fix: pinelli/3usecase cleanup

Similar to cleanup done in #255.

Author: wesleyboar

user-guide/usecases/pinelli/3usecase.md

@@ -2,8 +2,7 @@
 
 **Bakhshandeh, M. – Florida Institute of Technology**<br>
 **Pinelli, J-P. – Professor - Florida Institute of Technology**<br>
-**Cocke, Steven – Professor - Florida State University**<br> 
- 
+**Cocke, Steven – Professor - Florida State University**<br>
 
 **Keywords:** hurricane, wind field, interpolation, conversion, exposure correction, Jupyter Notebook, DesignSafe, Florida
 
@@ -15,18 +14,19 @@ Since 2017, Applied Research Associates (ARA), under contract with the National
 
 The workflow reads the wind-field data, performs interpolation to estimate winds at each property coordinate, and applies terrain adjustments to compute actual terrain-level wind speeds. For instance, given any hurricane dataset (e.g., Hurricane Michael), the notebook interpolates ARA’s 10-m open-terrain winds to specific property locations and modifies them based on local roughness and coastal proximity. The result is a spatially resolved dataset of adjusted 10-m wind speeds suitable for exposure modeling and vulnerability assessment.
 
-**Key steps:** 
+**Key steps:**
 
-- **Interpolation:** Derives property-level wind speeds from gridded ARA/NIST hurricane data.  
-- **Conversion:** Transforms wind speeds between averaging times (3-sec gusts, 1-min sustained) and exposure categories (marine, open, urban).  
-- **Terrain Adjustment:** Applies roughness-based corrections and coastal modifiers using GeoTIFF rasters.  
+- **Interpolation:** Derives property-level wind speeds from gridded ARA/NIST hurricane data.
+- **Conversion:** Transforms wind speeds between averaging times (3-sec gusts, 1-min sustained) and exposure categories (marine, open, urban).
+- **Terrain Adjustment:** Applies roughness-based corrections and coastal modifiers using GeoTIFF rasters.
 - **Visualization:** Generates interactive Folium-based maps and static Matplotlib plots for analysis and validation.
 
 ### Resources
 
 #### Jupyter Notebooks
 
-The following Jupyter notebooks are available to facilitate the analysis of each case.  
+The following Jupyter notebooks are available to facilitate the analysis of each case.
+
 You can access and run them directly on **DesignSafe** by clicking the link below:
 
 | **Scope** | **Notebook** |
@@ -38,16 +38,16 @@ You can access and run them directly on **DesignSafe** by clicking the link belo
 
 The following DesignSafe resources were used in developing this Use Case:
 
-- [Jupyter notebook on DesignSafe JupyterHub](https://www.designsafe-ci.org/rw/workspace/jupyter/)  
-- [DesignSafe Publication: Wind Speed Estimation and Conversion (DOI: 10.17603/ds2-kcxr-2683)](https://doi.org/10.17603/ds2-kcxr-2683)  
+- [Jupyter notebook on DesignSafe JupyterHub](https://www.designsafe-ci.org/rw/workspace/jupyter/)
+- [DesignSafe Publication: Wind Speed Estimation and Conversion (DOI: 10.17603/ds2-kcxr-2683)](https://doi.org/10.17603/ds2-kcxr-2683)
 - [ARA / NIST Hurricane Wind Field Datasets]
- 
+
 ### Implementation
 
 #### System Requirements
 
-- **Python 3.8+**  
-- **Jupyter Notebook environment** (DesignSafe JupyterHub or local)  
+- **Python 3.8+**
+- **Jupyter Notebook environment** (DesignSafe JupyterHub or local)
 
 ####  Dependencies
 
@@ -65,18 +65,19 @@ Before running the notebook, make sure the following Python libraries are instal
 |  | `contextily` | Static basemaps (tile layers for Matplotlib) |
 |  | `matplotlib` | Static plotting and visualization |
 
-
 #### Steps to Run
-1. Download or clone the repository:  
+
+1. Download or clone the repository:
    ```bash
    git clone https://github.com/mbakhshandeh2023/WindSpeed-Estimation-UseCase.git
    ```
-2. Open the Jupyter notebook (`Wind_Speed_Estimation.ipynb`).  
-3. Update file paths in `config1.txt` as needed.  
-4. Run all notebook cells sequentially to perform interpolation, conversion, and mapping.  
+2. Open the Jupyter notebook (`Wind_Speed_Estimation.ipynb`).
+3. Update file paths in `config1.txt` as needed.
+4. Run all notebook cells sequentially to perform interpolation, conversion, and mapping.
 5. Outputs (CSV files and HTML maps) will appear in the `Interpolation&ConversionOutput/` directory.
 
 ### Maping and Visulization
+
 This usecase presents two complementary approaches to visualize estimated actual wind speed data, each with distinct strengths and suited for different purposes:
 
 #### I.	Static Geospatial Map with Matplotlib and Contextily
@@ -93,21 +94,26 @@ This method creates an interactive web map that can be embedded in Jupyter noteb
 </figure>
 
 ### Background
+
 This use case was developed to support advanced hurricane wind hazard and vulnerability modeling in Florida. It integrates validated wind engineering relationships, spatial interpolation methods, and terrain adjustment techniques within a reproducible Jupyter-based computational framework.
 
 The use case is based on the Jupyter notebook published in the DesignSafe-CI Data Depot — “Wind Speed Estimation and Conversion” (DOI: 10.17603/ds2-kcxr-2683
 ) — and extends that work by providing a structured example for researchers and practitioners to reproduce, adapt, and integrate into broader catastrophe modeling workflows.
 
 ### Citation and Licensing
+
 If you use this repository, please cite:
 
 > Bakhshandeh, M., Pinelli, J.-P., & Cocke, S. (2025). *Wind Speed Estimation and Conversion.* DesignSafe-CI. DOI: [10.17603/ds2-kcxr-2683](https://doi.org/10.17603/ds2-kcxr-2683)
 
-**License:** BSD 3-Clause License  
+**License:** BSD 3-Clause License
 
 ### Data Sources and Confidentiality:
+
 The Jupyter notebook also requires input files containing the latitude and longitude of the analyzed properties. These data can be obtained either from insurance claim records or from publicly available reconnaissance datasets (such as those hosted on the DesignSafe Reconnaissance Portal).
+
 For insurance-related data, confidentiality restrictions may apply. Users are responsible for ensuring that any proprietary or sensitive information is handled in compliance with data privacy agreements and institutional policies. The dataset used in this notebook does not contain any company identifiers or personally identifiable information, ensuring confidentiality is maintained.
 
 ### Acknowledgment:
+
 This research was supported by the **National Science Foundation (NSF)** under **Award No. 2022469**, through the **NHERI DesignSafe Cyberinfrastructure**. The opinions and conclusions expressed are those of the authors and do not necessarily reflect the views of the NSF.