Introduction:
These past few years have felt like the Earth is very unhappy with us humans, what with the catastrophic Earthquakes in Haiti and Chile last year, along with just plain bad ones in Italy, Baja and China, and now the devastating earthquake/tsunami crisis happening in Japan right now. Fires as well have become an increasing concern as global warming heats/dries out parts of the world. In 2007 we had the series of wildfires in Southern California that burned thousands of properties, and then the Station fires in 2009. Last summer hundreds of wildfires broke out across Russia, causing thousands of people to die from smog poisoning.
With all that in mind I (and I suspect many other students) wanted to create a hazard map of California that takes both fire and earthquakes into account. In addition to this, I wanted to focus in on LA County, my home, and determine the most and least risky places to live. In order to do this I also needed to include population density information into the risk assessment – the greater the density the more damage likely.
As denizens of California, especially Southern California, We are intimately familiar with the risks posed by both earthquakes and fires. Fortunately, due to better disaster preparation, building codes, and luck, we have mostly avoided true catastrophe (we took the lessons learned from the Great Earthquake of 1906 to heart). Take for example the Loma Prieta earthquake of 1989, which at a 7.1 magnitude killed 64 people. Contrast this with an earthquake of 6.9 magnitude that happened in Armenia 10 months later (where there are no earthquake-proof codes), had a death toll of over 25,000.
Despite our level of preparedness, the possibility of disaster always hangs over us. For as long as I can remember, my house and schools have been fully stocked with earthquake kits, fire and earthquake drills are routinely mandated, and no matter where I go hiking, there always seems to be a big patch of scorched earth and dead trees left over from a recent fire. The fact is that we live in an area with a dry climate prone to fires that also happens to be situated on top of several fault lines. The question is not if a big earthquake or fire will come, but when.
Methods:
Although the concept of my map was pretty simple, there was quite a bit of spatial analysis and various other steps involved, so I will attempt to be as clear as possible in my methodology.
Part one: Hazard Map for the whole state of California, taking into account risk from Earthquake, fire and population density.
1. Creating a classified hazard map of earthquakes
Data needed: state map plus county boundaries; earthquake point layer
a. Step 1: Input Earthquake epicenter coordinates and magnitudes into an excel doc. My list was of significant earthquakes since 1900, meaning they had a magnitude of 6.5 or higher, or they caused over 200,000$ worth of damage.
b. Step 2: Add X, Y data to state map as a point feature class.
c. Step 3: Convert points to raster data using spatial interpolation based on magnitude. This way the map would appropriately reflect the danger associated with each earthquake depending on distance to the epicenter. I decided to go with the kriging method, because the tutorial we used in class for the interpolation lab says that kriging “assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variance in the surface.” This sounds appropriate for earthquakes, as the factors that caused one earthquake are likely to affect the ground near to it.
d. Step 4: Reclassify the new raster. I chose for there to be five classifications, somewhat arbitrarily, because I thought it wasn’t too many or too few to clearly reflect the analysis on the map.
2. Creating a classified fire hazard map
Data needed: fire hazard layer (vector)
a. Step 1: Convert the fire hazard layer, which was a vector shapefile, into raster format, based on the hazard level attribute, using the spatial analyst tool.
b. Step 2: Do a reclassification as in the earthquake layer.
3. Creating a population density map to asses areas of maximum potential damage
Data needed: Census population data
a. Step 1: Convert the shapefile to raster based on population density
b. Reclassify
4. Create two final hazard maps, one displaying the regions according to likelihood of earthquake or fire, and the other incorporating the population data to assess at risk of high damage.
a. Step 1: For the earthquake/fire map, do a simple raster addition, adding the two reclassifications together. Label this map: Hazard Risk
b. Step 2: Do the same but this time add the population density reclassification to the other two. Label this map: Hazard impact, since population density mostly affects how bad the damage will be.
Part two: focus in on LA County and determine the cities both at the highest and lowest risk of a damaging disaster
1. Create LA County layer with damage assessment classification
a. Step 1: I did a simple select by attribute: name = LA county, to highlight the parcel, then extracted it into a new layer
b. Step 2: Created a spatial analyst mask using the new county layer as my extent, and then did an empty raster calculation on the risk classification map (i.e. I clicked evaluate without any specifications). This created a copy of the map with the county extent.
2. Convert the raster to vector, so that I could extract parcels based on attribute in the next step
a. Step 1: Simple raster to vector tool spatial analyst toolbar, with the transformation based on risk level. The vector shapefile it created was somewhat rough looking, but that is to be expected considering it was trying to turn square pixels into amorphous polygons
3. Create two maps: one showing the areas of LA County with highest risk, and one showing areas with lowest risk.
a. Step 1: Select features by attribute, the attribute being the three highest risk classes.
b. Step 2: export those into a new layer, so I could highlight them with different symbology in final product
c. Step 3-4: do the same with the low-risk parcels
Part three: Assembling it all into a coherent final product:
1. One map showing the two full-state extent hazard and damage classifications
2. One map breaking the classifications down into the three component maps: Earthquake point interpolation, fire hazard and population density
3. One map showing the classification for LA County, and the areas of high and low risk.
Results:
Looking at the hazard map for earthquakes and fires, we can see that most of the areas associated with high risk values are along the coast and up in Northern California, with the highest being in Ventura County and the Bay area. This is consistent with our knowledge of where the fault lines run through and to where there are a lot of fires. The biggest area with practically no risk involved is in the bottom of La County. This makes little sense to me, as I know that there were three earthquake epicenters in the area, and we get fires. Perhaps there was an error in the data? When I look at the map that includes population density, however, the map makes more sense, as the areas of highest risk values are clustered around population centers, with the sparsely populated areas getting lower values. The surprising area to me is the line of medium/high and high risk values stretching down the middle of northern California. There must be very high risk of fire or earthquake. To examine further we must look at the maps of each individual risk. In terms of frequency of risk levels (aka what risk levels are most prevalent), here is a graph showing the distribution: 
While this chart doesn't give us real world information, like what cities these classes cover, for example, it does tell us the majority of California is at a relatively low risk level. But if you happen to be in the area those high risk pixels cover, watch out!
The earthquake map shows the areas of highest risk to be just north of Ventura County, the bay area, and the Humboldt region in northern California. The Bay area and Ventura county results are unsurprising considering they are on faults. The Humboldt risk value is explained by a series of offshore earthquakes this past century. The areas of lowest risk are certain areas in central California and in extreme southern California.
The fire map is slightly more interesting, for it reveals the area of highest risk to be the counties touching the coast line, and the ring of mountains around the central valley. It is this last part that explains why the part of this spine threading northern California has such a high hazard assessment – though it is sparsely populated it has a high enough fire risk to make it stand out on the map.
Finally, the population density is pretty clear. We all know that the bay area and LA/San Diego counties will have the highest densities, and the area bordering Nevada and Arizona will have the lowest.
Moving on to the LA county close-up, one thing jumps out at me immediately: not all the classes are represented – the two highest are absent altogether. This is good news for us LA dwellers. However, the bad news is that West LA, as in Santa Monica, and north La, as in Simi Valley, are at the most risk out of anyone. This is mainly due to fire hazard. Although of course, everywhere in LA has a high population density, adding on to the score. As for the areas of lowest risk, they appear to be the San Fernando Valley, Lancaster/Palmdale area, and parts of Long Beach and Orange County. This is unfortunate news for us Westwooders. Conclusion:
I feel that this was a very worthwhile project altogether. There were, of course, some flaws – for example, I’m not sure if interpolating historic earthquake points is a totally legitimate way of calculating earthquake risk, but it was all I could think to do - and I really would have liked to include some other important risks we face here in California, such as flooding and landslides, but I just couldn’t find the data. Obviously if I was really in charge of putting together a hazard map for California, I could spend my time looking for quality data, and even spend money getting it. However, I feel that accuracy aside, the project was good for me to reinforce all the spatial analyst tools we have learned this quarter, as well as other random tools, such as querying, projection changing, extraction, and adding X, Y data points. In addition, I spent a long time putting together my three maps using all the display tricks I know, and I had fun doing the project.
Sources:
For California county boundaries:
Los Angeles County GIS Data Portal - http://egis3.lacounty.gov/dataportal/?category_name=boundaries_political
For Earthquake points:
USGS, by way of the CA State Department of Conservation
http://www.conservation.ca.gov/cgs/rghm/quakes/Pages/eq_chron.aspx
For the Fire Hazard Map:
California Department of Fire and Forestry
http://frap.cdf.ca.gov/data/frapgisdata/select.asp?theme=5
For the Population Density Map:
California Department of Fire and Forestry
http://frap.cdf.ca.gov/data/frapgisdata/select.asp?theme=2
For Earthquake information:
“Earthquake hazards and risks”, adapted from lecture notes of Prof. Stephen A. Nelson Tulane University
http://earthsci.org/processes/struct/equake2/EQHazardsRisks.html
