's flight between New York and Paris. There was so much detail with the drawings of the different countries. It was really interesting to witness how incredible the map looked with just pencil drawings. It was obvious that there was a huge amount of work put into creating the map. I was very interested also in the cartographic illustration of early Rome and the illustration of what the lifestyle was like in those days. I really liked the ancient Babylonian map also, which was created on brown paper and had pencil drawings of the different city features. The Native American maps were really interesting also, which showed maps that the tribes created which showed the territories that they claimed as their own. These maps were created for the government so that the tribes could keep their land.

There were some downsides to the exhibit. First of all, I thought there should of been more recent, new maps displayed. Every map seemed to be of ancient origin. The only new maps that were displayed in the museum were those that were created by people illustrating their neighborhoods in Baltimore through maps and people who created maps for general areas of Baltimore. I would of liked though to have seen some digital cartographic maps displayed, just so I could visually look at the difference between the ancient and new, recent maps. Some more maps could of shown more recent maps that people could relate to in this day and age, such as remote sensing type maps or chloropleth or proportional symbol maps.

The exhibit was worth seeing though, I hope another exhibit comes around with all the ancient maps and many new age maps displayed. I would really like to see this aspect!

My lab 10

Hi all, I did my lab on map overlays of Berkeley, California and Key West Florida. Enjoy! Here is my website

http://mason.gmu.edu/~dlader/myladerlab10.kmz

Here is my map overlay of Berkeley, California

Here is my map overlay of Key West, Florida

My lab 9

My image of dorms 55, 54, 51 and 49 in Presidents Park in GMU

Here is my lab 9 where I did a sketchup of GMU dorms 49, 51, 54 and 55. http://mason.gmu.edu/~dlader/gmudorms.kmz

Interactive map

Hi everyone, this is an interactive map of battles from Vietnam or Korea http://dsc.discovery.com/convergence/combat-zone/battlemaps/battlemaps.html

Multivariate Maps (3+Variables)

Mulitvariate mapping is a very efficient and cheap way to display data. It requires a little more work but you are able to display tons of data.

In 1973, Herman Chernoff introduced a visualization technique to illustrate trends in multidimensional data. His Chernoff Faces were especially effective because they related the data to facial features, something which we are used to differentiating between. Different data dimensions were mapped to different facial features.

Chernoff faces

Display multivariate data in the shape of a human face. The individual parts, such as eyes, ears, mouth and nose represent values of the variables by their shape, size, placement and orientation. The idea behind using faces is that humans easily recognize faces and notice small changes without difficulty. Chernoff faces handle each variable differently. Because the features of the faces vary in perceived importance, the way in which variables are mapped to the features should be carefully chosen.

They use facial features to represent trends in the values of the data, not the specific values themselves. While this is clearly a limitation, knowledge of the trends in the data could help to determine which sections of the data were of particular interest.
Here, the faces are described by a few facial characteristic parameters:

Eye Size
Pupil Size
Eye Slant
Darkness of Hair/face
Size of Mouth
Position of Eyebrows

http://kspark.kaist.ac.kr/Human%20Engineering.files/Chernoff/Chernoff%20Faces.htm

Here is a Cheroff Face map showing death penalty execution.

http://aaronrb204.blogspot.com/

Here is another website that I thought was pretty cool because it shows the Chernoff Faces in 3D:

http://filer.case.edu/~dbh10/eecs466/CFaces.png


Another example of multivariate maps that people display data for is climate maps. Here I found a map that is showing how certain parts of the United States effects the vegetation growth. They are using multiple colors to display their information.

http://images.google.com/imgres?imgurl=http://gis.esri.com/library/userconf/proc98/proceed/TO350/PAP333/P33329.GIF&imgrefurl=http://gis.esri.com/library/userconf/proc98/proceed/TO350/PAP333/P333.HTM&h=511&w=764&sz=168&hl=en&start=2&um=1&tbnid=6hmkzGq6kYm05M:&tbnh=95&tbnw=142&prev=/images%3Fq%3D%2Bmultivariate%2Bmaps%26um%3D1%26hl%3Den%26sa%3DG

This is the way that the national map of multivariate vegetation patterns appears under the new RGB color scheme. The individual clusters essentially merge with neighbors, and the map changes into a spectrum of color gradients which reflect the dominant suites of variables affecting vegetation growth in each region of the country. The red Southwest is dominated by physiographic factors like slopes and elevation. The blue Northeast is dominated by thermal factors. The green Southeast has rather poor soils, on the whole. The upper Midwest is very light blue because of the cold continental winter. The Pacific Northwest and the Central California valley are light green - fairly favorable conditions for plants.

WORLD MAP OF KOPPEN-GEIGER CLIMATE CLASSIFICATION

Here is a multivariate map that is showing the main climates, the precipitation, and the temperature. This is my favorite map because I feel this is a very clear map that is able to be read with ease:

http://images.google.com/imgres?imgurl=http://squ1.org/files/wiki/climate/koeppen-map-large.gif&imgrefurl=http://squ1.org/wiki/Climate_Classifications&h=790&w=1200&sz=134&hl=en&start=1&tbnid=94J1fNAfDFBfsM:&tbnh=99&tbnw=150&prev=/images%3Fq%3Dhttp://koeppen-geiger.vu-wien.ac.at/%26hl%3Den

As you can see from these maps, multivariate maps give map makers the ability to display a lot of information on a map. You are able to take a variety of objects and place them all on the same map. Yes, they are a little harder to interpret if you don’t know what your looking for but it gives a detailed representation of data at a cheaper price.

Multivariate Mapping

Multivariate Mapping or Analysis is the investigation and analysis of more than one statistical variable at a time. This type of analysis is used to display the statistical relationships amongst objects or variables. It can be used to display statistically consumer strategy purposes. It is a very useful way to display statistically significant information for transportation and remote sensing purposes, or in other words satellite imagery. It is also a great way to explain climate characteristics such as with temperatures around the U.S. and types of soils that occur.

These types of maps can be created using ERDAS, Arcgis, or just simply through Adobe Illustrator. These maps can be created through ERDAS by principle component analysis or by a color classification system, where the user can assign 7 different bands of the elecromagnetic spectrum different colors, which is especially useful for classification for different land cover features. Multivariate Mapping can be analyzed through Arcgis by displaying graduated color and symbol maps displaying say type of habitat for graduated color maps and number of animals in a certain habitat. Through Adobe Illustrator, one can create multivariate maps by using the paint, lasso tool, knife tool for displaying temperature and the scale and select tools.

This website is about how multivariate maps can help out with corporations creating products for the consumer. It starts out talking about how multivariate maps help to analyze the covariation amongst variables in different data sets and to describe the relationship between them. Multivariate mapping methods can be placed in three categories: data reduction, which is reducing the difficulty of co-variation to show underlying factors; classification which groups objects which are the same on the basis of co-variation and data relating, which relates one or more data sets to show related factors. Different methods in this group analyze different types of responses and use a different approach to achieve the same objective. An example of a bi-plot map is displayed on this website, which shows the relationship between 3 different types of food consumer companies. A new product development strategy is shown which shows a series of product concepts explaining existing and potential new products. The map show the relationship between consumers, concepts and expectations from consumers to the desired product being made.  
http://www.camo.com/rt/pdf/brochure/unscrambler/consumer_to_scientist.pdf-

Multivariate maps are displayed very well in atlases which show as much data as possible in one map. In interactive multimedia cartography, there are many individual variables that can be put together in many ways. Visual variables that are put into these maps are points, lines, areas, surfaces and volumes. Surfaces can be displayed as two spatial dimensions; size and value. Five texture related visual variables have been identified for multivariate mapping, including arrangement, size, shape, density and orientation.  Each visual variable shows different data features. The visual variable approach by Wilson states for the classification of variables that exterior and interior texture patterns have to be split, a visual variable contains contiguous and categorical data, a minimum number of different values should be easy to tell apart and when two visual variables are combined, they should be visually seperable. A map is multivariate usually if more than one retinal value is used to obtain information and if more than one data variable is visualized. Proportional dot maps are a example of good visual variables for the multivariate map. Generic multivariate mapping is the graphic visualization of atlases by data and instructions. This mapping approach deals with different map layer types and different multiple visual variables. Each map layer has its own set of aggregated visual variables. The multivariate symbolization approach can be used in two levels. First, multiple visual aspects can be applied to areas like colored circles. To improve the ability for a person to be able to interpret the map, an attribute can be assigned to one or more visual variables. On the second level, charts categorized by interval graphs or multiple intervals are created containing different thematic variables, such as age that relate with the visual variables. An XML-based interface called Map Description Files contains all visual, variable characteristics for the multivariate map. It contains an area layer for polygons, a line layer for polylines and a point layer for points dealing with symbols or charts. 

http://www.atlasderschweiz.ch/pdf_publi/ICC07_HuberSieber.pdf- 

Microanalysis maps are remote-sensing based maps for which multivariate mapping applies. Automatic classification groups pixels in a thermal image together into different clusters related to different measurements. The K-means technique performs a partitioning of data into different numbers of classes. Objects are usually assigned to the class whose center is the closest to a chosen distance, as with Euclidean distance. Then there is the C-means technique, for which the centers of the classes are updated and this process is repeated until convergence happens. Then objects are assigned to the class for which their degree of membership is at the maximum extent. The K technique is the best technique for convergence and the best technique overall out of the two. The C technique does have the advantage though of computing the degree of confidence of this classification for every pixel. There is a technique for finding arbitrary shape by first coming up with an estimation of the probability density function, which occurs from the Parzen window technique in the parameter space after linear or non-linear mapping and one N or M dimensional smooth kernel h is then combined with any point that is a representation of pixel i and then these kernels are added. The estimated pdf is probably composed of a few peaks, or in other terms modes. The number of classes detected in the pdf depends on the width of the kernel. 

http://www.aber.ac.uk/~ecmwww/journal/smi/pdf/smi97-01.pdf-