My GIS Experience

The Image to the left shows the Asian population by percent in the United States as depicted by the Census 2000. The picture shows that general distribution of Asian communities across America. It is easy to see the heaviest populated areas are those on the Eastern and Western Seaboards. Middle America's stark Asian population can also be seen from the colors on the mapped image. California, in particular, has the greatest population by percent of any other state in the United States.

The image to the right shows the African American population by percent across the United States in reference to the Census 2000. The map is very interesting because it shows the specific concentration of Black communities across the Louisiana, Mississippi, Georgia, and Alabama regions, as well as the Virginias and other Southern states. Some counties within the specified regions have a percentage of in excess of 85% Black. In comparison, again, there are counties in middle america that do not even show up on the scale indicating an exceedingly low population by percentage.

The below image shows the Latino/Hispanic population by percent in the United States in reference to the Census 2000. Like the previous two mapped images, this image gives valuable insight to where the greatest and least percentages of Latino/Hispa

nic Americans reside. While the vast majority of the nation yields between 0 and 2% Latino/Hispanic residents, the Southwest holds the most population by percent than anywhere else in the US, with percentages capping out around the 40% margin.

My Census 2000 chloropleth map series were a yet another GIS experience. As the last project of the class, it was valuable to work through the ArcGIS program to find information pertaining to the population percentages by county across the nation. The maps themselves are could play a crucial role visualizing census data for infinite business and economic functions. While the mapped images above provide valuable insight, they are not the be-all-end-all of clorepleth maps. I personally used a 5 category breakdown to classify the different percentages, but for economical reviews, it would be necessary to go into further detail to review the populations more accurately.

Overall, my experience with ArcGIS and the Geography 7 geographic information systems has been positive. The learning curve is steep when dealing with the ArcGIS program, but it serves a valuable purpose to better understand such a crucial piece of data classification. ArcGIS can be used to see the extent of Fire Damage, or to see how populations of races vary among different counties in the United States. Throughout the labs, I have been able to experience just the rudimentary aspects of ArcGIS, but the labs have allowed me to see some of the various uses for GIS and just how much information can be gathered and visualized using ArcGIS.

Mapping the Station Fire in ArcGIS

The Station Fire was a fire that originated on Wednesday the 26th of August at around 3:30 p.m.. In its entirety the fire burned a total of 160,577 acres. The station fire destroyed 209 structures, including 89 homes. The wildfire coined its name from its origin. It began in Southern California's Angeles National Forest by U.S. Forest Service station on the Angeles Crest Highway.

The Station Fire burned over the span of three months; specifically from the 26 of August to the 16 of October. The fire, which is the 10th largest in California history claimed two firefighters lives when their fire engine plummeted off of the Angeles Crest Highway during the fire fight. The fire forced evacuations from the neighborhoods of La Canada Flintridge, Glendale, Acton, La Crescenta, Pasadena, Littlerock and Altadena, as well as the Sunland and Tujunga neighborhoods.

Not only were numerous cities evacuated, but roads were closed too. The roads were closed for numerous reasons, but the most common was for damage. Many roads unfortunately laid in the path of the blaze and were over taken. Major roads including Highway 2, Highway 39, N4, N3, and Highway 14 were closed for various periods of time during the extent of the fire. The map above shows the relation of the fire to the roads that were closed.

The assignment requires a map showing the extent of the fire as well as a thematic map displaying a specific aspect of the fire. I was able to incorporate both into one visual model with the above image which shows the fires growth from August 30th to September 2nd, as well as the roads that were caught in the blaze's path. Angeles Crest Highway was closed for the longest due to road damages even after the fire was contained. While most of the pavement stayed intact, road signs and debris flows left the road unusable for some time.

The Station Fire affected so much more than just road's and their availability. The road closures are just one aspect of numerous other issues that were associated the Station Fire. Other possibilities of thematic maps could have been debris flows, fire fuels, camp grounds destroyed by the fire, and so much more. I chose the road closures, because it is interesting to see what roads were closed and how that could have also affected evacuation plans. Maps like these are useful for such purposes, and also aid in the logistics for future developments.

Bibliography

Station Fire Update Sept . 27, 2009. InciWeb. Accessed 2009-09-28. Archived 2009-09-30.

"Station Fire Evening Update Aug. 31, 2009". InciWeb (United States Forest Service). 31 August 2009. Retrieved 3 September 2009.

"Firefighters Killed in 'Station Fire' Remembered". KTLA-TV (Channel 5). 1 September 2009. Retrieved 3 September 2009

"Station Fire." Inciweb.org. US Forest Service. Web.

"Angeles Forest officials use Station Fire to fine tune Twitter policy." SCPR.org. Web.

Digital Elevation Models

Slope DEM
Hillshade DEM
Aspect DEM
3D DEM

The chosen field of view for the Digital Elevation Models is a segment of the Sierra Nevada Mountain Range in Central California near Mount Whitney. I chose this area, because the Sierra Nevadas are among the tallest in the continental US and would yield a great DEM. All information was gathered using ArcGIS supplemented with information from the USGS Seamless server.

Extent Information:

Top: 36.9794444437 degrees

Left:-118.0908333333 degrees

Right: -117.873888888 degrees

Bottom: 36.790833326 degrees

Spatial Reference:

GCS North American 1983

Map Projections: The Real Deal

Maps are used by millions of people on a daily basis. It is easy, however, to take for granted what, exactly, the map user is looking at. The earth is a 3-Dimensional sphere, but maps, on the other hand, depict it in a planar 2-Dimensional format. A globe is not convenient to carry in ones pocket, and thus the map projection is necessary. The process of transposing a 3-Dimensional object onto a 2-Dimensional plane, nevertheless, comes with a fair share inaccuracies. Primitive map projections came with shining a light from the inside of a globe onto a wall, then tracing the features, thus creating a flat usable surface. Problems quickly arose, when obvious distortions in distances and shapes became prevalent. Efforts to preserve different aspects of globes such as distances, shapes, scale, and area gave birth to different mathematically based projections such as equal area, equidistant, and conformal map projections.

The GCS WGS 1984 and Mercator are both examples of conformal map projections. Conformal map projections preserve local angles. For example, a standard conformal map will have the equator in the middle. The further north and south towards the poles, however, the angles are not preserved and distortion is created, often increasing the size of land masses near the poles. The Conic and Sinusoidal maps are equidistant projections. Equidistant projections preserve distance from a standard point or line. Equidistant maps do not come in flat surfaces and are not ideal for daily use. They are used in finding accurate distances because there is little to no distortion at the polar regions. The final map projection used in these exercises are equal area map projections. The Mollweide and Hammer equal area projections satisfy the preservation of area on a 2-Dimensional surface.

No map projection is perfect. Each projection focuses on a specific aspect, but leaves other aspects immensely inaccurate. In general, Mercator projections are among the most common with user-centric programs such as Google Maps. Even though distortions are prominent at the poles, conformal projections are ideal for plotting routes and viewing land masses for directional purposes. Other map projections, such as equidistant maps are not necessarily visually friendly, but are ideal for plotting the actual distance or finding the shortest distance for navigation.

This exercise is perfect for examining the different types of map projections and what differentiates them. The dichotomy between conformal, equidistant, and equal area maps is necessary to understand. Understanding the key uses is equally as crucial. The general knowledge of depicting what projection is best to reference if asked to accurately determine the distance between two objects. In this case, a conformal map would not be best. Furthermore if, asked to evaluate the actual area of a continent near a polar region, a conformal would again not be they key to success. With this base knowledge of map projections, and a greater understanding of map projections are the next steps in truly adjusting to geographic information systems.

Equidistance Projections

Conic Equidistant Projection
Distance between Washington, D.C. and Kabul, Afghanistan: 6,983 miles
Sinusoidal Equidistant Projection
Distance between Washington, D.C. and Kabul, Afghanistan: 8,112 miles

Equal Area Projections

Mollweide Equal Area Projection

Distance between Washington, D.C. and
Kabul, Afghanistan: 8,072 miles

Hammer Equal Area Projection

Distance between Washington, D.C. and
Kabul, Afghanistan: 8,372 miles

Conformal Projections

Mercator Projection

Distance between Washington, D.C. and Kabul, Afghanistan: 10,073 miles











GCS WGS 1984 Mercator Projection

Distance between Washington, D.C. and Kabul, Afghanistan: 8,087 miles