Integrating Artificial Intelligence, Semantic Indoor Geospatial Modeling, And Network-Based Routing for High-Precision NG911 Emergency Location Intelligence

Abstract

This quantitative study examined whether integrating artificial intelligence, semantic indoor geospatial modeling, and network-based routing improved high-precision Next Generation 911 emergency location intelligence. A multisite, repeated-measures experimental design was implemented across 18 multistory buildings representing residential, hospital, educational, transportation, commercial, and industrial environments. The study established 540 surveyed reference points and retained 12,614 of 12,960 primary trials, producing a 97.3% retention rate. Eight smartphones representing four device classes collected Wi-Fi, Bluetooth, ultra-wideband, inertial, magnetic, barometric, and visual observations. Three configurations were compared: conventional coordinate-only localization, artificial-intelligence localization with semantic matching, and full integration with semantic modeling and network routing. Mixed-effects regression, external holdout validation, calibration analysis, and multilevel sequential mediation with 5,000 bootstrap resamples were applied. Full integration reduced mean horizontal error from 7.84 to 2.36 meters and vertical error from 4.18 to 1.12 meters. Correct-floor identification increased from 72.6% to 96.4%, and correct-room identification increased from 51.8% to 91.7%. Adjusted horizontal and vertical reductions were 5.42 and 3.04 meters, with large standardized effects of −1.52 and −1.32, respectively. The odds of correct-floor and correct-room identification were 9.74 and 10.31 times greater under full integration. Semantic interpretation accuracy reached 95.1%, valid-route generation reached 96.8%, successful route completion reached 96.2%, and operational record completeness increased from 63.4% to 94.8%. Mean end-to-end processing time was 1.92 seconds. Semantic matching and route validity mediated 67.2% of the total relationship between artificial-intelligence localization and emergency location intelligence. Holdout evaluation showed limited performance loss, while stress tests retained fallback localization above 78% and successful diversion above 85%. Model fit was strong, with CFI = .976, RMSEA = .034, and 74% of final outcome variation explained by the complete integrated statistical framework. The findings demonstrated that coordinated positioning, semantic interpretation, and routing produced more accurate, calibrated, resilient, and operationally actionable NG911 indoor location intelligence than isolated coordinate estimation.