Final Project

Introduction: As my time here as a student at UCLA is rapidly drawing to a close I can't help but wonder what lies in store for me in the near future. Fearful of uncertainty I decided to make a map that would show me my optimal living location in Los Angeles after college. My project uses four criteria by which I can determine my new home. First and foremost I would like to work as a librarian after college so that I can read books in the down time of my work as well as honing other skills, such as GIS. Therefore I must live fairly close to a library. Being as commuting anywhere in LA is a hassle, the closer I can live to my work the better. Outside of work, my life largely revolves around outdoor recreation. Therefore my second and third criteria are focused on this. The second criteria is relative proximity to a park so that I have an area to use as I will most likely be living in an apartment with no backyard after college. The third criteria is how much it rains in the area since I can't do outdoor activities if it is raining. My fourth criteria is based on centrality in LA. As I have noticed that I have not ventured much into downtown over the past 3 years I would like to live as close to the center of Downtown LA as possible. These four criteria will be mapped and added together to give me a composite map in which I can find an optimal living location.

Methods: To objectify my first criteria (distance to work) I downloaded a shapefile of all the libraries in Los Angeles county. I then used the Euclidian Distance tool to map the distance at any given point from a library. I then reclassified the distances into five different zones and gave them a point scale appropriately. The closest zone was given a value of 0 and the other zones were given values of 3, 6, 10 and 15 respectively moving outwards from libraries.

To objectify my second criteria (distance to parks) I downloaded a shapefile of all the parks in Los Angeles county. I then used the Euclidian Distance tool to map the distance at any given point from a park. After that I reclassified the distances into five different zones and gave them a point scale appropriately. The closest zone was given a value of 0 and the other zones were given values of 1, 2, 4 and 6 respectively moving outwards from parks.

To objectify my third criteria I downloaded seasonal averages of rainfall from weather stations in LA. I then used Inverse Distance Weighted spatial interpolation to generate data for the area in between where the measurements were taken. I then reclassified rainfall to my point scale. Areas with the least rainfall were given a value of zero. Other areas were given values of 2, 4, 7, and 10 respectively increasing with increasing rainfall.

To objectify my fourth criteria I chose Los Angeles city hall as the point to base centrality off of. I then geocoded the address of the city hall. After that I used the Euclidean Distance tool to show increasing distance from this point. Once the distances were broken up into 6 classifications I then reclassified the zones according to my point scale, 0 being the closest and 10 being the furthest with values of 1,3, 5, and 7 lying respectively in between.

I chose to give the most value in my reclassifications to my first criteria as I will be going to work every day and is the most important criteria for my new location. Thus it holds more weight in the point scale with a maximum value of 15. Being as there are so many parks in Los Angeles I chose to give the least weight to my second criteria and thus the maximum value is 6.

To gather a final map in which I took into consideration all of these criteria I used the raster calculator and added together the four layers that show the reclassified values of their respective criteria. The final result was a map in which my optimal living location would be the areas with the smallest value.

Results: Unfortunately I have little to write here as can be explained through my email discussion with you.

Conclusion: In conclusion I have used ArcMap and its GIS capabilities to find an objectively optimal living location for after college. To do this I mapped each of my four criteria, gave them point values respectively and created a composite map, via the raster calculator, in which all criteria were taken into consideration. This project offered me a chance to use GIS in a real life application pertinent to my future.

Link to google doc of my project:
https://docs.google.com/leaf?id=0Bz77O-B_FFspNmE3MzdjZGEtY2I3ZC00YzNhLTkxYjMtMjM4MmJmNjVhMmM3&hl=en_US

Most recent email:
Hey Jida,

I'm here in my hotel in Mexico trying to finish my final project but the ArcMap program keeps crashing before i can even open my project. A few times i have even been dropped from the remote access all together. I have been trying every way I can think of to open this file for the last two hours straight to no avail. When i went down to the front desk to pay for the internet, i asked how the internet connection was and from what i could gather with my minor knowledge of Spanish it is pretty mediocre at best. I have uploaded my file to google docs and will share the file with you which you will probably receive an email about shortly after this. The file should contain four data frames from what I can remember. The first being a working data frame which would eventually hold the composite raster calculation of three reclassified layers. The other three data frames were to display each reclassified layer individually on the final layout. I will still post my write up to the blog. Additionally I will add this email in case you don't see it before you grade my project.
I hope this does not bring down my final grade as the current state of my project does not reflect my work in this class. I know if I were sitting in front of a working computer right now I could produce my final project from scratch for you in under an hour and a half as compared to the numerous hours I have spent working on this in class, in the lab, in your office with you and trying to use the remote access here. Please email me back if you have trouble downloading the file from google.

Thanks,
Vinnie Ciardi

Week 5 lab

Rainfall for this season to date is much higher than the season normal in just about every part of LA. I believe that IDW method worked a lot better for the interpolation of this data set than the spline method. The spline method probably did not work very well because there was a larger change from location to location than I initially expected in the data set and spline interpolation works better in data that have less sharp changes. Additionally I think that the number of points I used for the spline interpolation to reference was too many for the density of the data set. IDW worked well because the sample stations were not uniform distances from each other but were still dense enough to create a reasonable estimation.

Lab 4

Sorry this wasnt posted yesterday. I went upstairs to post this after talking to you about it after the quiz but the lab was already closed in Bunche too and I hadn't yet exported a jpeg of the map. Thanks Vinnie

The first step in creating this model was collecting the data. I used the California Department of Forestry and Fire Prevention website to get the land cover data. I got a dem from the USGS seamless site but it for some reason didn’t work. Thankfully I had remembered that I already had one for LA County on my USB drive. Lastly I found a perimeter of the station fire from an old project. Once I had compiled my data I started working on it. I made a hillshade map from the dem would layer under the semi-transparent thematic layers and the fire perimeter.

                In order to make the final fire model I used two layers which are contributing factors to fire spread. The first layer is a map of the land cover type reclassified to show the potential fire hazard of the area based on vegetation. This reclassification is into points that are called NFPA Hazard Points. These points would later be added to those from the respected areas of the second map which was a slope map. In this map I used the spatial analysis tools to get a map which showed the slope in a percentile ranking in for everywhere in LA. I then reclassified these into a point system consistent with the NFPA Hazard Points. Once I had these two maps created I used the raster calculator to combine the maps into one fire model that showed the potential hazard based on both slope and fuel. I think it’s interesting to see how closely the fire follows the areas of hazard as predicted by the model.

1.   1.  The most populated countries in order are: China, India, United States, Indonessia, Russia, Brazil, Pakistan, Japan, Bangladesh, Nigeria. I arrived at this by opening the countries attribute table and ranking them by population by clicking on the  POP_CNTRY field name.

2.   2.  15 I did this by doing a select by attribute query system = amazon and then opening the selected features in a table which told me there were 15.

3.    3. There are 60 cities with 500 km. I did this by selecting the rivers and then selecting all cities within a 500 km search radius via a select by location query

4.     4. 516490670-64193450= 452297220 Is the answer. Got the population by the statistic tab of the result of the spatial query minus the population of Iran

5.     5. The least populated is Vatican city and the most populated is Ethiopia. I got this by  doing a select by attributes landlock = y. this returned all the landlocked countries which I then ranked by population.

6.    6.  Solvenia, Bosnia &Herzegovina, Croatia, Slovakia, Austria, Czech Republic, Yugoslavia, Romania, Poland. I did this by doing a spatial query, Verspezm being the selected feature, cntry02 the target layer and cities the source layer with a 300 km distance

7.     7. Cameroon, Central Africa Republic, Libya, Niger, Nigeria, Sudan. I found this by doing a spatial query in which Chad was selected and then I chose to select from the cntry02 layer any country that shares a border from the drop down.

8.   8.  Russia, 97; United States 93; Thailand, 72; turkey, 67; Poland, 50. I found these by doing a spatial join with cntry02 being my target features and cities being my join feature. From there I opened the cntry02 table and ranked them by the join count.

9.     9. The rivers running through Sudan are the Blue Nile, 1893 km; Nile, 3023km; and the White Nile, 5046 km. This totals 9962 km. I arrived at this by doing a spatial query for all rivers that intersect Sudan. Once these rivers were selected I projected them onto a sinusoidal projection in order to have the GIS program be able to calculate the lengths of the rivers. From there I added a new field to the attribute table of the newly projected selected rivers titled ‘length’. I then used the calculate geometry school.

10                Russia, 1537; Canada, 1355; united States 759; China 226; Sweden 173; I did this by doing a spatial join between the lakes and cntry02. Then I opened the spatially joined files attribute table and sorted by join count.

11.                         Canada 443,509 km/sq; USA 196,856; Russia 138250 km/sq;  Kahzakstan 70,869; China 51,272; I first projected all of the lakes onto a sinusoidal projection so that the program could measure their area. I then added a new field named ‘area’ in the new projection attribute table. I did a spatial join between countries and lakes. This didn’t work had no clue how to do it the right way so I looked at the cumulative area of the countries with the most length amongst some others and thus came up with the answer J.

Lab 3

Part 1

Part 2

Week 2 Lab

This lab focuses on the 50 closest Taco Bell locations to my apartment. I found the addresses of these Taco Bell’s on their website in a location finder. On the map above you can see all 50 locations, a ½ mile buffer zone around each, the mean center of the Taco Bell’s, a point showing the location of my apartment and major highways. I chose this topic because Taco Bell is my favorite fast food and being as I don’t have a car, it is important that I know where a near Taco Bell is. Additionally I wanted to know some information regarding the clustering of Taco Bell.

Upon making this map I soon realized that Taco bell’s in Los Angeles are practically everywhere. This led me to my first question: ‘How many of these Taco Bells are within one mile of each other?’ in order to answer this question I created a ½ mile buffer zone around each Taco Bell. When the buffer zones intersect it means that the two corresponding Taco Bells are within a mile of each other. I found 11 Taco Bells that were within a mile from another Taco Bell. After doing this I made a buffer layer with dissolved boundaries to better show which fit the criteria.

Additionally I thought it was interesting to see that many of the Taco Bells were pretty far from my apartment. This then made me wonder where the mean center of these 50 Taco Bells was. Using the spatial statistics tools, I was able to measure geographic distributions and plot the mean center on the map.

Week 1 Lab

Station Fire Lab

                 Hearing about a natural disaster that has caused destruction in people’s lives hits close to home in a figurative sense, but what happen if a natural disaster hit close to home in a geographical sense. Well in late August of 2009 it did. The Station Fire as it was named occurred in Los Angeles, just north of Pasadena. The fire is believed to have first begun on August 26, 2009, but did not reach its full potential until early September (Inci Web). This time of the year set up optimal conditions for fire spreading as the hot LA summer moths had just finished up. The “bone-dry conditions in an area that has not seen a major fire in more than 60 years pushed” the station fire to great extents (CNN.com).

                This fire took quite a while to contain and left a devastating impact on the area and its people during its existence. By September 19^th, over 20 days after the fire began, it was finally 93% contained (NASA.com).  The fire was finally fully contained on October 16^th, nearly two months after it began (Inci Web). The fire burned 160,577 acres which is equivalent to 250 square miles (Inci Web). In its path the Station Fire destroyed 89 homes and threatened countless others (Wikipedia). The biggest loss in this fire though was the lives of two firefighters, Arnaldo Quinones and Tedmund Hall (LATimes.com).

                In my reference map one can see the sheer size of the station fires. Additionally we are able to see the where the fire spread from and how quickly it spread likewise. Originally confined to a small patch around the 2 highway, it is clear to see that in a matter of three days the fire had increased its size exponentially. The fire boarder many largely populated areas, with three of the four side of the fire nearing or engulfing a highway.

                After taking 2 months to finally get the fire contained one can only wonder how much chaos the area would be in had they had lost large institutions, such as schools in the fire.  This is of a particular threat in this situation due to the fire’s proximity to highly populated areas. In my thematic map I show which LA schools were threatened by the fire. To do this I mapped multiple fires perimeters from different times in the fire. I also mapped all the schools in the area and created a one mile buffer zone around them. Any schools that were within one mile of the fire perimeter were considered threatened. To see how many schools were threatened I intersected the area of the fire perimeters with the LA schools and their buffer zones. The thematic map only shows the schools that were considered threatened by the fire.

                Fire is a powerful force of nature and should not be toyed with. In incidents like this GIS programs can become powerful tools for fighting fire. By mapping the current area of the fire and other things that factor into fire spreading such as wind and soil moisture, people can make educated guesses as to where the fire will extend to next. Additionally using intersections of layers and buffer zones can help give information to firefighters as to which areas should be more closely watched, such as schools.

Works Cited

 “2009 California Wildfires.” Wikipedia.com. 2009. June 8 2011. <http://en.wikipedia.org/wiki/2009_California_wildfires>

“Angry fire roars across 100,000 California Acres.” Cnn.com. 2009. CNN. June 8 2011. <http://articles.cnn.com/2009-08-31/us/california.wildfires_1_mike-dietrich-firefighters-safety-incident-commander?_s=PM:US >

Garrison, Jessica et.al. “Station Fire claims 18 homes and two firefighters.” Latimes.com 2009. LA Times. June 8 2011. <http://articles.latimes.com/2009/aug/31/local/me-fire31>

 “Station Fire.” InciWeb.org. 2009. Incident Information Systems. June 8 2011. <http://www.inciweb.org/incident/1856/>

“Station Fire Burn Scar.” NASA.org. 2009. NASA. June 8 2011. <http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=40245>