FSLens: A Visual Analytics Approach to Evaluating and Optimizing the Spatial Layout of Fire Stations

TL;DR

FSLens is an interactive visual analytics system that helps evaluate and optimize fire station layouts by analyzing fire data, exploring resource gaps, and generating multiple solutions for improved fire service efficiency.

Contribution

This paper introduces FSLens, a novel visual analytics approach integrating fire data analysis and multi-criteria decision-making for fire station layout optimization.

Findings

Effectively reveals fire occurrence patterns.

Identifies under-resourced areas for fire services.

Assists decision-makers in selecting optimal layouts.

Abstract

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.