Why 27.306(x)^2 - 238.96(x) + 510.08?

To get ArcGIS to properly calculate the radius of the buffers for each earthquake I looked at Keefer, 1984 - Landslides caused by earthquakes. In his paper he noted that with an increase in earthquake magnitude landslides occurred in a larger area around the epicenter. I had to work backwards from one of his figures (Figure 2B) as he gave no formula for the curves in his figures. 

Keefer, 1984 Figure 2B

I snipped the figure out of the PDF and brought it into Illustrator. I could now find out what the distance from the epicenter each magnitude was plotted as. I made a grid and measured the distances, and with a little algebra could find out each one. I then plotted those into Excel and had it make me a curve. Finding that curve by hand would have been rather difficult. 

Keefer's curve as plotted in Excel. 

The curve was polynomial with the formula being y = 27.3606(x)^2 - 238.96(x) + 510.08. This is pretty close. It is actually gives a value a bit larger than what it should truly be. As you can see the trend line matches more closely beginning around a magnitude of 7 and above. I tested it using a magnitude of 7 which had an original value of 163. The formula gave 175. Close enough. A little large is probably better than smaller. One important thing to note is that earthquakes with a magnitude below 5.06 cannot be used. This is because the calculation gives a negative number below 5.06. 

These columns were added to ease computation of the radius.

To make this whole this work I had to make new fields in the attribute table. I found it worked best when I used one for 27.306(magnitude)^2, 238.96(magnitude), and 510.08 as its own column. For whatever reason ArcGIS didn't like to calculate correctly when I plugged in the formula straight forward. So I broke it into pieces and fed each one back in. It also calculated the radius of the buffer circle in meters, so I had to convert to km by multiplying by 1000. These buffers were way bigger than I would have expected. For a magnitude 7 it drew a circle 350 km in diameter!

Just about anywhere along the Alaskan coast would have been suitable, however a larger city seemed more suitable for this study.

Earthquake induced landslide hazards in Anchorage, Alaska and Los Angeles, California - A comparison

For this project I decided to compare the risk of earthquake induced landslides around Anchorage, Alaska and western Los Angeles, California as both areas are affected by active seismicity. 
To do my comparison I first acquired data on cataloged earthquakes in Alaska and California. The following were done on data for both study areas:

- Drew buffers around earthquakes where the radius = 27.306(x)2 – 238.96(x) + 510.08, based on Keefer, 1984 and derived using Excel. I explain it here.
- Slopes were derived from 10 DEMs.
- Ran Con on the rasters of the slopes for all slopes above 30° and all slopes above 45°, then converted them to polygons.
- Land cover rasters were converted to polygons and all but the urban sections were removed. 
- All slopes of 30°+ were selected within 50m of urban areas, buffered and dissolved. Same for 45° for 50m and 150m.
I chose 30° and 45° as they are a bit below and above, respectively, the angle of repose for most geological materials. 

Figure 1 - Earthquakes in Alaska with buffers drawn around them. All earthquakes were magnitude 6 and above.

Figure 2 - Earthquakes in California above magnitude 5.06.

Significantly more areas with steep slopes near or intersecting urban areas were found in California. The Anchorage DEM covered approx. 5824 km2 with roughly 37 km2 at risk (Figure 3). The California DEM covered approx. 10192 km2 with about 1262 km2 at risk (Figure 4). 

Figure 3 - Alaska, Anchorage Area

Figure 4 - California, Los Angeles Area

To ‘ground truth’ how well my model worked I used Google Earth’s Street View in a few selected places. Street View seems to have corroborated my GIS findings as seen in figures 6 and 8.

Figure 5 - One of the few areas with steep slopes in the Anchorage Area. 

Figure 6 - Google Earth Street View Image of the the area in the above map. Going south along Hatcher Pass Road, north of Fishhook, Matanuska-Susitna County, Alaska, . It is evident that the DOT is aware of this area as a hazard as the outcrop has been cut back from the road and a fence has been put in to keep loose boulders from making it to the road. The rocks here are sedimentary.

Figure 7 - A section of the Pacific Coast Highway near Big Rock, Los Angeles County, California. Significant areas at risk can be seen here, the red being areas with slopes of 45° or more within 50m of urban areas (roads included).  

Figure 8 - The same stretch of the Pacific Coast Highway shows in Figure 6. Note how close the slope comes to the road, and how steep it is. 

This model is far from perfect as it doesn’t consider lithology, soil conditions, engineered slopes, the role of vegetation, or precipitation, which all factor into landslide occurrence.

The full report can be viewed here.

Converting a Land Use raster to Polygons, and keeping it usable

So, you have a land use raster (or any other raster with different values) but it is ugly, so you want to convert it into polygons. Of course you want to run Raster to Polygon, and sure it will work, but oh noes! All my polygons are the same color! What to do?

A land cover raster, but the cells are massive, and rather ugly. 

First run Raster To Polygon. I recommend clipping the raster to whatever area you are working in because a state sized raster will take quite a while to convert to polygons. Before you hit run change the FIELD (optional) from Value to LAND_COVER. Now it will properly assign the polygons to the LAND_COVER attributes.

Next go to Properties -> Symbology -> Categories -> Unique values. In the Value Field drop down select LAND_COVER. If you have a style layer you can import it using the import button. If not you will have to manually change each color if you want to match the original raster. Or you can choose from a color ramp. The order of the values be changed by clicking on the value and using the arrows to the right to move them up or down (great if they are numeric values).

Here are the two side by side for comparison:

And using the Identify tool shows that they retained their LAND_COVER values, and it looks a whole lot better. Now spatial queries should be easier to do.

Geomorphology and Watersheds

For our first project I decided to create two watersheds using ArcGIS 10.6 and compare the stream average gradient and sinuosity of all the streams in each watershed. Initially I wanted to see if urban land cover might be a driving factor in increasing stream gradient and sinuosity. 

Since we are here in Auburn, Alabama I created a watershed around Auburn, and for comparison created one just to the east where, from the map, I could see far less development. I used Arc’s Hydrology tools to trace out streams and delineate watersheds. I created a file geodatabase with the features classes, one for each watershed, and traced streams I wanted to compare.

I found a Python toolbox called Calculate Sinuosity which conveniently can iterate through a shapefile or feature class of streams, calculate the sinuosity, and adds that to a newly created field in the attribute table. I spent a lot of time trying to figure out how to make ArcGIS calculate stream gradient before re-discovering Tom Dilts’ custom toolbox Stream Gradient & Sinuosity Toolbox for ArcGISv10. It didn’t like my streams in the feature class, so I decided to use Stream to Feature to convert my streams raster (which I had clipped to each watershed) into polylines, and fed that to the tool - success. I then used the statistics function to find average gradient/slope, sinuosity, and length.

I found that the two watersheds were nearly identical. Chewacla Creek Watershed, which Auburn is in, has 20% urban cover compared to 1.77% in the Little Uchee watershed, and yet sinuosity and gradient were very similar. There was greater relief in the Little Uchee, but more urban cover in Chewacla. I suspect these two might balance one another. Or other factors are at work; more work needs to be done.

Attribute	Chewacla Creek Watershed	Little Uchee Watershed
Area	162.22 km2	165.92 km2
Streams	377	376
Relief (meters)	120.082	148.025
Average slope	0.033°	0.041°
Average sinuosity	0.907	0.897
Average length	991 meters	962 meters
Urban cover	20.89 %	1.77 %
Forest Cover	65.08 %	92.38 %
Agricultural cover	7.56 %	3.42 %
Open water	6.44 %	2.44 %