FINAL PROJECT

QUIZ #2

1.) 1. China (biggest) 2. India 3. U.S. 4. Indonesia 5. Russia 6. Brazil 7. Pakistan 8. Japan 9. Bangladesh 10. Nigeria

2.) 1. Nigeria (most) 2. Ethiopia 3. Democratic Republic of the Congo (DRC)

3.) South American Countries with lowest pop (ascending order):
Lowest: Grenada (95,608 people)
Second Lowest:St. Vincent and the Grenadines,
Third Lowest:St. Lucia
Fourth Lowest: Barbados
Fifth Lowest: Suriname (428,026 people)


4.) 15

5.) Cities within 500km of the Amu Darya and Syr Darya rivers:
Leninobod
Jalabad
Zareh Sharan
Turgay
Zhezkazgan
Taldykorgan
Kyzylorda
Almaty
Bishkek
Talas
Karakol
Nukus
Shymkent
Dashkhovuz
Urgench
Naryn
Tashkent
Namangan
Andizhan
Osh
Gulistan
Fergana
Dzhizak
Navoi
Bukhara
Samarkand
Kashi
Chardzhev
Karshi
Dushanbe
Ashgabat
Kulob
Qurghonteppa
Mary
Termez
Feyzabad
Taloqan
Konduz
Mazar-E Sharif
Sheberghan
Mashhad
Aybak
Baghlan
Meymaneh
Mahmud-E Eraqi
Charikar
Qal eh-ye
Asadabad
Bamian
Mehtar Lam
Kabul
Chaghcharan
Mayda Shahr
Herat
Srinagar
Peshawar
Baraki Barak
Islamabad
Rawalpindi
Gardez
Ghazni
Dzhambul

6.) 516,490,670

7.) Least populous: Vatican City
Most populous: Ethiopia

8.) within 300km of Veszprem, Hungary:
Poland
Czech Republic
Slovakia
Austria
Slovenia
Hungary
Romania
Croatia
Bosnia & Herzegovina
Yugoslavia

9.) Monaco

10.) Niger, Libya, Sudan, Central African Republic, Cameroon, Nigeria

WEEK 9

I preferred to use the Inverse Distance Weighting and Kriging methods in comparing this season’s rainfall to normal precipitation amounts. I decided not to use splining, as that would be better in predicting thermal values, such as temperature, more than kinetic values, such as rainfall. However, I felt kriging to be the most superior to all three, as it not only revealed values throughout Los Angeles County, but was a geostatistical meaurement also including accuracy of the measurements, relating to the "hard science" Theissen method used in hyrdologic science.

After converting the degrees, minutes, and seconds to decimal degrees and determining the difference between season normal and current season precipitations in Los Angeles County, I converted the Excel file to a .dbf, then projected the .dbf as a shapefile onto the polygon of Los Angeles County, both in the North American Datum 1983 Geographic Coordinate System. I then used spatial analyst to create kriging and IDW models of the season to date, normal, and differing precipitation amounts of the three models.

Overall, the season normal was larger than the season to date. This is not a very accurate depiction of data, due to the fact that the 2009-2010 season has not concluded, and this season will most likely have higher rainfall records. This weekend’s storm has already raised season to date amounts higher than that of my models presented, which were created the Thursday before the storm. I feel that ordinary kriging provided the best representation because it is a visual depiction of the hydrologically trusted Theissen method, relating the variance of rain gage amounts to the distance between each gage. In both kriging and IDW, I expanded the amount of points from the default (12) to 20, in order to cover the entire county boundary. Through kriging, it is evident that there are increased precipitation differences from the center moving northeast in Los Angeles County. The kriging model depicts the trends more acutely through the geometric polygons, as opposed to the more vague, circular IDW model with increased precipitation differences from the county center moving southwards. I chose the IDW method, assuming that all of the gages posted would provide rainfall recordings. I felt that the amount of gages provided was dense enough, but that is subjective and could be considered too small a network for accurate depictions to another geographer. However, I feel that overall, the kriging method was most accurate and presented the best spatial depiction of the data.


SOURCES:

Earls, Julie. http://proceedings.esri.com/library/userconf/proc07/papers/papers/pap_1451.pdf

Shi, Yunfei. http://spiedl.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=PSISDG00675300000167531I000001&idtype=cvips&prog=normal

WEEK 7: Fire Hazard Mapping

My spatial analysis of the Station Fire was constructed using a variety of data sources and through following the technique demonstrated in the fire model tutorial. My data was acquired through free data providers, where I was able to access a DEM through the USGS’ Seamless website (http://seamless.usgs.gov/website/seamless/viewe r/htm), a vegetation shapefile through the California Department of Forestry and Fire Protection (http://frap.cdf.ca.gov/data/frapgisdata/download.asp?spatialdist=2&rec=hardwoods), and a fire perimeter polygon through the Rocky Mountain Geographic Science Center (http://rmgsc.cr.usgs.gov/outgoing/GeoMAC/2009_fire_data/California/Station/).

I followed techniques as taught by the fire hazard mapping tutorial, correcting my data to correlate in resolution and in geographic projection. I then created a hillshade from my DEM layer, from which I was able to create a slope layer through the spatial analyst tool, allowing an assessment of fire hazard based on steepness. Next, I converted my vegetation polygon from a vector to raster image, allowing reclassification of data. I also extracted the vegetation type from the vegetation polygon, allowing a classification of land cover. The land cover classification provided a clear image of fire boundaries and fuels in relation to vegetation. Finally, using the raster calculator, I combined the vegetation (fuel) hazard with the steepness of slope fire hazards, providing a robust assessment of fire hazards in the Station Fire.

While building the necessary map layers, problems arose, involving data collection and mismatched data projections. The most frustrating obstacle posed during the spatial analysis was by far the task of collecting data. While the DEM shapefile was found quickly through USGS Seamless.), a concise perimeter and an adequate vegetation layer were found only after tedious and time consuming internet-searching. For the vegetation layer, contrasting projections, or incorrect coordinate boundaries were common obstacles, as well as the fundamental problem of incomplete data. For example, some layers only incorporated hardwoods, were obsolete, or were poorly classified. Another problem pertained to the Station Fire perimeter, involving contrasting coordinate boundaries, mismatched projections, or a polygon of only one stage of the fire (opposed to a summary of the fire’s progression).

WEEK 7

Suitability analysis provides techniques relevant to the debate over landfill expansion in California. Such techniques can provide analysis of one single limiting factor, or incorporate a variety of factors involved into one layer. This technique can provide the physical limitations to expanding a landfill, potential human hazards that could ensue after expansion, and exposes limitations involved in spatial suitability anlaysis.

Suitability analysis through GIS can provide useful spatial interpolation of a potential landfill expansion in the Central Valley. Soil drainage, land cover, nearby streams, elevation, and distance to landfill can all be compared and contrasted against each other in deciding where to expand the landfill. In addition, these factors can be combined into one data frame, allowing decision makers to evaluate all factors over the same region. Other important factors, such as particulate and smog emissions, chemical leaching, and toxic runoff can be presented by using relevant buffer regions.

The health hazards can be analyzed with the use of buffering systems. Contaminant leaching of mercury, toxic waste, arsenic, and other harmful chemicals can be presented with the use of GIS. Furthermore, different buffers for specific chemicals over different time periods can be created, illustrating persons at risk based on the duration of exposure and chemical load. These buffers can provide minimum risk assessments due to the

Unfortunately, there are several problems that hinder spatial suitability analysis in a landfill expansion project. There is a subjective nature of factors, causing heated debate between economic, health, environmental, and aesthetic factors. The importance of one factor over another is subjective to each person’s perspective, and this also gives rise to questioning who makes the choices, and gives a party the power to expand a landfill to one person’s preferences over another’s.

Although multiple factors can be presented to aid in a solid, thorough analysis of the best-fit location of landfill expansion sites, some factors cannot be translated into spatial data. For example, a large majority of reformers will push for higher standards and improved quality of life with transparent and true data, but do not want the site anywhere near areas involved in these reformers’ daily lives (Not in My Backyard -NIMBY). Factors such as these cannot be objectively incorporated into GIS suitability analysis due to the arbitrary nature of this factor. Another factor that poses a problem are areas that are frequently visited. Although one's home may not be in a high-risk area, a home of a friend, family-member, or place of work may, making one highly vulnerable to a hazard regardless of home location.

GEOG 168: QUIZ

I am against the decision to prohibit marijuana dispensaries within 1,000 feet of schools, libraries, and parks due to the subjective nature of the order, as well as the repercussion of concentrated marijuana districts.

Firstly, the rules to this order are highly debatable. Although schools, parks, and libraries are areas of high concentration, there are other areas of high “child concentrations.” Such locales include museums, daycare facilities, and orphanages. Furthermore, there are also institutions for “part-time” child congregations, such as churches where youth groups meet, boys and girls clubs, and after-school programs. If all such institutions were included, there would be a very minimal selection of potential marijuana dispensary locations. Furthermore, all such locations not included that do have child-related activities would be served an injustice at being placed in a marijuana dispensary zone.

In addition to the ruling’s vulnerability to debate, the repercussions of the marijuana dispensaries’ relocation would produce marijuana districts, as illustrated in the map above. This is highly insensitive to any homes in these districts, where children and families live. Furthermore, businesses targeted toward children and families could suffer if falling in a family-unfriendly marijuana zone.

For these reasons, I feel that this is a poorly-planned policy.

Week 5: Digitizing