Lab 7

     As the name suggests, the first map shows the black population of almost every county in the United States. The data are from the US Census Bureau, and were collected in 2000. It measures the population of interest as a percentage of the total county population. These data are represented by various colors, and is displayed on the Lambert conformal conic projection of the contiguous United States. A yellowish color denotes a relatively low percentage (less than or equal to 5 percent) whereas a reddish color denotes a relatively high percentage (55-90%). The map legend breaks the size of the black population into 6 categories: 0.0103-5%; 5-15%; 15-25%; 25-35%; 35-55%; and 55-90%. There is a concentration of counties with a large black population in the southern United States, most notably in the states of Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina, and Virginia. Most of the remaining states have a majority of counties that contain a black population less than 5 percent of the total population, with a few counties in the midwest that have a relatively large black population. Counties that are white indicate that there were insufficient or no data.

     The second map shows the asian population of nearly every county in the United States. Like the first map, the data used in this projection are also from the US Census Bureau, collected as part of the 2000 census. Again, the population of interest (asian) is measured as a percentage of the total population of the entire county. Counties with different asian populations are represented with a different set of colors, and this map utilizes the North American conformal conic projection as well. A light blue color represents a relatively small asian population (less than 1 percent) whereas a dark blue indicates a county with a relatively large asian population (greater than 20%). The map legend breaks the sizes of the asian populations into 6 categories: 0.0085-1%; 1-3%; 3-5%; 5-10%; 10-20%; and 20-50%. Counties with the largest Asian populations are found along the west coast of the US, with a concentration of these counties near the bay area of San Francisco, as well as Los Angeles. The Asian population of the remaining counties is sparse and relatively low, with a few counties throughout the midwest and east coast that have large Asian populations. Counties that are white indicate that there were insufficient or no data.

     The third map shows the population of some other race in almost every county in the United States. Like the other two maps, the data used are from the US Census Bureau from the 2000 census. The population of interest, which is neither black nor asian, is again measured as a percentage of the total population of the whole county. Counties that have different sized populations of "some other race" are again represented by varying shades of a color. Like the other two, this map also uses the same Lambert conformal conic projection on which to display the data. Light green represents counties with a relatively small "other race" population (less than 2 percent), whereas dark green represents counties with a relatively large "other race" population (greater than 20%). The legend breaks the sizes of the "other race" population into 6 different categories: 0.00795-2%, 2-5%, 5-10%, 10-15%, 15-20%, and 20-40%. Counties that have a large "other race" population are found throughout the western portion of the US, especially in the states of California, Arizona, New Mexico, and Texas. Based on existing cultural and social knowledge, one might infer that "some other race" might refer to Hispanic or Latino. Counties that are white indicate that there were insufficient or no data.

     All three of these maps were created by joining spatial data and attribute data. Every map used the same spatial data, which was the Lambert conformal conic projection of the contiguous United States, with the county boundaries present. Each map had its own attribute data, which gave the size of certain populations of interest within counties. Furthermore, each map tells its own story, and certain social or historical implications can be made by a simple analysis of the projections and their data. These three census maps provide an excellent example of the advantages of "joining" data sources, and how GIS can be used to aid in the interpretation and analysis of data of all kinds.  They are easily understandable and straight forward, thus making them universal and intended for all.

     At the beginning of the quarter, I had absolutely no knowledge of GIS, or how it is used. I have a relatively mild understanding of geography, and I like to see myself as more geographically aware than the average person, but I had no prior knowledge of GIS. It quickly became evident that GIS is a critical component of a large number of fields, and has developed alongside a rapidly evolving technological world. My ineptitude with computers served only as a catalyst for frustration, as the labs at the beginning of the quarter seemed exceedingly difficult and time consuming. As ArcGIS was used more and more in each subsequent lab section, I slowly built up my confidence, and simple tasks and map-making in ArcGIS became easier. I now sufficiently understand the important of geographic information systems, and how it is utilized by almost everyone on a daily basis. Additionally, I also have an understanding of the limitations technology places on GIS, and how knowledge of computers is often required for a productive and stress free GIS experience. GIS is critical in how we perceive and analyze our world, and is only becoming more and more important for all of us.

Lab 6

     Four maps were created in this lab section, a shaded relief model that layered a hillshade and a color ramped DEM model, a slope map, an aspect map, and finally a 3D map. The potential user-friendliness of this aspect of ArcMap was almost immediately evident, as the maps were quickly and easily created. Each offers a slightly different visual representation of the same raster elevation data, with one focusing on slope, another on orientation, another on elevation, and another on a three dimensional representation. This lab made it evident that DEMs can be displayed in a vast variety of ways, with just a few previously mentioned. This allows for a multi-faceted approach to analyzing certain types of GIS data, thus broadening the potential applications for DEMs and related GIS programs. Furthermore, they specifically function to improve the efficiency and accuracy of spatial analysis, which is a critical component of GIS. This became strikingly evident upon the creation of the 3D map during the lab, since it presented us with perhaps the most "realistic," and easily analyzed visual representation of the data. However, as with many other facets of GIS, DEMs are not without their pitfalls. These representations are limited to the hardware and software being used, and must also keep up with improvements made in technology. And even though the maps created in the lab were constructed with relative ease, individuals seeking to display some form of elevation data may be limited by their lack of knowledge and unfamiliarity with ArcGIS and other programs. Finally, one might also encounter problems when using DEMs to represent reality. Temporally affected data, such as lakes and rivers that dry out but return at certain times of the year (or the solid ground beneath them) may be difficult accurately represent on a DEM.

3D DEM

Lab 5

     Perhaps the significance of map projections is indicated by their very definition: the way in which we perceive the world. Since it is simply not possible to accurately transform the three dimensional surface of the earth into a two dimensional map, every projection preserves certain physical aspects, while distorting a few others. These simple distortions, while often subtle, can be extremely significant and must be taken into consideration when choosing an appropriate projection. The main elements of a projection that are either preserved or distorted are distances, scale, bearing, direction, area, and shape. The vast number of existing map projections is indicative of the versatility of maps, and their many different uses. Different projections can be used to convey a wide array of information, and a particular map's purpose or function usually dictates what projection will be used.

     One of, if not the most well known map projection is the Mercator projection. It is a conformal cylindrical projection produced by Gerardus Mercator in 1569, and quickly became the primary map used for nautical navigation since a straight line on the map represents a constant course, which is arguably its most significant potential use. However, land masses that are farther away from the equator are more distorted in terms of area than land masses closer to the equator. As the map continued to gain popularity, it became the primary projection of the world typically found in US classrooms in the 20th century. This was critical, as the average person is not cognizant of the many distortions associated with map projections, and could believe that the sizes of certain countries and regions compared to others are accurately depicted. This is entirely false, and the relative sizes of regions like the United States, Russia, Africa, Greenland and Australia convey subtle messages that can subconsciously be interpreted as one region having "importance," or some other critical aspect, over another region. As a result, contemporary atlases no longer use the Mercator projection, and instead use equal-area projections more often than conformal ones. Along with the Mercator, another conformal projection is the Stereographic projection. It is advantageous when mapping the Earth's poles, and also has applications in photography when capturing wide-angle views.

     

     

     Another significant type of projection is the equal are projection. As the name implies, it preserves surface area and accurately depicts the relative sizes of regions. An example is the Mollweide equal area projection, also known as the elliptical projection, which distorts angles and the accurate shapes of regions in order to maintain accurate relative sizes. Created in 1805 by astronomer Carl Mollweide, this particular projection is advantageous for mapping the entire globe, as well as the sky. As a result, the Mollweide projection is found in a great number of 19th century star atlases, and is frequently used to display astronomical observations, such as cosmic microwave background radiation in full-sky format. An additional type of equal area map is the Bonne projection. Named after Rigobert Bonne, this pseudoconical projection displays every parallel as an arc of a circle. Like the Mollweide map, the Bonne projection maintains accurate area, and distorts shape. These distortions are more severe farther from the center of the map, but are minimal towards the center. The scale is accurate along the straight, central meridian line.

     

     Along with the equal area projections, another widely used projection is the equidistant projection, in which the distances from a standard line or point are preserved. One example is the azimuthal equidistant projection, which may have been used by ancient Egyptian star maps dating back to the 11th century. The main advantage of this projection is that all points on the map are at correct distances, as well as correct angles from the center point, which is commonly the north pole. This has various social advantages, as the United Nations uses this particular projection in their emblem, since there seems to be no implied "primary" countries or regions. The distortion of shapes and areas is present in this projection, and they are more extreme farther from the center of the map. Another equidistant projection is the conic equidistant one, which is derived from a conic section of the earth's surface. Distortion is minimal along the central parallel, and is constant along any given parallel. This type of projection is useful for displaying only one hemisphere, and is neither equal area or conformal. These six map projections described offer only a brief look at the vast variety of modern and old projections.

Lab 4

I was unable to complete the lab due to technical difficulties. I reached page 22 in the tutorial. Since I wasn't able to paste an image of the end result, I decided to include what I was able to complete:




      After attempting to complete this lab and experiencing ArcGIS for the first time, I have a much better understanding of the potentials and pitfalls of GIS. Perhaps the most apparent potential advantage of GIS is its availability, as anyone can go online and download the programs. Interacting with the program was a rather interesting experience, and the instructions in the tutorial were mostly easy to follow. Not too long after starting the tutorial, it was apparent that there are likely a great number of applications for ArcGIS that go beyond the relationship of airport land and sound contours. ArcGIS allows for the conveyance and display of information that can easily be understood by many people. If the information being conveyed is urgent in nature, for example the extent of damage of some natural disaster, GIS allows for a relatively quick data analysis and has the potential to be critical in certain urgent situations. 

      In conjunction with making information easily displayable to the general population, GIS encourages the sharing and spreading of information since it's part of a web 2.0 that is becoming more and more critical to society. Since almost anyone has access to GIS, and because it has applications in many different fields, including the sciences, engineering, and many more, it's easy to see why GIS would be an efficient medium through which to spread and distribute vital information. ArcGIS is only part of a rapidly growing network of geographic information systems which ultimately makes our lives easier and more efficient.

      However, in addition to the various potentials of GIS and ArcGIS in particular, there are also a number of pitfalls and disadvantages. Although the programs themselves are widely available, the average person may lack the necessary computing skills that would make the GIS experience easier and more pleasurable. ArcGIS assumes the user has a certain knowledge of computers, even in the tutorial, and a person lacking these skills and knowledge may encounter some difficulty using the GIS. Furthermore, reliance on technology presents its entirely own set of potential problems. I experienced this when I was unable to retrieve the data I had saved on my flash drive when I tried to continue my work on the lab. Problems like these are exacerbated by a lack of computer knowledge. 

      As GIS is primarily reliant on technology and computers, another possible pitfall is compatibility. Since the world of technology is constantly evolving and improving, GIS must also keep up. Advances in computing warrant similar upgrades for GIS. This requires continuous maintenance for the creators and editors so that the programs run most efficiently.