Skip to main content

Module 6: Damage Assessment

This week I learned how to utilize ArcGis Pro to make out the pathway of a past hurricane, Hurricane Sandy, along the North East coast of the USA in 2012. Later, I was also able to assess structural damage on the coast of New Jersey from Hurricane Sandy. 

Below you can see the map I created of Hurricane Sandy's pathway/timeline. If you look closely, you can see via the symbology what category storm Sandy was along its path along with information on its wind (mph) and pressure (barometer) information. 


In the second part of the lab, I assessed the structural damage of Hurricane Sandy in a select area in New Jersey. I created a new point feature class and subsequent domain with categories no damage, affected, minor damage, major damage, and destroyed to categorize each parcel. After creating the points and designating their damage category, I analyzed the data future by determining what damage feel 0-100m, 100-200m, and 200-300m from the coast. 

1.      Created a new polyline feature class for the coastline to create polyline of the Atlantic coast.

2.      Created a new polygon feature class to box out the distance 0-100m, 100-200m, and 200-300m from the coast to our analysis area.

3.      Used select by attributes to create a new layer for each of the 3 distances.

4.      Spatial joined each of the 3 distance layers to the structure damage point feature class.

5.      Used select by attributes to determine the numbers for table below via the newly spatially joined tables.

Structure Damage Category

Count of Structures

0-100 m from coastline

Count of Structures

100-200 m from coastline

Count of Structures

200-300 m from coastline

No Damage

0

0

0

Affected

3

0

24

Minor Damage

0

4

17

Major Damage

0

32

4

Destroyed

8

5

0

Totals

11

41

45



I found on the coast (0-100m) was the worst damage, with entire buildings decimated and destroyed. As I moved away from the coast (200-300m) there was less structural damage and more inundation I assume, given the displacement of sand. I would want to do further analysis before trying to extrapolate the data as there are many details I do not know. This particular section may have been at the heart of the storm. Other buildings along the coast could have also used different building designs/architecture with higher hurricane ratings. There are too many unknown variables to confidently assume a relationship between distance to the coastline and structural damage at this time. 

View my ArcGis StoryMap here: https://arcg.is/1iGLaH. 

Labels:

Comments

Post a Comment

Popular posts from this blog

Positional Accuracy: NSSDA

 In this analysis, I compared the street and road intersect data collected for Alburquerque, NM by the City of Alburquerque and the application StreetMaps. I used an orthophoto base layer as the reference for this analysis, to compare and determine the accuracy of both the City and Streetmap layers using NSSDA procedures. The most difficult part of this analysis for me was how to determine what 20% per quadrant looks like. Because the reference map was divided into 208 quadrants, I had to determine how to subdivide all the quadrant's equality into 20%. After multiple trials and error, I decided to subdivide the entire area (208 sub-quadrants) into 4 equal-area subsections. In this way, I could do 5 random right intersection points per subsection or 20% per subsection.  Map 1: City of Albuquerque city map data.  Map 2: City of Alburquerque SteetMap data When selecting a random intersection to place the points within each quadrant, I choose a location that had data f...
Post a Comment
Read more

Isarithmic Mapping

  Map 1: Annual Precipitation, Washington State Map 1 is an Isarithmic map that follows the continuous phenomenon of rainfall in Washington state over a 30 year period. The data was created by the PRISM group at the Oregon State University in 2006, and then downloaded and amended by the U.S. Department of Agriculture, Natural Resources Conservation Service, National Geospatial Management Center in 2012. Eden Santiago Gomez, analyzed the data on 5/2/2021, to create the map above. Santiago Gomez created continuous tones for the data, also adding a hillshade effect. She then converted the floating raster data into Integer data via the geoprocessing tool Int (Spatial Analyst Tool) to bring out hypsometric tinting. Lastly, she added contours of the data via the Contour List tool.   How the precipitation data was derived and interpolated? The PRISM system has been continually developed over the past couple decades, utilizing physiographical maps and climate fingerprints as its ...
Post a Comment
Read more

Supervised Classification

 This week we learned how to perform supervised and unsupervised classification on ERDAS Imagine. On the figure 1 map below, you can see my attempt at classifying land use/cover of Germantown, Maryland. This week my map falls short of the assignment goals, as I was not able to get ArcGIS pro to replicate the recoded feature classification outputs I performed in ERDAS. I am not sure what I did wrong, but in the future, I will make sure to analyze signature points more carefully via their histograms and mean plots to identify potential errors.  Figure 1: Supervised classification of Germantown, Maryland with a smaller distance map of the main feature map. 
Post a Comment
Read more