Virginia Earthquake Magnitude 5.8 August 23, 2011

Introduction

     Earthquakes happen unexpectedly without warning, sometimes resulting in fatal disasters. Recently, a 9.0 magnitude earthquake hit Japan, causing a tsunami that brought injuries and deaths among thousands. California is awaiting another massive earthquake. Many believe that the time is past due since 1994 when a 6.7 magnitude hit in Northridge. It is essential to prepare with canned foods and plenty of water. Children in schools practice drills to cover their head with one hand and the other holding a leg under the table. The majority of residents in California and most of the West Coast are ready for an earthquake. However, what happens if there is an earthquake rattling the East Coast of the United States? Surprising, a 5.8 magnitude quake shook Virginia on Tuesday, August 23, 2011, at 17:51:04 (UTC). Now, it is important to note that unlike the West Coast, the continental crust at the East Coast is older, colder and denser. Therefore, shaking can transmit about three times the distance. The quake was centered in Louisa County where the North Anna Nuclear Power Station was shut down. Atlanta, Georgia to Illinois to Detroit, Michigan to New York felt it. Even President Obama felt it while playing golf on his vacation in Washington, DC. The East Coast was not ready for an earthquake. Various reactions are predictable: What happened? Is that an earthquake? BOMB! Terrorist Attack! Everyone evacuates the buildings and stand on the streets confused and shocked. Luckily, no severe injuries or deaths were reported. How many people did the Virginia earthquake affect? My goal is to show the population count according to specific distances from the epicenter. It is interesting that this quake was the most powerful earthquake to strike the East Coast in 67 years. This is definitely worth researching. On a side note, top Universities affected by the Virginia earthquake was also recorded just out of curiosity because I assume that if there is another earthquake in California anytime soon, I will most likely be in the UCLA campus.

Method

     In order to accomplish this project, it required many steps. First, I had to obtain data. Data searching was time consuming considering the fact that census tracts 2011 for the U.S. were not available for download. UCLA GIS had census tracts 2008 so I used this one instead since the tract polygons are consistent. The only problem was that this census tracts file was a SDC file. I had to convert the SDC file into a Geodatabase using Feature Class to Geodatabase (multiple) under To Geodatabase in Conversion Tools. The output had to be saved into a blank folder. An error popped up when saving it into a folder with existing files inside it. Converting the SDC file was necessary to allow editing the attribute table. The epicenter was found in USGS and formatted into an Excel sheet that was saved as a 93 version and exported into a single database through ArcCatalog. Then, the x and y coordinates were added as data in ArcGIS.

     After creating multiple buffers of 100 miles apart up to 500 miles from the epicenter, I selected by attributes. It was a repetitive process of selecting by attributes and selecting by location. The buffer layer of the epicenter was the layer in focus when selecting by attribute. Unique values were chosen for “FromBufDist” and “ToBufDist” separated by “AND” and using the “=” sign. For example, FromBufDist=0 AND ToBufDist=100, depending on what buffers to highlight. After the buffers are highlighted, Target Layer for selecting by location was the converted geodatabase file for census tracts. The source layer was the buffer layer of the epicenter and spatial selection method was target layer features are within the source layer feature. When clicking OK, the target layer features are selected on the map layout.

     Finally, we can start editing. I opened the census tracts attribute table and showed selected records. A new field was added and named. Field calculator was used to give an integer for the values selected. The next part is the easy part. Open census tracts properties and choose pop2007 under the symbology tab. Add all values. The Count column includes the total count according to the selected target layer features within the source layer feature but this is not the total population count. The population sum can be found under statistics in the attribute table. The column with the integers assigned can be changed to help show the exact number on the legend. This process was repeated for every 100 mile buffer distance. Colors representing each population within 100 mile buffers were adjusted and darkened to show clarity. A map with no field calculating is also included above just for reference purposes. Here, population size for each county can be generally determined within 100 mile buffer zones.

     The top Universities in the United States was found in U.S. News & World Report. Addresses for these Universities were gathered using Google Maps and individual University sites. The addresses were inputted into an Excel sheet and exported as a dbase through ArcCatalog. An address locator is not possible to make with the states file that was downloaded from UCLA GIS. Therefore, I used the default address locator provide in the Geocoding tool, which proved efficient. The same was done for the North Anna Nuclear Power Station, but in a separate Excel sheet. Since the purpose was to show the Universities affected within the 500 mile buffer, I selected by attributes using FromBufDist as 0 and ToBufDist as 500. Select by location with target layer as the Universities layer that was geocoded and the source layer as the buffer layer of the epicenter. Again, spatial selection method was target layer features are within the source layer feature. In the attribute table of the Universities layer, there is an option to export into a new table. Show selected records and export. Lastly, use the new table to create the report.

Results

     Although the earthquake was centered in Virginia, 100 miles from the epicenter had the least population count affected by the shake with a total count of only 7,942,887 people. The population count for 100 to 200 mile buffers was the second least at 16,300,404 people. The most population count affected by the shake was within 200 to 300 mile buffers. This area included parts of Pennsylvania, New Jersey, Ohio, New York, Kentucky, West Virginia, Virginia, Tennessee, North Carolina, and South Carolina with a total count of 34,504,956 people affected. It was also unique to find that areas within 300 to 400 mile buffers and 400 to 500 mile buffers had almost the same number of people affected by the earthquake. The count for 300 to 400 mile buffers was 27,028,910 people. The count for 400 to 500 mile buffers was 30,905,272 people. The total number of people affected within all buffers equal to 116,682,429 people. The counties in the color white represent the population of the whole United States outside of the buffers. It is important to note that this result only refers to the population affected in the U.S. within 500 miles of the epicenter. The news reports that Canada and as far as Greenwich at the south of London, England felt the quake.

     The earthquake affected more than half of the 100 top Universities from the US News & World Report. A total number of 55 Universities were affected within 500 miles of the epicenter. Also, it is evident that the North Anna Nuclear Power Station was extremely close to the epicenter. The University closest to the epicenter was University of Virginia in Charlottesville. Most of the Universities affected were situated along the East Coast of the US.

Discussion/Conclusion

     This project is purely based on statistics and data. It is not a real and definite presentation of the exact population count affected by the earthquake, but rather a reasonable estimate. The reason I state this is that the vibration of the earthquake predominately shook the East Coast of the United States. Also, there could be a number of people out of state for vacation or not present due to other reasons. Naming this project “A hypothetical approach to how many top Universities and people could have been affected by the Virginia earthquake, given that vibrations spread across evenly in all directions,” would be more accurate. However, the statistics and buffers given in these maps prove how devastating the earthquake could have been if the magnitude was higher. A lot of people could have been injured or dead. Nuclear power plants would be damaged and there would be another mayhem much like Japan's tsunami. The extent of how much of the total population possibly felt the earthquake is shown within the buffers. Obviously, the closest population and Universities from the epicenter felt it. Basically, the whole East Coast of the US had to have felt the quake. The rest is based on personal experiences and comments but I assume that the majority of the population felt it. It is interesting to know how far an earthquake can shake depending on where it is situated. I assume that grocery stores of the East Coast are selling water fast. Suddenly, the East Coast is preparing for what the West Coast has prepared for a long time.

References

Dominion. 2011. “North Anna Nuclear Information Center.” http://www.dom.com/about/stations/nuclear/north-anna/north-anna-nuclear-information-center.jsp

Google Maps. http://maps.google.com/maps?hl=en&tab=wl

Technica, Ars. Wired Science. 2011. “Why the East Coast Earthquake Was Felt So far Away.”

http://www.wired.com/wiredscience/2011/08/virginia-earthquake-distance/

UCLA GIS. http://gis.ats.ucla.edu/

U.S. Geological Survey. “Magnitude 5.8 – VIRGINIA.” http://earthquake.usgs.gov/earthquakes/recenteqsww/Quakes/se082311a.php

US News & World Report. 2011. “National University Rankings.” http://colleges.usnews.rankingsandreviews.com/best-colleges/rankings/national-universities

Spatial Interpolation

 

     The final lab is spatial interpolation. The LA County has hired Intermediate GIS students to conduct spatial interpolation on precipitation. Precipitation data was gathered from the county's Water Resources homepage. Points for both normal and total were formatted on an excel sheet. Evenly distributed points were taken. Then the points were inputted on ArcMap by adding x and y coordinates of calculated degrees. Spatial interpolation allows better predictions of the area around the points. I chose the IDW and Spline methods. I used these methods because I thought it would be easier to compare with each other. In my personal opinion, I thought that the IDW method was better than the Spline method because the interpolation values were more similar. The lower and higher precipitation values are more similarly represented on the map for the IDW method. The Spline method resulted in a negative value for normal precipitation. I believe that the IDW method allows easier comparing between total and normal precipitation. Spline method makes it more difficult to compare because values are more different between normal and total precipitation. I avoided using Kriging because the result seemed too general. It spread and trends were hard to identify. Both maps show similar distribution of rainfall across LA County. It is evident that rainfall is continuously distributed across the county. The maps show that LA County has received most rainfall in the eastern side of the county. Normal precipitation Spline method shows more white (highest precipitation) than does the Total precipitation Spline method. In contrast, the IDW method proves more similarities on the highest precipitation between total and normal values. Both maps have positive values for precipitation. The Spline method shows differences on the upper parts of the county, as well as the eastern side. Due to these differences in values, IDW proves to be better for spatial interpolation.

Quiz 2

1. Rank order the ten most populous countries of the world. [6 points]
1) China
2) India
3) United States 
4) Indonesia
5) Russia
6) Brazil
7) Pakistan
8) Japan
9) Bangladesh
10) Nigeria 
Open attribute table from cntry02 layer and change POP_CNTRY to sort descending. Answers are under sovereign

2)  15 rivers
Open attribute table from rivers layer and change system to sort ascending. Amazon rivers are listed.

 3) Amu Darya: 52, Syr Darya: 37, total cities: 89

Open attribute table from river layer. Select the two cities. Navigate to Selection and Select by Location. Selection method is select features from and target layer is cities. Source layer is rivers and spatial selection method is features within a distance of source layer. Check apply a search distance, 500 km, then OK. Open attribute table from cities and print screen. Paste in paint because directly pasting on blog slows the computer. Save the images as JPEG and insert image here.

4) 452,300,000
Select by attributes. "CNTRY_NAME" = 'Iran'. Select by location. Target layer is cntry02. The source is cntry02. Spatial selection method within a distance 300 kilometers. Open attribute table from cntry02 layer. Show selected records. Unselect Iran. Look at sum in statistics on POP_CNTR.

5) Most populated: Ethiopia, Least populated: Vatican City
Open attribute table for country layer. Find landlocked and highlight it. Select by attributes. "Landlocked" = 'Y' Open attribute table again and sort descending. Find most and least populous countries.

6) Austria, Bosnia & Herzegovina, Croatia, Czech Republic, Poland, Romania, Slovakia, Slovenia, Yugoslavia.
Open attribute table of cities. Select Veszprem. Select by location. Target layer cntry02. Source layer cities. Spatial selection method is Target layer (s) features are within a distance of the Source layer feature. Apply a search distance 300 Kilometers. Everything else is default. Click OK. Then open attribute table of cntry02. Show selected records. Answers are listed and exclude Hungary.

7) Cameroon, Central African Republic, Libya, Niger. Nigeria, Sudan
 Open attributes table from country layer. Highlight Chad. Select by location. Target layer: cntry02. Source layer: cntry02. Spatial selection method: Target layer features touch the boundary of the source layer feature. Everything else default. Click OK. Navigate to attribute table of country layer. Show selected records. Answers all listed and exclude Chad.

8) 1) Russia (97), 2) United States (93), 3) Thailand (72), 4) Turkey (67), 5) Cote d'Ivory and Poland (both 50)
Open Arctoolbox. Expand analysis tools. click on statistics and click frequency. Input table as cities and frequency field (s) as cntry_name. Click OK. A new table is created. Open new table. Sort frequency descending. Answers are listed. 

9) 2950km + 211km + 599km =  ~3760 km.
Open attribute table of country layer. Find and select Sudan. Use the measuring tool to measure in kilometers the three rivers on Sudan. Estimate the measurements and add the three together.

10) 1) Russia (1516), 2) Canada (1340), 3) United States (743), 4) China (219), 5) Sweden (168)
Same as #8 but input table should be lakes. Use the same frequency tool and choose cntry_name as frequency_field. Click Ok. A new table is created. Open new table. Sort frequency descending. Answers are listed.

11) 1) Canada (443517.19 sq km), 2) United States (196848.52 sq km), Russia (138250.78 sq km), Kazakhstan (70899.672 sq km), 5) Tanzania, United Republic of (53529.613 sq km)
Open attribute table for lakes and go to table options and add field. Name the field. Type should be long integer. Calculate Geometry for the new Area field. Change units to square meters. Under geoprocessing, choose dissolve tool. Input features as lakes. and dissolve_field(s) as cntry_name. Click OK. A dissolved layer will be created. Open attribute table from new dissolved layer. Add new field as before. Name the new field. Type is float to get decimal. change units to square kilometers. Sort descending. Answers are listed.

12)

Open attribute table from dissolved lakes. Navigate to join and relate. Join country layer with the lake dissolves layer. add a new field. Name the field. Use field calculator and find lake dissolves area/ pop_cntry. Edit symbology by changing color ramp and reclassifying the values. 

Los Angeles County Fire Risk Map

     Creating a fire risk map for the Station Fire in Los Angeles County involved several detailed steps. Fire perimeter data and the DEM was provided by the instructor for student convenience. Coordinates for the DEM was changed in order to make the slope model. The coordinates were changed to UTM zone 11 by selecting metric based system project coordinate system and NAD 1983. Therefore, the slope can successfully be created after the hillshade model. The slope was reclassified with appropriate values. The areas with flatter slopes have lower hazard points and areas with steeper slopes have higher hazard points. A steeper slope has a greater risk of catching fire. Additional data on land cover needed to be obtained from the FRAP website. I chose the fuel rank data. Adding the fuel rank data to the map was a simple step. The final component of the fire risk map was combining factors. The slope model and the fuel rank data had to be calculated using the raster calculator. The two were added together to make a summary map. The final product shows the areas at greatest risk in red, orange and yellow. The areas of lowest risk is shown in green. The final product fire risk score proves that the area within the Station Fire has high risk of catching fire because of steep slopes. The fire perimeters of various dates from August to September are included in the map for reference on how the fire grew over time.
     The most difficult problem I encountered during this lab assignment was assigning values for reclassification of the slope. It was difficult to determine the ranges for the classes. Also, I was unsure on how many perimeters I should include in the map. I included one from each date for clarity. I further provided an inset map to indicate where the fire is located in the Los Angeles County. Lastly, it turned out that I did not need to make a slope for my fuel risk layer. This was not required for the raster calculation to make the final product.

Final Project Proposal

Topic: Earthquake hit Virginia, CA and rattled New York, Washington D.C., and others in the east coast.

Methods: Obtain land data from UCLA map share or TIGER website. Make up to 3 maps focusing on the main areas affected by the earthquake. At least one of these maps should show elevation. Also, indicate the areas that were most devastated by the earthquake. Geocode cities and major cities of each state using excel sheets. Add buffers around the earthquake epicenter to show what extent impacted nearby states. Show population data according to each state per province. The earthquake hit today so it is necessary to gather more information and do further research on the subject matter.

Medical Marijuana Dispensaries Policy Brief

     Medical marijuana dispensaries should be at least 1,000 feet away from places where children congregate. It is potentially dangerous to expose children to drug related activities and stores. Children are vulnerable to peer pressure and inappropriate activities. Drugs can affect growth and learning abilities. Drugs can prevent a child from maturing into a adult. Children on drugs will perform poorly in school and have problems socially and physically. Marijuana slows the memory growing process. It is unsafe for children to have access to these types of stores in their neighborhood.
     The map shows various medical marijuana dispensary and elementary school locations in East Los Angeles. Medical marijuana dispensaries are labeled as green points. Elementary schools are labeled as school symbols. Evidently, medical marijuana dispensaries are definitely 1,000 ft away from local elementary schools. Buffers defining the distance from medical marijuana dispensaries are in red.
     Elementary school children will become corrupted. They will not behave properly at home nor at school. As a matter of fact, they will behave poorly. They will start fights with other children. Perhaps, they will join a gang or spend days throughout their lives doing nothing but playing computer games. They are in danger of ruining their futures and health. Smoking at an early age is hazardous to the health. Parents are strongly urged to keep children away from dangerous areas such as areas with medical marijuana dispensaries. Marijuana leads to other more harmful drugs that children are better off not knowing about. Children at a young age need to experience the world by playing with friends and doing educational activities. It is not a bright future for children if they only do drugs and not have a goal in their life. Drugs are not the right toys to play with for children.
     Medical marijuana dispensaries need to ignore the costs involved in keeping their stores away from children. The stores need to collaborate and work together to maintain a safe neighborhood for children. 

References
http://gis.ats.ucla.edu/Mapshare/
http://local.yahoo.com/CA/Los+Angeles/Education/K-12/Elementary+Schools
http://legalmarijuanadispensary.com