FSLens: A Visual Analytics Approach to Evaluating and Optimizing the Spatial Layout of Fire Stations
Longfei Chen, He Wang, Yang Ouyang, Yang Zhou, Naiyu Wang, Quan Li
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.
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Install the CLIlune papers fulltext 941448d6-e947-446c-bee4-7f933c5b708cCited by top-tier papers2
- CSLens: Towards Better Deploying Charging Stations via Visual Analytics - a Coupled Networks PerspectiveYutian Zhang, Liwen Xu, Shaocong Tao, Quanxue Guan et al.IEEE VIS 2024 · 3 citations
- TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical TrialsRui Sheng, Xingbo Wang, Jiachen Wang, Xiaofu Jin et al.IEEE VIS 2025 · 1 citation
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- mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series ForecastKe Xu, Jun Yuan, Yifang Wang, Cláudio T. Silva et al.CHI 2021 · 18 citations
- PromotionLens: Inspecting Promotion Strategies of Online E-commerce via Visual AnalyticsChenyang Zhang, Xiyuan Wang, Chuyi Zhao, Yijing Ren et al.IEEE VIS 2022 · 15 citations
- DeepVideoMVS: Multi-View Stereo on Video With Recurrent Spatio-Temporal FusionArda Düzçeker, Silvano Galliani, Christoph Vogel, Pablo Speciale et al.CVPR 2021
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