Quiz 2

 1) (go to attribute table of cntry02, click pop_cntry, right-click choose sort descending)
China
India
United States
Indonesia
Russia
Brazil
Pakistan
Japan
Bangladesh
Nigeria

2) (go to river's attribute table, group Amazon together, count the number of rivers)
15 rivers

3) (go to selection "attribute table of the river layer, find the 2 rivers, and select them using ctrl and mouse click, then go to selection > select by location, selection method is select features from, target layer is cities, then source layer is rivers, spatial selection method is "features within a distance of source layer" check apply a search distance, 500 km, then ok, then the map would light up in a region, then go to cities, attribute table and print scree)

4) (go to country's attribute table, sort descending, find and select Iran > select by location > select features from > target layer is cntry02 > source layer is cntry02 > spatial selection method is within a distance > apply a distance of 300km > ok > open country;s attribute table, show selected > use statistics on pop_cntry)

 total population of countries: 516490670

5)  (go to country;s attribute table, select the Y of landlocked countries, click show selected records, right click on pop_cntry, click on sort descending, look for the most and least populous countries)

Most populous country: Ethiopia 53142970
Least populous country: Vatican City 860

6) (go to cities' attribute table, select Veszprem, go to select by location, select features from > target layer is cntry02 > source layer is cities > target layer's features are within a distance of the source layer feature > apply a search of 300 km > go to country's attribute table, click selected features)

Poland
Romania
Yugoslavia
Czech Republic
Hungary
Austria
Slovakia
Croatia
Bosnia & Herzegovina
Slovenia

7) (go to attributes table of country > find and select Chad > select by location > "select features from" > target layer is cntry02 > source layer is cntry02 > spatial selection method is "Target layer features touch the boundary of the source layer feature" > OK > open attribute table of country > show selected records > exclude Chad and copy the rest of the countries that border it.

Cameroon
Central African Republic
Libya
Niger
Nigeria
Sudan

8) (ArcToolBox > Analysis Tool > Statistics > Frequency > Input is cities > Frequency fields is CNTRY_NAME > open the newly created frequency table > sort descending on frequency tab > copy the first 5)

97 Russia
93 United States
72 Thailand
67 Turkey
50 Cote D'Ivory and Poland

9) (use measure and approximate the length of the three Niles flowing in Sudan)
2838 + 322 + 614 = 3774 km


10) (open attribute tables of lakes and eyeball to see which country has the most number of lakes > select all of the lakes of one country on attribute table > go to select by location > target layer is lake, source layer is lake > look at how many lakes there are in that country, repeat for top 5 countries)

1516 Russia

1340 Canada

743 USA

219 China

168 Sweden

11)  dissolve lakes layer > input lakes > dissolve: country_name > add field area > calculate geometry in sq km > sort in descending order

443517km2- Canada
196848km2- United States
138250km2- Russia
70900km2- Kazakhstan
53530km2-United Republic of Tanzania

12) Map showing world countries and lake boundaries.

 

Join dissolved lake with country > symbology > value > area > normalization > population

Lab 4 Fire Model

      The combination of the digital elevation model (DEM) and land cover analysis used in this lab has introduced me to a new way of analyzing spatial data. Using the DEM shape file provided in the class website, as well as the GIS techniques that i have learned so far; namely, creating slope and reclassification, i was able to create the slope map which shows how susceptible certain areas of the mountain range is to fire. As you may know, the elevation of slope determines the rate of fire spread, therefore using this method to evaluate the hazard level in mountainous areas would allow better precautions to be run in the most efficient places. On the other hand, to create the Land cover map, reclassification took up a large proportion of its creation. I downloaded the land cover data from the FRAP website, and the data include a wide range of different land types, such as agriculture and urban. The reclassify tool allowed me to rank each land type according to their susceptibility to catching fire. As a result, water was classified as 1 because it is the least likely land type to catch fire, while dry wood or shrubs are given a high number as they are vulnerable to fire. Lastly, the largest map on the top left corner is the result of the slope and land cover map combined. With reclassified data, i was able to show different levels of risks within and out of the fire perimeter.

     When creating these maps, it was inevitable to encounter all sorts of problems as this was my first trial of these ArcGIS functions. Some minor problems include the fact that i have always forgotten to check the spatial analyst box under customize; thus, my tools always failed to work. Also, i had a hard time choosing from the vast variety of color ramps; picking one that suits my personal liking and one that will suffice to convey the purpose of the maps was troublesome. A considerably major challenge i encountered was the part where i had to decide what number to assign to each land type in order to reclassify; i first followed the general rankings of the land types on the tutorial which ranked them as light to heavy hazard, and i assigned my ten land types in numeric order. However, i found out later that these numbers can be overlapped according the my own preference and of course basing on the reasonable assumptions that suggest which land cover types fall into which risk level. This lab was a fun experience in the course of this class.

Lab 3 - Landfill Project

Research Brief of Final Project - "Does Income Influence the Prevalence of Obesity"

1) Project Topic
"Does Income Influence the Prevalence of Obesity in LA?" Since the fast food industry seems to be more prominent in the city of LA, i believe that the industry must have an impact on the population. Further, i realized if a poorer person eats fast food often due to the need to accommodate their hectic schedule and monetary limits with fast dining time/cheap food, wouldn't it lead to obesity? In addition, poorer people eat fast food because they may not be aware of the health consequences they may cause, and that they are cheap; on the other hand, wealthier people may be more cautious in what they eat, and so they wouldn't mind paying more for equally efficient prepared foods but of healthier quality. Thus the topic arose.

2) The Progress on Data Collection

We will use data from "County of LA, Department of Health Services, Public Health" which will show distribution of obesity around LA. Secondly, we would use Google map to locate the 5 major fast food franchises which are McDonalds, KFC, Burger King, Taco Bell and Carl's Junior. Lastly, we would utilize the income distribution information from the USGS website.

3) Propose Methods
There will be 3 maps:
1) map showing distribution of income/household
2) map showing distribution of fast food
3) map showing the prevalence of obesity

These 3 maps would show the relationships between income level and obesity in LA.

Example of an obesity map:

  Source:
County of LA, Department of Health Services, Public Health
http://publichealth.lacounty.gov/ha/reports/habriefs/lahealth073003_obes.pdf

Lab Quiz - Geocoding (Marijuana Dispensaries in LA)

I am against the decision to require marijuana dispensaries in the city of Los Angeles to be at least 1000 feet away from places where children congregate. This is because from the 50 dispensaries I have found using Google map, at least half of them are located within the 1000 feet buffer of schools, parks and libraries. If the policy was passed, it would require the state to utilize a lot of budget to eradicate those dispensaries that are already in the area; given today's economy state-wise, it is not a good idea to do so. Furthermore, even though these dispensaries are within close proximity to where children congregate, but in order for a person to access these dispensaries, they would need to have proof of doctor's prescription and a cannabis card; thus, children wouldn't be able to acquire marijuana as they wish. I believe that if the city council were to pass this new policy, the policy would have to apply to all other addictive products as well, such as cigarettes and alcohol, since these addictive products are even easier to be attained by the general population.

Recent scientific discoveries have found out more positive effects of marijuana, it not only treats multiple diseases, It also elevates people's mood. Hopefully as marijuana's benefits become more well-known by people, it would be acknowledged by the city council.

p.s. handed in on time through email.

Lab 2: Geocoding - Storage Location Reflects Regional Social Status

       As a college student, I realize that there is the tendency of having to move constantly due to the limitation of dormitory vacancies. In order to facilitate moving, many students would resort to renting public storage spaces during the transition period of moving from the dorms to their new apartments. Since I have personally experienced renting storage spaces, I went through the process of researching for cheap and close-by storage places and have become curious about how these storages are distributed around a city as large as Los angeles in relation to the needs of differently social classes in respective city areas. 

Map Showing Average Listing Price for Los Angeles

       Using the address locator in geocoding and a base map which i downloaded from Tiger/Line, i have created a map showing the distribution of storage places in Los Angeles. In addition, according to this map showing the average listing price of homes in Los Angeles found on Trulia.com, which reflects the social class of different regions of the city; there seems to be a strong correlation between storage locations and social class. The red regions seen on the Trulia map represent areas of high value properties, whereas the green regions represent the opposite (low value properties). Referring back to my storage location map, there are less than 10 storages altogether located in the Santa Monica, Belair, and Beverly Hills regions where they fall into the high value property category, while there is a significantly large number of storages in Downtown which falls in the low value property region. This phenomenon is due to the fact that people who live in the area of relatively low value properties have less economic stability which increases the possibility of having to move away and would need storage spaces to aid moving. Also, since lower income housing has limited space for its owner to store their belonging, people would store items at a storage space. Another reason for this phenomenon is that there is a large amount of offices in the business district of downtown, and these offices might require the use of storage space to store away their files and documents. 

      I enjoyed using geocoding in this lab because it allowed me to analyze spatial contents in terms of their  relationship with the area they are in. In other words, it is reminiscent of looking at an area in the bird's eyes view, and being able to oversee different objects while understanding their relationship with each other.

Works Cited

"Los Angeles Home Prices and Heat Map - Trulia.com." Trulia - Real Estate, Homes for Sale, Sold Properties, Apartments for Rent. Web. 14 Aug. 2011. <http://www.trulia.com/home_prices/California/Los_Angeles-heat_map/>.

Lab1, Review of ArcGIS Resource

                                 Downtown Greenvalley Water Mains and Water Use

                                                     Potential Wastewater Plant Sites