Module 5: Damage Assessment

This lab assignment had many moving parts which required skills learned in previous classes and labs, for which I'll share on of the required deliverables.

With a set of pre/post storm images the required these to be placed as a mosaic layer to use to perform an assessment of structure damage caused by a storm. Once these were created and added to a map with the parcel and assessment boundary. It was easy evaluate the structures using the Mosaic Layer -> Swipe or Flicker tool to create point features identifying structures and assigning it level of damage from a domain of five categories.

The methodology used to determine a structures damage was by first reviewing the overall assessment area. The first indicator all the structures within the area were affected is by the amount of sediment and debris deposited by the storm surge. Streets and parking lots where covered completely with sediment and could not be seen. This was an indicator that the structures where likely flooded and therefor affected. Reviewing each structure parcel by parcel, given those which did not have any visible structure in the post-storm image where code as destroyed. Those with a majority of the structure standing but sections severely damage where code as major damage, those with visible roof damage, but the integrity of the structure remained enacted, coded as minor damage. Below is a screenshot of the points collected during the evaluation.

Once the structure damage evaluation was completed, a line feature was added which represented the coastline adjacent to the subject area. Leveraging the multi-ring (buffer) tool the line feature used to create a 100, 200, and 300 buffer area. That area was then spatially joined to the structure damage feature and a summary statistics table was generated counting the structure damage types by the joined distance. Below is the results of my analysis.

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
Minor Damage	0	17	3
Major Damage	2	9	0
Destroyed	8	4	0
Totals	10	39	29

Module 3: Visibility Analysis

Until reasonably the data used and map produced have been 2-dimensional, here in module 3 we explored data in a 3-dimensional space. Assigned four Esri web course, 

• Introduction to 3D Data
• Performing Line of Sight Analysis
• Performing Viewshed Analysis in ArcGIS Pro
• Sharing 3D Content Using Layer Packages

The first lesson, Introduction to 3D Data, explored the basics of 3-dimentional data with an overview of vertical coordinate system. The next two web course provided step by step instructions on performing line-of-sight and viewshed analysis with ArcGIS Pro and finally wrapping up with a course on sharing 3D content. 

Some best practices when authoring a 3d scene:

• Be sure to have all source data and scene in the same coordinate system.
• Structure the content and decide what needs to be seen
• Define and area-of-interest (AOI) for the scene
• Set the required 3D symbology for the feature layers being publish
  

Module 2: LiDAR

 

Module 1: Crime Analysis

This weeks lab provided insight into different methods used with crime hotspot analysis.  and explored the differences between global and local approaches. 

The lecture defines global clustering as a pattern which produces a single statistic with confidence intervals and local clustering which produces a hotspot.  Some of the more notable methods for determining local clusters are grid-based mapping, local Moran's I, kernel density, Gi*, and Nearset Neighbor Hierarchical (NNH). These method combined data aggregation provide valuable insights for decision makers to improve their community.

Kernel Density Hotspot

Geographic Boundary Hotspot

Some examples of hotspot mapping found in the lab is shown above, which show a kernel density of assault and choropleth map of burglary rate. Below are examples of grid-based, kernel density, and local Moran's I hotspot mapping.

Finally, the lab concluded with creating a table which provide a matrix (not shown) to evaluate the different methods. Utilizing the information found in the map and the matrix the kernel density and local Moran's I offered the greatest crime hotspot insight, where the the local Moran's I performed slightly better.