Lab 7

The first map of the Census 2000 Racial Composition by County series presents the percentage of Blacks by county in the Continental United States. As seen in this first map, the Black population in the United States is heavily concentrated in the South. Additionally, throughout the majority of the continental United States the percentage of Blacks that populate a given county is very low. Nevertheless, in urban pockets of the United States one sees a substantial increase in the percentage of Blacks in the county population. However, as previously stated, the Black population by county is largest in the Deep South with the largest proportion of blacks in Mississippi and Alabama. For instance, the largest percentage of Blacks occurs in Jefferson County, Mississippi, where Blacks occupy 86% of the total population. When comparing this spatial data with other census data, one notices a striking correlation between areas with heavy concentrations of Blacks with areas of lower income and education levels.

The second map presents the United States Asian population by county in the year 2000. With few exceptions in urban pockets, the population of Asians is relatively sparse in the majority middle America. In the North East and West Coast one sees, however, the largest populations of Asians in the United States. For example, the four counties with the largest concentration of Asians all are within the San Francisco Bay Area. That said, the percentage of Asians in any given county is very low: most counties in the United States have fewer than a 2% Asian population and the largest population in the continental United States in San Francisco County is only 31%.

The final map demonstrates the Census defined "Other Races" as a percentage of total county population in the United States in the year 2000. As one can infer from this map and the Census data source, the category is predominately occupied by Hispanic or Latino individuals. As a result, the largest density of individuals classified as "Other Races" occurs in the American Southwest from Texas to California. With very few exceptions, significant county population percentages of "Other Races" is restricted to counties within these states. For example, the largest proportion of "Other Races" are in Imperial County, CA and Guadeloupe County, NM with populations of approximately 40%. When one compares this data with other Census information one notices a striking correlation between areas of heavy unemployment. Furthermore, the geographic distribution of agricultural zones and proximity to the Mexican border points to large immigrant populations.

Taken as a whole, the ability to utilize Census data in ArcGIS is extremely powerful. The functionality of layering in GIS applications when combined with the multitude of information categories provided by the US census allows individuals to present a holistic view of the United States population. As seen in the maps that I constructed, GIS allows users to demonstrate population trends among races in an abstract form that enables ease of communication. Although some information is obscured in the form presented (such as country wide population percentages), individual county population populations when combined in a national spatial distribution ultimately convey a powerful message whereby information can be inferred.

Lab 6

The set of maps and graphics above utilizes a digital elevation model sourced from the USGS Seamless data. The extent of the model covers an area in the Lower Sierra Nevada Mountain Range from 36.8136N, 118.4758W to 36.4164N, 117.7400W. The digital elevation model and the associated maps use the North American Datum of 1983 as the Geographic Coordinate System. I personally chose to study this location due to its rapid change in elevation. For instance, this location in California includes Mt. Whitney with a peak elevation of 14,505 ft. Directly to the east of Mt. Whitney and other mountains of similar elevation lies Owens Valley at an elevation of approximately 4,000 ft. As a result, my models highlight a significant change in elevation of over 10,000 ft. This change in elevation is particularly striking if one views the 3D rendering of this location visible in the top right of the graphics collection. Furthermore, on the eastern side of Owens Valley another slope rises before falling to the lowest point in the extent of the data. This lowest point is at an elevation of approximately 1,000 ft. and is located in the north east of Death Valley National Park. Taken as a whole, these models demonstrate a change in elevation of over 13,000 ft. One of the world's greatest athletic challenges, the Badwater Ultramarathon, includes the area studied due to the drastic changes in elevation.

Lab 5

This week's lab concerning map projections highlighted the various distinctions between conformal, equal area and equidistant map projections. The process of projection with ArcGIS proved to be quite simple, however provides an amazing amount of information regarding differences in map projections. This method ultimately elucidated aspects of map projections such as shape, appearance, area and distance. Taken as a whole, these differences shed light on the myriad of ways one can mathematically project the world and the inherent discrepancies in geographic interpretation associated with different projections.

In particular, the difference in shape of the map provides information about it's projection method. For instance, the Stereographic projection visible in the conformal map projections projects the world onto a plane with a shape of a circle. On the other hand, cylindrical map projections such as the Cylindrical Equal Area projection and the Equidistant Cylindrical projection project the map in a rectangular shape. These differences in shape can be traced back to method of projection: in the case of the Stereographic projection, a sphere is projected onto a flat plane whereas a cylinder is used the case of the cylindrical projections. As a result, the 30°x30° graticule layer present in all projections also has line shapes dependent upon the geometric object used in the projection process. As one can see in the aforementioned projections, the Stereographic map contains curved lines whereas the graticule lines remain at right angles in the cylindrical projections.

Furthermore, the differences in preserved properties between the maps is extremely striking. For example, when one compares the area of landmasses in a conformal projection such as the Mercator projection to an equal area projection such the Eckert IV, extreme area exaggerations are visible. More specifically, when one observes areas below the standard parallels in the Mercator projection (such as Antarctica), it is immediately apparent in the way area is distorted. Although in both conformal projections the areas above and below the standard parallels are grossly exaggerated, these projections do maintain consistent shape of landmasses.

In a similar manner, the comparison of distance between two points on the various map projections highlights significant discrepancies in distance. When calculating the distance between Washington D.C. and Kabul, Afghanistan using different projections one can come to completely different conclusions. For instance, the Cylindrical Equal Area projection provides a distance of approximately 10,000 miles whereas the Equidistant Cylindrical projection provides a distance of approximately 5,000 miles. Furthermore, even among equidistant projections there exists a significant difference due to the distinction between the point on the map from which distance is equidistant. In the case of the Azimuthal Equidistant Projection, distance is preserved originating from points at the poles, yet the Equidistant Cylindrical Projection originates from the standard parallel at the equator. This discrepancy in distance highlights the importance of utilizing map projections designed for regional distance calculations in order to maintain accuracy.

Lab 4

ArcMap Review

My experience with ArcMap came with much excitement, however I also found myself to be extremely frustrated at times due to the use of novel software. Although ArcGIS has a steep learning curve associated with its use, I was still able to experience a taste of the software's power through the ArcMap tutorial. 

I found the ability to manipulate data layers in ArcMap to be extremely interesting. As one can see from the associated graphic, I was able to utilize data layers in different forms and at different scales. For example, I used the land use data layer to highlight the area of the airport at multiple scales. As a result, the airport's role in the geographic distribution is understood in three different contexts: population density, land use, and school distributions. Given this information, one can make an informed judgment over the implications of theoretical airport expansion. Even with limited experience in this field of spatial data manipulation, I am already able to envision many of the possibilities associated with the practice. 

Additionally, I found that one of the major benefits of ArcMap is the ability to convey information in multiple graphical formats using the same data source. In the context of the tutorial, I was able to present information regarding land use within the noise contour in both a bar graph and map format. As a result, one can use ArcMap to reinforce data presentations that are particularly relevant to one's objective. In this case, I was able to highlight the the number of parcels within the noise contour based upon the land use type. Although one can estimate this value through the map representation, the bar graph provides further detail in quantitative form. 

As another testament to the potential of ArcGIS, I discovered the benefit of graphical customization in the process of the ArcMap tutorial. Through the method of customizing layout, legends, scale bars, and color, I was presented with the power of ArcGIS. Due to this level of user control, the possibilities for graphical presentation are endless and are visible in the different legends and color schemes used in my presentation. However, this empowerment of the user also leads to a converse effect that is the associated difficulty of such a learning curve. By enabling extensive graphical customization, the ArcGIS software thereby precludes ease of access and user friendly environments. 

Although I found the integration of tables and databases with ArcMap to be particularly enabling, this feature set also forces the user to maintain a highly systematic and organized file system. In particular, the ability to join and relate data sets proved to be one of the most beneficial aspects of the ArcMap tutorial. Due to this ability, I was able to place combine population density data in the context of the county. Nevertheless, this process reinforced the concept that one must maintain a normalized data set using an organized file system. Furthermore, this process established the notion of the importance of using a primary key in order to relationally join different tables.