Quiz 2

1. China, India, USA, Indonesia, Russia, Brazil, Pakistan, Japan, Bangladesh, Nigeria. Select by attribute from cntry02 layer, type in query “POP_CNTRY” and get unique value. Get from the highest one and  to the 10^th.
2. 15 rivers in Amazon. Open the attribute table in rivers layer, select by attribute, enter query system="amazon".
3. 61 cities 
4. 516500000
5. The most populous landlocked country is Vatican City and the least populous landlocked country is Ethiopia. Open the attribute table in cntry02 layer, select by attribute, enter query landlocked=’Y’ and then Popcntry<=5000 on least populous and >=5000000 on most populous to see the result.

6.    6.  Poland, Czech Republic,Slovakia,Austria,Slovenia,Hungary,Romania,Croatia,Bosnia & Herzegovina, Yugoslovia

7. Libya, Niger, Sudan, Nigeria, Central African Republic, Cameroon. Selected Attribute using query CNTRY_NAME=’Chad’ found out which country share the common border on the map. Select by location and using the selected features touch the boundary of the source layer to find the answer.
8. Russia, United States, Thailand,  Turkey, Vietnam. 
9. There is a build in function called calculate the geometry in the attribute table. First, select attribute to locate where the country of Sudan is, then select attribute by location to locate the river inside the country of Sudan. I wonder if I can use the calculate the geometry in the attribute table to actually calculate the length of the river.
10. First, I join the table in lakes and country layer. I opened the attribute table, trying to use the build in function of field calculator, but it didn't work for me on several times. I know I need to use the field calculator to choose several of the countries. 
11. Again, I joined the table in lakes and country layer. I opened the attribute table, trying to use the build in function of field calculator and entering the query of equation to calculate the total area of lakes because there is a field with area. I know I need to put an equation in the field calculator to calculate the total area of lakes. 
12. 
    

Lab 4: Station Fire Hazard Analysis

Write-up on process and challenges

First of all, we need to look for data for this lab. I downloaded the digital elevation model from the USGS seamless viewer website. I picked the area where the station fire boundaries and the surrounding areas were and downloaded the DEM. I downloaded the station fire perimeters from the Los Angeles County GIS Data Portal. I was a bit confused at first on what data I should use as they have surface fuel and surface fuel rank. I finally used the surface fuel data from the California Department of Forestry and Fire Protection to finish my map.

The first map I created was the Slope Hazard map. First, I changed the projection of the DEM data to the NAD Projection UTM Zone 11. Then I got an accurate slope map. I followed the tutorial and then reclassify the slope value into NFPA hazard points. This is the first reclassification in this lab. The result image was then overlay with the hill shaded image to get a better visual appearance.

Second, we need to create and reclassify the surface fuel model data. When I opened the surface fuel data, there are about 13 classes of vegetation and they were all assigned with the numbers. I had to go back to the FRAP website to check on the definition of each and add on some descriptions beside those numbers. Then I reclassified the 13 vegetation classes into Non-Fuel, Light, Medium, Heavy and Slash, five different classes. And then I assigned these five classes into the NFPA hazard points as demonstrated in the tutorial.

The last map is the product and purpose of this lab. We need to use the raster calculator to “combine” both reclassified slope and surface fuel data into one map. After having both slope and surface fuel layer activated, we need to enter the formula into the raster calculator. This is the most important part of the lab because it will reclassify the data into a slope/surface fuel map. After summing both classified data, slope/fuel hazard can be assessed. The slope/surface map can be a useful tool in reality such as the safest locations to build residences can be determined, past fire extents can be assessed and fire suppression opportunities can also be explored.

I had a big challenge in doing this lab because I changed all the data projections to NAD 1983 UTM Zone 11. When I reclassified the surface fuel to NFPA hazard points, the image became blurry and could not be used for further investigation or analysis.  I had to re-download the data and do it again. One of the things I learned about in this lab is that I would only change the projection to do the slope map because of the projection would affect the calculations of the slope. The other challenges will be reclassification. Since the reclassification result really base on what I want to reclassify or what I want to group, it’s important to know different types of vegetations and their ability to catch fire. This is so important because the hazard map is completely based on a personal reclassification. Overall, I like using the raster calculator to combine the data and create a new map. This could be really useful to produce a map with two types of related data in GIS. 

Week 3 Lab: Suitability Analysis with Raster Data

Final Project: Introduction

What does it determine in the library location across Los Angeles County and the accessibility of library?

The project will look at several things. First, library locations geo-coding, located all the libraries across Los Angeles county. Second, schools  locations geo-coding, the schools is to determine whether library will be located near schools, whether students in the school can enjoy a walking distance to the library. Third, the median/average of household income across Los Angeles County. This would be an interesting factor that will there be more/less library accessible to rich/poor. Those are the three things that the project will focus on.

Quiz #1

I am against the decision that requires medical marijuana dispensaries in the City of Los Angeles to be at least 1,000 feet from places where children congregate, such as schools, parks and libraries. I support the statement by the given map. The star represents most types of the schools including elementary school, middle school, Junior and Senior High school but excluding colleges and universities. The green polygon represents the park location or area in the city of Los Angeles. The triangle represents the location of the library and the circle represents the location of dispensaries location that is allowed to operate according to the L.A. City Attorney.  Under the Los Angeles medical marijuana ordinance, only those dispensaries that registered with the city by Nov. 13, 2007, will be allowed to operate. I particularly focus on the location of both library and schools because there will be most numbers of children in the two locations. As shown on the map, the schools and libraries have the 1000 feet buffer. Of the 39 dispensaries that are mapped, there are around 12 of the dispensaries are completely located in the 1000 feet buffer.   What does this tell us? That means almost half of the dispensaries are located within 1000 feet buffer. These dispensaries have to find another place for relocation. These may cause a lot of economic problems for the dispensaries because they may have to consider about the rent and relocation fee and it would be unfair for some of the dispensaries.  It is because they are not “purposely” open or locate their dispensaries that are closed to school, libraries or even children. This causes more problems because dispensaries are also important in the community.I am strongly against that because it would be the choice of the children to get into these dispensaries but not because of moving further away to these locations that can fix the problems.

There is another map showing the dispensaries that are within 700 feet buffer of schools and libraries. I think for those super close to the schools and libraries. I consider 700 feet as a super close distance because it would be a normal walking distance for the children in my point of view. Those dispensaries that are within 700 feet should be relocated because children may get to these dispensaries really easily.  It is very important to limit the access of children to dispensaries because they can be really easily to get the drugs and medicines. It is needed to ask those dispensaries which are close to these children "concentrated" places to relocate to some places. The big issue on that will be dispensaries are not only used by children but also adults. It may cause a lot of problems as well especially in the community level because adults have to go further way to look for dispensaries. I have a suggestion that it should limit the distance between each dispensaries so it won't be too many or too concentrated number of stores in one location but then local people are still happy with that because they can still reach the dispensaries.

Week 2 Lab: Geocoding

Geocoding Result: Store Location of In-N-Out

Write-up:

For this week lab, we learned how to use the ArcGIS software to geocode locations on the map with addresses. I worked on the distribution of In-N-Out fast food store locations across California and Los Angeles. In general, In & Out has 204 stores in total across California. In particular, there are around 60 stores located in Los Angeles County, almost 30% of the total number of stores in California. This lab will look at what locations of In-N-Out has chosen and the distribution trend across California and Los Angeles.

On the left side of the map, it is the map of store locations across California. Major streets and freeways are both shown on the map. The green dots represent the In-N-Out store locations. Most of the stores are concentrated in the Northern and Southern California. There are only a few number of stores in the middle part of California. This maybe due to the reason of low population in the rural area of the middle part of California. For the majority of the store locations, they were built along the freeway systems and only few stores that were not built on the freeway systems. For example, the stores in the Fresno country are not close to the I-5 freeway. However, the street layer may give out some clues on why these stores were not built along the freeway. Although they were not built along the freeway, the stores are built in a populated area. It is because there are complex streets and roads structures around the store location. For most of the stores that were built along the freeway or close to the ramp, their target customer would be the freeway users. With that means, the target customer for the stores in Fresno may not be the freeway user.

On the right side of the map, it is the map of store locations across Los Angeles with 2 miles buffer. In Los Angeles county, there are around 60 stores in total and not all of them were built along the freeway systems. One interesting fact is that, stores are spreading all over Los Angeles. The buffer shows the distance between each store locations are very close together. Most of the buffers are overlapped which means you can reach a store within 2 miles. With the heavily populated Los Angeles County, store may not be located close to the freeway system because stores can serve the local community (the same case as Fresno).

Geocoding requires address database so the accuracy of the geocoding results really depends on the database. The database has 0 unmatched results with the geocoding results in the ArcGIS. Although I did not use the address locator in this lab, it would be a good way to use the address locator when there is unmatched result.   

Data: 204 In-N-Out Stores in California (Click to enlarge the data sheet)

Geog 168 Summer Lab 1