Week 8 Lab Write Up

The first map I created is the one titled “Percentage of Black Population by County.” For this map, I decided to shift the break points for the data and make them significantly lower than their “natural” location. Whereas the original set of break points included break points relatively evenly spaced up to approximately 90 percent, I chose to have five of my six ranges below 10 percent, with the final range spanning the whole range from 10 to 90 percent (which is represented by a deep red hue). This had two results on the data. Because the top range spans a wide 80 percent range, essentially the entire southeastern region of the map turned red, which visually appeared to increase the number of blacks in the region when compared with the coloration of the map when the original break points were in place. This also visually made it seem that the region with a higher percentage of black individuals expanded north along the east coast of the United States, when in fact a large number of the northern counties in this region that are exhibiting the reddish hue actually contain black population percentages closer to the 10 percent section of the top percentage range. Secondly, skewing the break points to have so many break points representing lower percentages illustrated how truly minimal or absent the black population is across the United States, especially in the northernmost regions. By skewing the break points as I did, I was neither able to show a complete national presence or absence of the black population; however, I was able to boldly contrast the regions where the black populations are the largest (the southeast) and where they are they are the smallest (the northern regions of the United States).

The second map that I created is the one titled “Percentage of Asian Population by County.” For this map, I left the break points relatively close to their original locations by simply rounding off the original ranges to make for visually cleaner resulted in both the legend and on the map. In doing this, two main visual goals were accomplished. Firstly, the percentage ranges of 5.00000-9.999999, 10.000000-14.999999, and 15.000000-24.999999 are spaced relatively evenly across the majority of the nation (minus the northernmost Midwest) in a visual sense. Secondly, by shifting the break points to 25-50 percent, only San Francisco County was able to fall into this range, thus making it the only county on the map with the hot pinkish hue. Because of this, San Francisco County is singled out as the county with the highest percentage of Asians in its population in the entire continental United States (had I also included Hawaii and Alaska on this map, the State of Hawaii would have also illustrated the same coloration of San Francisco County in various counties). In California, the map shows a visual lightening of color in the regions the farther away one travels from San Francisco, which could be appropriately assumed to show a point of immigration (from Asia) and dispersal (as the hue of the counties lightens with distance from San Francisco County). Due to the alteration of the break points for this map, I was able to show a national presence of Asians in counties across the United States, that San Francisco County contains the highest percentage of Asians in the continental United States, and the general path of the immigration of Asians from San Francisco County throughout the State of California.

The third map that I created is the one titled “Percentage of Some Other Race Alone Population by County.” For this map, as there was no percentage of “some other race alone” in a single county higher than 40 percent, I chose to use six break points, with a range of either five or ten percent between them. Had I increased the size of the percentage ranges (for example, turning 30-40% range into a 20-40% range), I could have increased the visual presence of “some other race alone” as I did with the graph that illustrated the “Percentage of Black Population by County.” However, the graph that I have created shows the highest percentage range existing primarily in the southwestern United States. Based on this map and the lack of a “Hispanic” category in the 2000 Census, it is feasible to conclude that this map shows the immigration of individuals of Hispanic dissent into the United States from the southwestern border from Mexico and Central America, provided that counties closer to this region exhibit higher percentages of “Some Other Race Alone” within their borders. However, the highest break point for this map (40%) is lower than highest break point for the other two maps (50% and 90% respectively). Therefore, while “Some Other Race Alone” may seem to have a larger presence on this map based on this sole set of data from the 2000 Census, if it were to be combined with the data from the graph titled “Percentage of Asian Population by County,” “Some Other Race Alone” may not appear to be as prominent.

In this lab, we learned how to portray data in ArcGIS, and in all honestly, I was surprised at how easy it was to manipulate the data to achieve a desired goal. By simply altering the break points for each map, it is very simple to change the illustrated results of each of the maps. With regard to the “Percentage of Black Population by County” map, altering the break points can make it seem like a certain population has more of a presence in a certain region. With regard to the “Percentage of Asian Population by County” map, altering the break points allowed me to highlight San Francisco County as the percentage with the highest percentage of Asians. By doing this, I was able to show a specific point of immigration and dispersal of the immigrant population to the surrounding regions. With regard to the “Percentage of Some Other Race Alone Population by County,” a more even distribution of break points was able to illustrate a gradual dispersal in immigrants entering the United States from the southwest border.

At this point, my overall impression of ArcGIS is becoming significantly more favorable of the program. I found it highly useful to learn how to take a consistent set of data and alter various aspects of the data frame (break points, color hues, color ramps, data ordering, etc.) in order to get the map to show a desired result. After this lab and the last lab (where we made three maps of a specific terrain, showing slope, hillshade, and aspect), I finally feel that I have an understanding of how ArcGIS is used to present data visually. I found the Week 7 Lab to be highly useful for understanding how to represent physical geographic data; however, I feel that the methods that were practiced in this lab are probably used more commonly for data representation. Last time I was asked to write about my impressions of ArcGIS, I felt that performing certain tasks required too many steps, thus making it too complicated; however, as time has progressed and I have acquired more experience working with ArcGIS, I feel that the program is becoming increasingly easier to use and performing certain actions, like creates joins, altering break points, create data layers, importing data, export data, storing data, and other similar actions are as second nature as the tasks and tools in a Microsoft Word program.

Week 7 Lab Write Up

The area that I selected for this lab resides between the latitudinal coordinates of 36.35°N and 36.71°N and between the longitudinal coordinates of -105.78°E and -105.13°E (thus residing within the northeastern region of California). The Geographic Coordinate System used to obtain these coordinates is the North American 1983 Geographic Coordinate System. The area is to a scale of 1:218,735. The elevation in the region shows a spread ranging from ~6,800 ft above sea level to ~11,900 ft above sea level. This spread of ~5,100 ft is best exemplified by the three-dimensional illustration that shows regions in the lowest elevation (~6,800 ft) as a vibrant purple and the highest regions (~11,900 ft) as a reddish-brown. The Aspect DEM map shows that a majority of the slopes are facing in a Southeast direction, which is symbolized by a red coloring. The Slope DEM map shows the slope of not only the mountains and landmasses, symbolized by a lime-green coloration, but it also shows the flow of rivers and the slope of snow packs, as exemplified by the blue coloration on the map. The Hillshade DEM map illustrates the natural shape of the hills by using a grayscale to illustrate the general physical shape of the land. I chose a sunset scheme of colors for this map to represent elevation, with the yellow representing the lowest elevations and the purple representing the highest elevations (while the reddish tones represent the middle-range of elevations).

Week 6 Lab Write-Up

In order to analyze geographical layouts of the globe, map projections are necessary tools that are used to convert the spherical composition of a globe to a flat “projection” surface. Essentially, a map projection is the conversion of a three-dimensional global representation into a two-dimensional form through mathematical transformation. For this assignment, I analyzed three different types of map projections: conformal, equidistant, and equal-area. The conformal map projections preserve the right angles formed by lines of latitude with meridians, equidistant map projections have distances that are uniform from the center to any other place on the map, and equal-area maps preserve the same proportional relationship of countries. However, map projections are problematic tools when trying to obtain an accurate representation of the Earth. The problem with the conversion is that it distorts the location and size and various features on the three-dimensional surface when it converts it into a two-dimensional plane, cylinder, or cone. For example, the conformal Mercator map projection shows a Greenland that is approximately the same size as North America and an Antarctica that is larger than all other existing continental landmasses. While the Mercator map projection does successfully preserve the shape and the direction of the original landmasses from the three-dimensional globe, the aforementioned distorted areas are quite easy to recognize. The 30° x 30° graticule also further accentuates the disproportionate sizes of the polar countries and continents.

Distortion depends not only on the map projection but also on the shape the two-dimensional map projection takes. For example, the aforementioned Mercator map projection takes on a rectangular shape, thus distorting the continental masses found in the Polar Regions. This happens because the top half of the three-dimensional globe, which typically tapers inward, is pressed flat to create the two-dimensional map projection. In contrast, the Stereographic map projection, another conformal projection, has a circular composition that results in the disproportional enlargement of Australia. Based on this example, it is important to remember that when working with map projections, distortion will vary based on the ultimate shape of the final two-dimensional projection.

The accuracy of map projections also is largely dependent on the purpose for which they are being used. For example, in a simple sense, a map projection can be used just to gain a better understand of the relative location of the Earth’s continental mass in relation to the rest of its mass. For example, map projections like the equal-area Mollweide and the equidistant Plate Carree map projections do a solid job (relatively speaking) in showing the general geographical layout of the Earth because they maintain a 30° x 30° graticule that lines up appropriately with the continents as it does on the globe. However, projections like the equal-area Bonne map projection (which is a three-dimensional shape that resembles a heart), the Stereographic map projection, and the Azimuthal Equidistant map projection all skew the latitude and longitude lines of the 30° x 30° graticule, making relative location and coordinate position both increasingly difficult to interpret. While the Mercator projection has a well-defined 30° x 30° graticule, the massive distortion of the Polar Regions decreases the accuracy of the relative location interpretation in higher latitudes. In order to figure out a specific location, surveying would likely be a more accurate tool than map projections, and it’s important to have a higher, if not complete, degree of accuracy when looking at a specific region, especially if it’s to do something like a construction projection where specific location must be certain.

By measuring the distance between two cities, one can determine how map projections alter the relative distance between two points. The Bonne map projection is understandably the projection that exhibited that closest distance between Washington, D.C. and Kabul (only 6,730.7 mi), given that the projection pinches the Earth back on itself, shortening the distance between objects on the other side. The flat, rectangular projections exhibit the farthest distance between Washington, D.C. and Kabul. For example, the Mercator map projection measured a distance of 10,112.12 mi. and the Plate Carree map projection measured a distance of 10,109.67 mi between the two cities. This increased distance is likely due to the stretching of the northern and southern hemispheres to line up at the 180° line of longitude. The Stereographic map projection (distance between Washington, D.C. and Kabul: 9,878.04 mi) and the Azimuthal Equidistant map projection (distance between Washington D.C. and Kabul: 8,341.41 mi) map projections both increase the distance between the two cities by rotating the North American and Asian continents away from each other in the distortion. The Mollweide map projection experiences the same distortion, except at a lesser extent with the distance between the two cities being only 7,925.56 mi. Because map projections all distort and alter different features based on the feature that they are trying to preserve, it is virtually impossible to get a complete accurate measurement of geographical relationships on the Earth’s surface from a map projection.

Week 4 Lab Write Up

The lab from week 4 was my first time ever being exposed to ArcGIS and I can honestly say that I found parts of it truly fascinating, yet some parts of my first experience working with ArcGIS were equally frustrating. At first glance, I found ArcGIS to be a truly amazing way to organize geographical data. I found the layering set-up of the data sheets to be completely ergonomic, as it simplifies the process of observing data on multiple forms and lays it out in a more pleasing visual layout. I also liked how simple it was to transfer data from one data sheet to another, allowing the construction of multiple images of the same thing in order to show different trends and impacts at a specific geological site. However, I did find it painstakingly frustrating how different data layers have to be backed up and saved as separate files through the process of exporting data layers, rather than having them readily accessible from within the program software itself. I had the same experience with tables and charted data. I found it amazingly simple how ArcGIS was able to compile various data into a single table and represent it graphically; however, searching through hidden files for the data layers was obnoxious.

I also found that the alternate data and layout views were serious helpful when organizing data. I really liked how the layout page was an accumulation of all of the images and graphs created on one ArcGIS spread, yet the program, under data view, allows you to alter the data of one image at a time (and the screen shows just the image being edited). I found this as a logical way to organize data and separate the focus of each individual image and graph. However, when in layout view, I found it irritating how the ArcGIS commands were not the same as standard Microsoft Windows commands. For example, on a typical computer, when you use the scroll on the mouse, the page scrolls from top to bottom. However, when using ArcGIS, the images zoom in or out. And in layout view, if one image is accidentally selected, that image alone will zoom in or out, which distorts the layout that you have already positioned. I wish that there were a way to lock images on the page unless you specifically command the program to edit them (or if this option already exists within the ArcGIS software, it would have been nice if the tutorial had mentioned it).

One thing that I both loved and hated about ArcGIS was the methods through which images are visually edited. I liked how ArcGIS allows for colors of data layers to be altered by either creating a separate color scheme for a range of data or by using contrasting colors for different layers on the generated image. However, I felt like the program took too many steps to reach the pages, or property windows, within the program that allow for color change. For example, right clicking properties, and finding the frame tab, and then altering the color in order to simply change the background is not as simple a process and as user-friendly as changing the background on a Microsoft Word document. I found the same frustration with changing the font size of inserted titles. For some reason ArcGIS was not allowing me to change the size of the font on the same page that I wrote the title on (even though it clearly displayed the font size with a down arrow next to it), so I had to go another route, using the drawing toolbar, highlighting the title, and then editing the font size using the drawing toolbar. It just seems like too many steps in order to simply edit a title.

In all honesty though, the only pitfalls that I can currently find with ArcGIS are centralized around the basic operation of the program and the way that the program requires one to perform certain tasks (maybe after I complete more than just one lab I will have a better idea of pitfalls outside of the software itself). However, even after just one lab, I can already see a massive potential for the use of ArcGIS. The way that data is compiled in the program is truly amazing and it makes complex information readily accessible and interpretable by and to anyone. Prior to taking this course, I did not have much of a grasp of geospatial information systems, thinking that it was only used to make maps. But after one tutorial with ArcGIS, I’m struggling to find a field of study/expertise/etc. where ArcGIS could not be used to convey information about location. I find that ArcGIS is an efficient way for information compiled by specialists to be shared with a group of people that do not necessarily contain the skills and knowledge necessary to interpret overly technical data, or the general public. While making a project in ArcGIS may be time consuming and have multiple steps, it is for this reason that I believe that is ensures the creation of images that portray only legitimate information. My reasoning behind this thought is that operating ArcGIS requires a degree of skill and knowledge as well as a vast amount of time and, unlike a post on the internet on a site like Wikipedia, which can take all of five minutes, the amount of time that an ArcGIS project takes I feel like should deter people from using the program to spread false information. Furthermore, ArcGIS inputs data, not generalizations and unformulated conclusions. Any faults in a project would have to be found in the data itself. Overall, I believe that given the wide variety of geographical information that can be compiled and simplified using ArcGIS, that ArcGIS is a great tool for sharing information.