Final Project - 30/10 Plan

Quiz 2 - Reviewing ArcGIS

Note: I got nitpicky and changed my color scheme to differentiate things better.


Part I [62 points]

1. Rank the order the ten most populous countries of the world. [6 points]

Go to ‘Select by Attributes’ > Layer: 'cntry02' > Choose 'POP_CNTRY' > Create expression “POP_CNTRY” >= 97228750 (because this is the 10th highest number) > Open Attribute Table for ‘cntry02’ > Show selected >Then ‘Sort Descending’ for 'POP_CNTRY'

ANSWER:

1)      China

2)      India

3)      United States

4)      Indonesia

5)      Russia

6)      Brazil

7)      Pakistan

8)      Japan

9)      Bangladesh

10)   Nigeria

2. How many rivers does the Amazon River System consist of? [6 points]

Go to ‘Select by Attributes’ > Layer: ‘rivers’ > Choose ‘SYSTEM’ > Create expression "SYSTEM" = 'Amazon' > Open Attribute Table for ‘rivers’ > Show Selected > Count

ANSWER: 15 Rivers

3. How many cities are within 500km of the Amu Darya and Syr Darya rivers? Attach a screen shot of a table for these cities. [8 points]

Go to 'Select by Attributes' for 'Amu Darya' > ‘Select by Location’ > Select features from: 'cities' > Source layer: 'rivers' > Check ‘Use selected features’ > ‘Target layer(s) have their centroid in the source layer feature’ > Check ‘Apply search distance of’ > Put ‘500’ and scroll down to ‘km’ > Open Attribute Table for ‘rivers’ > Choose selected > Do the same for 'Syr Darya'

ANSWER for Amu Darya: 52 (can see 52 out of 2533 selected on the attribute table)

ANSWER for Syr Darya: 37 (can see 37 out of 2533 selected on the attribute table)

4. To the nearest 100,000 what is the total population of countries within 300 kilometers of Iran (not including Iran)? [8 points]

Go to ‘Select by Attribute’ to find 'Iran' > ‘Select by Location’ to have countries that are ‘within the distance of’ 300 km from Iran > Open Attribute Table for 'cntry02' > Show Selected > Unselect 'Iran' > Use Statistics to see 'Sum'

I got 2 different answers for this because the ‘Select by Location’ tool would sometimes change the selected areas depending on whether “Ok” or “Apply” were clicked first. The first time I did it, the selection included a lot of countries in Europe and Africa. The second time I went through the procedure, the selection excluded many of these countries.

First ANSWER: exact number is 3,356,737,001 and rounded is 3,356,700,000

Second ANSWER: exact number is 452,297,220 and rounded is 452,300,000

5. Identify the most and least populous countries of the landlocked countries of the world. [6 points]

Go to 'Select by Attributess > Create expression "LANDLOCKED" = 'Y' > For Most: Sort Descending, For Least: Sort Ascending

ANSWER for MOST populous landlocked country: Ethiopia

 

ANSWER for LEAST populous landlocked country: Vatican City

6. Identify all countries within 300 kilometers of Veszprem, Hungary (not including Hungary). [10 points]

Go to ‘Select by Attributes’ to find 'Veszprem' > ‘Select by Location’ to find all countries within the distance of 300 km > Unselect Hungary from the list

ANSWER:

1)      Austria

2)      Bosnia & Herzegovina

3)      Croatia

4)      Czech Republic

5)      Poland

6)      Romania

7)      Slovakia

8)      Slovenia

9)      Yugoslavia

7. What countries border Chad? [8 points]

Go to ‘Select by Attributes’ to find 'Chad' > ‘Select by Location’ to find all countries 'touching the boundary' of 'Chad' > Open Attribute Table for 'cntry02'

ANSWER:

1)      Cameroon

2)      Central African Republic

3)      Libya

4)      Niger

5)      Nigeria

6)      Sudan

8. Rank order of the five countries that have the most cities based upon the data. And what is the city number for each? [10 points]

Using ArcToolbox to go ‘Analyst Tools’ > ‘Statistics’ > ‘Frequency’ > Input: ‘cities’ > Open Attribute Table

ANSWER:

1)      Russia (97)

2)      United States (93)

3)      Thailand (72)

4)      Turkey (67)

5)      Cote d'Ivory and Poland (tied at 50)

Part II [38 points + 5 bonus points]

9. Approximate the total length (km) of all river portions /segments flowing in the country of Sudan? [8 points]

Using ‘Draw’ tool > Trace the rivers within Sudan > View ‘Properties’ > View ‘Length’ > Add the total of the km together > 2023.5 + 1621 + 148 = 3792.5

ANSWER: 3792.5 km

OR Using ‘Measure’ tool > Trace the rivers within Sudan > Add the total of the km together > 2837 + 321.6 + 613.8 = 3772.4

ANSWER: 3772.4 km

10. Rank order of the five countries that have the most lakes in terms of number. And what is the lake number for each of the five countries? [10 points]

Using ArcToolbox to go ‘Analyst Tools’ > ‘Statistics’ > ‘Frequency’ > Input: ‘lakes’ > Open Attribute Table

ANSWER:

1)      Russia (1,516)

2)      Canada (1,340)

3)      United States (743)

4)      China (219)

5)      Sweden (168)

11. Rank order of the five countries that have the most lakes in terms of area. And what is the total lake area (square km) for each of the five countries? [10 points]

Open the Attribute Table for ‘lakes’ > Add Field: ‘AREA’ > ‘Calculate Geometry’ > Go to ‘Geoprocessing’ > ‘Dissolve’ > Input: ‘lakes’ > Output:’ dissolve_lakes’ > Dissolve_Field(s): ‘CNTRY_NAME’ > Open the Attribute Table for ‘dissolve_lakes’ > Add Field: ‘AREA’ > ‘Calculate Geometry’ > Under ‘AREA’ choose ‘Sort Descending’

ANSWER:

1)      Canada (443,517)

2)      United States (196,849)

3)      Russia (138,251)

4)      Kazakhstan (70,900)

5)      United Republic of Tanzania (53,530)

12. Produce the following map: a world country map of lake area per capita (area of lake surface per person). [15 points]

Open the Attribute Table for ‘dissolve_lakes’ > ‘Join and Relates’ > ‘Join..’ > Join ‘cntry02’ by ‘CNTRY_NAME’ > ‘Add Field’ > Type ‘AREA_PP’ > Choose ‘Float’ > Click on the new column and choose ‘Field Calculator’ > Create the expression [dissolve_lakes.AREA] / [cntry02.POP_CNTRY] > Under ‘AREA_PP’ choose ‘Sort Descending’ > Change the Symbology

Some information during the join did not relate and was presented as 'Null', therefore some countries are not displayed on the map. Here is my process/ANSWER:

Lab 5 - Spatial Interpolation

Post your output to your blog and write a paragraph discussion about recent vs. normal rainfall for LA County, and which interpolation technique you believe is best for the data and why. I chose to use spline and IDW interpolation techniques because they provided smoother maps of the data, and while kriging may provide a better method of modeling it didn’t present a good visual interpretation. IDW seems to provide the best results (for example, spline modeling gives negative rainfall which is clearly impossible), and this makes sense because the amount of rainfall should depend on the rainfall in a neighboring cell.

Lab 4 - Fire Model

This week's lab involved creating surface fire models for La Cañada-Flintridge, CA, where the Station Fire broke out around summer to fall 2009.  My fire hazard model focuses on land cover and slope in relation to fuel hazard. By gathering data digital elevation model (DEM) data, land cover/vegetation data and perimeter data from USGS Seamless Server and the California Department of Forestry and Fire Protection (FRAP), I was able to design a map for slope within the perimeter (bottom right; using the exercise model as a reference for hazard), land cover (bottom left; after re-categorizing hazard based off of dryness criteria), combined hazard of land cover and slope (top left; depicting the fires that have the least to greatest likelihood overall) and Los Angeles County (top right; to show where the Station Fire was relative to the rest of Los Angeles).

In the Wildland/Urban Interface exercise fire model, we worked with raster images for vegetation type, fuel and slope. We used ArcGIS’s spatial technology to create classification systems for forest fuels and topography that gauge fire hazard. Referencing the National Fire Protection Association’s NFPA 1144 (Standard for Protection of Life and Property from Wildfire), we learned the relationship between slope and fuels while using Digital Elevation Models to determine risk. These skills ultimately help with preventing wildfires that may potentially occur around homes and addressing ones that pose the greatest probable risks overall. 

With that said, the exercise greatly helped with creating my own model. I first had to download the DEM file for Los Angeles County. I then converted the projected coordinate system of that file to UTM (NAD 1983 Zone 11N). I then downloaded a land cover file of Los Angeles County from the CA FRAP website. This is where the work became more tedious. The file had over 200 rows of data in its 'Attribute Table'. Using what I learned from the exercise, I had to determine how to reclassify the data according to the vegetation type’s likelihood to be fuel for a fire. This ultimately came down to 6 categories in which I have shown below. The table shows the types of land cover and vegetation that the values under the 'Land Cover' map's legend reflects.

Additionally, I determined the slope of the DEM and land cover files and created a Hillshade in which I used to transparently overlay on top of my slope and land cover maps. I added major roads in my combined map to help visualize the difficulty of accessing these hazardous areas. The steps that I faced the most difficulties in this lab were related to reclassifying and layout. Aside from the time it would take to determine my own legitimate criteria, the Spatial Analyst tools were also always malfunctioning due to: file names that were too long, forgetting to check certain boxes, issues with setting perimeters and reclassifications not appearing properly. Because of my perfectionism, laying out the right color schemes, sizes of the maps and proper legends also consumed a lot of time. Ultimately, the lab helped determine that the location of the Station Fire, a typical mixed shrub and forest environment that was not only dry, but was also at a higher elevation then the rest of the surrounding area, explains the frequent occurrences of fires in this area.

Project Proposal

1. Project topic

2. Progress on data collection

3. Proposed methods

The goal of my final project is to assess the suitability of the proposed projects for the Los Angeles 30/10 Initiative. This initiative allows Metro to build 12 key mass transit projects in 10 years, rather than 30. The plan was approved by Mayor Villaraigosa and has already been taken into effect. The data I would be using would involve: current transit lines, proposed transit lines, population densities, income levels, etc. of Los Angeles County. My proposed method would involve 1) Digitizing the current and proposed transit lines (if existing shapefiles cannot be found), 2) Adding population data to assess areas with the largest concentrations of people, 3) Adding income level distribution data to assess areas with people less likely to use public transportation, 4) Create criteria for suitability, 5) Do a suitability analysis using ArcGIS and 6) Write an executive summary describing feasibility and effectiveness of the proposed plans. I am currently in the process of looking for shapefiles using the Los Angeles County Data Portal, UCLA GIS and LA Metro websites.

Lab 3 - Suitability Analysis with Raster Data

Quiz 1 - Geocoding Marijuana Dispensaries

After looking at 64 medical marijuana dispensaries, I have decided to not be in favor of the Los Angeles City Council’s decision requiring 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. As seen through my Geographic Information Systems findings, this policy seems to be very unrealistic. Although there is a lot of space available outside of the 1,000 feet radii where children congregate, currently as it stands there are far too many overlapping dispensaries with schools. Despite much difficulty finding fully updated data for parks and libraries around the county, as we can see from Beverly Hills, Culver City and much of the city of Los Angeles (displayed on the map on the bottom left), there are already many medical marijuana dispensaries that are within the buffer zones of schools and parks. Although not very many conflict with libraries, schools seem to be the biggest factor in implementing this policy. On average, a small storefront is roughly 1900 sq. ft. store and can run $13 per square foot (at the very least), making it cost approximately $2058 per month (but one can only dream in this current economic situation). With that, this may be very costly to push out and relocate all of these dispensaries. Also, it needs to be taken into consideration that in these dispensaries still act within accordance with the law. Store owners are only allowed to sell medical marijuana given a prescription and users are not allowed to use it in public places. Because it is not socially and economically practical to implement such a policy, with already so many dispensaries within the buffer zones of schools and some parks and libraries, the Los Angeles City Council should rethink their decision.

Below is a list of where I found my information:

LA Weekly (to find addresses of marijuana dispensaries in Los Angeles County)

http://www.laweekly.com/directory/marijuana-dispensaries/

GIS at UCLA's Mapshare Program (to receive shapefiles for LA county and schools within LA county)

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

LA County GIS Data Portal (to receive shapefiles for parks and libraries in LA county)

http://egis3.lacounty.gov/dataportal/