Lab 8

In this lab we mapped the United States Census Data for 2000 to determine if there were any trends.  Out of the total 3,141 recognized counties of the country, 3,111 were in the continental United States.  We examined the percentage of the population in each county of three races:  Black, Asian, and Other Races ranked by percent as defined by the US Government Census Bureau.  The maps are as follows and show very distinct patterns.         

The US Census (http://quickfacts.census.gov/qfd/meta/long_68184.htm) defines a Black or African American as: 

"A person having origins in any of the Black racial groups of Africa. It includes people who indicate their race as 'Black, African Am., or Negro,' or provide written entries such as African American, Afro American, Kenyan, Nigerian, or Haitian."  

As we can discern from the above map depicting the percentage of black residents in each county, the majority of United States residents that are classified as African-American reside in the south.  The extent of the high concentrations of black residents stretch along the coast from as far south as Texas to as far north as New York and Maine.  There is also some clustering of very high percentages of African Americans in Southern California and there seems to be a linear trend of high black population spanning northeast across the midwest up into Illinois and the Great Lakes.  This map also shows that in terms of area, the majority of the United States is sparsely populated with people of African American heritage outside of the regions mentioned above.  Based on the cut-off points in this representation, counties with a black population of 0.010289% to 4.328816% is most apparent.  The highest category on this representation ranges from 53.197016% to 86.488706% inferring that those areas shaded in a very dark green will have a very significant proportion of African American inhabitants.   

The Census defines an Asian by the following: 

"A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam. It includes 'Asian Indian,' 'Chinese,' 'Filipino,' 'Korean,' 'Japanese,' 'Vietnamese,' and 'Other Asian.'  

By contrast to the African American map, the Asian population seems to be very clustered on the west Coast particularly in Southern California, Northern Washington, and Southern Nevada with several spots in the midwest and in the northeastern United States.  One might deduce that the West Coast's adjacency to the Pacific Ocean might be a cause for the high percentages of Asians in those counties.  Asians appear to be a minority to blacks.  In considering the entire continental United States, the 0.008506% to 0.942961% Asian population appears most often; we can deduce that in many areas, there are not a lot of people with Asian roots.  Compared to the previous map, the highest category represented ranges from 20.448128% to 46.038719%.

The US Census defines a person of "Some Other Race" as the following:

Includes all other responses not included in the 'White', "Black or African American', 'American Indian and Alaska Native', 'Asian' and 'Native Hawaiian and Other Pacific Islander' race categories described above. Respondents providing write-in entries such as multiracial, mixed, interracial, Wesort, or a Hispanic/Latino group (for example, Mexican, Puerto Rican, or Cuban) in the 'Some other race' category are included here.

Compared to the varied and widely distributed locations of those counties with high ratios of Black or Asian residents, the map showing the "Some Other Race" shows a very unique trend.  As one moves from the East coast to the West Coast, the percentage of those who classify themselves as "Some Other Race" continues to increase.  We see very lower percents from the Atlantic Ocean (ranging from 0.007950% to 1.766826%) increase very dramatically around Texas yet again to reach the Pacific Ocean (ranging from 22.271270% to 39.079523%).  The midwest and southern parts of the country are rarely inhabited by those of an "Some Other Race."  California, Oregon, Washington, Texas, Arizona, New Mexico, and Colorado are the states with counties that have a high number of people of "Some Other Race."  

To summarize, although there may be many factors at hand behinds these trends that we have seen by constructing these choropleth maps, one might speculate that population density, locations of major cities, and proximity to a coast line might cause Black, Asians, or people of "Some Other Race" to gather in large proportions.  Due to the vast plains of the midwest and low population density, there might not be a lot of desire for Blacks, Asians, or people of "Some Other Race" to inhabit those states.  One might also need to take into account the historical nature of the areas in question.  For example, the Black concentration in the Southern United States may have lingered from the area's former slave industry.  Likewise, California's history as a place for immigrants from Asian countries may have a correlation to the high percentages of people with Asian heritage.  Furthermore, one must also take into account the construction of these maps.  I no reason behind my choice of 6 bins or categories in these representations.  Depending on how one separates the data values, whether equal interval, natural breaks, quantile, quartile, etc. will alter the trend that one sees.



To end this quarter, I would like to say that my experience with Geographic Information Systems has been one of great interest.  Sure, ArcGIS may cause some frustrations when one is first introduced to the program, but once the learning curve has been surpassed, one can see the amazing products that GIS can provide.  I found that the concepts that the class dealt with were very fundamental not only to geography, but to many of my other classes as well.  When one sees a map--whether it be reference, theme, or dynamic--one must delve deeper into its meaning, dissect it and learn its true representation and meaning.  GIS is a powerful tool, I can only begin to imagine its possibilities.  It creates logic and reason to phenomena seen in the real world.  The power of representation is in the hand of the geographer, GIS is their tool, and they must use it wisely to represent the most accurate, true, and objective data.

Thank you, Jida and Professor Shin, for the wonderful class.  It was a spectacular quarter.

  

Lab 7

Location of 2009 Los Angeles County Station Fire with Neighboring Citie

The 2009 Los Angeles County Station Fire was one of the most devastating and and detrimental fires to happen in Southern California. It has been classified as the 10th largest fire in the history of California and the largest in the history of Los Angeles County. Many lives and habitats were threatened by the fire and unfortunately two firefighters were lost in battling the massive and engulfing flames. Beginning August 26, 2009 and ending October 16, 2009, the fire burned primarily on Mount Wilson destroyed approximately 160, 577 acres, 209 buildings, and 89 homes. The fire was started in the Angeles National Forest near the areas of La Cañada Flintridge, Glendale, Acton, La Crescenta, Littlerock, and Altadena and as seen in the above map provided by the Incident Information System Organization. Officials stated the probable cause of the fire was indeed arson and a chemical substance was found at the location of the fire's ignition source that may have caused an exponential growth of the fire's flames.

Using data from the Los Angeles County Enterprise GIS website, the boundaries of the fire from August 29 to September 2. The above map indicates the extent of the fire on the initial date of the fire (August 29, 2009 at 2:48 AM) in yellow shading, the median date (August 31, 2009 at 2:34 AM) in blue, and the terminal date (September 2, 2009 at 7:02 AM). The fire, starting from its original extent of,

    West: -118.211222;

    East: -118.147167;

    North: 34.271518;

    South: 34.224312 decimal degrees

expanded to the north, west, and east to end at

    West: -118.339177;

    East: -117.972885;

    North: 34.438232;

    South: 34.210177 decimal degrees.

To analyze the damage done by the Station Fire, I have chosen the Significant Ecological Areas surrounding the Angeles Mountains as defined by the Los Angeles County of Regional Planning.  All of the Significant Ecological Areas have been denoted in a pinkish color, whereas those areas denoted in spotted orange in the map shown above, show where there has been fire damage or was at risk of damage. However, in order to begin our analysis, we must first define what a "Significant Ecological Area" or "SEA" is. Taken from the Santa Clarita Organization for Planning the Enviornment's website, a SEA contains the following characteristics:

1. Is the habitat of rare, endangered, or threatened plant or animal species.
2. Represents biotic communities, vegetative associations, or habitat of plant or animal species that are either one-of-a-kind, or are restricted in distribution on a regional basis.
3. Represents biotic communities, vegetative associations, or habitat of plant or animal species that are either one-of-a-kind, or are restricted in distribution in Los Angeles County.
4. Is habitat that at some point in the life cycle of a species or group of species, serves as a concentrated breeding, feeding, resting, or migrating grounds, and is limited in availability
5. Represents biotic resources that are of scientific interest because they are either an extreme in physical/geographical limitations, or they represent an unusual variation in a population or community.
6. Is an area important as game species habitat or as fisheries.
7. Is an area that would provide for the preservation of relatively undisturbed examples of the natural biotic communities in Los Angeles County.
8. Is a special area, worthy of inclusion, but one which does not fit any of the other seven criteria.

The Significant Ecological Area layer was superimposed upon the Digital Elevation Model of Los Angeles County to give an overview of the fire's path.  One can clearly see two SEAs in which the fire caused some destruction and two more areas in which had the fire continued to burn, would have surely been affected by the Station Fire:  The Santa Clara River to the northwest of the fire boundary and the Tujunga Valley/Hansen Dam to the southwest were affected.  Consequently, the Kentucky Springs, directly north of the fire's center and the Verdugo Mountains south of the Tunjunga Valley were both at risk.    

In these areas, the following biological organisms were affected:

•  The Santa Clara River and the stickleback fish habitat.  This river represents the last major unchanneled river in Los Angeles County.
• In the Tujunga Valley/Hansen Dam Area, the dam and many publicly owned equestrian ranches.
• The Joshua Trees of the Kentucky Springs.
• Many California native plants such as the oak woodland plant community, the chaparral, oak woodlands ecoregion.  

One might hypothesize that the Santa Clara River helped retard the growth of the fire in the north direction.  A same allegation can also be said for the Hansen Dam of the south where the Tujunga Wash River flows. 

Currently three forms of rebuilding are in place:

 

• Fire Suppression Repair
• Burned Area Emergency Response
• Long-term Recovery (BAER). 

Bibliography

"Chapter 6.  Open Space and Conservation."  Department of City Planning.  Web.  22 Nov.  2010. <http://cityplanning.lacity.org/cwd/framwk/chapters/06/06.htm>.

"InciWeb the Incident Information System: Station Fire."  InciWeb, the Incident Information System.  Web.  22 Nov. 2010.  <http://inciweb.org/incident/1856/>.

"Significant Ecological Areas."  Santa Clarita Organization for Planning The Environment.  Web.  22 Nov. 2010.  <http://www.scope.org/sea/index.html>.

Wikipedia contributors.  "2009 California wildfires."  Wikipedia, The Free Encyclopedia.  Wikipedia, The Free Encyclopedia, 1 Nov. 2010.  Web.  23 Nov. 2010.

Wikipedia contributors.  "Verdugo Mountains."  Wikipedia, The Free Encyclopedia.  Wikipedia, The Free Encyclopedia,  20 Oct. 2010.  Web.  23 Nov. 2010.

Lab 6

The following Digital Elevation model was taken from the United States Geological Survey of 1983 in the area of Southern Oregon/Northern California.  The extent of the Model is as follows:  W -124.455278, E -123.615278, N 42.776944, S 42.211389 decimal degrees.  The reference system was defined by the USGS and was the Geographic Coordinate System of North America 1983.  I chose this area because of its sheer beauty and very mountainous terrain.  The above map was taken from Google Maps to show the approximate area that the Elevation Model is describing.  I purposely chose to include a portion of the ocean in my map to see the visualization of the mountains and the oceans on the far left.  Since the majority of the map includes the Klamath National Forest, the Digital Elevation Model produces some very interesting lines.  Personally, it almost looks like the arteries and connections of the human brain. 

 

Lab 5

The above image is an map of the world based on the Geographic Coordinate System by the World Geodetic System of 1984.  Such a representation of the world is done by referencing a three-dimensional ellipsoid of the earth to calculate distances between two points.  This is the most accurate method to obtain distances between two points as it uses datum based on the actual curvature of the Earth.  Using the ArcMap's measurement tool, the distance from Washington D.C., United States of America to Kabul, Afghanistan is approximately 6, 954.752973 miles.  In this map, one can see that the sizes of the continents and respective countries are true to the real-world.  Although one is able to use this data in a computer or geographic positioning device, one must often utilize a physical map where an electronic version is unavailable.   The transformation of the world onto a two-dimensional, tangible map must unfortunately incur some distortion.  There is no "superior" map projection available; only those best suited to one's purpose.  Three categories of map projections were implemented in the ArcMap program: Conformal, Equal Area, and Equidistant to 1) compare the visual distortion and 2) compare the alteration of the true distance between the two cities.  

The two maps shown above are Conformal Projections based on the North America and South America Conics.  Lambert Conics, being one of the the most widely used in map making, preserve angular measurements.  The visual distortion of these two projections is remarkable.  The continents in the North America Conic have been significantly reduced in size and are barely visible whereas the South America Conic repairs this issues but in doing so, segments the European and Asian continents.  To produce such a projection, a cone is placed on the Earth and the information is transferred to the cone.  A cut is then made on the cone itself to flatten it, giving these maps a "pie shape."  Lambert conics are either 1) tangent to one parallel or 2) secant to two parallels of the Earth.  This type of map projection is very important when one needs to gain one's "heading" or direction of travel--an very important aspect in sea-faring or aeronautics.  Unfortunately using this system, one cannot gain an accurate measurement of distance.  The North America projection gives a distance between the two cities of 6, 702.869643 miles and is close to the true distance, off by a mere 3.62%.  However, the South America conic is very inaccurate.  It gives a distance of 13, 117.192883 miles, a substantial 88.61% difference.     

The above maps are Bonne and Goode's Homosoline Equal Area projections.  These projections, while preserving the relative sizes of the continents to their true values, produce very odd looking maps.  Although visually the Bonne Projection looks like a heart, it does preserve distance along each parallel and central meridian.  The approximate distance between the two cities from the Bonne Projection is 6, 765.256.451, a minuscule difference of 2.72%.  Due to the distortion, such projections are generally reserved for small areas and not global representations.  On the other hand, the Goode's Homosoline is preferred for global maps.  There are two versions of the Homosoline projection 1) land orientated and 2) ocean oriented.  For this context, the land oriented map was selected.  Despite the fact that the sizes of the land masses are correct, the map is very broken, almost if the globe was "unwrapped" or "peeled."  The distance between Washington D.C., USA and Kabul, Afghanistan is far from correct.  Goode's Homosoline calculated a distance of 15, 642.332504 miles, a massive 124.92% difference.  Equal Area projections are most often used for documenting changes in natural resources.  For example, the changes of foliage due to deforestation can be accurately measured due to the unwavering size of land masses in the map.          

Equidistant projections preserve distance along a given line, usually the meridians.  The Equidistant Conic projection can also be created along one parallel or two parallels using the same cutting method as the Lambert Conformal Projections.  However, the distortion in shape is significant.  The Antarctic continent is depicted as all encompassing and many countries, Australia in particular, have been "squished" vertically.   The Equidistant Conic projection gives a distance of 6, 994.945309 miles, a minuscule .58% difference, and the closest result by a projection to the actual distance given by the GCS WGS map.  Moreover, the Equidistant Cylindrical projection does the opposite of the Equidistant Conic.  Instead of compressing the continents, the cylinder appears to vertically stretch the shapes.  Horizontal distance seems to be fairly inaccurate, giving a distance of 5, 069.388001 miles, a difference of 27.21%.  Equidistant maps tend to be used for small areas to minimize the blatant visual distortion.      

Lab 4

 

ArcGIS is a very powerful tool for geographers of all skill levels.  Rather than presenting people with sterile statistics and mundane data, it is often times more effective to use maps to convey one's message.  GIS allows for one to manipulate layers, attributes, and spatial data and inevitably utilize this information in public policy making or private decisions.  With a click and a drag, layers of information can be hidden, made more pronounced, or edited.  As seen in the documentary shown in class regarding Geographic Information Systems, often times a language or educational barrier may prevent communication between two parties.  In such cases, a visual aide can bridge this gap and create a springboard for innovation and change.  GIS has many potentials and pitfalls.  

One of my first thoughts of the ArcGIS suite and specifically the ArcMap program was how intriguing it was.  Unfortunately, due to the high resolution-- although great for viewing a lot of detail--made ArcGIS very slow at rendering images.  This phenomenon was exacerbated when I was working through a Remote Desktop Connection.  Being a self-proclaimed “computer geek,” I was amazed at the capabilities that the software had and was instantly wanting to explore it.  The graphic interface was very organized and clean.  The menu bar was very straightforward allowing the user to annotate the map as they pleased.  A few clicks on the “Insert” menu and one could add a title, legend, scale bar, north indicator, etc.  The “wizards” that appeared for every annotation proved to be very helpful to the novice as the systematic processes provided very detailed directions.  To the expert, GIS enables the user to represent the data in a variety of ways to best suit ones purpose.  If one representation of map elements is not satisfactory, another one can be selected.  The many color schemes, for instance, allowed for an eye appealing customization of data; e.g., when representing the parcel land types in the Land Use data frame. 

However, after the initial learning curve was overcome, I quickly realized how cumbersome ArcMap could be.  After learning the basic concepts of how ArcMap adds elements to current projects, I found that adding elements now became a tedious process.  The keyboard shortcuts defined in the help menu were not common to many other applications and were very awkward to use.  For example, adjusting the page settings, a simple task in many other programs, became lengthy as one needed to be in Layout View and then access it from the File menu.  The wizards that were initially helpful became an inhibitor to one’s goal.  One could have easily produced the same results in one window rather than separating the options into different panes.  For example, the graphing utility, although capable of producing an indisputable representation of data, is much more difficult than programs such as Microsoft Excel.  

Because GIS is a system ordinarily reserved for government and higher-education institutes, it only allows for a select number of people to utilize this expensive mapping and analysis program.  Having completed the tutorial on four separate occasions, I personally found the ArcGIS interface both intriguing and cumbersome.  Although such a system might be state-of-the-art in many regards, the amateur user finds themselves overwhelmed with the plethora of options laid before them.  In order for one even begin to understand ArcGIS one requires countless hours of practice and practical application.