Fernanda Molina and Andrea Wan

It was Fire Prevention Month in October, a month dedicated to improving fire safety awareness. This interactive narrative visualization attempts to investigate the many fire occurrences that have happened in Pittsburgh. Understanding the many types of fires that occur, as well as where and how frequently they occur, sheds insight on why fires occur in our community. The data analysis presented here reveals which fire incidents are the most prevalent, and hence which fire safety methods are most important to the community. The data we're looking at comes from the Western Pennsylvania Regional Data Center and contains a variety of criteria linked to fires that the Pittsburgh Bureau of Fire has responded to since January 2013.

https://docs.google.com/document/d/1PRNFIGeYvFSHjdqIyugaHdZ6K_8RGz9HZUfGA3sCe0I/edit?usp=sharing

https://youtu.be/2ijNBxMCZus

https://observablehq.com/d/5fd1b51fecd1c282

View this notebook in your browser by clicking on the link above

In order to create this interactive visualization, we took many pivots throughout our process. We first began by wanting to do an interactive/application that can help to explain the process of creating a 3D model. Since that did not really invovle a dataset and our proposal got rejected, we then thought on using a dataset about Fish Species; we were planning on doing a narrative about eating sustainability. However, we found the dataset was hard to load into Observable. We also thought about doing a narrative on susbtance abuse, but none of the datasets were clean or had much useful information. Therefore, we settled for a dataset on fire incidents in Pittsburgh.

With this dataset, Fernanda was in charge of all the SQL queries as she had a good amount of experience using PostgreSQL and creating load files. Andrea explored some different visualization options involving animation and the use of 3D graphics. Both of us worked on creating the visuals with Vega-Lite as we had gained experience from previous assignments. We worked to find new ways to make charts, add specific interactions such as tooltips, and improve the accessibility of our visualizations through custom color schemas. Furthermore, we both contributed to adding to the narrative and analyzing the results given out by our interactive charts. Overall, the hardest part was thinking about how to use SQL to manipulate the data to make interesting observations. That being said, we did face challenges in creating the map and also figuring our how filtering works with the different types of charts. There were also challenges with making the animations and 3D graphics interactive, whcih is why we opted to show them as videos rather than Three.js modules.