I chose to map deaths due to alcohol impaired vehicle accidents for 2008.  The data was divided by state across the US.  This information is extremely useful for the State Governments.  A state’s category as a high fatality state is brought before the local government and  the issue can be dealt with increased patrols and community awareness.   Also, knowing the changes in alcohol fatality numbers from year to year demonstrates the effectiveness of preventative measures or the need to improve them.

I found the data on a website for Alcohol Alert! , a product that can be installed in bars or restaurants.  For a price it measures one’s blood alcohol content and thus determines the individual’s ability (or inability) to legally drive.  Although an interesting product, this does not prevent an intoxicated driver from getting behind the wheel and seems ineffective.  As far as the accuracy of the site’s data, I doubt it is 100% accurate, as exaggerating the numbers would seemingly make the product a necessity.

The following map is made with equal number intervals and is in blue and red for easy contrast.  There are 5 classes, as more than 5 shades of color become difficult to differentiate among.

For the second map I kept the colors and numbers of classes the same for easy comparison to the first map.  However, I changed the way in which the data was sorted into class: this map is divided by a method called Jenkins’ Natural Breaks. 

The natural breaks method divides the data based on ‘clumps’ of information.  It provides a much more accurate selection of the high fatality states.  If you look at the classes, there is a large jump from the purple – red to the red.  While each class includes increases by 300 or 400 fatalities, the last red group jumps to thousands.  The natural breaks method separates the extreme states. 

The equal number method, on the other hand, splits the classes so that each contains an equal number of states.  This accurately disperses the low fatality states but crowds medium to high fatality states into one group.  Notice that the red group includes the entire uppermost half of the statistics.  Not only does this show an inaccurate view of the states in red, it also offers no clue as to where the states in red fall, as red includes such a large range.

For instance, observe Illinois.  In the first map the state is red, among the highest in alcohol fatality rates.  But when the red class from the first map is broken down, Illinois actually falls in as having an average rate as shown in the second map.  These dramatic differences are due to the way in which the data is classified.

After the devastating January 2010 earthquake in Haiti, a NASA satellite took a radar image of Haiti.  The article explains that the radar image shows the fault responsible for the earthquake (black arrow).  With this image NASA scientists determined that the fault has ruptured another 25 miles westward, possibly leading to future earthquakes.  In a few months another radar image will be taken of the area and compared to the recently taken photo.  Scientists will then be able to measure land movement and have a better estimate for future quakes.

The closeness of the capital of Haiti (yellow arrow) to the fault is clearly depicted, as well as information regarding the fault’s physical state.  Although the map seems relatively simple, it is complex in that the fault cannot really be seen and this is a false color image.  The map’s accuracy and clarity makes it a reliable source for future predictions of the fault.

Although the radar map does not offer details regarding the earthquake’s effects, other maps do.  This article from CNN.com discusses  the devastation that the country experienced and the large task of rebuilding the capitol.  A series of satellite photos act as a map and allow a “before and after” view of several severely affected locations.  The map emphasizes the destruction caused by the earthquake, and makes the reader sympathetic.  The map does not, however, include any written details or labels.  If labels or descriptions were added, such as marking points mentioned in the article on the map, the two would convey the information more easily and clearly.

Although this class is about the Digital Earth, the place I would like to visit someday is the Moon.  It is hard to predict what walking on a different celestial body would be like and the only foresight I can offer is one based on pictures and the experience of others.  My initial description entailed a black sky, dusty grey landscape illuminated by the distant sun, and the silence of space.  My initial and final descriptions were the same after looking at ground view photos at the Apollo 16 landing site.

As for a location I would not want to travel to, I chose the city of Tokyo in Japan.  My initial view of the city is one overly crowded with people, vehicles, and buildings.  I understand this is the basic definition of a city but I thought of Tokyo as more crowded than a typical large city.  Also, the difference in culture is not something I would enjoy, especially since i’m not much of a seafood person.  However, after looking at street views of the city, it was not as bad as I initially thought.  Yes, it was crowded, but not to the extent I imagined.  I didn’t learn much about the culture with Google Earth but it portrayed the city differently than I thought.

With the street view, or even the overhead view, Google Earth grants an observer a first person view of the surrounding culture and environment.  For instance, in Tokyo, Japan I learned that drivers travel on the left side of the road, an example of the differing culture.  However, this is not to say that Google Earth provides an accurate representation of reality.  The program offers only a view and lacks the smells, sounds, and feelings of a place.  Google Earth cannot provide the smells of traditional food in Tokyo, the feeling of low gravity on the moon, or the peaceful silence of outer space.  In other words, Google Earth only provides a glimpse; the reality of a location must be experienced firsthand.

Google Earth has thousands of uses other than virtual tourism.  Since it is an advanced map, it can be used to display the varieties of data and information we discussed in class.  However, the ability to observe and study specific details of celestial objects would be useful for astronomers, scientists, and even students.  Google Earth could offer the ability to observe other planets, suns, interstellar space, and universes with detail and ease.  For example, Astronomy deals with a gigantic amount of data.  Google Earth could serve as a centralized database for this information.  Also, almost all that Astronomy deals with is too distant and hostile for humans to experience them.  Thus, most of what we know about the universe comes from observing light from far away objects.  Google Earth, or Google Universe as it should be renamed, would provide those observations (photos, information points, points of interest) similarly to the ease and detail that is currently Google Earth.