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.