Lab#7

        The first map is a representation of Black alone population in counties by percent. This data came from the 2000 census report. For this map, I chose shades of purple to show the distribution of black population throughout the United States. The legend showed a range of percent starting at 0 and topping at 86. The darker the shade the higher the population. For example,  counties with 0 to 3% Blacks are lighter in shade, which spreads over most Northern states. A darker shade would mean higher percent, so the darkest purple in the legend would represent 59 to 86% Black population. There is an overall a higher percent of Blacks in the Southern-east states, such as Alabama and Georgia.Then there is also a small cluster of high percentage of Blacks in California and states that border it.
         For the second map showing Asian alone population in counties by percent, I used shades of green. From the display, there isn't a significant pattern of Asian distribution by counties. The range of Asian along population starts at 0 and up to 46%. There is good percentage, ranging 9 to 46%, of Asian population residing near the Pacific Coast, mostly in the state of California. Other than that, there is scattering of low and high Asian population throughout the United States.
         The last map showed Other Race alone population distribution by counties in percent. The Other Race will be categorized with Hispanics because Black, Asian, White, Native American, etc had their own set of data. In this map, we can see a prevailing trend of high concentration of Hispanics along the Southern-west states, such as California, New Mexican, and Texas. This highly concentrated population can range from 11 to 39%.
          In this exercise, I enjoyed the free range of colors we can use to display our maps. The legend are especially helpful to inform the readers of where has the higher concentration of population. The coloration eases our visual understanding. The actual display of a map is much easier to comprehend the data than just by reading the tables. Thematic maps also allow us to make observation of areas that have the most or least of a certain population. We also can point out clusters and trends of population. Ultimately, colored maps are aesthetically pleasing unlike boring old numbers. To further elaborate on my maps, I include a legend, a scale bar, and a north arrow to help orient all my audience the same way.
         With the help ArcGIS, I can join shape files with tables thus making the maps appearing the way they are. In addition, the preset color ramp saved me time from choosing a color. The different way to sort data to make it look less messy and more presentable. For example, applying the same map projection to all layers to make the look reasonable. Throughout the weeks of doing labs, I gain a better understanding of how to combine data and join table to make maps meaningful. Each lab is mind refreshing and increase my efficiency in ArcGIS usage.

Lab #6

  

Extent:
Top:36.3199999992
Left:-112.341666666
Right:-111.721944444
Bottom:35.9616666659

Geographic Coordinate System:
GCS_North_American_1983
D_North_American_1983

    For this exercise, I pick a section of the Grand Canyon and did elevation analysis on it. Grand Canyon is a national park located in Arizona. This beautiful landscape runs 277 miles long, 18 miles wide, and up to 1 mile deep. It was believed that the land was slowly eroded and carved through by a river, what is now known as the Colorado River. A reason for having low elevations such as seen in the slope data. The deepest point of the canyon is over 6000 feet, while its highest elevation is at 8800. There are huge difference between its lowest and highest points of elevation is due to the river erosion and uplifting. I find this place to be noteworthy and interesting to do a digital elevation model on it because of its elevation differences. The 3D map was especially striking to me since I got a get a three-dimensional feel to the difference of the elevations as I move and rotate the map around.

Lab #5

 

     

     In this week’s lab, I got to test out different map projections. Map projections are putting a three dimensional earth on a two dimensional surface. Since there is no perfect representation of the 3D earth, we can only get different types of map projections for different purposes. Like said, all map projections can preserve certain properties and distort others. In this exercise, I get to generate two maps in each of the three major map projections categories: conformal, equidistant, and equal area.

       Conformal maps have its unique set of properties. Conformal maps preserve angles and direction but distorts shape. This meant the angle between any two lines on the sphere must be the same between their projected counterparts on the map and scale at any point must be the same in all directions. Conformal maps are mostly used for local purposes and seldom for world maps since angles can only be preserved to a certain extent, thus shape or area are greatly distorted when the area it trying to represent is too big. In Mercator, we can see that Antarctica is greatly stretched and looks as it got equal landmass as to all the combined continents. In Stereographic, instead of looking like a planar as in Mercator, it’s spherical and it looks like the upper border of Canada can almost touch the upper border of Russia.

        Equidistant projections, as suggested by its name, are maps that preserve distance. Such projection maintains scale along one or more lines, or from one or two points to all other points on the map. That said, we could measure the distance between two points more accurately than the other two projections, conformal and equal area. Equidistant projections are very important in aviation purposes; we would want to know the accurate distance when we are traveling. In comparison, we would travel 2,000 more miles using the Mercator map than if we were using Azimuthal Equidistant! Even though such projection is very effective in preserving distance, it distorts area like conformal projections. As seen in Equidistant Conic, we can see that Antarctica is distorted in such a way that it appears to stretch across the entire bottom globe.

          Finally, equal area projections, the last of the three major projections, preserve area. Equal area maps are especially useful when comparing land area between places. For example, Antarctica looks more appropriate in size in both maps, Eckert VI and Mollweide. Even though it has advantage in maintaining area, it distorts shape. It become evident when we approach the poles; the shape of the poles can be flattened out as in Eckert VI or squeezed in Mollweide. 
            I had a lot of fun exploring different map projections and compare them to each other. This exercise teaches the different purposes of each map projections. Though each map projections have its own special preservative properties, they can also introduce distortion. Thus, I have to be extra careful to figure out what exactly I am looking at and what information I can draw from it. It’s also an important knowledge to have because now I know the usage of each map projections and save it for future references.