Lab 5 Answers

#1 - 99 Counties

#2 - Fulton County

#3 - 39 Cities

#4 - 3851.14 Miles

#5 -

1367445.89 Acres

#6 - 522

#7 -

#8 -

Doncaster Indian Reserve 17

Kitigan Zibi Indian Reserve

Kahnawake Indian Reserve 14

Kanesatake Indian Reserve 16

Akwesasne Indian Reserve 15

Lab 4

For my first map using the Black Population census data, I chose to depict the population rates using the Natural Breaks method in ArcGIS. From these classifications, I manually adjusted the break values to more easily digestable number values that were close to the Natural Breaks. I accounted for the actual Natural Break values in the information in the lower right, which is unfortunately unreadable after uploading these images to this blog. This data classification provides good distinction in the South, but also reveals how homogenous the rest of the country is. For the layout of my map, I chose to balance the map by placing the insets and legends in both of the bottom corners.

With the Asian-American population map, I made minor adjustments to the layout, and I think it provides a more visually appealing design. In retrospect, it may be better to have pushed the upper neatline to the edge and put the title inside the mapped area. In its current form, the legend stands out heirarchically because it overlaps the neatline, but if I had pushed the neatline to the top, this may have not been the case. For my data classification, I chose the geometric intervals method in ArcGIS. This method offered an opposite effect from the African-American population map: while there is distinction between classes across the nation, there is no distinction amongst counties with a substantial Asian-American population since the top class ranges from 8.56% to 46.04%.

In the 3rd map, I chose to classify by standard deviation in order to present the data in a different way. While this method makes a more visually interesting map than the other two, it would also be the hardest for a layperson to understand. I also chose to use a horizontal layout for the legend, which fit neatly above the Great Lakes.

All three maps presented a similar challenge: depicting data which is not well-dispersed throughout the mapped area. However, this presents an opportunity to use the maps to show the proper patterns of where the populations have tended to cluster. This series would not work well together due to the different classification methods I used and different layouts. If I was to create a series, I would have stuck to the Natural Breaks method I employed on the first map.

Following this exercise, I believe GIS is a very powerful tool for spatially depicting data. I think the classifications in choropleth maps presents a challenge, and understanding the purpose and intended audience is critical for presenting data. These maps presented similar data in different ways which would have not been appropriate for all purposes or audiences.

Lab 3

This lab exercise helped to reveal the strengths and weaknesses of various map projections by providing a good way to compare them together at one time. While map projections are designed to preserve a particular aspect of the map while allowing distortions in other aspects, this exercise also showed that there are many differences between even the same types of projections.

While it is common for conformal maps to distort area sizes near the poles, the two projections used in this exercise show significant differences in their distortion near the poles. A clear example of the distortion can be seen by comparing the distances between the 60 degree latitude lines and the poles. The Mercator projection stretches these areas of the globe so much that the scale of the Mercator map is twice as small as the Stereographic map, but the mapped areas are nearly the same size. This shows why conformal maps are not ideal for thematic maps which compare data distributions, especially if the mapped area involves latitudes outside of the tropics.

The equal area projections proved that they also distort shape and distance, like the conformal projections. Unlike the conic equal area projections for North America, these global equal area projections would not be optimal for thematic maps either. Though they would offer an accurate comparison of the data distribution due to the preserved area sizes, these projections distort the shapes of many countries from the shapes people are accustomed to seeing.

The equidistant projections present perhaps the greatest pitfalls. Since they are designed merely to preserve distances from all points to one particular point, they do not necessarily preserve distances between any point and any other point. I think a map user who did not understand that could potentially assume the equidistant maps preserved all distances. Clearly, the Equidistant Cylindrical projection displays an incorrect distance between Washington and Kabul. Additionally, this particular projection shares many of the shortcomings of the conformal maps. While the conic projection was very close to the actual distance of ~6919 miles with this particular example, its major weakness comes in the massive distortions of shape and area in the southern latitudes.

This exercise reveals why it is important to select a proper projection to fit the purpose of the map. The differences in projections offer a variety of strengths to be exploited and weaknesses to be avoided when creating a map.

Lab 2B

This lab exercise was a beneficial introduction to the use of the ArcGIS program, and highlighted some potential strengths and weaknesses of GIS. Being concurrently enrolled in GEOG167, I have already been exposed to ArcGIS; however, this exercise showed me many functions which I had not discovered through the "crash-course" style exercise of the first week in 167.

A major strength of GIS generally, and the ArcGIS program specifically, is the ability to combine a multitude of information to create meaningful map products. The ability to create different layers and different frames allows users to examine several aspects of the map at once. This exercise revealed this strength of GIS by examining the affect of the noise contour from the proposed airport on key areas within the city. ArcGIS users can create maps designed for very specific purposes by showing the spatial relationships between different kinds of information. For example, the first map's purpose is clear: to highlight that there is one school that would be affected by the noise of the airport. This was accomplished by overlaying the noise contour with the local schools. The ability to sort and visually depict different kinds of data makes GIS a powerful tool.

However, this exercise revealed a large weakness of GIS: the anonymity and impersonality between the GIS user and the subject matter. From the beginning of the exercise, this county is nameless and our purpose in creating this map product is not revealed. For the purposes of this exercise, as a tutorial - this information was not necessary. A new user of ArcGIS is capable of going through this well-written tutorial, and create a map product, as described in the instructions. However, there is a potential for anonymity and impersonality of this exercise to exist throughout the field of GIS. The separation of a GIS user from the subject matter of the map is a problem that could result in the creation of maps which do not fully achieve their purpose. So while this exercise was merely an instructional tool for learning ArcGIS, it reveals a weakness of GIS. Users of GIS should have an interest in the product beyond that of merely trying to finish their work.

Therefore, this exercise proved to be very useful and instructive - not only from the functional standpoint of knowing how to use ArcGIS, but also by revealing more behind the concepts of map development. I was able to apply the skills learned in this exercise toward my products for GEOG167. I believe my products are far superior this week, after having gained more familiarity by practicing with this tutorial. Additionally, I was able to experience the impersonal nature of creating a map. I think it would be beneficial to be an engaged user GIS whenever possible. A detached approach to creating a map could work well when creating a reference map, but I believe thematic maps require a level of engagement between the user and the subject. Both functionally and conceptually, this has been a valuable exercise.

Lab 2A Discussion

Since I enjoy snow skiing and am new to California, I chose to create a Google Map which would depict the ski areas which are within a reasonable driving distance for an extended weekend trip. Therefore, I could not only create a project for this class, but also have a functional map I could use this winter when I have a free weekend and want to go skiing.

Creating a map which would be functional for my personal use allowed me to explore the core strength of Neogeography: allowing individuals with no formal training in cartography to create functional maps which are beneficial and/or entertaining. With relative ease, I was able to navigate the map, find ski areas, plot them, and organize them in a fashion which would remind me which areas I would be most likely to try to visit in the future. I developed a color scale to describe my assessment of each area after visiting their website and reviewing their information. Red respresents the worst areas - ones I do not plan to visit. Green represents areas I absolutely would like to visit. Blue areas are areas I'm sure I would enjoy, but not to the degree of the Green locations, and Yellow areas are places I believe are viable, but lack the size/diversity needed to sustain interest for a long visit.

I then created two areas: the Green area represents my estimation of a "Day trip zone" - given the right conditions, I could potentially wake up early, travel, ski, and return the same day. Ski areas in this zone would be preferred over others of the same caliber. Conversely, I created a red zone around Lake Tahoe to indicate the areas which are clearly inside the tourist trap zone which would likely be extremely busy on weekends and holidays. Despite the quality of the areas in that zone, I would likely only visit these areas when taking days off during the middle of a week, outside of peak season.

Throughout the exercise, I encountered several shortcomings associated with Neogeography. The largest problem was a lack of accurate information. Google Maps had accurate information for most of the areas, but some areas did not have their locations inserted properly to find them with the search function. After careful browsing of the map, and comparing map information to the ski area website, I was able to locate these areas. Conversely, in many locations there was an overabundance of information. A search for "Ski Area" yielded not only the ski area, but any store or hotel in the area which associated themselves with skiing. Problems like these discourage some people from using Neogeography.

Another interesting issue I found was some potentially unscrupulous tactics by ski areas competing for business. For one particular ski area, a Google map search yielded a result with an accurate name and location, but the website associated with the area was for one of the nearby competitors. Since I believe Google relies on "self reporting" of information on their maps for these search results, this may have been a deliberate attempt to steal customers.

These issues with Neogeography highlight the "Garbage-In-Garbage-Out" concept. Google Maps is an outstanding tool, but its potential is maximized when information is loaded into it correctly, and when users can filter or manipulate searches in order to find exactly what they want to find.

Overall, Neogeography offers many opportunities for individuals to create meaningful maps for themselves and others. With this exercise, I was able to create a product which could potentially be useful for me for many years. However, if I had merely taken the Google result and plugged it into my map without double-checking the information, I think about 10% of the ski areas would have either had some inaccurate information or been missing altogether. Like all technology, Neogeography is an outstanding tool, as long as those who use it are critically engaged in the process.

Lab 2A

View Regional Ski Areas in a larger map

Lab 1B - Topo Map Answers

1. Beverly Hills Quadrangle
2. NW - Canoga Park; N - Van Nuys; NE - Burbank; W - Topanga; E - Hollywood; SW - Blank (Pacific Ocean); S - Venice; SE - Inglewood
3. 1995
4. What datum was used to create your map?
5. North American Datum of 1927
6. At the above scale, answer the following:
a) 1200 meters
b) 1.894
c) 2.64
d) 12.5
7. 20 feet
8. What are the approximate geographic coordinates in both degrees/minutes/seconds and
decimal degrees of:
a) 34 deg 4' 30" N, 118 deg 26' 0" W
b) 34 deg 0' 30" N, 118 deg 30' 0" W
c) 34 deg 6' 0" N, 118 deg 24' 30" W
9. What is the approximate elevation in both feet and meters of:
a) 560 feet
b) 140 feet
c) 700 - 900 feet depending on where in the park
10. Zone 11
11. 11N 361500 3762950
12. 1,000,000
13.

14. 14 deg East
15. South
16. UCLA Campus:

Lab 1, Map #3

For my final map, I wanted to find a Reference Map, so I searched for "Map of Western United States" to commemorate my journey from College Station, Texas to Los Angeles, California. I found this nice historical map from 1897 from the United States Digital Map Library (http://usgwarchives.net/maps/usa/usmaps/west1897.jpg). This map stood out to me amongst the field of modern maps my search yielded because of several features:

#1) On both the left and right borders, you find the names of Asian and European/African places which fall along the same latitudes depicted on this map. This feature would give foreign viewers a better sense of where they are compared to their homeland, and gives domestic viewers a better sense of where they fall in relation to the rest of the planet. This is an interesting alternative to the use of an inset to show a small world map with a box over the Western US to show the borders of this map.

2) Another interesting feature is this map still lists longitude under both the old convention (a country's capitol) and the standard of Greenwich.

3) Finally, I enjoyed seeing a few features which were important to a 19th Century cartographer, like "Gold Mines" and old Forts.

Lab 1, Map #2

This map was found by doing a Google search for "Map of College Football Teams." I chose this map from http://southerncollegesports.com/football_location_map2.html because I like college football and this map depicts all Division 1 programs, and this happens to be a map I had not seen before. This map's strength is showing not only where each college exists, but also what conference each team represents through the use of color-coded labeling.

But I also chose this map because of its numerous shortcomings. Obviously, it was created without the use of proper mapping tools/software (looks like an MS Paint product) and without any regard for accuracy. Being familiar with Texas geography, I can find clear faults with the placement of several of the teams in that state. Clearly, it is designed by a College Football fan, for College Football fans, and it's an assumption on the part of the creator that his viewers will come with a knowledge of each team's abbreviation. Even so, the author of this map does a poor job of ensuring that his labels can clearly be read. This is particularly a problem in the Mid-West, where the Big Ten and MAC teams are clustered. Finally, it's notable that this map shows a bias in its coloring. The map was presumably authored by a Southerner, and as such, the author saw it fit to depict the PAC-10 teams in the color pink - which may seem innocuous to a casual viewer, but I believe it was more than likely a direct jab at the West Coast style of football.

Lab 1, Map #1

This map by Charles Joseph Minard was the first map I wanted to post for this assignment because it is an outstanding example of depicting statistical information spatially. I found this particular image on the Wikipedia entry for Charles Joseph Minard (http://en.wikipedia.org/wiki/Charles_Joseph_Minard)

Minard creates this Thematic Map to depict the advance and retreat of Napoleon's Army as it marched into Russia to attempt to take Moscow. The use of different line thicknesses to represent the size of Napoleon's Army allows this map to help the viewer comprehend the staggering troop losses throughout the campaign. I originally saw this map in a book by Edward Tufte (The Visual Display of Quantitative Information) that my wife used for her Communication Studies research last year as she finished her Ph.D (forgive the obligatory spouse-bragging), and I liked the map from the moment I saw it - even though I don't speak any French, nor am I particularly interested in Napoleon. It's a stunning representation of the total loss of Napoleon's Army. (Note the graph on the bottom which shows the temperature at various points along the retreat. I feel cold, too.)