Lab 8 - Census 2000

Other races

 Most of the people who listed themselves as “Some Other Race” in the 2000 census were Hispanic (ADNET 2000). I have classified the data into seven categories, so that the map can portray the extremes of the concentration of the population of the race and the viewer can understand the overall distribution with as much detail as possible. According to the above map, we can see that the counties in the South and South Western of the continent have the highest percentage of their population with people of “Some Other Race” compared to other areas. Then there is another concentration of high “Some Other Race” population in Florida. When we regard this map at a macro-level, we can see that Mesoamerica and South America are closest to the South and Southwest regions in which these high density of “Some Other Race” groups exists, therefore it makes sense that since the Hispanic population, which has a close proximity to these areas, would prefer to first settle in these regions and then slowly expand inwards to other states/counties. Similarly, Florida and its nearby regions is very close to Puerto Rico, and if the first immigrants settled first in these regions, the later generation would prefer residing in those already established communities of their own race. Nevertheless, this map also has its biases. Viewers have to bear in mind that even though Hispanics were the largest group who reported that they belong to “Some Other Races,” population density of some other races are also included in this map. Moreover, the visual effect of the map might be misleading because even though the spread of the density of the population of “Some Other Race” might be significant, the overall highest ranking population of “Some Other Race” is only 39.1%, which is less than half of the whole population. Therefore, in order to remedy this, I have added to the legend the highest percentage of the overall population of a county. So that viewers would know even though the population might cluster in specific areas, the population of “Some Other Race” itself is not so huge compared to the whole population.

Black population

 Immediately after looking at this map, we have the impression that the black population concentrates in the Southeast of the U.S. Continent. I have classified my data into six different categories since even though the population might concentrate in the SE, it could lead to a lot of biases and making invisible the other areas in which the black population is also concentrated. Therefore I also used two different colors to show that there is also a medium high concentration in the West coast, which might be also significant to some map viewers. The significantly small concentration of Black population (as shown on the map by the range 0-1%) in many other regions might due to certain factors including ethnic issues and group preferences, which might include segregation in the low black population concentration areas and the firm establishment of communities within the high black population concentration areas. On the other hand, the African Continent is much closer to the Southeast area of the U.S. Continent than the Western area, therefore it might make sense for the first immigrants to settle in these areas and then slowly expanding to the nearby counties. Similarly, most of the black immigrants arrived earlier in history were from the Caribbean (Kent 2007). Given the close proximity of the Caribbean and the area on the map which the concentration of Black population is clustered, we can say that this factor, together with the factor regarding the African Continent, contribute to the firmly rooted communities created by the immigrants and therefore the high concentration of Black population in this region of the Southeastern of the U.S. Continent.

Asian Population

 Compared to the Black map and Other Race map, the Asian map seems comparatively more evenly distributed across the U.S. Continent but still has its own concentration in the West coast. Since it was hard to tell the significant clusters when I tried to classify the data into more categories, therefore I have decided to break them into four categories so that the overall pattern can be portrayed and that the areas with high Asian population density can be highlighted. Interestingly, when compared to the other two choropleth maps, the Asian population consists of a high density in Hawaii (highest when compared to the other states). Because of the clusters in the West Coast, the high Asian population density in Hawaii, and also the close proximity of these areas with the Asian Continent, we can infer that the first Asian immigrants settled first in these areas and then slowly expanding their territories with the U.S. Continent. And in fact, since Chinese is one of the largest Asian population in the United States, therefore the huge number of Chinese who rushed into the West Coast in search for Gold (Le 2010) might also account for why this area has such a high cluster of Asian population. Nevertheless, this map also contains certain biases. Asians do not only consist of Chinese, they consist of other larger population as well, including Koreans, Japanese, Indians, Filipinos, etc. That is why the map viewer has to be especially careful when decoding the implications of the map. And this might also contribute to one of the reasons why the Asian population above portrays the population as so evenly spread compared to the other two maps. Since these different ethnic groups might have settled in the U.S. Continent at different periods or may be simply because of difference of culture, therefore they have set up their own unique communities or “turfs” that their own respecitve group might be more attached to. Lastly, similar to one of the drawbacks of the first map (“Some Other Race”), although this Asian Map shows how some of the regions might be containing a high concentration of Asian population, the highest percent of population density is only 46%, which is still less than half of the whole population of an area.

Concluding discussion about the census maps

Creating the census maps was not difficult except for the part in which we had to classify the data into different categories. In all of the three maps, I used “manual” or “optimal” classification scheme by actually first using “natural break” and then further breaking the data into smaller categories so I could see the minor changes which might be useful when we want to analysis with more details. It would be easy for the map maker to make more sense out of the maps because of the combination of the excel files/metadata of the map. But the viewer of the map might not have access to these extra sources, therefore, as the map maker, I tried to create the overall pattern as clear as possible and as much detail as possible, but at the same time making the classification more systematic without complicated decimals.

When we look at the three maps, we can see that they all have very distinctive patterns but they all have a thing in common, that is, these populations tend to cluster in certain areas rather than evenly distributed across the continent. Even when the viewer does not know the history of the United States, they might be able to infer the reasons for concentration of the population of certain races when looking at the choropleth maps. Since human beings do not randomly select their place of habitation, therefore there must be some factors that pull them into residing in those areas together. For example, the reason why these races tend to concentrate in one area might suggest that their population has first set up in that area and therefore the later generation of the population might be able to fuse into those communities more easily. Together with my analyses, the map shows that the geography at a macro scale and also multiple human factors contribute to the preference for the clustering of populations of different races. And by making use of these maps, viewers can not only able to understand the concentration of different races in different counties, but also predict how these population spread across space and time.

Overall impression of GIS:

 Even though this class is just an introductory course to GIS, I felt like I have slowly progressed from an amateur cartographer to a more professional one. From the beginning of using googlemaps to create thematic maps simply by plotting points and drawing lines using an existing map to downloading our own maps and editing them to form Digital Elevation Models and choropleth maps, I slowly started to manipulate the basics of a more professional cartographic tool – ArcGIS. And most of the times we were left to explore the program ourselves, and this had led me to better manipulate the program. “Practice makes perfect,” when I repeatedly used the program, I started to remember which buttons to click to do certain actions including adding an insert map and also selecting attributes using buffers. In fact, I find myself addicted to the use of ArcGIS because once I started doing my project, I did not want to stop using the program because of the multiple options that I could employ to make my maps unique and informative, including the color, the north arrows, sizes, etc. Nevertheless, my addiction led to some negative effects. For example the physical damages that resulted from sitting in front of a computer for hours.

Despite the positive side of ArcGIS, I sometimes found the program confusing because of its rigidity, including how the undoing process could not allow me to undo minor changes, how when I forgot to turn on the “3D Analyst” and “Spatial Analyst” tools I could not make any changes to my Digital Elevation model, and how when I did not save certain files to another location, I could not export or save my map to another location. In addition, the random dysfunctional cases including the sudden termination of and the slow response of the program have led to my frustration.

Overall, I found ArcGIS and the class beneficial because, as an archaeologist, the use of maps is very crucial to the understanding of archaeological findings and its environment, both as a map maker and a make viewer. I really hope to continue expanding my ArcGIS knowledge such as collecting my own data and also making more complex maps so that I could be able to master ArcGIS to create useful maps for the contribution of Archaeology in the future.

Works Cited 

ADNET. "Some Other Race." Census Bureau Home Page. 2000. Web. 02 Dec. 2010. <http://www.census.gov/mso/www/rsf/racedata/sld008.htm>. 

Le, C.N. 2010. "The First Asian Americans" Asian-Nation: The Landscape of Asian America. <http://www.asian-nation.org/first.shtml> (December 2, 2010).

United States. Population Reference Bureau. Immigration and America’s Black Population. By Mary M. Kent. Population Bulletin, Dec. 2007. Web. 2 Dec. 2010. <http://www.prb.org/pdf07/62.4immigration.pdf>. 

Lab 7

Figure A

Figure B

The Station fire in L.A. County was considered to be the biggest and most damaging California wildfire in 2009. From Figure A above, we can see that the earliest affected area (the actual fire was reported at 3:30 p.m. of August 26, 2009) shown on the map, August 29, 2009 at 2:48a.m. started in the Angeles National Forest. The fire then spread North according to Figure A, resulting in huge amounts of damages including deaths of two firefighters and more than 2000 buildings and 10,000 residences were affected. From the reference map (Figure A), we can see how the fire expanded its boundaries from August 29 to September 2 of 2009 rapidly within a week. Since the Station Fire was so devastating, therefore I believe it would be beneficial to look at its effects in both micro and macro scales. Figure B above indicates both the populated areas within 2 miles of the fire perimeter, it also shows three different types of vegetation which had a lot to do with the spread of the fire and also its consequences. This thematic map helps to illustrate my points below.

I used a Digital Elevation Model for both of the maps. Since if we simply look at a map without its elevation information, then we would only be able to know that the fire spread in the Northern direction. But with the help of a DEM (Figure A), we can actually see how the fire expanded to the higher elevation areas in the mountains. On the other hand, Figure B illustrates the vegetation features within the perimeter of the fire. Within the wider boundary, it is densely packed with shrubs and there are also a vast area of conifer and hardwood forests. The Station fire did not spread to the urban areas in the South where there is a lower elevation and the vegetation pattern does not extend. The Northward spread of the fire was due to uphill wind and high temperature, but when we put this map into consideration, we can see that an important factor contributing to this spread was the presence of the dense vegetation layer. The burning of vegetation facilitated the rapid spread of the fire. In this figure, populated places within and near the fire perimeter are illustrated as well. Even though the area within the larger fire boundary consisted a lot of forest area and was not in the urban area, there were still people residing in this area, therefore we can see how the fire negatively affected people living in the area where the fire occurred, but at the same time, it also affected others who lived near the perimeter as illustrated in the figure.

On a micro scale, we can look at the local effects of the Station fire in terms of the population. The populated places illustrated in Figure B are defined as “places with clustered or scattered buildings and a permanent human population” by the Cal-Atlas website (http://atlas.ca.gov/). According to the reported statistics, more than 93 buildings were destroyed by the fire. Therefore people within these populated areas were being directly affected, having to relocate themselves due to the destruction of their buildings. On the other hand, I created a two mile buffer around the perimeter to locate the populated places within two miles of the fire. This would allow us to look at the population in close proximity of the affected area. The people in these areas together with those within the perimeter might be affected directly physically (by the fire itself or by smoke and ash) and psychologically. Overall, the Station fire was devastating to people in a local scale. On the other hand, although not being illustrated in this DEM, wildlife populations might also be affected. When we look at the affected area, the vast vegetation and forest areas suggest a suitable habitat for wildlife. After the fire, this area would be not suitable for wildlife to inhabit anymore, and due to the close proximity to the urban areas, they could not move Southward (it would be hard for them to adapt if moved there), thus they could only move to other vegetated area or Northward to even higher elevations.

On a macro scale, the burning of vegetation by the Station fire led to a wide range of consequences. Since the shrubs were so densely packed together, therefore the fire was able to spread rapidly. Hardwood forests were also present in the affected area, and they constitute a “fire-sensitive community” (http://www.forestencyclopedia.net/p/p162) since thicker trees are able to retain more heat, therefore the fire was able to expand quickly and persist. The burning of the area led to the emission of a lot of greenhouse gases including carbon dioxide. As the burnt area was so vast, more than 140686 acres, therefore the amount of “hundreds of years worth of stored carbon dioxide” would definitely be huge and devastating. The emission of aerosol on the other hand created smoke that hindered the rescusing process, but at the same time it could also “influence the formation of clouds and precipitation” (Janha”ll, Andreae, and Poschl 17185). Therefore the burning of dense vegetation could lead to larger climatic consequences.

The two DEMs above provide viewers an overview of how the fire spread and how the different features within and near the boundary of the affected areas could contribute to significant consequences in both micro and macro scales. Despite the simplicity of this map, it is able to provide a clear description of the natural landscape and features of the areas immediately related to area where the Station fire occurred.

Works Cited

Bloomekatz, Ari B. "Station Fire Is Largest in L.A. County's Modern History | L.A. NOW | Los Angeles Times." Los Angeles Times. 2 Sept. 2009. Web. 23 Nov. 2010. <http://latimesblogs.latimes.com/lanow/2009/09/station-fire-is-largest-in-la-county-history.html>.

Deioma, Kayte. "California Burning - Station Fire Looms Over LA." Los Angeles Travel - Guide to Los Angeles Travel. 29 Aug. 2009. Web. 23 Nov. 2010. <http://golosangeles.about.com/b/2009/08/29/station-fire.htm>.

InciWeb. "InciWeb the Incident Information System: Station Fire." InciWeb the Incident Information System: Current Incidents. 11 Oct. 2009. Web. 23 Nov. 2010. <http://www.inciweb.org/incident/1856/>.

Janha ̈ll, S., O. Andreae, and U. Po ̈schl. "Biomass Burning Aerosol Emissions from Vegetation Fires: Particle Number and Mass Emission Factors and Size Distributions." (2009): 17183-7217. 2009. Web. 23 Nov. 2010. <http://www.atmos-chem-phys-discuss.net/9/17183/2009/acpd-9-17183-2009.pdf>.

Konoplk, E. "Fire And Northern Hardwood Forests In The Southern Appalachians — Forest Encyclopedia Network." Encyclopedia Collection — Forest Encyclopedia Network. 14 Nov. 2008. Web. 23 Nov. 2010. <http://www.forestencyclopedia.net/p/p162>.

NASA. "NASA - Biomass Burning Fact Sheet." NASA - Home. Feb. 2001. Web. 23 Nov. 2010. <http://www.nasa.gov/centers/langley/news/factsheets/biomass.html>.

Saugus Union School District. "Station Fire Update for Friday, September 4th." Saugus Union School District. 4 Sept. 2009. Web. 23 Nov. 2010. <http://www.saugus.k12.ca.us/station-fire-update-friday-september-4th>.

Wapedia. "Wiki: 2009 California Wildfires." Wapedia. Sept. 2009. Web. 23 Nov. 2010. <http://wapedia.mobi/en/Station_Fire_(2009)>.

Lab 6

3D Images of the area

 

Area on google map illustrated by the blue pushpin

Extent information:

Top: 20.3255555537 Degrees

Left: -102.469444443 Degrees

Right: -100.839444443 Degrees

Bottom:  19.3866666648 Degrees

Information about GCS:

GCS_North_American_1983

This Digital Elevation Models above illustrate an area in the city of La Piedad de Cavadas, Mexico, which is located East of Laguna de Chapala. When comparing the DEM with the google map shown above, we can understand much more about the relief of the area. From the DEM images, we can infer that the area contains quite an amount of flat areas and even though there are also mountainous areas, they are not particularly high in elevation and only a few areas of slopes are steep. This might actually contribute to one of the advantages to the raising of livestock in La Piedad de Cavads, which is prominent in the area. The 3D images above can further enhance the topography of the area and allow us to see how the some areas are not only flat, but are indented. When combing all the images, we can conclude that the area is not highly dominated by mountain ranges nor flat land, rather, the flat land to protruded areas ratio is quite proportionate and it is also proportionate in terms of where they are located, and that the slopes are mostly gentle.

Lab 5 - Projections in ArcGIS

The process of map projection transforms the 3D world into a 2D model. It allows people to analyze the details on a 2D surface rather than a 3D globe because it can allow easier measurements. There are countless number of projections because they are all based on mathematics and coordinate systems, and these projections are then categorized into different groups including Conformal, Equidistant, and Equal Area. In fact, for every map projection, there will definitely be some sorts of distortions in terms of area, shape, distance, scale and direction, etc. Nevertheless, in different projections, some of the above properties are preserved and create benefits for different functions of the projections.

The Conformal projections as shown in the figures above, Stereographic and Mercator, show that this category preserves local shapes and angles. As we can see, the parallels and meridians intersect at 90 degrees angles. Therefore this type of projection is good for navigation including sailing. Since the angles do not shift while taking a bearing in certain directions. Nevertheless, Conformal projections are distorted in other ways to make it disadvantaged in certain areas. For example, shapes are not preserved within larger regions and that areas are not proportionate to the areas on Earth. When we refer to the Conformal projections figure above, we can see that Greenland in both conformal projections are elongated vertically and horizontally, for Mercator and Stereographic respectively. On the other hand, Antarctica in the Mercator projection appears to be much larger than the Antarctica in the Stereographic projection.

On the other hand, Equal Area projections preserve the area because the areas maintain proportional relationship with the Earth's. We can see from the two Equal Area projections above (Bonne and Mollweide) that the parallels and meridians are not like those in the Conformal projections, instead, they do not meet at 90 degree angles. Although the area is preserved, angles, shapes and scale are distorted. And we can see from both of the Equal Area projections above that the shapes of the regions along the margins are distorted the most, rather, the areas in the middle seem less distorted. This kind of projection is good for analyses interested in calculating areas where shape is not important.

Lastly, Equidistant projections preserve distances from the center point of the map outward. Since it is impossible for any projections to preserve all the distances, therefore distances are actually maintained at a few points only. Moreover, the scale at different areas of the projection would be different. The scale of other points would be sacrificed in order to preserve the distance between points relevant for the projection. Apart from these limitations, this kind of projection also distorts the area and shape. As we can see from the two Equidistant projection maps above (Plate Carree and Equidistant Conic), that Plate Carree projection stretches the shapes of the regions sideways and the shape of Antarctica in the Equidistant Conic projection is heavily distorted.

Lab 4 - ArcGIS

Using ArcGIS to create a map is far more complicated than using Googlemap or other tools for neogeographers. Instead of having a pre-existing map for us to plot the information that we have for each place, ArcGIS requires the users to import their own desired layers and compile them into a map with our previously recorded information that we are interested in. Although it seems more complicated than Googlemaps when so many different layers overlay each other, it is actually a benefit for users. Users thus have the ability to control each and every layer at a time and combine them afterwards to see the effect, moreover, the layers are shown very systematically on the left hand column and the users can turn them on and off at ease when working on each layer.

In the previous exercise in which we had to work on googlemaps, I did not encounter any problem exploring how to use the system myself. Googlemap is very user-friendly and very self explanatory. On the other hand, for this lab, if I did not have the 50-page guideline, I am sure it would be quite difficult for me to explore without guidance. Even though it might be easy to add data/layers and to add legends and titles, these are only basic steps to create a very basic map. In order to manipulate at a deeper level for example joining the different graphs and also drawing streets and editing information, I am sure it would not be easy for an amateur to figure it out. That is why ArcGIS is for professionals and googlemaps are for amateur cartographers. Although ArcGIS is very systematic, after repeating the steps on the guideline, I am now able to manage the basic use of it without constantly referring back to the steps. For example, adding frames, resizing frames, and shifting from frames to frames are relatively easy. I am sure after a few more uses, amateur users will be able to manipulate the system quite easily.

ArcGIS also has another benefit for creation of professional maps. It allows users to easily convert qualitative data as shown on the map into quantitative data on graphs and vice versa. Users can therefore make use of the numbers to create graphs and make relevant calculations to understand the relationship of a place and its attributes. For example, in this exercise, we can understand the relationship of the population density and a particular place with or within the noise contour area. Users can thus understand the situations and connections between these data and make possible changes or proposals for changes to improve the lives of the people. On the other hand, ArcGIS can also combine qualitative and quantitative data. For example the drawing of a street. The system allows us to plot the starting point on the map and then we get to enter the length of the street. This allows the accuracy of the location and length of the street.

Apart from these benefit, it has its other pitfalls too. When I used this program, I realized the undoing process is very troublesome. ArcGIS can only allow us to undo or redo the macro details like the movements of the frames and adding layers but not the micro details like the change of color and the inserting of words, for these changes, we have to reset them manually. Moreover, as I have mentioned in the first paragraph that we have to import the data from other folders (including ArcCatalog), I realized that ArcGIS does not allow us to search for a place by its address and area code. And it is different from googlemaps in that it does not allow us to have a street view of the places therefore ArcGIS is very technological and computerized.

Overall, there are aspects that I appreciate but there are also some that I dislike about ArcGIS. Even though it is very hard to manipulate at first, it is relatively easy to handle after a few tries by following the guidelines. And it allow us to do more than we can on neogeographic tools, for example we can be able to import different data we are interested in and allow the system to correlate them for us. Nevertheless, the using of the system is still very complicated because it includes a lot more than just the basic adding of streets and points at a superficial level. Rather, it includes a lot of quantitative and qualitative data which, although allow us to understand the attributes of a location, need a lot of interpretation and analyses.

Lab 3 Neogeography

View Along the Yangzi River in a larger map

According to archaeological accounts, the Yangtze River/Changjiang already existed when China started its civilization more than 5000 years ago. Despite its importance, a lot of people tended to focus on looking at the Yellow River, which is North of the Yangtze, because most of the really important cultural artifacts and archaeological sites which suggest the power of China were found along it. However, nowadays, the focus is more and more given to the Yangtze river because of the increasing amount of development along it and people's realization of its importance to the Chinese civilization. As a Chinese archaeologist, I believe it is important for me to increase people's attention to the river because of the increasing finds and developments along it and the huge amount of resources it provides to the livelihood of the Chinese. Therefore I have created this map "Along the Yangtze River" linking the four rivers (Tuotuo River, Tongtian River, Jinsha River and Changjiang) which combined into the Yangtze River, as indicated by the blue line. On the other hand, the red line shown on the map depicts a route linking important archaeological sites and modern developments in the major cities (Shanghai, Jiangsu, Anhui, Jiangxi, Hunan, Hubei, SiChuan, Yunan, and Qinghai) that the Yangtze River flows through. By plotting these points on the map, we can see that the Yangtze River is important in both past and present, and since not a lot of people know the actual flow of the Yangtze, therefore this map allows viewers to visualize how the river flows from West to East.

Neogeography allows both amateurs and experts to easily create a map that they desire without the need to handle complicated technological resources. For example, my map was easy to create simply by searching for the specific places that I typed in on googlemaps and also by tracing the different rivers and connect them with a line because of the pre-existing information provided. On the other hand, it allows viewers to easily visualize and understand what I tended to present, that is, the river itself and also the locations of the archaeological sites and the modern developments. And it does not require additional specialized knowledge about geography or cartography to understand what I presented. Moreover, users can be able to post pictures or videos thus merging both cartographical and real world representations so to create an easy-to-read and resourceful map to convey their main purpose and increase the significance of the issue portrayed.

Nevertheless, after plotting this map, I realize that neogeography has its own pitfalls too. First of all, the data being added and stored cannot be too large. For example, when I tried to plot blue line from Shanghai to Qinghai precisely by zooming in and plotting in the actual curve of the meander, it was not possible for googlemaps to save and therefore I have to plot it with separate straight lines. Second, the resolution of the locations when zoomed in might be low and therefore could not allow us to precisely look at the actual features on the ground. Third, the points that users intend to plot might sometimes be inaccurate because the maps might not contain all of the geographic information either due to the fact that it is not updated or that the place was too insignificant to be recorded in the first place. And the information that the neogeographer provides might be too subjective and might therefore mislead the viewer.

Sources:

Yangtze River source: http://img8.itiexue.net/1092/10926752.jpg

Qingzang Railway:http://www.youtube.com/watch?v=ZrXguB8o8bk

Lijiang Ancient City:http://www.youtube.com/watch?v=oiK0rw-E0eE&feature=related

San Xing Dui Archaeological remains:http://lh5.ggpht.com/_N6v9j1PlOvc/SchOz3bhQeI/AAAAAAAABJQ/db-o-PwiFFA/s800/20090306-15天津-三星堆-成都.jpg

Three Gorges Dam, Yichang, China:https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9CJQLyufJ6LzlGvWs_jW_mKPROxJO2_9DRQX60GegKSXo4jn1BRVYEaHVwvP32Q3cuQSoEeXGHUVm3lqAuDB4EHKtzETQFKAnuE48-w3Se0iWtjMTMR0D7PAmH4asqck4LWCgHrpsBLE/s320/三峽大壩1.jpg

Changsha Hunan Museum:http://www.youtube.com/watch?v=PcYthkmUYCc

Eastern Zhou Dynasty tomb:http://culture.people.com.cn/GB/22219/6779297.html

Xiangshujian Pumping Water and Storing energy power station:http://ido.thethirdmedia.com/article/frame.aspx?turl=http%3a//ido.3mt.com.cn/article/200803/show928543c33p1.ibod&rurl=&title=%u5B89%u5FBD%u8003%u53E4%u53D1%u6398%u51FA%u516D%u5343%u5C81%u9057%u5740_%u5B89%u5FBD%u5C06%u5DE2%u6E56%u6253%u9020%u6210%u4E3A%u5168%u56FD%u8457%u540D%u7684%u65C5%u6E38%u5EA6%u5047%u80DC%u5730%20---%20ido.3mt.com.cn

Words more than a thousand years older than Oracle Bone inscriptions:http://news.sina.com.cn/c/2003-10-24/1719985504s.shtml

Gyangfulin Culture Archaeological site:http://news.163.com/08/0626/21/4FD42DA9000120GU.html

Yangtze River Mouth:http://news.xinhuanet.com/photo/2006-09/28/xinsrc_13209032822070151670374.jpg

Lab 2: USGS Topographic maps

1. What is the name of the quadrangle?

Beverly Hills

2. What are the names of the adjacent quadrangles?

Conoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, Inglewood, Pacific Ocean

3. When was the quadrangle first created?

1966

4. What datum was used to create your map?

North American Datum of 1927, North American Datum of 1983

5. What is the scale of the map?

1:24,000

6a. 5 centimeers on the map is equivalent to how many meters on the ground?

Based on the scale of the map, 1:24,000
1cm (on map) = 24,000cm (on ground)
5cm (on map) = 24,000X5 = 120000cm (on ground)
As 1m = 100 cm
Therefore 5 cm on the ground is equivalent to 1,200m on the ground.

6b. 5 inches on the map is equivalent to how many miles on the ground?

Based on the scale of the map, 1:24,000
1 inch (on map) = 24,000 inches (on ground)
5 inches (on map) = 120000inches (on ground)
As 1 mile = 63360 inches
Therefore 5 inches on the map is equivalent to 120000/63360= 1.894 miles on the ground (rounded off to the nearest thousandths).

6c. One mile on the ground is equivalent to how many inches on the map?

Based on the scale of the map, 1:24,000
1 mile (on map) = 24,000 miles (on ground)
1/24,000 mile (on map) = 1 mile (on ground)
As 1 mile = 63360 inches
Therefore 1 mile on ground is equivalent to 63360/24000 = 2.64 inches on map.

6d. Three kilometers on the ground is equivalent to how many centimeters on the map?

Based on the scale of the map, 1:24,000
1 km (on map) = 24,000 km (on ground)
1/24,000 km (on map) = 1 km (on ground)
3 km (on ground) = 3 X 1/24,000 = 1/8000 km (on map)
As 1km=100000cm
Therefore 3 km on ground is equivalent to 100000/8000=12.5 cm on map.

7. What is the contour interval on your map?

20 Feet

8. What are the approximate geographic coordinates in both degrees/minutes/seconds and decimal degrees of:
a) the Public Affairs Building

34°4'30"N, 118°26'00"W
34.075°N, 118.43°W

b) the tip of Santa Monica Pier

34°0'27"N, 118°30'00"W
34.0075°N, 118.5°W

c) the Upper Franklin Canyon Reservoir

34°7'12"N, 118°24'24"W
34.12°N, 118.41°W

9. What is the approximate elevation in both feet and meters of:
a) Greystone Mansion (in Greystone Park)

580 feet
176.784 meters

b) Woodlawn Cemetery

140 feet
42.672 meters

c) Crestwood Hills Park

700 feet
213.36 meters

10. What is the UTM zone of the map?

Zone 11

11. What are the UTM coordinates for the lower left corner of your map?

(3763 N)or 3763 X 1000 meters = 3,763,000N and (362 E) or 362 X 10,000 meters = 362,000E

12. How many square meters are contained within each cell (square) of the UTM gridlines?

(363,000 - 362,000) X (3,764,000 - 3,763,000) = 1,000,000 meters square

13. Obtain elevation measurements, from west to east along the UTM northing 3771000, where the eastings of the UTM grid intersect the northing. Create an elevation profile using these measurements in Excel (hint: create a line chart). Figure out how to lavel the elevation values to the two measurements on campus. Insert your elevation profile as a graphic in your blog.

14. What is the magnetic declination of the map?

14°

15. In which direction does water flow in the intermittent stream between the 405 freeway and Stone Canyon Reservoir?

From North to South

16. Crop out UCLA from the map and include it as a graphic on your blog.