Damage Assessment

 Final Results

 

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	13
Affected	0	8	17
Minor Damage	0	14	4
Major Damage	0	11	4
Destroyed	9	6
Totals	9	39	38

Coastal Flooding Analysis

In this week's lab, the focus was on the analysis of coastal flooding. The first part of the lab was focused on the aftermath of Hurricane Sandy after making landfall along the coast of New Jersey in 2012. The data we worked with was LiDAR data downloaded from the NOAA website. When analyzing LiDAR data in GIS, the .laz file must be converted to a .las file because GIS does not support .laz. So, the first step in this lab was to convert the data into a .las file. To do this, the Spatial ETL tool was used. 

Once the data was converted, the next step is to create digital elevation models (DEM's) so we can create an elevation change layer to better see the impacts a storm surge. Working with this kind 

The map below shows the elevation changes along the coast of Mantoloking, NJ after Hurricane Sandy.

 

Forestry and Lidar

 

Crime Analysis

Working with Geometries

    In this final week of GIS Programming, we were tasked with creating a text file that reads data from the shape file rivers.shp. Below is the final result from the text document created as well as a flow chart demonstrating steps to complete the assignment.

 

Exploring and Manipulating Data

In this week's lab, we were tasked with creating a new geodatabase and copying data to that gdb. Once that was complete, we had to populate a dictionary using some of the data from the new geodatabase. With all of the practice using Python these last several weeks, it really feels as though I've made progress in learning this skill. So, the first part of this lab went smoothly for me. However, the part of the assignment in which we had to create a Search Cursor, a dictionary, and populate the dictionary really gave me some trouble.

Below is a screenshot of the geodatabase (jag198.gdb) and data copied over to it. 

 

Geoprocessing with Python

By now, we're all very familiar with using geoprocessing tools in ArcGis. But this week gave us the opportunity to learn how to utilize those tools with Python scripting.

After each tool is performed, your script will print a message with information about its successful execution and a time stamp of when the tool was executed. The screen shot above is an example of such a message. For this exercise, we were tasked with writing a code that would add x,y coordinates and a 1000-meter buffer to the designated shapefile. 

Since I am still very new to writing code, the Esri website was very helpful in guiding me through this process.