M5 - Damage Assessment

Introduction

After Hurricane Sandy made landfall, conducting a precise damage assessment was crucial for effective disaster response and recovery. Utilizing Geographic Information System (GIS) technology, we systematically analyzed the damage to structures along the affected coastlines.

Steps to Complete the Damage Assessment

To carry out the damage assessment, we followed a structured approach using GIS tools for data visualization and analysis:

  • Data Preparation: We started by importing both pre-storm and post-storm imagery into the GIS platform, ensuring that all necessary layers, such as parcel boundaries and the study area polygon, were correctly set up.
  • Digitizing Points on Structures: Next, using the pre-storm imagery as a reference, points were placed on every visible structure within the study area. This step was crucial for ensuring that no buildings were overlooked.
  • Comparative Analysis: By utilizing the swipe tool, we compared pre- and post-storm imagery to visually assess the extent of damage for each structure. We then assigned attributes to each point, categorizing the damage from "No Damage" to "Destroyed."
  • Data Entry and Validation: Attribute data was meticulously entered into the GIS database, with predefined domains used to minimize errors. For structures that spanned multiple parcels, additional points were added to ensure each unit was assessed individually.
  • Final Analysis and Visualization: Finally, the data was analyzed to detect damage patterns, especially in relation to the structures’ proximity to the coastline. The results were then visualized on a map, clearly highlighting areas that suffered significant damage.

Final Results


The analysis demonstrated a strong correlation between proximity to the coastline and the severity of damage. Structures located within 100 meters of the shore were found to be heavily impacted, with many either destroyed or severely damaged. As the distance from the coastline increased, the extent of damage generally decreased, with fewer structures experiencing major impacts. These findings highlight the critical influence of shoreline proximity on storm damage, offering valuable insights for improving disaster preparedness and response strategies.

This GIS-based assessment not only provided a clear understanding of Hurricane Sandy’s impact but also illustrated the effectiveness of geospatial analysis in post-disaster scenarios. The insights gained can be leveraged by local governments, emergency management agencies, and urban planners to enhance coastal resilience and better prepare for future events.





Comments

Post a Comment

Popular posts from this blog

M1 - Python Environments & Flowcharts

Image
  Python Module 1 Recap: From Scripts to Flowcharts and Zen Principles Converting 3 Radians to Degrees To convert 3 radians to degrees, we use a simple formula: degrees = radians * 180 / pi Below is the flowchart illustrating the conversion process: Running the calculations in IDLE gives us:  ≈ 171.887 Interpretation of the Zen of Python The "Zen of Python" showcases the philosophy and guiding principles of Python programming. It emphasizes simplicity, readability, and clarity in code. In other words, “Zen of Python” promotes straightforward and elegant solutions over complicated ones. By valuing explicitness over implicitness, the basis of Python encourages clear and understandable code. Moreover, The principles highlight the importance of practicality and avoiding unnecessary complexity— why use 4 lines of code when 1 will do the same thing? This mentality helps to balance between purity and practicality. Overall, the “Zen of Python” advocates for writing code that is easy
Read more

GIS Map Projections

Image
  This week's lab exercise looked at the importance of map projections in GIS, especially when using multiple datasets. The map above shows four counties in Florida that are displayed using the Albers Conical Equal Area, NAD 1983 UTM Zone 16N, and NAD 1983 HARN State Plane Florida North FIPS 0903 (US Feet) map projections. As the table within the map shows, the areas of the counties vary depending on the map projections. The best map projections are ones that shows the least distortion between map features. For example, The UTM projection is best when used the map features fit within one UTM zone, which is not the case for showing the entire state of Florida. The same can be said for the Florida State Plane that is dependent on the region of interest. Therefore, the map projection would need to be Albers since it minimizes distortion between its standard parallels.  Overall, this lab illustrates how important it is to use accurate map projections since it can cause distortions betw
Read more

ArcGIS Pro Orientation

Image
  Module 1 Lab Assignment This first lab assignment's objective is to prepare and show what is expected for future lab assignments. The primary learning outcomes involved: Getting familiar with accessing the ArcGIS software through Argo Apps. Assessing data storage drives. Preparing project folders for each project. The map shown above is a visualization of the Cities and World Countries data files found in the OverviewArcGIS file in the R drive. In order to view the shapefiles in a way that is informative, we had to add a Folder Connection to the data we downloaded as well as update the symbology to show graduated colors for the field POP_2007 with Natural Breaks and 7 classes. It is important to use a single color from light to dark when using sequential data. While I wish this had been a smooth and uncomplicated process, I struggled with learning UWF's Argo Apps since that is how we access ArcGIS Pro without downloading it to our personal computers. While I am familiar with
Read more