Lab 8

Station Fire Near Acton Courtesy of the LA Times

This report examines the 2009 Station Fire in Los Angeles County during August and September of 2009. The extent of the fire totaled 251 square miles (160,577 acres) and ultimately destroyed 209 different structures, 89 of which were households (InciWeb). As seen in the reference map of the greater Los Angeles area in Figure 1, the Station Fire burned in the Los Angeles National Forest on the slopes of Mount Wilson just north of Los Angeles. As evidenced by the Digital Elevation Model provided by USGS, the fire resided primarily in higher elevations above 1,500 ft. The station fire is the 10th largest fire in Californian history and the largest fire on record for Los Angeles County. The fire was ultimately proclaimed by officials to be caused by arson (Winston).

Although the Station Fire was not 100% contained until October 15th, 2009, my temporal presentation follows the fire from its inception at 08/29/2009 until 09/02/2009 during which the fire reached its peak extent. As shown in Figure 1, the fire spread expanded primarily in a northern direction and also expanded both westerly and easterly over the examined period. Although the fire is often assumed to be wind-driven much like other Southern California fires, Los Angeles County Fire Chief P. Michael Freeman has reported that the fire was not due to infamous Santa Ana winds (Gleeson). Upon closer temporal examination, it is evident that fire traveled along the highest elevations in the region and expanded along greater elevations rather than travelling in a topographic direction downslopes. 

Furthermore in Figure 2, I analyze the ultimate spread of the fire in the context of the aspect of the region. In regards to temporal analysis, I compare the station fire perimeter on 08/29/2009 to its peak extent on 09/02/2009. The spread of the fire appears to have been unaffected by aspect as the fire was able to expand across slopes facing multiple directions. Upon further examination of the aspect presentation, one is able to recognize the force of the fire as it traversed multiple ranges.

Figure 3 assesses what communities were at risk given the spread of the fire and is presented in the form of buffer analysis overlay against population density of nearby communities. In my analysis I performed a multiple ring buffer analysis at distances of .1, .25, .5, 1, 1.5 and 2.5 miles to elucidate the communities put at risk by the Station Fire. Upon immediate investigation one notices that the cities of La Crescenta, Montrose, La Canada Flintridge and Altadena all fell within 1.5 miles of the fire's southern perimeter. Additionally, as the fire traveled north the city of Acton ultimately fell within a 2.5 miles of the northern perimeter. During the period study these communities faced mandatory evacuations and the fire threatened 12,000 structures as previously stated. The fire ultimately was responsible for the destruction of 89 homes (InciWeb).

In addition to the general communities that were threatened by the blaze, Figure 3 also investigates the type of community that was threatened by analyzing population density. As seen in the map, the fire predominately threatened sparsly populated areas. When one compares this to the temporal analysis in Figure 1 it becomes clear that the fire north away from highly dense communities towards less dense areas. 

In Figure 4, I supplement Figure 3 by addressing the major roads and parks that were threatened by the fire. As with Figure 3, I utilize a multiple buffer analysis to present what types of communities were at risk. The map highlights how the nearly the entire fire occurred within the bounds of Angeles National Forest and that several smaller parks fell within the projected buffer to the south of the fire.  This finding is consistent with the fact that the main portion of the Angeles forest was closed during the duration of the burn. Furthermore, the map demonstrates that multiple major roads were directly threatened by the fire. These roads include the Angeles Crest Highway, Upper Big Tujunga Canyon Road, and the Angeles Forest Highway as these roads traveled directly within the fire extent. All of these roads remained closed during the studied period and incurred significant destruction. For instance,  Caltrans dedicated over $30 Million dollars to the reconstruction of the portion of the Angeles Crest Highway that was damaged (Phillips).

As a final measure of analysis, Figure 4 presents the distribution of hospitals in the fire's surrounding area. This distribution is significant due to the potential danger presented by the fire to both residents and firefighters. In fact, two firefighters died during the course of the blaze as their truck fell down a hill while evading flames. From the distribution present in Figure 4, it becomes apparent that the distribution of hospitals closely resembles the population distribution as one would expect. However, as the majority of the fire burned in more remote locations, hospital access could have proven to be difficult given the large hospital distance from much of the fire's extent. 

References:

1) Gleeson, Gene. "Station Fire Teaches Fire Department Lessons." ABC News, JUNE 10, 2010. <http://abclocal.go.com/kabc/story?section=news/local/los_angeles&id=7484272>

2) Incident Information System, "Station Fire Final Update Sept. 28, 2009." SEPT 28, 2009. <http://www.inciweb.org/incident/article/1856/9647/>

3) Winton, Richard. "Substance found near Station fire ignition point is key evidence in arson probe." Los Angeles Times, SEPT 09, 2009. <http://latimesblogs.latimes.com/lanow/2009/09/station-fire-ignition-arson-investigation.html>

4) Pringle, Paul. "L.A. County fire doubles in size; more homes destroyed; Mt. Wilson threatened." Los Angeles Times, 08 31, 2009. <http://latimesblogs.latimes.com/lanow/2009/08/la-county-fire-doubles-in-size-more-homes-list-mt-wilson-threatened.html>

5) InciWeb: Incident Information System, "Station Fire." <http://www.inciweb.org/incident/1856/>

6) Phillips, Darsha. "7-mile stretch of Angeles Crest Highway reopens ." ABC News, JUNE 03, 2011. <http://abclocal.go.com/kabc/story?section=resources/traffic&id=8168328>

Figure 1
Data Provided by LA County Enterprise GIS, UCLA Mapshare, USGS Seamless

Figure 2
Data Provided by UCLA Mapshare, LA County Enterprise GIS, USGS Seamless

Figure 3
Data Provided By LA County Enterprise GIS, UCLA Mapshare, US Census TIGER

Figure 4
Data Provided By UCLA Mapshare, LA County Enterprise GIS

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. 

Lab 3

Solar Eclipse Travel Map

View GIS Lab 3: Solar Eclipse Trip with map description in a larger map (suggested)

Neogeography Write Up
My experience in creating a custom map clearly elucidates many of the benefits of neogeography, which center primarily around the advantages of crowd-sourcing and user generated content. First, the integration with business information provided by other users can be seen as a significant aspect for the potential of neogeography. As implemented in my map, this integration allows individuals to supplement custom maps with predefined information about a specific location. Second, the option of creating lines along streets within the Google Maps toolkit allows individuals to publish accurate travel directions with ease. In the case of my map, this feature was used heavily in order to map a road trip between destinations in the Southwest. By implementing road travel information, neogeography as an entity extends spatial awareness of the general public and allows individuals to share individual travel with others. In today's era of smartphone proliferation, neogeography enables the dispersal of individual location data through platforms such as Google Latitude that integrate with mapping platforms such as Google Maps. Finally, as another major point of neogeographic potential, products such as Google Map Maker allow individuals to contribute to the overall content of maps by providing polygonal and nominal content. In my experience with locating the the area covered by the Very Large Array in New Mexico, I noticed that this information was not presented on Google Maps. Nevertheless, I am able to provide this information for future users of the service after accurately outlining such a structure and submitting my edits through Google Map Maker. 
Although plenty of advantages stem from the use and proliferation of neogeographic tools, several issues arise from the use of neogeographic information. The most apparent problem associated with neogeography relates to the issue of inaccurate content provided by volunteers. For example, in my experience with locating the Lightning Field in New Mexico, the suggested location provided by another user was nearly 3km away from the true location. Another example can be seen in the use of supplementary information such as business information. I personally noticed multiple mistakes in some of the user provided data and therefore decided to forego the inclusion of such information in my custom map. This problem is most recognizable in Google's photo geotagging platform Panoramio, in which most of the location data is dramatically incorrect. Another major concern with neogeography are the multiple standards in terms of geographic data that mapping platforms such as Google Maps create. As mentioned by Turner in Introduction to Neogeography, Google and other companies have in essence created a new datum that is significantly different from other established datums such as the North American Datum. As a result, scholarly geography is faced with the issue of determining the standards of authority.
In my evaluation of the aforementioned pitfalls and potential benefits of neogeography, I ultimately contend that the advantages of neogeography significantly outweigh the disadvantages. I strongly support the empowerment of individuals to share their local knowledge and believe that as technology advances many of the concerns such as misinformation and authority will fade.

Lab 2

1. Beverly Hills
2. Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, Inglewood
3. 1966
4. 1929 - National Geodetic Datum of 1929
5. 1: 24,000
6. At The Above Scale
1. 1,200 meters
2. 1.894 miles
3. 2.64 inches
4. 12.5 centimeters
7. 20 feet
8. Geographic Coordinates
1. Public Affairs
1. 34° 04' 40",  -118° 26' 15"
2. 34.0778°,  -118.4375°
2. Tip of the Santa Monica Pier
1. 34° 00' 45",  -118° 29' 50"
2. 34.0125°,  -118.4972°
3. Upper Franklin Canyon Reservoir
1. 34° 07' 15", -118° 24' 30"
2. 34.1208°,  -118.4083°
9. Approximate Elevation
1. Greystone Mansion
1. 560 feet
2. 171 meters
2. Woodlawn Cemetery
1. 140 feet
2. 43 meters
3. Crestwood Hills Park
1. 700 feet
2. 213 meters
10. UTM Zone 11
11. 11N 361500 3763000
12. 1,000,000 square meters
13. Elevation Profile

    14. 14°

    15. South

    16. UCLA Map

Lab 1

This map shows the global distribution of cigarette smokers as a percentage of a nation's total population by gender. The data source comes from the Tobacco Atlas provided by the American Cancer Society and World Lung Foundation published in 2012. The map was created by The Economist and uses the 2010 data provided by the Tobacco Atlas. The map is particularly striking because it illuminates first and foremost the larger number of male cigarette smokers. In most of the world, the percentage of female cigarette smokers is relatively low and in almost all nations the percentage of male smokers is larger than the percentage of female smokers. Furthermore, the graph highlights regions of the world where cigarette consumption is highest (at least among men) such as Russia, China and Indonesia. 

This map presents the plurality ethnic background by county in the United States. The map uses data from the U.S. Census Bureau taken from the 2000 census and is provided by Wikipedia. The map is insightful because it demonstrates how clusters of ethnic backgrounds have formed in the United States. A number of patterns also arise when examining the map. First, one can see how counties that are geographically close to the Mexican border contain a Mexican ethnic plurality. Second, German ancestry is the ethnic plurality for the much of the Northern continental U.S. Third, one can recognize a "belt" formation that spreads across the southern states where African Americans are a plurality. These points are further reinforced when examining the secondary map in the upper right hand corner, which presents the plurality ancestry by state in the United States. 

This map demonstrates the US population change by county between the years 1930 and 1940. Population changes are exhibited both by percentage changes in the first map and absolute numeric changes in the second map. The map is particularly interesting as it shows the effects of the Dust Bowl in the 1930s on migration patterns within the United States. As one can see, populations declined most drastically in the Midwest, which in some cases exceeded population losses of over 25%. Furthermore, the map highlights individual counties of significant growth during this period. For instance, the map establishes the extreme population increase in Los Angeles county of more than 500,000 people. As a result, I find it intriguing how the map presents the effects of the largest internal US migration in the 20th century.  The map data comes from the 1930 and 1940 US Census and was provided by the US Census Bureau.