Week 8 LAB

The first map in this post represents census data from 2000 showing the density of the Black population throughout the nation.  The darker green areas represent a higher black population in those particular counties, which are clustered around the South and Southern-Eastern coastline of the United States. The darkest green counties clustered across the southern areas of the nation can have as high as an 86% Black population density.

The second map shows the density of Asian populations in the continental United States.  The darkest blue areas representing the greatest density of Asian population are clustered in the coastal California regions of the Bay area and Los Angeles areas with some further clustering in the New England territories of the United States with some counties retaining between a 25-42% Asian population.

The final map in this post documents the population density of some other race across the United States.  The darkest orange areas in the mid-western states of California, Arizona, New Mexico, and Texas contain numerous counties with the highest density of this particular population. As much as 42% of the population in these dark orange counties are made up of this other race.

Taken together, these three population maps provide an interesting picture of the continental United States as related by racial population density.  The areas with the highest population density of any given racial group (represented by the darkest counties in these maps) have very little overlap.  With the exception of the high Asian and other race populations in California, all three high density clusters in these maps are located in three different areas around the nation.  For the Black population, this area is the Southern U.S. For the Asian population, this area is coastal California and the Northern East coast. Lastly, for the other race population, the high density populations are largely clustered in the mid-western part of the United States.

The use of GIS in the creation of these maps makes analysis of population density across the nation easier.  The process of creating these GIS, particularly the combining of Excel data onto the ArcGIS program, proved difficult. However, the end results shed light on just how beneficial GIS can be when analyzing different areas or trends.

Week 7 LAB

 

The use of the ArcGIS program in the creation of these four map models (Shaded Relief Model, Slope Map, Aspect Map, and a final 3-D Map) reveals some of the pitfalls and potential of using ArcGIS for spatial analysis projects.  When looking at these three models together, one can get a better picture of how spatial data relates.  This would prove to be especially beneficial in situations where one wanted to look at the terrain of a given elevated area in order a building project, road, or pathway. However, this potential is somewhat marred by a possible pitfall of using the program for these purposes.  The use of this program for spatial analysis is limited to what one can read from the given data fields, but this does not necessarily lend itself to figuring out the best possible use of the land unless all known variables are analyzed. Aspect, slope, ect. are not the only variables that come into play and ArcGIS may not be able to adequately present all of the necessary data projections for complete spatial analysis.

Week 6 LAB

For this week's lab, the mapping of six different types of globe projections revealed the attributes and limitations of three types of projections: conformal, equidistant, and equal area. Two examples of each of the three major types of projections are posted to provide examples of variances within the specific types of projections.

The first set of maps, Mercator and Stereographic, are examples of conformal projections.  These projections are useful in that they preserve all the angles within a localized area. Specifically, the Mercator projection is used commonly, despite the severe area distortion of land masses like Africa and Greenland, making the two seem like they are roughly similar in size, when they are not in reality.  Although right angles may be preserved in conformal projections, the occurrence of such area distortions are a significal pitfall of these particular map projections.

In the second set of maps, Equidistant Conic and Equidistant Cylindrical, are specific examples of equidistant map projections.  In these projections, angles and area may not be preserved on a map, but the distance from a particular point or set of points are preserved.  This may prove especially useful when the primary goal of a map projection is to ensure that point distances are maintained above all else (as in this lab where the distance between two cities, Kabul and Washington D.C., is a key component).

The final set of maps, Cylindrical Equal Area and Sinusoidal, are equal area projections.  Unlike the previous two projections, these maps work to preserve global areas. However, there is a trade off as angles and distances may be distorted in various places on the map.  The three projections all trade off on accuracy on issues of distance, area preservation, and angle preservation.  However, if one is familiar with the potential benefits and inaccuracies that come with each particular projection, one can pick a projection that best accomplishes whatever goal the map is meant to achieve.

Week 4 LAB

Working with ArcMap throughout the course of this week’s lab was a completely new experience.  I do not consider myself in any way computer savvy, so working my way around a brand new program to complete a lab on a subject I am only recently acquainted with proved to be challenging.  However, the ArcMap program and the tutorial that came with it made the process easier.  Although it took me a period of time to become comfortable with the layout and basic functions of ArcMap, by the end of the tutorial, I found the program to be very user-friendly even for GIS novices like myself.

Certain features within the ArcMap program, like the “Zoom to Layer” application, were irreplaceable time savers.  One of the biggest challenges upon starting the lab was accidentally zooming in too close on certain data field layers or even completely losing them in whatever particular field view I had placed myself in.  Similarly, the table of contents displaying all of the different data frames and layers proved to be an organized and efficient way of keeping track of the different data sets that I was working with throughout the lab.  Other actions asked of me during the tutorial, like joining two tables together, required outside assistance.  Luckily, once the process was shown to me I was easily able to repeat the process again the next time I completed the tutorial.  Overall, the ArcMap program was interesting to work with.  Although I was worried about exactly how much I did not know how to do when I started using the program, I now feel comfortable carrying out the tasks asked of me throughout this lab.  

Using ArcMap has helped to reveal some of the various potentials and pitfalls that surround the use of this program in regards to GIS.  The extensive tools and functions within the program prove to be extremely beneficial when working on data analysis.  Specifically  with this lab, looking at population density within the noise contour of an airport would allow for analysis of the extent to which airport noise effects the areas surrounding the airport boundary.  This could prove useful when deciding what to build in the areas within the noise contour.  For example, one particular school (Northwestern Prep) fell within the boundary of the noise contour of the airport.  Should the county decide to build another school, they can take a look at the data frames built within the ArcMaps program so that their school is not placed within the noise contour boundary like Northwestern Prep was.  This is an example of the potential that GIS and the ArcMap program both have in helping with analysis of real scenarios.

The use of the ArcMap program also revealed some pitfalls that befall GIS.  Although the program has made it so that anybody can work their way through the program, that does not mean that the capacity for human error goes away.  While the program is relatively easy to use with time and practice, it is not a perfect program.  There are still scenarios where human error can distort any potential gains from GIS analysis.  For example, the ArcMap program has a feature that allowed me to extend a specific road near the airport and even build a brand new one.  The first time I attempted to complete this feature, I built my road incorrectly.  The program made it simple to extend the existing map roads, but I was still able to erroneously utilize that function the first time around.  Had I not fixed this problem, this could have led to some misinterpreted GIS analysis should someone be analyzing the roads layer on the ArcMap data frame.  The capacity for human error is not something that the ArcMap program can completely do away with and this provides a potential pitfall in its use with GIS.