## 2 Methods
### Case Study: Proof-of-Concept Demonstrations  
Following the HCD process and using the PTM analysis of user stories to further define each information variable, we developed three proof-of-concept analyses on a single megafire event, the 2021 Colorado Marshall Fire. Here, we describe the methods used to create proof-of-concept analyses, which were used in continued iterations of qualitative research to gather feedback from potential users. The Colorado Marshall Fire occurred on New Year's Eve (30 December 2021 to 3 January 2022) in a suburban neighborhood in the central United States with a traditionally low fire hazard. Tens of thousands of people were evacuated and over a thousand homes burned. The Marshall fire occurred in the wildland urban interface (WUI) with human development adjacent to a large expanse of naturally preserved open space. The transport network is dominated by slow speed, curved residential streets. As the fire traveled through the WUI with gusts greater than 100 miles per hour [51], evacuees flooded onto small, circuitous streets and sat in traffic trying to get away from the fire blowing overhead. Because of recent investments in climate resilient infrastructure and the need for people to evacuate away from fire hazard, we applied analytics for the following: (1) social media filtering, (2) evacuation rate predictions, and (3) predicting the annual fire hazard from present to 2070. Social media filtering provides context for the chronological order of events and public perception of the fire as it was happening. We use social media data collected using the Twitter API version 2 [52] to extract all original tweets (no retweets) and containing the hashtags _marshallfire_ or the words \"_Marshall Fire_\". Tweets were collected from 30 December 2021 through the end of January. We collected beyond the containment date to capture the post-fire discussion related to the evacuation and returns. We pulled 26,788 tweets, 1756 of which related in some way to the evacuation. These 1756 tweets were further stratified using a neural net classifier to identify the tweet contributor role [11; 53; 54]. The content of these 1756 tweets was examined manually, including the related conversational threads and linked sources, to build a timeline of events We filtered media sources and focused on official messaging and contributions from people directly impacted by the event. We identified the key issues and communication disconnects related to the evacuation as it evolved and the socio-technical innovations supporting decision making and communication. Based on our initial analysis, we expanded the data collection to include the names of the towns under evacuation orders (Louisville, Superior, Boulder, and Lafayette) and tweets related to evacuation or traffic. For example, we searched for original tweets for the town of Louisville using terms _Louisville_ plus either the word _traffic_ or _evacuation_ during the evacuation timeframe. After eliminating noise (e.g., tweets reportingtraffic_ in _Louisville_, KY or marketing tweets promoting _superior_ internet _traffic_), this resulted in the addition of 232 tweets. Simulations of evacuation traffic [55] away from the Marshall fire used three inputs: a network representation of the roads provided by Open Street Map, a fire perimeter from the National Interagency Fire Center [56] showing the extent of the burned area, and a list of origins and destinations for travelers evacuating the fire. We made a naive estimation of origins to be one vehicle leaving each destroyed structure. Structure locations came from the Boulder County Sheriff's office and were geocoded to match building records from the Zillow ZTRAX dataset [57]. We estimated isoclines within different amounts of travel time. We used existing research to predict future fires from 2020 to 2060 in the contiguous U.S. [58]. We overlaid major road networks to better see where infrastructure exists, and hazards were changing.