Lab 8

This first map shows the concentration of African-American blacks throughout the United States. It is clear that blacks are most strongly concentrated in the southern United States such as Virginia, Alabama, Mississippi, Georgia, and Louisiana. Besides the south, we find that blacks are most concentrated in counties with large urban cities (i.e. Chicago, New York...) and their surrounding suburbs. This distribution seems to be an obvious result of Black America's history, from slavery in the South on to the relatively free movement of blacks into large cities, and the the eventual 'black flight' of many African Americans into the suburbs in the '60s.

This map shows the percent concentration of individuals identifying as any of many Asian ethnicities. Judging from the map, American Asians are predominantly focused in the more urban regions of the U.S. After some outside research I found that Los Angeles has the highest number of Asians, followed by New York, New Jersey and Long Island. Because the "Asian" racial category is so diverse, the disbursement must be based on many different factors. While the west coast hosts the highest percentages of Asian. Many Japanese and Chinese first settled in Hawaii, and then later worked as laborers on the transcontinental railroad, concentrated mostly in the West. They settled in the bay area and Los Angeles, establishing Japantowns and Chinatowns, and later Koreatowns and others, in regions of the cities and their suburbs.

This map was the most interesting to work with. It shows the concentration of "Some Other Race" throughout the US. This is interesting to me because, at first, I was at a loss for what "Some Other Race" referred to. After a bit of outside research I learned that this category is held for those individuals who do not identify with any of the races presented on the census. Apparently, it was designed for those of mulatto or mestizo race, i.e. people of Hispanic or Latin American origin. Because it is not a specific category, other individuals may choose to select it, but in 2008, "15.0 million people, nearly 5% of the total U.S. population, were estimated to be "Some other race" with 95% of them being Hispanic or Latino" (Wikipedia.com) We find these people most concentrated in states bordering Mexico and on the (once Mexican) southern West Coast.

These three maps show the distribution of three main minorities in the US: African-Americans, Asian-Americans, and "Some Other Race," in this case primarily Hispanics and Latin Americans. Firstly, we can infer that these concentrations are founded on racial histories. Blacks are predominately reside in the south, where they were historically concentrated as slaves. Asians predominate the West Coast due to their work on the Trans-continental railroad. Hispanics and Latin Americans occupy border states and previously Mexican land. In addition to the historical factor of these distributions, each of these races has created appealing communities which attract more of the same race. Major cities throughout the US include suburbs populated primarily by specific races, i.e. Chinatowns, black suburbs, and Mexican-American communities. It was very interesting to work on these maps and interact with the data in order to watch these complementary racial distributions take shape on the screen. The visualization of these population distributions is important to understand what regions of the US are populated by who, why they might live there, and how this racial make-up might effect politics, economics, and education in different regions.

GIS is a very useful tool that I have become very interested in learning to use and apply to everyday happenings. I think that it is very important that people become comfortable comprehending spatial data and phenomena. Through GIS the general population might better understand what factors contribute to their own lives by being presented with not only the hard numbers but that data organized coherently and relatively on a map. The basics that I have learned in this class have strongly affected my conception of what is mappable, why different maps are made, and the power that presentation has on the effect of the map. Learning about different projections opened my eyes to the ease of misinterpreting space and relative size and distance, making me aware of the potential for misuse of maps. The software has really impressed me and I am excited to work with it in the future. It's clarity and ease of use does not do it's power and usability justice, and I am definitely excited to work with different data from all over the place.

Lab 7

For this project, I chose to address the communities and cities affected by the rapid expansion of the Los Angeles Station Fire of 2009 from August 28 to September 2. While it could be said that all of greater Los Angeles and even surrounding areas were affected by this wildfire, several specific cities were hit hardest as the fire approached their boundaries. This was the largest fire ever for the Angeles National Forest as well as the largest in modern Los Angeles history. 209 structures were destroyed, including almost 90 homes (http://inciweb.org/incident/article/9640/). It cost more than 90 million dollars just to fight it, with almost 1000 firefighters assigned by September 15th (http://inciweb.org/incident/article/9535/).

The Station Fire tore through the Angeles National Forest area north of La Cañada, Tujunda, Altadena, and neighboring cities. In only two days, from early morning on the 28th (green patch on map) to early morning on the 31 (pink patch), the fire area multiplied intensely. By 9:00 PM on the 31, the fire had already consumed 105,296 acres of forest land and destroyed 53 structures while only 5% contained (http://inciweb.org/incident/article/9360/). Though I show the fire’s progression until only September 2, it was not declared fully contained until 7:00 PM on October 16 following a moderate rainstorm which allowed firemen to hike into the forest and contain what little had not burnt out (http://inciweb.org/incident/1856/).

In creating this map, it was important to show the hillshade gradients in order to understand how and what allowed the fire to expand in the way it did. We can see that the forest area north of the affected cities climbs up into the San Gabriel Mountains. Comparing the shape of expansion to the underlying terrain (i.e. various valleys and peaks) helps explain how the fire traveled throughout the area. The different colored patches represent the chronological expansion of the fire. In 5 stages throughout 4 days, the fire exploded through the region. From the original relatively symmetrical patch, the direction of growth is clear as it climbs up the sides of nearby peaks. The light green region to the south of the fire zone indicates populated regions within Los Angeles, including the labeled cities reported to have been most at risk and experiencing the highest levels of evacuation orders. It is interesting to see how the fire was kept to the very most edge of these zones, suggesting firefighters worked hardest to keep the flames from consuming city regions. It is also interesting to see that there are actually areas where the fire zones overlap slightly with populated places.

During the span of the forest fire, much of La Tujunda, La Crescenta, La Cañada Flintridge and Glendale were under large-area mandatory and voluntary evacuations. For months after the fire, these cities and surrounding areas were at extreme risk of flash flooding, mudslides, and debris flow as the rainy season approached (http://latimesblogs.latimes.com/lanow/2009/10/rain-coming.html). Also affecting these communities was the dangerous air quality. At one point, the air quality index was set at 398. Any number over 100 is considered unhealthy (http://latimesblogs.latimes.com/greenspace/2009/08/air-quality-at-hazardous-levels-in-foothill-cities.html).

This fire burned out of control for weeks. Many believe this could have been avoided had a certain law that prevented airborne firefighting during the night been overturned beforehand. These laws prevented firefighters from addressing the blaze at its onset, instead opting to hit it in the early morning. In addition, fire officials admit to have miscalculated the strength of the fire and were caught unprepared on the second morning. By that night, the fire was blazing unstoppably into the forest. One spokeswoman noted that “[t]hey didn’t really hit [the state agency] up for heavy resources until the morning of the third day” (http://www.latimes.com/news/local/la-me-fire27-2009sep27,0,6025715.story)

Works Cited

Bloomekatz, Ari B. "Rainstorm Heading to Southern California Raises Mudslide Concerns [Updated]." L.A. NOW. The Los Angeles Times, 12 Oct. 2009. Web. 27 May 2010. .

Lin, Rong-Kong. "Los Angeles Fire Map: Mt. Wilson, Tujunga, Acton, Altadena, Pasadena, Sierra Madre." Los Angeles Times: Local. The Los Angeles Times, 11 Sept. 2009. Web. 27 May 2010. .

Pringle, Paul. "Station Fire's Strength Was Miscalculated." Los Angeles Times: Local. The Los Angeles Times, 27 Sept. 2009. Web. 27 May 2010. .

Seib, Al. "Air Quality at Hazardous Levels in Foothill Cities." Los Angeles Times: Environment. The Los Angeles Times, 31 Aug. 2009. Web. 27 May 2010. .

"Station Fire Evening Update Aug. 31, 2009." The Incident Information System: Current Incidents. InciWeb, 31 Oct. 2009. Web. 27 May 2010. .

"Station Fire News Release Sept. 15, 2009." The Incident Information System: Current Incidents. InciWeb, 15 Sept. 2009. Web. 27 May 2010. .

"Station Fire Update Sept. 27 2009" The Incident Information System: Current Incidents. Inciweb, 27 Sept. 2009. Web. 27 May 2010. .

"Station Fire." The Incident Information System: Current Incidents. InciWeb, 10 Nov. 2009. Web. 27 May 2010. .

http://gis.ats.ucla.edu//Mapshare/Default.cfm

http://seamless.usgs.gov/website/seamless/viewer.htm?startbottom=5.0&starttop=85.0&startleft=-170&startright=-60.0&limitbottom=-85.0&limittop=85.0&limitleft=-179.5&limitright=179.5

Just for fun, here is a cool timelapse video of the fire from far off in LA.

Lab 6

For this project I decided to work with data representing Catalina Island off the coast of Southern California. The island is 22 miles long and eight miles across at its greatest width. The highest point is 2097 feet atop Mt. Oriziba. It is the only Channel Island with a functioning city, called Avalon, which has a population of about 3200 people, and there is a smaller unincorporated village called Two Harbors with about 250 residents. I have visited several of the Channel Islands before, but Catalina has become my favorite only because of Avalon, a very interesting city where most travel by golf cart. I thought it would be interesting to work with the island on this project because I wanted to see how the gradients and 3D would look in this extreme circumstance of having a mountain emerge out of an otherwise completely flat ocean surface. I had some trouble with the 3D projections, I could not find a color gradient that accentuated the island best. If I were to do this project again I would choose a different color gradient to better express ridge lines and such, but I am otherwise satisfied with my final outcome.



North American Data of 1983

West: -118.684722

East: -118.225000

North: 33.517222

South: 33.275833

Lab 5

Understanding map projections is important when you are trying to find locations or measure or plan any sort of route or path. Every map made requires some sort of distortion of Earth's true dimensions, and so knowing which map (and form of distortion) to choose is vital in order to be as accurate and precise as possible. When navigating, for instance, conformal maps are most helpful as they preserve shapes at local levels and line angles throughout. When comparing geographic distributions, we would want to use an equal-area map which will allow us to compare area. If we wish to derive distances from a central point, an equidistant map would be our best choice. Note, however, that each of these maps has its downfalls and none is well fit for anothers primary use.

Conformal maps preserve local shapes while allowing for a relatively straightforward navigation. Meridians and parallels intersect at right angles, which provides us the ability to align our compasses with the squared latitude and longitude directions. The Mercator projection is widely used as world map as it represents Earth as a simplified squared North-East-South-West grid. Because of the usefulness of this projection when navigating, most map-based web services (google maps, bing.com, etc) use some form of web-adapted Mercator maps, as shown to the right in figure 1. However, these maps are not useful when comparing area. Because of the way in which the spherical meridians are manipulated to create right angles upon a cylindrical plane, area is greatly exaggerated towards the poles. At a local scale, however, this gradual stretching of area does little to manipulate human-scaled shapes, such as buildings and streets, and so also remain useful for simple, localized referential navigation. Buildings retain their true shapes, as do local regions and other objects on large-scale maps.

Equal-Area preserves an area on a map. In these maps, each quadrilateral formed by two parallels and two meridians on the map represents the same amount of area on the globe. These maps are useful when comparing geographic distributions because area is represented truly. It is important to employ these maps when trying to represent any sort of geographic distributions, whether of land or other phenomena such as resources and population, because otherwise observers might be led to see the world wrongly, a misfortune that may influence their perception of the world for years after. For example, when we compare our Mercator maps to our equal-area projections, we can clearly see the flaws in the conformal maps. The difference in size between Greenland and Africa is represented truly in our equal area maps, yet in the Mercator maps Greenland is projected and appears to challenge Africa in size. In reality, Greenland is small, closer to the size of Mexico than Africa.

Equidistant maps show you the true distances from the center of the projection. The good thing about this is that if you can find a map centered on your city of choice, distance is preserved going outwards from that point in any direction. That is, any line spanning from the center to point A on the map will cover the same distance on the earth if we were to take that line to connect the center with another point of equal distance away from the center on the map as point A. This is useful when working with specific cities, but proves problematic when trying to measure distances between two points on the map which are not the center. If a map is centered on Atlanta, for instance, and represents the whole of the United States, the measurement of distance from Atlanta to New York would be true to the distance on the globe. However, if we were to measure the distance on the map between New York and Los Angeles we would not attain a true measurement of the distance between these cities on the globe.

Each map projection has its strengths and weaknesses, and we must understand these in order to decide which projection is the best fit for our purposes. Luckily, GIS software allows us to easily switch between different projections using the same true sphere-based data. We can easily switch between a conformal map to an equal area map and so easily observe and measure the differences in representation. While the different projections within each type of projection still puzzle me, the understanding I am gaining by working with ArcMap is invaluable as far as better utilizing my data through different maps. Each projection is a different tool in our hands to better represent what we are trying to show with our data.

Lab 4

In this project, I took my first major steps into the world of ArcGIS software. Working through the tutorial I was able to experiment with different basic tools within the software and learn how to label maps and objects on them, size different representations accurately relative to each other, and better conceive how different spatial data represent real space and intertwine to create a comprehensive representation of an area. Since I was able to go through the different layers of the map I could learn about different related factors individually and so better come to conclusions about the relationships between these areas (i.e. the schools relating to the tract zones relating to the noise contour). In learning how to place new roadways I was able to dip into the manipulation of existing map data, which was interesting because the program makes it so simple. I was also able to consider how the simplicity of using and manipulating the maps representing the imported spatial data might hinder the potential for true accuracy.

This GIS technology is, I feel, essential to the analysis of space as it exists within modern thought. As space becomes more and more complex, building upon itself to the point where new networks hardly take up any real "space" at all, we increasingly need the capabilities of GIS to help us manage the ever developing and complexifying nature of the spaces in which we live our lives. Thankfully, there is the data out there as people from all disciplines and all walks of life collect increasing amounts of spatial data for both their experiments and to supplement their everyday lives. With GIS, this data is easily manageable and made expressible in many different forms. As we pile streets over sewers, neighborhoods over streets, and both landline and wireless communication networks over these neighborhoods, the layers off distinct but interrelated data also pile up. Since GIS allows for us to take these layers into account and distribute our data specifically to these, we can visually assess the otherwise abstract notions of spatial zoning, boundaries and overlap. Not only that, but GIS lets us link factors between layers, connecting them so that changes made on one side will no go unnoticed by affected elements within another layer.

As helpful as GIS is, we must work with caution and skill. Working with the pure data is tedious and sketchy. Any mismatch can lead to huge problems because of the way GIS links all the separate data together. When working with my data I had to work around the fact that half of the tutorial's files were safe on my flash drive and the other were on the computer system. Thankfully they worked together, but had I tried to change computers I'm assuming the software would not have been able to make a whole out of the little data I had transferred. When working with the layering order I quickly caught that my 'arteries' layer had slipped beneath an opaque parcels layer. Had I not caught that (very obvious) flaw, my map would have been basically useless unless we were only interested in regions and not pathways. Editing data and working with automatic calculations (i.e. creating population density gradients and new roadways) is not only relatively complicated relative to manipulating images on the screen, and creates risk of mistyped functions and false coordinates. Basically, while the software is solid, it can become awful prone to human error when handled incompetently or incautiously.

In conclusion, I had a lot of fun with this project. While I know I need to practice with ArcMap much more to start getting the ropes down, I feel like I learned a lot about a piece of software I had never used previously. It was encouraging to learn that the software we will be using in the class seems very user friendly and straight-forward, and I am excited to learn more of the capabilities within it. I am worried about handling what I assume will be an increasing amount of more diverse and complex data, but know from this project that as long as I keep it organized and in one spot the software will prove to be an essential tool throughout the rest of my time studying and working in geographic fields.