Methodology

Levels of DH + Acknowledgements

Sources

To find literary sources to supplement our research, we used the UCLA Library’s online search engine. We searched for specific key terms pertaining to our project such as “wildfire.” “fire,” “drought,” “California,” “displacement,” and “vulnerability.” We utilized the search engine by using search filters “AND” and “OR” to present us with articles, journals, and books that mention all keywords or one of two keywords. The UCLA online library allowed us to easily and efficiently access scholarly resources. Moreover, some articles were found on JSTOR using the same search technique. 

The SVI data came from the CDC website, and the data was collected from different Census tracts from California from 2018 to 2022. Some of their numbers were gathered from the American Community Survey (ACS). The California Wildfire dataset came from the California Department of Forestry and Fire Protection. The dataset includes information about the names of wildfires, when the wildfire started, and the causes of the wildfire. To make our map of California wildfires, we used an additional data set from Kaggle which the original poster credits the California Department of Forestry and Fire Protection (CAL FIRE), National Interagency Fire Center (NIFC), and Fire Integrated Real-Time Intelligence System (FIRIS) for the provision of the dataset. The dataset spans from 2013-2024 and provides information about wildfire incidents and the structural damages the wildfires caused. This data set provided specified counties, and longitude and latitude information which allowed us to easily create the map using Tableau. The Historical Drought Data came from the National Integrated Drought Information System. It includes information on the percentages of California that experienced different levels of drought severity and wetness. However, this dataset did not include geographical data, so we could not use it to visualize what specific areas in California were affected by droughts leading to wildfire risk.

  1. Social Vulnerability Index: https://www.atsdr.cdc.gov/place-health/php/svi/index.html
  2. California Wildfire: https://gis.data.cnra.ca.gov/datasets/CALFIRE-Forestry::recent-large-fire-perimeters-5000-acres-1/about
  3. Kaggle California Wildfire Dataset (for mapping): https://www.kaggle.com/datasets/vijayveersingh/the-california-wildfire-data/data
  4. Historical Drought Data: https://www.drought.gov/historical-information?dataset=0&selectedDateUSDM=20250121&state=California

Process

We combed through the variables manually in order to focus on the variables pertaining to our research questions. 

We used Timeline.js along with an ABC7 news article to create our timeline depicting the dates of the largest 15 California wildfires.

Challenges surrounding our data included inconsistent formatting among datasets such as inconsistent formatting of dates among the three datasets that we used. Some column names were ambiguous, and we had to look at the documentation of the dataset to determine the meaning of some variables. For the California Wildfire dataset, there was limited information only 96 columns that only spanned four years (2019-2023).

Presentation

Platform: We used WordPress to create our website with the assistance of the Elementor, Superblank, and Astra (template) plugins. The Superblank plugin helped to start on a blank canvas and working with Astra gave us the bones to design with tools from Elementor. 

Design: We implemented a vibrant orange and blue color scheme across our website to align with our theme while ensuring visual cohesion. The high contrast between these colors enhances readability. Additionally, we utilized icons from Figma to maintain a minimal and consistent aesthetic.

Visualizations: We used Tableau to turn our data into visualizations since it was the most user-friendly platform for our team. Some of our visuals were also made in R as some team members had experience with the programming language. Lastly, we used HTML to embed Tableau visualizations, add interactive buttons, and build timelines. For the timeline specifically, we used TimelineJS.

Acknowledgements

We would like to thank Professor Sabo for providing us informative, thorough, and engaging class presentations that allowed us to learn the skill sets necessary for this project. Moreover, we would like to thank our TA, Cameron Manning, for his assistance during section answering our every question!