Lab 08. Race Proportion (by County), Census 2000 Data

The Asian Population Proportion map shows that counties on the coast of California (particularly near Los Angeles and San Francisco), counties on the coast of Washington, and counties on the coast of New England have higher than average proportions of Asian residents. The population of Asians compared to total population in the county ranged from 1 at 0.08% of the population (in  Monroe County, Kentucky) to 403,371 at 46.04% of the population (in Honolulu County, Hawaii – not pictured). The county with the largest absolute Asian population was Los Angeles County, California at 1,137,500 residents (11.95%).

The Black Population Proportion map shows that counties in the Southeast (excluding the Florida Peninsula, and most of the narrow strip of coast along the Gulf of Mexico) have higher than average proportions of Black residents. The population of Black residents compared to total population in the county ranged from 1 at 0.01% of the population (in  Webster County, West Virginia) to 8,424 at 86.49% of the population (in Jefferson County, Mississippi). The county with the largest absolute Black resident population was Cook County, Illinois at 1,405,361 residents (26.14%).

The Other Race Population Proportion map shows that counties in the Southwest, counties along the Mexico-U.S. Border, counties in Southern California, and counties in eastern Washington have higher than average proportions of Other Race residents. The population of Other Race residents compared to total population in the county ranged from 1 at 0.01% of the population (in  Martin County, Kentucky) to 55,634 at 39.08% of the population (in Imperial County, California). The county with the largest absolute Other Race resident population was Los Angeles County, California at 2,239,997 residents (23.53%).

Extrapolating from the patterns on these maps and some knowledge of U.S. history, one can generate several theories as to the distribution of Asian, Black and Other populations throughout the U.S.  I am basing my explanations on the belief that populations of people tend to stay at their population’s historical first point of settlement.

Residents of Asian ethnicity tend to populate the West Coast. This is not that surprising. Given California’s proximity to Asia, it was the natural port of entry for many Asian immigrants to the U.S. The California Gold Rush in the 1850s drew a massive amount of Chinese laborers to California. Numbers of new Chinese immigrants remained high until congress passed the bigoted Chinese Exclusion Act of 1882 (there were no such restrictions on European Immigrants until much later). Japanese immigrants began to arrive in the mid-19th century as well. American wars were largely responsible for an influx of Filipino immigrants (after the Spanish-American War) and Vietnamese immigrants (after the Vietnam War).

I hypothesize that Black populations in the South are at such a high percentage because of the legacy of slavery. Africans were kidnapped and transported to the South where they were bought by plantation owners throughout region. After the American Civil War, the federal government granted former slaves ownership to 40 acres of land for farming. These plots were typically located on the area of the former plantation. Land “ownership” – serious flaws in the Reconstruction after the Civil War resulted in many of these plots falling right into the hands of the former plantation owners – kept many former slaves from leaving the South.

Strangely, Latino population data specifically was not included in the database I examined; instead, it seems to be embedded in the “Other Race” category of data. Possibly, this is because “Latino” is generally recognized as an ethnicity, rather than a race. From this point forward, I will use “Latino” and “Other Race” category interchangeably. Well before the West Coast and Southwest were annexed by the U.S., large populations of people of Latin American and Spanish descent populated these areas. Missions, Ranchos and other types of settlements soon spread these populations throughout the Southwestern U.S. More recently, an influx of itinerant workers and permanent immigrants have made their way across the U.S.-Mexico border. Many of these people, looking for work, settle close to their point of entry.

My limited experience with GIS while enrolled in Geography 7 has left me with a respect for the depth of information that spatial data can contain. Analyzing and presenting the data is not as simple as creating a graph in Excel. There is no linear input-output relationship when dealing with GIS data. Choices from color scheme, to classification method, to content scale have a profound effect on the way the data is received. Before enrolling in this class, I was perplexed by how many GIS-related classes UCLA offered. Now after taking this course, I realize that specializing in GIS involves more than just mastery of a software platform.

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Lab 07. The Station Fire

On August 26, 2009, at 3:30 p.m. authorities believe a transient began a small fire at the foot of an oak tree, near the southern fringe of Angeles National Forest. Fueled by the shrub and grassland ecosystems nearby (parched after a 100-plus degree heat wave), and stoked by the Santa Ana winds, the fire grew and spread to eventually engulf the entire Angeles National Forest. Over the ensuing three weeks what came to be known as the Station Fire tore through the mountainous region east of Los Angeles, consuming over 160,000 acres and displacing thousands of residents.

Angeles National Forest hosts a variety of habitats, including deciduous and evergreen forests, grasslands, shrub and scrubland. During a fire event, these vegetation types certainly play a role spreading, or containing of the fire. In the Station Fire, fire engineers observed that trees such as Junipers and pines have “a propensity for carrying fire.” Embers from the tops of trees are carried much further than those in generated in the low-lying Chaparral. In the dense Chaparral ecosystems containing shrubs and scrubs up to 6 feet tall, observers reported flames reaching as high as 75 to 100 feet into the air. Forested areas also tend to retain heat more so than Chaparral, which burns more intensely at a faster rate.

In the first four days of the blaze, the Station Fire exhibited the most expansion. By day five, the fire had occupied most of the area it would spread to by the time it burnt out several weeks later. So, fire expansion analyses can best be performed on data pertaining to the behavior of the fire over these first four days. The fire originated in a habitat with evergreen, deciduous, shrub and scrub habitat (phase I). Then, over a period of 12 hours, it moved northwest and southeast, following areas populated with evergreen and deciduous forest (phase II). During this time the fire neglected areas populated with shrub and scrub habitats. That is until sunset, when it quickly spread north through a valley with mixed shrub, scrub and grassland habitats (phase III). Moving northward past the valley, the fire encountered mixed deciduous and evergreen forests as well as an increase in slope/elevation (phase IV). At this point, the fire expanded west and east indiscriminately through both forests and chaparral, and high and low elevations.

These “patterns” should be taken with a grain of salt. Other variables such as diurnal wind direction, firefighting efforts, and slope surely played significant roles, and most likely diluted vegetation’s effects on fire progression; however, there are specific instances in the progression of the fire where vegetation type can be isolated as the most likely vehicle for fire expansion. During phase II, the fire spread across a valley and an elevated region, more or less omitting slope as a strong factor controlling the direction of the fire’s progression. Also, in phase II, the fire traveled in a direction that was not associated with wind direction. In other words, the fire traveled in an east-west direction, while winds traveled in a north-south direction. During phase II, the one consistency that seemed to appear was that the fire traveled along a line of trees (see Land Cover [NCLD 2001]). This pattern would lead one to believe that at times, land cover can play a role in fire spreading.

Eventually, through backfires, fuel breaks, liberal dumping of flame retardants and (most significantly) a moderate rainfall, the station fire was fully contained on October 16, 2009. In total the fire caused the deaths of two firefighters and injured 22 others. The Station Fire was the 10th largest in California since 1933.

Work Cited:

Sources for maps:
U.S. Department of the Interior, 2010. National Resource Information Portal. National Park Service, 22 November; http://nrinfo.nps.gov/Map.mvc/GeospatialSearch.

U.S. Environmental Protection Agency (EPA), 2010. NCLD 2001 Land Cover Class Definitions. Multi-Resolution Land Characteristics Consortium (MRLC), 20 November; http://www.epa.gov/mrlc/definitions.html#2001.

Sources for report:

Associated Press (AP), 2009. Station Fire ‘goes where it wants, when it wants.’ The Los Angeles Independent, 01 September; http://www.laindependent.com/news/56493872.html.

CNN, 2009. Firefighters close to containing half of Station fire. CNN, 02 September; http://articles.cnn.com/2009-09-02/us/california.wildfire_1_angeles-national-forest-massive-fire-fire-officials?_s=PM:US.

KPCC Wire Services (KPCC), 2009. Station Fire still burns in Angeles National Forest. KPCC, 05 September; http://www.scpr.org/news/2009/09/05/station-fire-still-burns-angeles-national-forest/.

Molly Peterson, 2010. A year after Station Fire, botanist, volunteers protect changing forest ecology. KPCV Southern California Public Radio, 25 August; http://www.laindependent.com/news/56493872.html.

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Lab 06. Manipulating DEMs

When we were assigned this lab, we were told to pick an area with a lot of vertical relief. So, of course I had to choose the Grand Canyon. The Grand Canyon is not hard to find on a map of the U.S. Using the Seamless USGS DEM data downloader I scanned the Southwest until  I came upon a gigantic gash. Just to be sure it was the Grand Canyon and not some other gigantic rift in the middle of the country I hadn’t heard about, I brought up Google Earth and put in the Lat/Long from Seamless. As Google Earth performed a flyover I could make no mistake in identifying the location as the Grand Canyon. Aside from the enormous amount of geotagged vacation photos, there is such a massive elevation change in a small, compact area that there was no doubt in my mind of the identity of the feature. Examining the 3D representation of the canyon I’ve created, you can see the elongated cliff face and associated dramatic elevation drop. In the slope map, a ring of red bounding the canyon floor delineates a 90 degree slope, i.e. cliff face. In the color-ramped DEM the cliff edge can be seen as the elevation drops from high (red) to low (turquoise) within a matter of pixels. Interestingly, there is also a miniature canyon that eventually runs into the larger feature.

Specifically the area I chose was bounded by (in decimal degrees):

West: -112.182500
East: -111.592500
North: 36.323333
South: 35.887500
North American 1983 is the geographic coordinate system (GCS) utilized by the DEM.
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Lab 05. Map Projections

Map projections are two dimensional representations of a three dimensional globe. Conceptually, projections are created by inserting a light source inside of a semi-transparent globe and wrapping the globe in a sheet of paper. The features of the globe will be “projected” on the surface of the 2D sheet. Depending on the orientation of the sheet, different projections are produced.

There are issues inherent within the translation process that can distort everything from angles and distances, to shapes and areas. These problems become apparent when trying to measure any of the aforementioned quantities. For example, in the maps above, I attempted to measure the distance between Washington D.C. and Kabul, Afghanistan. For this I used a straight line measurement tool in the ArcMap program. As one can see the distances vary greatly from map to map, even on those maps that are “equidistant,” (more about this later).

Luckily, certain projections can preserve certain measurements. Conformational projections such as the Mercator and Gall Stereographic projections preserve angles. Equidistant projections preserve the distance between points. Equal Area projections preserve the area of polygons. There is an important caveat to this however. Notice how on the equidistant projections, the distance between Kabul and D.C. still differ between the Equidistant Conic and Equidistant Cylindrical projections. This is because within projection categories, maps can preserve different aspects of distance, or angles, or areas etc. For example, in the conformational projections, the Mercator projection preserves the angles of rhumb lines, or lines of constant course; however, the Gall Stereographic projection preserves the shapes of circles.

The specific qualities of projections, if known, can prove to be useful. Mercator projections for example were ideal for navigators on sailing ships because straight lines drawn on the map correspond to real-world courses. If you were to draw a straight line on an Equidistant Cylindrical projection, the line would not correspond to your real-world course. Again, this is because moving over a 3D surface is different from moving over a 2D surface.

 

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Lab 04. ArcGIS Introduction: Proposed Airport Expansion Map

The above maps are part of a hypothetical presentation on the effects of expanding an airport in a dense residential area. The spatial relationships between noise pollution, schools, population density, land use, the existing airport, and proposed airport expansion area are all depicted. All maps were created using ESRI’s ArcMap, U.S. Census data and data provided by UCLA’s Geography Department.

Upon opening ArcMap for the first time, it was obvious that it was a professional program. Gone was the friendly GoogleMaps browser interface with the ludicrously simple “Create new map” link. The linear and “user-friendly” neogeographic mapmaking experience of Google had been replaced by such esoteric ESRI-isms as “Cadastral Editors,” “Raster Painting,” and “COGO.” After exploring the program briefly, I understood why the UCLA Geography department is offering five GIS courses this winter quarter. It was clearly a complex program and it would take time to master.

With most complex software programs, complexity translates into power. ArcGIS is no exception; it is an analytical beast. In terms of data extraction, manipulation and presentation ArcMap is unrivaled. ArcMap can extract data in a variety formats (aerial photos, tabular data, spatial data, images) and manipulate them into a uniform format. This format homogenization process allows one to compare apples to apples. Once the data is homogenized, statistics based on the relationships between these datasets can be generated. This allows for the testing of hypotheses.

ArcMap is also a powerful visualization tool. Most humans are incapable of interpreting large sets of numbers, they need to see the data presented with the patterns highlighted. ArcGIS can depict these patterns in a digestible way.

Isn’t this:

easier to understand than this:

Overall, ArcGIS is presents a steep learning curve and is not for the lay-user. This is the largest problem with ArcGIS. It is also an inherent, unavoidable one. A program that can do so much must be complex, and is therefore not accessible to the casual user.

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Lab 03. GoogleMap of Crime Around UCLA, 2010

Description of Map:

This map is a compilation of data from preventative “Crime Alert Bulletins” published by the UCLA PD, showing the location of crimes around UCLA/Westwood Village, from 1/1/2010 to 10/17/2010.

Whenever a crime is committed and the police believe there is a possibility of recurrence, they post a synopsis of the crime, suspect description etc. on the UCPD website. I have gone through the bulletins from January 1, 2010 to the present and geocoded them. Note that some of the police bulletins are not specific about location of the crime, hence some of the place markers can be considered approximations.

Purple icons signify robberies. Yellow icons signify crimes of a sexual nature. Green icons are miscellaneous.

(source: http://map.ais.ucla.edu/portal/site/UCLA/menuitem.789d0eb6c76e7ef0d66b02ddf848344a/?vgnextoid=e95bad9386bab010VgnVCM100000db6643a4RCRD)

Critique of Neography:

Neogeography is a term used to describe user-generated maps. Coined after the advent of web 2.0, neogeography has grown and blossomed in recent years. Maps like John’s Backpacking Trip Through India, or Best Running Trails in Los Angeles now pepper the blogosphere, making valuable (and invaluable) and previously inaccessible geo-information (and misinformation) available to the public. Programs with application programming interfaces like Google Maps make it possible for anyone to publish anything.

In terms of free thought and dissemination of information, web 2.0 in my opinion is comparable to the invention of the printing press, the Renaissance, and the preamble to the Declaration of Independence all bundled up into a little ball of free-thinking delectability. Anyone, and I mean anyone wealthy enough to afford an hour at an internet cafe living in a nation without internet censorship can blog, twit, facebook, and otherwise ithink. Neogeography is simply a facet to this explosion of knowledge and creativity.

User-generated content is unique in that there are no boundaries. The formatting guidelines to which government maps are restricted, or the content specifications that traditional maps are held to don’t have to play a role in u-gen maps. Users can generate unique and some may say trivial subject matter, such as McRib sightings in the lower 48; or can add to something as scathing as a worst landlords watchlist; or see how much energy their roof could generate if it were covered in solar panels. The only limit is the bandwidth of the user’s imagination. In such an environment, creativity is fostered, and innovations become inevitable. This blank slate in my opinion is the foremost strength of user-generated content.

However, with a lack of boundaries and rules comes the inevitable negative. Users can post anything and aren’t held accountable for the content they generate; they are operating in an unregulated environment. This can lead to lapses in accuracy or ethics on the user’s part. Due to this possibility of inaccuracy, user-generated maps must be viewed through the lens of a skeptic. Don’t think that I believe all u-gen maps shouldn’t be trusted, just those where data sources and/or methodology are not transparent. The idea is to leave as little room for skepticism as possible. By citing sources and describing the methods used to generate the map, users can achieve credibility.

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Lab 02. USGS Topographic Maps

1. What is the name of the quadrangle?

Beverly Hills.

2. What are the names of the adjacent quadrangles?

Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, and Inglewood.

3. When was the quadrangle first created?

“Topography compiled 1966.”

4. What datum was used to create you map?

North American Datum of 1927 and 1983.

5. What is the scale of the map?

1/24,000.

6. At the above scale:

(a) 5 centimeters on the map is equivalent to 1,200 m on the ground;

(b) 5 inches on the map is equivalent to 1.89 miles on the ground;

(c) one mile on the ground is equivalent to 2.64 in on the map;

(d) and 3 kilometers on the ground is equivalent to 12.5 cm on the map.

7. What is the contour interval of your map?

20 ft.

8a. The coordinates (lat/long, decimal) of the following locations approximated from the map are:

(a) UCLA’s Public Affairs Building:  34°04’10”N, 118°25’30”W; 34.069N, 118.425W;

(b) the tip of the Santa Monica Pier: 34°00’30”N, 118°30’W; 34.000N, 118.500W;

(c) and the Upper Franklin Canyon Reservoir: 34°07’00”N, 118°24’30”W; 34.117N, 118.408W.

8b. The coordinates of the following locations derived from Google Earth:

(a) UCLA’s Public Affairs Building:  34° 4’28.65″N, 118°26’21.55″W; 34.075N, 118.439W;

(b) the tip of the Santa Monica Pier: 34° 0’27.25″N, 118°29’58.95″W; 34.008N, 118.500W;

(c) and the Upper Franklin Canyon Reservoir: 34° 7’11.49″N, 118°24’36.12″W; 34.120N, 118.410W.

9. The approximate elevations (feet, meters) of the following are:

(a) Greystone Mansion (in Greystone Park): 580 ft, 180 m

(b) Woodlawn Cemetery: 40 ft, 12.19 m

(c) Crestwood Hills Park: 700 ft, 210 m.

10. What is the UTM zone of the map?

The map is in Zone 11.

11. What are the UTM coordinates for the lower left corner of your map?

3763000N, 362500E.

12. How many square meters are contained within each cell (square) of the UTM gridlines?

1,000,000 square meters.

13. Create an elevation profile using these measurements in Excel. Obtain elevation measurements, from west to east along the UTM northing 3771000, where the eastings of the UTM grid intersect the northing.  Figure out how to label the elevation values to the two measurements on campus. Insert your elevation profile as a graphic in your blog.

 

* Elevation at UCLA; **Axis labels are abbreviated and signify UTM easting coordinate 3( _ )000E.

 

 

14. What is the magnetic declination of the map?

The declination of the map is 14 degrees from true north.

 

15. In which direction does water flow in the intermittent stream between the 405 freeway and Stone Canyon Reservoir?

The water flows downhill, which in this case is south.

 

16. Crop out (i.e., cut and paste) UCLA from the map and include it as a graphic on your blog.

 

Cut from USGS 7.5 minute series topographic map of the Beverly Hills Quadrangle, California-Los Angeles Co.

 

 



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