Connor Matthews

Lab 4

Introduction:  In this lab I examined the tsunami evacuation zone if the “Big One” (referring to an earthquake off the northern west coast of the U.S.) were to be triggered.  The purpose of this lab was to find suitable areas of relief in the case that this earthquake was to happen, for preparedness to evacuate large amounts of people.  In order to look at demographics of this tsunami statewide, I had to use DOGAMI’s tsunami information along with census data of census blocks to clip out my desired affected area. After I had clipped out the blocks within the tsunami zones I summarized both the population and housing units by county to get the two tables shown in my map.  I then needed to figure out the total area of city limits affected, which was done using clip along with a city limits data set and the DOGAMI tsunami data set.  The total acres affected were: 39,165.16.  I then figured out the distance of highways that would be affected using the same clip analysis step used previously for the city limits.  The total mileage of highways affected is: 255 miles.

In order to create suitable multi-category recovery zones, I had to set a Lincoln county DEM to my total extent being looked at.  I then created raster fields of the Highway network, the tsunami data layer and the Lincoln county DEM.  In the end I want to combine these three raster data layers to show the most suitable land for recovery zones.  I had to reclassify each raster layer into ten categories 1-10 (10 being the most suitable) with respect to closeness to highways, in areas of low slope, and close to the evacuation zone but not within it.  Combining these three raster models gives me my mutli-criteria evaluation model that I made and ran within the model builder shown below.

After getting my combined evaluation model I reclassified it into two categories.  The first being recovery zone suitable and the second being not.  I chose to classify the suitable recovery zones from 7.1-9.7 and the non-suitable 0-7.1 to get two different areas.  I then used the majority filter tool and the raster to polygon to finish my polygon recovery zone areas.  After getting these areas, I examined the Highway and city limits data layers with respect to the recovery zones to choose best suited cites for the tent cities.

Final Map:

Conclusion: My findings in my research suggest suitable land in case of a natural disaster that can be used for “Tent City” recovery cites for victims.  With my findings of using the Multi-category evaluation model I can now look at potential locations close to highways for emergency evacuation.  After spatially analyzing the recovery zones with respect to highways and city limits I created three somewhat central locations for the emergency tent cities.  With these findings I have better prepared Lincoln County in the event of a catastrophic earthquake causing detrimental tsunami damage.

References:

http://www.oregon.gov

https://www.census.gov/

Class Lab folder

DOGAMI

Lab 1

Connor Matthews

Q1) The source tab shows the extent of the map, as well as the data source which includes two important features like the Geographic Coordinate System and Datum.

Q2) The coordinate system in this layer is a Geographic coordinate system, GCS_North_American_1983. GCS is based on a sphere whereas a PCS is defined on a two dimensional surface that has constant lengths, areas and angles across the two dimensional surface.

Q3) The Albers equal area conic projection curves the map like it is wrapping around a sphere. The Mercator, Plate Carree, and Robinson projections are all on two dimensional planes the Mercator is skewed by being wider north to south because it’s a cylindrical representation. The Plate Carree projection is skewed east to west and stretches out the map and the Robinson is on a plane that is skewed from NE to SW, and NW to SE making these two points of the U.S. closer and the other points farther apart than the other projections.

Q4) The Albers equal area conic projection has Chicago and Philadelphia closer together than the Mercator or Plate Carree projections because its projection is modeling the actual representation of how the world is shaped. Unlike the other projections that don’t represent the spherical qualities of the world.  The Robinson projection puts Chicago and Philadelphia similarly close to the Albers projection as well as most angles you look at the Robinson projection isn’t off by more meters than the other two projections because it is slanted bringing the cities that are closer together by the spherical quality of the earth, closer together by being at a slanted orientation.

Q5) The Albers equal area conic projection puts a slight curvature over the whole area of the map, curving slightly upward.  The Mercator projection is stretched north to south, where the Plate Carree projection is being stretched east to west elongating the map horizontally. The Robinson projection is at a slight slant from top right slanting down towards the left making the entire area seem smaller because it is being looked at an angle instead of an overhead view.

Q6) L.A. to Chicago: Albers (2,811 Kilometers), Mercator (3,600 K), Plate Carree (3,538 K), Robinson (3,135 K)   Houston to Chicago: Albers (1,510 K), Mercator (1,860 K), Plate Carree (1,589 K), Robinson (1,684 K)

The distances portray the spatial differences I outlined in Questions 4 and 5 showing that yes the Albers skews places closer together than other projections, and the Mercator stretches the map vertically and the Plate Carree stretches the map horizontally.

Q7) This data set contains a lot of different variable per each state however the one I will be using is state population data from 2010, showing the most populated to least populated states.

Q8) I used the Classifications, Quantile, Natural Breaks, and Equal Interval.  The Quantile classification sets a bias by grouping 4 quartiles with the range of 25% for each interval break.  The Natural breaks classification sets the bias of setting equal intervals per state, trying to get equal amount of states in each category to try and compare similar state population sizes.  The Equal Interval does not pay attention to how many states it wants in each category, making it the least biased classification and shows outlying and extreme data compared to the other two classifications

Map of 4 different map projections

Map of 3 different category classifications

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