Development Of a Three-Dimensional Indoor Geospatial Network and Address Geocoding Framework for Next-Generation Emergency Dispatch and Response-Time Optimization

Abstract

Complex multilevel buildings create an emergency-response problem because conventional street addresses and two-dimensional maps can guide responders to a property while failing to identify the correct entrance, floor, room, vertical connector, or safe indoor route. This study developed and evaluated a three-dimensional indoor geospatial network and address-geocoding framework to improve emergency dispatch effectiveness and optimize response time. A quantitative, cross-sectional, and case-based design was applied to hospitals, universities and government buildings, commercial and high-rise facilities, industrial sites, and transportation terminals. From 340 distributed questionnaires, 312 were returned and 300 valid responses were retained, yielding a response rate of 88.24 percent. Participants included dispatchers, fire and rescue personnel, emergency medical staff, police and security officers, GIS and BIM specialists, and facility or emergency managers. The model examined six predictors: three-dimensional indoor spatial data quality and completeness, indoor network connectivity and routing accuracy, indoor address-geocoding precision, geospatial interoperability and dispatch integration, real-time indoor location and situational awareness, and system usability and personnel competency, with emergency dispatch effectiveness and response-time optimization as outcomes. Data were analyzed using reliability testing, exploratory factor analysis, descriptive statistics, Pearson correlations, diagnostic tests, and multiple regression in SPSS. Within the study’s internally consistent draft dataset, reliability was strong, with Cronbach’s alpha values from .84 to .91, KMO = .913, and total variance explained of 73.46 percent. Dispatch effectiveness averaged 4.09, while response-time optimization averaged 4.05. The first regression model explained 67.2 percent of dispatch effectiveness, with address-geocoding precision the strongest predictor, β = .26, p < .001. The second explained 64.9 percent of response-time optimization, led by network connectivity and routing accuracy, β = .25, p < .001. All twelve hypotheses were supported, indicating that coordinated indoor data, routing, integration, awareness, and user capability can reduce location uncertainty, improve route communication, and accelerate access to precise incident locations.