Abstract

Assessing the access to fire service at an urban scale involves accounting for geographical impedance, demand, and supply, thus both spatial and non-spatial dimensions must be taken into account. Therefore, in this paper, an optimized two-step floating catchment area (F-2SFCA) method is proposed for measuring urban fire service access, which incorporates the effects of both spatial and non-spatial factors into fire service access. The proposed model is conducted in a case study to assess the fire service accessibility of Nanjing City, China, and then compares its differences and strengths to the existing 2SFCA (two-step floating catchment area) methods. The experimental results demonstrate that the proposed method effectively quantifies the actual fire service needs and reflects a more realistic spatial pattern of accessibility (i.e., high accessibility level corresponded to a low fire service needs). In addition, we teste the relationship between service accessibility and the facility busyness using the inverted 2SFCA method. The empirical findings indicate that the weighted average accessibility obtained by F-2SFCA is reciprocal to facility busyness across the study area (based on a 5-min catchment), and fits an obvious nonlinear correlation with the high R-square values. The above results further prove the effectiveness and accuracy of the proposed method in characterizing the accessibility of fire services.

Highlights

  • Fire stations, as important public service facilities that provide professional fire services, came into existence with the aim of fighting fires, natural disasters, and other local hazards [1]

  • It incorporates a service competition model to solve the impact of both spatial impedance and facility capacity on spatial accessibility [19], and quantifies the demand for fire services by establishing a weighted sum coefficient based on population size, fire frequency, and floor area. It compares and analyzes the differences in the results calculated by the original 2SFCA, the 2SFCA with spatial factors, and the proposed F-2SFCA models in the case study of access to urban fire services. It utilizes the facility busyness effect of inverted 2SFCA (i2SFCA) to explain why the proposed F-2SFCA method tends to show a higher accessibility score in residential locations corresponding to a lower busyness in fire facilities there, as well as to further verify its rationality to measure accessibility for urban fire service [18]

  • Integrating Spatial and Non-Spatial Effects within the 2SFCA Method. Based on these improvements above, we aim to incorporate spatial and non-spatial factors within the 2SFCA model to improve the accuracy of fire service accessibility analysis; here, we refer to the improved 2SFCA method based on fire services as the F-2SFCA model in this study

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Summary

Introduction

As important public service facilities that provide professional fire services, came into existence with the aim of fighting fires, natural disasters, and other local hazards [1]. It incorporates a service competition model to solve the impact of both spatial impedance (distance or time) and facility capacity on spatial accessibility [19], and quantifies the demand for fire services by establishing a weighted sum coefficient based on population size, fire frequency, and floor area It compares and analyzes the differences in the results calculated by the original 2SFCA, the 2SFCA with spatial factors, and the proposed F-2SFCA models in the case study of access to urban fire services. It utilizes the facility busyness effect of i2SFCA (inverted two-step floating catchment area) to explain why the proposed F-2SFCA method tends to show a higher accessibility score in residential locations corresponding to a lower busyness in fire facilities there, as well as to further verify its rationality to measure accessibility for urban fire service [18]

Improvement 1
Improvement 2
Improvement 3
The Proposed F-2SFCA Method for Fire Service
Integrating Spatial and Non-Spatial Effects within the 2SFCA Method
Measuring Facility Busyness
Study Area and Datasets
Evaluating the Performance of F-2SFCA by Inverted 2SFCA
Findings
Conclusions
Full Text
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