This data viewer was developed in the research project SALUS Wildfire Risk Solutions to visualize the simulated propagation probability maps and extracted infrastructure from OpenStreetMap. The data viewer was developed with the libraries Dash and Plotly in Python and based on an example from the Dash gallery. The dashboard consists of a map window displaying wildfire propagation polygons in a range of heat colors, a blue buffer zone around the perimeter and exposure data in form of points or lines located inside these areas. The chart window plots temporal evolution of exposure data located in the different areas. Point data is summarized as sum of all data points (e.g. buildings) for each polygon category per hour. In case of linear data (e.g. roads) it is sum of length per category and hour. Other tools in the dashboards are the dropdown menus for:- change exposure category- choose a different case study- switch between three basemaps- slider to navigate through the simulated event

This work is part of the SALUS project, funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”, with CPP2021-008762 being the reference that appears in the grant resolution; MCIN being the acronym for the Ministry of Science and Innovation; AEI being the acronym for the State Research Agency; 10.13039/501100011033 being the DOI (Digital Object Identifier) of the Agency; and PRTR being the acronym for the Recovery, Transformation and Resilience Plan.