Fire Boundary final Lab

From viewing the numerous fire boundary shapefiles, one can easily see the pattern of spread and distribution.  On August 29, 2009 the boundary was very local and small.  As time went on, the overall size of the fire boundary increased exponentially, with the largest increase in size between any two times occurring from 12:25 am to 9:14 pm on August 30.  The spread of the fire seems to have moved primarily in the northwest direction, further up the Santa Monica Mountains. 

The theme of the map in which I decided to focus on was how California Points of Interest were affected by the spread of the fire.  Points of Interest are defined as parking, fuel, food, government, churches, tourist attractions, schools, hospitals, parks, lodging, sports, peaks, towers, and view points.  While this may seem like a large amount of data points, I felt that this was a good representation of how anthropogenic destinations were as a collective unit affected by the negative aspects of the fire.  The points that were included within the fire boundaries were no longer accessible to humans, therefore the direct inhibitory effects can be measured.  Using the GIS program, I was able to spatially select and identify all California Points of Interest within the fire boundaries for three separate time profiles.  As the boundary grows, the number of points affected increases as well.

This first map shows the extent of the fire in relation to Points of Interest on August 29 at 2:48 am.  Only 13 total POI's are present within the boundary at this time, although one can easily not all of the points that lie directly adjacent and around the boundary.  Many of these become a part of the boundary as the fire spreads, as would be expected.  The initial boundary size was 5,200 acres.

This second map shows the extent of the fire in relation to Points of Interest on August 31 at 2:34 am. As is evident, the boundary is much bigger than it was previously, expanding in size from 5,200 acres to 14,000 acres in only 48 approximate hours.  Many more points of interest are included within this new boundary, increasing from 13 points to approximately 140 points.  This means that more areas of human interaction were negatively affected by the fire, as would be expected as the fire grows and migrates to more settled areas.   

This third map shows the final extent of the fire, which represents the maximum boundary that is present within the shapefile data.  The size of the boundary in this map is now 140,000 acres, which is ten times the size of the previously mapped boundary.  Approximately 500 points of interest were affected by this boundary size, which represents a significant amount of human points of interaction that are no longer accessible.  This provides evidence for strong social implications surrounding the explicit danger from the actual fire itself.



Sources:

•The fire perimeters:

http://egis3.lacounty.gov/eGIS/index.php/category/gis-data/fire

Points of Interest

http://www.mapcruzin.com/free-united-states-shapefiles/free-california-arcgis-maps-shapefiles.htm

Lab 7

This first map shows the population breakdown by US county for percent Asian. I used a green-based graduated color system to represent the density of percentage change.  As the map shows, the highest density of asian population percentage are present along the west coast, as well as the Northeast United States.  The more inland off both coasts, the less asians make up a part of the total county population.

This second map shows the population breakdown by US county for percent Black. I used a red-based graduated color system to represent the density of percentage change.  As the map shows, the highest density of black population percentage are present within the Southeast United States.  There also appears to be a slightly higher black population percentages within Southern California. Much of the northern part of the United States is characterized by low densities of black citizens.

The third map shows the population breakdown by US county for percent "Some Other Race". I used a random graduated color system to represent the density of percentage makeup.  As the map shows, the highest density of 'some other race' population percentage are present within the Southwest United States.  The Midwest and Northeast are mainly characterized by counties of low percentages of other races.

I am very impressed with the power and capability of GIS.  My ability to spatially and even temporally manipulate and represent data is extremely important in conveying points in a scholarly manner.  This course has taught me many the basic fundamentals needed to operate the program, yet I still want to learn many more of the advanced functions and processes.   I plan to use GIS in my future work endeavors no matter what field it may be.

Lab #6

The area that I selected is located within the east Bay Area of Northern California.  I found this area particularly interesting because of the fact that it includes deep depressions in elevation from the actual bay, as well as peaks from near hills and mountains. The extent information goes as follows:
N: 38.11969           W: -122.23729
S: 37.50723            E: -121.78501
The coordinate system used for these images is the GCS North American 1983.

Lab 5 part II

Lab 5

Map projections come in a variety of styles and shapes, with each one highlighting different geographical parameters.  The reason for such differences comes from the multitude of ways one can take the three-dimensional surface of the earth and lay them out on a two-dimensional surface.  Depending on which type of distortion has occurred, a specific map projection can show one or more—but never all—of the following: true distances, true directions, true areas, or true shapes.  By understanding the type of distortion that occurs within each projection, one can specifically choose to view a particular projection for the most accurate information on one of these four parameters.  

            Conformal maps are an example of a projection that preserves shapes and angles locally.  This means that on a large-scale, broad shapes and angles will not be accurately represented, thus altering the exact distances between points on the map.  When looking at a 7.5 minute quadrangle map, conformal projections are normally the best option due to the smaller scale.  The common Mercator map is a popular conformal map that is frequently used in today’s society.

            Equal area projections preserve general areas of geographic parameters.  This type of projection helps to fight against maps that contain gargantuan-sized Greenland land-masses, and provides the viewer with a more accurate description of relative area.  Comparison of size between two continents, or even two cities will be much easier and straight-forward.  The drawback to using equal area projections though is the fact that distances are distorted.  If one wants to find a projection that illustrates the clearest distances between objects, an equal area map would not be the best one to choose.

            One final type of map projection is the equidistant model.  These are unique in that distances from the center of the projection to any other place on the map are uniform in all directions.  For someone concerned with exact and precise distance measurements, equidistant projections will provide the most accurate data compared to real-world numbers.  The one draw back though is the fact that area and shape may be distorted in order to account for the equidistance.  

Technical difficulties occurred while re-formatting maps to jpeg form.  I will email the pdf version to Cameran and then try to re-post them as soon as possible on the blog.  Sorry for the inconvenience.

Lab 4

           The world of GIS is incredibly vast and expansive, giving its users power over geographical information and its association to any parameter of interest.  In my mind, the single greatest component of GIS is the configuration of layers.  To be able to isolate, order, and compare multiple data sets dealing with different issues, and look at them together is an incredibly useful tool.  The spatial overlay of numerous parameters can be used in any field, and the ability to easily create layers, turn them on or off, alter their properties, and highlight their location makes disseminating information much easier.  

            With this powerful tool comes a bit of a lag time though.  The one frustrating thing about working with ArcMap within GIS is that when you zoom in or out, or even drag your map the smallest amount to a new location, it takes a bit for the program to produce the desired view.  I find myself watching the little globe at the bottom of the screen turning round and round while I wait for the map to update.  It doesn’t seem so bad within this explanation, but when you have to wait for that globe every single time you alter the visual outlay, it does become annoying.  As technology continues to improve with time, I see this problem becoming less and less of a big deal.  ESRI will eventually create a program with minimal to no lag time, with no more waiting for everyone’s favorite globe to stop turning.  

            Another annoying aspect of GIS is the dreaded red exclamation point—indicating a broken file pathway for your data.  It is incredibly disheartening opening up a map expecting to see a certain product, only to have all of the data missing and not showing.  Oftentimes it is very confusing and laborious to go back within the files and select where you got your data from, especially when you are dealing with hundreds of types of data. If I were to make a suggestion it would be for GIS to save the exact pathway within itself, so that if the files are broken, the user can easily see where they must search to acquire the correct data.

            Overall, I see GIS as an incredibly important and useful tool in any field, but more specifically within the environmental science field as well.  I currently work for an environmental consulting firm that deals with litigation against big-time industrial polluters, and the functions that GIS allows us to use to show pollutant migration and residential zones affected is paramount to our success.  We actually have three associates who specialize in GIS and only work on maps and different visual representations of the environmental situations all over the country.  Their ability to isolate facilities and highlight their polygon, shade over areas of contamination, and provide better understanding of the topology are the most simple and powerful pieces of evidence in a court of law.  I definitely see myself working with GIS in the future and am excited to continue to learn about the program and everything that it offers.

Lab #3

Here is the link to my Google Map.  It lists all of the locations of Travel Channel's "Man vs. Food" first 3 seasons, as well as the food eaten, and the result of the "duel".

http://maps.google.com/maps?hl=en&tab=wl

Neogeography is an incredibly important field in the individual citizen's right to create spatial and temporal relationships on any aspect they want to explore.  I see it as being similar to Open Office, in that it allows any person to be able to create a spatial outlay of any type of information without having to pay for expensive software like GIS.  Also, it is much easier to share these maps with the entire internet community, leading to a vast network of individual interests becoming interconnected.  From a creative point of view, I think neogeography allows greater communication of geographic knowledge.

One of the pitfalls of this free, open service though is that it can lead to inaccurate or biased information to be distributed all throughout the web.  Say for example a student is studying a certain topic for school, comes upon a map they find on google, and uses its information as being completely trusted and correct.  How can that student guarantee the validity of spatial outlay and data incorporated within the map?  I think the best way that neogeography can be implemented in our society is if we can implement a system of checks, so as to minimize the amount of illegitimate information flowing through the web.

Lab #2

1. Beverly Hills Quadrangle

 2.        A) Canoga Park

B) Van Nuys

C) Burbank

D) Topanga

E) Hollywood

F) Venice

G) Inglewood

3.      3.   1995

4.      4.     North American Datum of 1927 (NAD 27)

·         Projection and 1000-meter grid

·         Universal Transverse Mercator, zone 11 10,000 foot ticks

·         CA Coordinate System of 1927 (zone 7)

        North American Datum of 1983 (NAD 83)

·         Dashed corner ticks

National Geodetic Vertical Datum of 1929

5.      5.     1:24,000 scale

6.       6.   At above scale

a.       5 cm on map = 1200 m on ground

b.      5 in on map = 1.89 miles on map

c.       1 mile on ground = 2.64 inches on map

d.      3 km on ground = 12.5 cm on map

7.       7.   Contour Interval = 20 ft

8.       8.   Geographic Coordinates

a.       Public Affairs Building

M/S/D: Longitude:  118®26’30’’   Latitude: 34®4’30’’

Degree Decimal: Longitude: 118.441667   Latitude: 34.075

b.      Tip of Santa Monica Pier

M/S/D: Longitude: 118®30’    Latitude: 34®30’’

Degree Decimal:  Longitude: 118.5    Latitude: 34.008333

c.       Upper Franklin Canyon Reservoir

M/S/D:  Longitude: 118®24’30’’    Latitude: 34®7’

Degree Decimal:  Longitude: 118.408333    Latitude: 34.116667

9.      9.    Approximate Elevation (feet and meters)

a.       Greystone Mansion: 580 ft  =  176.8 m

b.      Woodlawn Cemetery : 140 ft  =  42.7 m

c.       Crestwood Hills Park: 760 ft   =  231.6 m

1110.   UTM Zone of map of Beverly Hills = Zone 11

1111.  UTM Coordinates for bottom left corner = Zone 11, Easting 361500, Northing 3763000

1212.  Amount of square miles of one UTM grid cell = 1 km x 1 km = 1,000,000 square meters

    13. Elevation profile along N3771000

14. Magnetic declination of map = 14 ˚

15. Water flow direction = North to South

      16. 

   

Map #1 - Baseball Teams of America


http://notinhd.files.wordpress.com/2009/08/baseball-map.jpg

This map, while not representing any established borders within the US is still a fun way to look at the general distribution of MLB fans across the forty eight states.  It lists the geographic area of fans of each team within major league baseball, and labels each area with the associated teams colors.  The aspects that I find most interesting are the locations in which many teams are only separated by a small distance. In much of the Midwest and South teams are spread out fairly well, which allows for the fan base to cover a broad area.  When focusing in on the Northeast or Southern California though, one can see that each team's geographic fan base is much smaller with many teams in much smaller areas.  It would be interesting to see how a GIS layer for police arrests during baseball season would look on top of this map.  Would areas of high team-overlap contain more arrests than less dense baseball areas?


Map #2 - Solar Radiation
http://morgansolar.files.wordpress.com/2008/08/us_csp_annual_may2004.jpg

This second map shows the distribution of incoming solar radiation within the fifty states.  The solar radiation attribute is measured in kWh/m^2/day, as indicated by the key in the bottom right corner.  From the map it is clear to see that the majority of solar radiation in the US is experienced in the Southwest, as shown by the higher concentration of red colored pixels.  This type of information is particularly interesting to me as an environmental science major, because it can be used on many levels from urban planning, to wildlife conservation, to renewable energy implementation.  This map is the type of map that I am used to working with for other classes, and is the type of representation that I want to be able to create for my future work.


Map #3 - Number of Unhealthy Days


Although this map looks very similar to the solar radiation map above it, the shading throughout the US here illustrates the mean amount of unhealthy days lived by Americans.  This is a perfect example of the incredibly diverse uses of GIS and how you can spatially represent data.  For example, I probably would have never thought that the most "unhealthy days" in our country occur in Kentucky.  Another technical aspect of this map that I really enjoy is the color shading.  It's a continuous transition of colors that convey a true distribution and also are easy on the eyes.  Maps like this are extremely important for the insurance industry, just another example of the many uses a map can serve.