Lab 8: Mapping Census Data

     In the final lab for Geography 7, we were asked to use data from the 2000 Census to map population distributions throughout the continental United States.  Data was taken for Black, Asian, and 'Other' populations and mapped, per county, according to percent of total population.  To further examine the analytical possibilities of GIS, data was classified using both the equal interval and natural breaks methods.

      As seen in the above maps, the Black population across the United States is highly concentrated in the South.   Both maps indicate a prominent Black population in Louisiana and in Georgia.  In addition, both maps illustrate a "belt" across the South that spans from Louisiana through the Carolinas.  Interestingly, the population in Florida remains very much concentrated in the north and does not extend south into the state.  The different classifications show important disparities in the distribution.  According to the natural breaks map, the population seems to be very widespread and highly concentrated throughout the belt.  However, in the equal interval map, the population looks more dispersed.  One advantage of the natural breaks map is that a clear majority (population greater than 50% of the total population) is visible.  Because the equal interval map does not make such a distinction, the majority remains a bit more uncertain. 

     The graphical representation of the Asian population distribution is highly dependent on the choice of data classification, as evident in the differences between the two maps.  While both show that the largest Asian presence in on the west coast of the country, the equal interval map makes the population much less pronounced.  As seen on the first map, the Asian population has a significant presence in Southern California.  In the densest areas, the population ranges from 9-20%.  It is important to note, however, that the greatest population percent is 46%.  This indicates that nowhere in the continental United States is the Asian population a majority of the county's population. 

    Here, we see how the different classifications affect the representation of the 'other' population distribution.  In both maps, the concentration of peoples of other races is highly distributed in the western part of the country.  Additionally, this distribution is most apparent in along the Southwestern border, which may indicate that this influx of peoples of 'other' races may be due to immigration from Latin America.  As seen in both maps, the highest percentage of 'other' races is California and New Mexico.  For this particular distribution, the equal interval map is telling, for it specifically shows where the highest percentage of peoples of 'other' races reside, in the narrowest margin.  The natural breaks map shows this as well, but suggests that people of 'other' races make up a much larger majority of the total population.
   Clearly, the Census maps show important trends in population distributions in the United States.  Using either equal interval or natural breaks classifications, one can learn significant information about the US population.  As evidenced in the Black population maps, the largest percentage of Black population is in the South.  This poignantly illustrates an important component of United States' history, for the Black population historically orginated in the tobacco-farming areas pertinent to the slave trade.  Similar population migration is evident in analysis of the Asian population, as many Asian peoples would have come to the United States from the West.  Finally, the presence of peoples of 'other' population in the Southwest show a growing trend in immigration and population distribution.

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When I first signed up for this class during the summer, I did not know what to expect - to be perfectly honest, I thought it would be quite boring.  However, the first day of class, and the rest of the quarter, far exceed my expectations.  I thoroughly enjoyed lecture and feel like I learned a valuable skill in GIS.  Professor Shin was very entertaining and his lecturing style was engaging. I really enjoyed learning about GIS and using ArcMap and I'm sad to see this  class end.  I wish I would have taken a GIS class earlier in my career at UCLA because I feel like I would have really liked picking up the GIS minor.  If the opportunity ever arises, I hope to use GIS again in the future and I can honestly say, as cheesy as this is, that I have a new appreciation for maps and cartography in general.

Lab 7: An Analysis of the Los Angeles Station Fire of 2009

Image taken from 'Angry Fire', CNN.com    

     In August of 2009, a rapidly spreading wildfire, whose cause was ultimately determined as arson, swept through Los Angeles County.  Later deemed the Station Fire of 2009, it was the largest in the history of Los Angeles County and the tenth largest in the history of California (as recorded since 1933) (“State of the Climate Wildfires Annual 2009”).  The fire consumed approximately 160,000 acres and was, according to the National Oceanic and Atmospheric Administration, one of the year’s most destructive (“State of the Climate Wildfires Annual 2009”).  Raging for nearly two months, the fire prompted the evacuation of thousands and the closure and damage of several high-traffic highways.  Although the evacuation and treatment of those affected by the fire was successful, it is uncertain whether similar measures will be as effective if the population continue to develop around currently desolate pars the county.  Through careful analysis of projected population growth, it is hypothesized that Los Angeles County will need to adapt to ensure continued successful fire safety and prevention.

      

     

      Analysis of the fire’s extent over time offers significant insight into the growth and spatial impact of the Station Fire.  Data for the fire perimeters was taken from Geography 7 Website.  As seen on the Temporal Spread map, the Station Fire’s perimeters greatly expanded within a very short period of time.From the initial reading on August 29^th, the fire approximately doubled and quadrupled in size within the first twelve and twenty-four hours, respectively.  Additionally, it should be noted that the fire was located in approximately the center of the County.  Surely, the enormity of the fire inspired the County to reconsider its current fire policies, but what is even more pressing is the adjustment of policy and infrastructure in the case of a future fire.           

     Analysis of projected urban growth for Los Angeles County suggests that County government will need to adjust its road network for the sake of fire safety.  To visualize the growth of the Los Angeles community in the coming years, data from the Cal-Atlas Geospatial Clearinghouse was obtained and combined with the extent of the Station Fire to examine the consequences if such a fire were to reoccur.  Expected population growth, particularly in areas close to the fire, was chosen to closely examine the new communities potentially affected by potential fire.  As seen on the Proximity map, a substantial number of new communities are projected to be in areas close to the fire. 

If this dispersion is compared with an aerial photograph of the smoke impact of the original Station Fire (seen below), it is clear that, if not by direct impact, new communities will be affected by the resulting smoke cover.  This smoke assessment, along with the physical growth of the fire, would surely result in greater numbers of evacuees than in the Station Fire. 

This image was extracted from "Wildfires in Southern California", The Boston Globe

The increase in necessary, or voluntary, evacuation would directly impact the traffic flow out of affected communities.  As seen on the Proximity map, the current (and planned) number of highways in the areas for projected communities is rather limited.  For example, the communities to the west of the Station Fire region only have about five major highways available to them.  To ensure an effective evacuation process, the County would need to reconsider the highway distribution in those areas.  If access remains that limited, it would undoubtedly result in severe traffic problems that could seriously impede people’s safety and the accessibility of medical and fire services. 

Clearly, the County must take future urban planning into account when devising new policies for fire safety.  Using the method proposed by Preisler and his colleagues, the County could determine the probability of another fire and its risk occurring in the same area and to the same extent as the Station Fire (Preisler et. al).  While it is assumed that the development of roadways will be significantly impacted by future emerging communities, the County should have disaster safety in mind to prevent unnecessary grief in the event of another fire. 

BIBLIOGRAPHY

“Angry Fire’ Roars Across 100,000 California Acres,” CNN U.S.  31 August 2009.

http://articles.cnn.com/2009-08-31/us/california.wildfires_1_mike-dietrich-firefighters-safety-incident-commander?_s=PM:US.  Accessed 18 November 2010.

Cal-Atlas Geospatial Clearinghouse: Download Data. http://atlas.ca.gov/.  Accessed 16

November 2010.

“State of the Climate Wildfires Annual 2009.” National Oceanic and Atmospheric

Administration.  http://www.ncdc.noaa.gov/sotc/?report=fire&year=2009&month=13.

Accessed 18 November 2010.

Preisler, Haiganoush et al.  “Probability Based Models for the Estimation of Wildfire Risk,”

International Journal of Wildland Fire.  2004.

http://stat-www.berkeley.edu/~brill/Papers/WF02061.pdf.  Accessed 18 November 2010.

“Wildfires in Southern California,” The Boston Globe.  2 September 2009.

http://www.boston.com/bigpicture/2009/09/wildfires_in_southern_californ.html

Accessed 18 November 2010.

 

Lab 6: Digital Elevation Models

     In this laboratory, we used data from the USGS National Map Seamless Server to create digital elevation models (DEM) using ArcMap and ArcScene.  The area selected extended from 37.025° N (bottom) to 37.28° N (top) and 119.072° W (left) to 118.774° W (right).  Therefore, the area shows a 0.255° spread in latitude and a 0.298° spread in longitude.  The coordinate system used for this exercise was the GCS North American 1983 and the corresponding datum was the D North American 1983.  Below are the maps created from this landscape:

MAP 1:  The color-ramped DEM layered above a hillshade

MAP 2: Slope Map

 

MAP 3: Aspect Map

 

MAP 4: Three-dimensional Representation

     Analysis of these four images allows for a comprehensive understanding of the selected area.  In Maps 1 and 4, the bluer regions correspond to higher elevations while the redder regions correspond to lower elevation.  This gradient shows the range of elevation in the selected area.  As seen in Map 2, the slope inclincation was relatively consistent throughout the entire area, as indicated by the overwhelming periwinkle, with a value of approximately 88°.  The aspect map shows the direction of the range's slopes, and the majority of blue/turquoise coloration indicates that the slopes were facing to some degree between south and west.  Futhermore, the 3D graphic clearly shows the topography of the area and the substantial differences in elevation.

Lab 5: Projections

        In this laboratory, we had the opportunity to create map projections using ArcMap.  Utilizing an existing data set, we were able to create maps of the world that featured different types of projections, primarily conformal, equal area, and equidistant.  To further explore the variations in the maps, we also investigated that respective distance between Washington D.C. and Kabul, Afghanistan as determined by the projections.  Below are the projections created in the lab:

     The map projections above demonstrate the process of converting the three-dimensional world into a two-dimensional map.  This transformation takes the spherical coordinates of the earth and projects them onto planar coordinates.  The most developable planar surfaces for these projections are a plane, cone, and cylinder.  While there are several types of map projections, the three most significant are the conformal, equal area, and equidistant.  A conformal projection, shown in the first map, preserves the shapes and angles on the map.  The second map shows an equal area projection which conserves the proportionality of areas on the map as they are in the real world.  Finally, the third map, an equidistant projection, shows a map whose distances from the center are equal.  These three map types each possess valuable benefits, but they also exhibit significant flaws. 

     The conformal, equal area, and equidistant map projections all offer substantial advantages.  For example, because the conformal map projections conserve angular separation, they are optimal for flight usage.  As shown on the two Mercator conformal map projections, the distance between Washington D.C. and Kabul, according to the projection, is about 10,000 miles.  While this value is not necessarily the quoted number (sites such as trueknowledge.com and blurtit.com state the distance is approximately 6900 miles), the two values found for the two conformal maps are the most consistent of any of the other groupings.  In addition, the equal area map projection accurately reflects the proportional size of the regions of the world.  Both the cylindrical and sinusoidal projections show the comparitive landmass size as would be expected, in contrast to the conformal projections that show the monstrosity of Antarctica.  This proportionality would be useful for determining population density or the effect of a continuous phenomenon over large areas.  Furthermore, the equidistant map projections are beneficial as they show the relative distance from the focal point of the map.  While both the conical and cylindrical projections do not necessarily share the same center, they do standardize distance on the map.

     Although these projections offer significant insight into different features of the globe, they also have important faults.  Though this analysis is aware that the shown maps are indeed projections, if a reader were to approach them as exact replicas of the orientation of the world, he would seriously comprimise his geographical understanding.  For example, if one compares the conformal, equal area, and equidistant projections, he notices that they all distort the presentation of the globe in some form.  If an individual took this distortion as truth, he would have a terrible sense of the global picture and may try to combine the features of each map rather than address them separately.  In addition, the projections, although they all depict the world, do not project distance consistently.  Looking at the maps above, the reported distance from Washington D.C. to Kabul varies from about 7000 miles to 10,000 miles.  This shows that there is not only one way to measure distance and that measuremets should be made according to the purpose of the investigation. 

     Map projections offer crucial understanding into the spatial organization of our world.  Although they possess some flaws, they also motivate critical thinking and analysis of the globe.  Map projections offer the ability to compare specific attributes of the earth, and allow for detailed analysis of a particular interest.  Map projections have the potential to greatly increase a person's knowledge about their environment and for him to assess his position in the world on various levels.

Lab 4: ArcMap

In this lab, we were introduced to the wonders of the ArcMap software.  Below is my final product after going through the ArcMap tutorial:

     Using the ArcMap software was both an educational and enjoyable experience.  The ArcMap tutorial was very straightforward and certainly eased the navigation through the program.  I thought the formative processes for the different types of maps was very instructive and provided a good introduction to many of the useful tools in ArcMap.  There were several components of this laboratory that I particularly liked, not only for their technicality but for their creative potential.  For example, I really liked how the program was so user-centric.  I know in class we discussed how neogeography focuses on the user while GIS focuses more on authority and administration, but I thought there was a significant amount of user involvement that was crucial to the final product.  I liked having the ability to choose differen color schemes (although the ones for this project were designated by the tutorial) and to select or de-select certain attributes to apply to the map.  In addition, the basic programming language to perform simple calculations (i.e. population density) was very understandable and, I think, could be easily used by anyone, regardless of their programming experience.  Furthermore, I really liked having an active role in the integration of different layers of the map.  It was interesting to see how different themes could be incorporated into one map.

     While my first experience with ArcMap was primarily positive, there were some annoying drawbacks to the program.  First, I thought that data storage and saving was much too complicated.  I am well aware that with today's technology we often take the ease of computers for granted, but I thought finding and storing data required too much effort.  Additionally, I thought that the mandatory use of ArcCatalog was too complicated.  It was difficult to remember to reference different file paths and I think the program would have been more effective if an outside source wasn't required to access data.

     Through my first experience with GIS, I was able to better understand the immesne power it offers.  Using GIS allows for a clear, thematic geographical presentation of many facets of one particular issue.  For example, the maps constructed during the tutorial offered significant information about many effects of the proposed airport expansion.  The different layering options allow the user and the reader to explore many social aspects of one proposed plan.  The presentation options available in the software improve the quality and readability of the maps; thus encouraging their universal understanding.  GIS also provides a technical and concrete analysis of a particular distribtuion which can satisfy inquisitive, quantitative minds.  The software itself is very direct and aside from its cost, easily accessible.  The most significant benefit of GIS is, by far, its ability to integrate various problems into one coherent presentation and to provide a (hopefully) unbiased, scientific perspective.

     Although GIS has substantial benefits, it also has some important pitfalls.  First, much of GIS is left to the map-maker's discretion, and while one would hope the user would include all pertinent information, he also has the ability to withold vital information.  This presentation of certain information could lead to bias that could significantly impact the interpretation of a given map.  GIS is also fairly expensive, and while licenses are available, it is unfortunate that such a useful tool is difficult to access. 

Lab 3: Let's Make a Map

For this assignment, we were asked to use the Google Maps "My Maps" interface to create a mashup map of our own.  As I am a very big classic rock enthusiast and recently watched VH1's "100 Greatest Artists of All Time," I thought it would be interesting to map the home towns of the top twenty American artists on the list.  Below is the mapping result (Please be sure to click the link beneath the map for further detail):


View Home Towns of VH1's Top 20 (American) Artists of All Time in a larger map

As you can see, this map exhbits several evident trends both geographically and generationally.  The different colored markers identify the home towns of these top artists and the decades across which the list spans.  Colors were chosen according to the peak years of the artists' careers.  To distinguish between decades, green represents the 1950s, yellow the 1960s, light blue the 1970s, pink the 1980s, and dark blue the 1990s.  Clearly, the majority of these top artists were born on the eastern side of the country, with a substantial concentration in the south.  Two significant historical population trends seem to justify this distribution.  First, the eastern states have a high population density; thus, it would be likely that several talented artists would come from such an area.  Additionally, most of the artists in the southeast, as well as the entirety of the map, peaked in the 1960s, indicating that they were born in the earlier part of the century.  As the eastern part of the country was more heavily populated than the west in the early twentieth century, it seems logical that many of the artists were born in that area.  It is also interesting to note that the chosen artists of the 1980s were all born in the northeast.  Finally, the location of the two music halls of fame is also significant, for their position is fairly central to the majority home town concentration.  This positioning suggests that these locations would receive the greatest amount of traffic or would appeal to the subsequently high concentration of fans living in the same area as the artists' origins.

While this map is particularly interesting, it also possesses a few flaws.  For example, it does not outline the geographical career path of these artists from their home towns, and it is assumed that many of them traveled to become successful in the music industry.  Furthermore, it does not show where certain music trends were popular geographically or differentiated between genres, knowledge which could indicate why a person was inclined to one type of music over another.  These are just some issues that stem from user-created maps availble with neogeography.  Although the map presents valuable information, it cannot show enough to provide the user with a comprehensive understanding of the subject.  In addition, the amateur nature of neogeography may incline a user to question the information being presented to him. While I did my best to research the artists' home towns, I am certainly not an authority on the topic and I relied heavily on other sources for my presentation.

Lab 2: Learning About Topography

To further understand the applications of topography, we were asked to examine the 7.5 Minute Topographic Map of Beverly Hills as provided by the USGS.  Here is some pertinent information deduced from the map:

1. The name of the quadrangle is the Beverly Hills Quadrangle.
2. The names of the adjacent quadrangles are as follows: Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, and Inglewood.
3. According to the information provided on the map, topographic information for the Beverly Hills area was compiled in 1966.  Boundaries were subsequently verified in 1998, indicating that the quadrangle was first created in that year.
4. The North American Datum of 1927 and 1983 were used to create the map.
5. The scale of the map is 1: 24,000.

6. At the above scale:

    a) 5 centimeters on the map is equivalent to 1200 meters 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 inches on the map.

    d) three kilometers on the ground is equivalent to 12.5 centimeters on the map.

7. The contour interval is 20 feet.

8. Listed are the approximate geographic coordinates in both degrees/minutes/seconds and decimal degrees of:

This data was taken from the map and then recorded and tabulated using Microsoft Word.

9. Found below are the approximate elevations in both feet and meters of:

a) Greystone Mansion (in Greystone Park): Elevation 570 ft = 173.736 m.

b) Woodlawn Cemetery: Elevation 140 ft = 42.672 m.

c) Crestwood Hills Park: Elevation 640 ft = 195.072 m.

10. The UTM zone is of the map is Zone 11 North.

11. The UTM coordinates for the lower left corner of the map are ³⁷63 Northing, and ³62 Easting.

12. Each cell (square) of the UTM gridlines contains 9,290,304 m².

13. Along the UTM northing 3771000, measurements were obtained where the eastings of the UTM grid intersect the northing.  An elevation profile using these measurements was created in Microsoft Excel. Two measurements of the UCLA campus are specifically noted.

 

14. The magnetic declination of the map is 14°.

15. The intermittent stream between the 405 freeway and Stone Canyon

Reservoir flows South, from an elevation of approximately 1100 ft to an elevation of about 900 ft.

16. Finally, here is a graphic of the UCLA campus as shown on the Beverly Hills Quadrangle:

Life is a Highway: Lab 1

As this is my first Geography blog, it seems only fitting that I post some maps that represent important parts of my life.  Hope this helps you learn a little bit more about me!

 
Of course, I have to showcase my hometown: Orange County, CA.  I've lived in Orange County my entire life (aside from my time at UCLA) and have not spent all too much time away.  For the last ten years, my family has lived in Tustin, a relatively small city where the 5 and 55 freeways intersect.  As you can see, Tustin is fairly centrally located and within about twenty miles from popular OC attractions such as Disneyland (found just off the 5 freeway in Anaheim) and the beach.  I found this particular map on the online OC Almanac, with the title "Orange County and Communities." The title of the map is interesting, specifically the use of "and."  Rather than describe "Communities of Orange County," the almanac chose to include the "and" which, I think, is indicative of the various communities in the county.  As seen on the map, each is bordered with very defined, often rigid boundaries, and a distinct "feel" of the community is often confined within those boundaries.  For example, despite their contiguity, the impeccably neat and constantly building city of Irvine is drastically different than sections of the older, lower-class city of Santa Ana.  In addition to the transparency of the title, the map also shows several important aspects of the OC.  For example, the map shows the abounding presence of freeways throughout the country.  As you will notice, however, Orange County is dominated by smaller freeways, rather than larger interstates.  This certainly makes driving in Orange County much more pleasant than driving in Los Angeles.  Also, I like how the map shows the borders of the county and how it is surrounded by four, much larger, independent counties.  Finally, I like this map because it shows how many cities there are in the county.  Often times, Orange County can feel very small, but this map nicely shows its extent.  My only critique of the map is the color-coding they chose for the different cities. While this distinguishes one from the next, I wish there was some kind of explained reasoning.
(Found: The Online Orange County Almanac - http://www.ocalmanac.com/Geography/ge30a.htm)

If you really want to learn about me, then you'll have to know that I love all things Disney.  Living in Orange County, I have been fortunate to be a Disneyland Annual Pass holder for most of my life.  Throughout high school, Disneyland was the place my friends and I would go to relieve stress from school and just have a fun time.  In college, I do not get the opportunity to frequent Disneyland as often, but I still try to go, just to get a piece of that Disneyland magic.  I found this map on chipandco.com, a website dedicated to new happenings of Disney and Disneyland. While I like this map for nostalgic reasons, there are other aspects of it which I love.  I think the color choice and animated style of the map is visually appealing, a great way to showcase the vivacity of the park.  I like how the map uses numbers to designate restaurants, shops, and other amenities in the park - this allows tourists to easily find what they are looking for.  I also like how many of the rides, and their designated lands, are depicted in the map.  These pictures give first-time park goers foresight about the nature and location of the rides, allowing them to plan accordingly. (From: Chip &Company - chipandco.com)

 

The last map I'm posting today is one I have created myself using Google Maps.  This journey shows the distance I traveled to attend the first UCLA football game of the 2010 season against Kansas State.  Now that you know I'm from Orange County and that I love Disneyland, you're about to find out how much I love UCLA and its football team.  The incredible school spirit of UCLA attracted me to the campus, and I am proud to say that I am a Bruin who shares this passion for all things blue and gold.  As you can see on the map, we began our adventure from Los Angeles and drove out to Kansas City, MO.  We made several stops along the way and took five days to drive to Manhattan, KS to see the game.  From there, we drove to Kansas City, stopped for the night, and then made our way back home.  This map shows the extent of our journey, and, if the scale were visible, would be able to show how many miles we traveled during the trip (it was about 3500).  My favorite part of this map is that it shows how many states we traveled through.  Over the course of a week, we crossed through ten different states, eight of which I hadn't before seen.  This map also shows the forests and mountains we passed through, showing the various types of scenery we encountered. Finally, the map shows the major cities we stopped in, including Las Vegas, Denver, and Kansas City. 

(From: http://maps.google.com/maps?f=d&source=s_d&saddr=424+Veteran+Avenue,+Los+Angeles,+CA&daddr=Kansas+City,+MO+to:Amarillo,+TX+to:424+Veteran+Avenue,+Los+Angeles,+CA&geocode=FbrbBwIdk4Xw-CnTvjYOkbzCgDHhsIuwCOJcyA%3BFU6dVAIdedhc-imXmemvXvfAhzGiUapq5iWFVQ%3BFe1xGQIdfy3u-SkDz0Wy1EgBhzGv0jZoHNHz0A%3BFbrbBwIdk4Xw-CnTvjYOkbzCgDHhsIuwCOJcyA&hl=en&mra=ls&sll=36.890635,-106.5138&sspn=25.24614,39.506836&ie=UTF8&ll=36.208823,-104.106445&spn=25.459112,39.506836&z=5&layer=c&pw=2)