arXiv 23 Oct 2025 · Statistics — Applications
arXiv:2510.26810 · PDF · DOI · OpenAlex · Extracted main text
Emergency medical services (EMS) response times are critical determinants of patient survival, yet existing approaches to spatial coverage analysis rely on discrete distance buffers or ad-hoc geographic information system (GIS) isochrones without theoretical foundation. This paper derives continuous spatial boundaries for emergency response from first principles using fluid dynamics (Navier-Stokes equations), demonstrating that response effectiveness decays exponentially with time: $τ(t) = τ_0 \exp(-κt)$, where $τ_0$ is baseline effectiveness and $κ$ is the temporal decay rate. Using 10,000 simulated emergency incidents from the National Emergency Medical Services Information System (NEMSIS), I estimate decay parameters and calculate critical boundaries $d^*$ where response effectiveness falls below policy-relevant thresholds. The framework reveals substantial demographic heterogeneity: elderly populations (85+) experience 8.40-minute average response times versus 7.83 minutes for younger adults (18-44), with 33.6% of poor-access incidents affecting elderly populations despite representing 5.2% of the sample. Non-parametric kernel regression validation confirms exponential decay is appropriate (mean squared error 8-12 times smaller than parametric), while traditional difference-in-differences analysis validates treatment effect existence (DiD coefficient = -1.35 minutes, $p < 0.001$). The analysis identifies vulnerable populations--elderly, rural, and low-income communities--facing systematically longer response times, informing optimal EMS station placement and resource allocation to reduce health disparities.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Carr, B. G., Branas, C. C., Metlay, J. P., Sullivan, A. F., and Cama… (2017) Access to emergency care in the United States | 1.000 | 11 | 3 | 100% |
| 2 | Kikuchi, T (2024) Dynamic Spatial Treatment Effects as Continuous Functionals: Theory and Evidence from Healthcare Access self | 1.000 | 8 | 3 | 100% |
| 3 | Kikuchi, T (2024) Nonparametric identification and estimation of spatial treatment effect boundaries: Evidence from 42 million pollution observati… self | 1.000 | 6 | 3 | 100% |
| 4 | Kikuchi, T (2024) Nonparametric identification of spatial treatment effect boundaries: Evidence from bank branch consolidation self | 1.000 | 6 | 3 | 100% |
| 5 | Larsen, M. P., Eisenberg, M. S., Cummins, R. O., and Hallstrom, A. P (1993) Predicting survival from out-of-hospital cardiac arrest: A graphic model | 1.000 | 5 | 3 | 100% |
| 6 | Saver, J. L (2006) Time is brain—quantified | 0.928 | 4 | 3 | 100% |
| 7 | Kikuchi, T (2024) Dynamic spatial treatment effect boundaries: A continuous functional framework from Navier-Stokes equations self | 0.874 | 7 | 2 | 100% |
| 8 | Kikuchi, T (2024) Spatial and temporal boundaries in difference-in-differences: A framework from Navier-Stokes equation self | 0.874 | 6 | 2 | 100% |
| 9 | McLafferty, S. and Grady, S (2012) Immigration and geographic access to prenatal clinics in Brooklyn, NY: A geographic information systems analysis | 0.874 | 6 | 2 | 100% |
| 10 | Kikuchi, T (2024) Stochastic boundaries in spatial general equilibrium: A diffusion-based approach to causal inference with spillover effects self | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dual-Channel Technology Diffusion: Spatial Decay and Network Contagion in Supply Chain Networks | 0.874 | 5 | 2 |
| 2 | Network Contagion Dynamics in European Banking: A Navier-Stokes Framework for Systemic Risk Assessment | 0.737 | 3 | 2 |