Mapping emergency medical calls : a retrospective spatiotemporal analysis of ambulance calls in Malta

Abstract

Background: Emergency medical services (EMS) represent a critical component of Malta’s healthcare system, yet little research has examined the spatial and temporal dynamics of ambulance demand. Understanding when, where, and under what conditions emergency calls occur is essential to improving operational efficiency, equitable service coverage, and data driven resource allocation. Objectives: This study aimed to analyse the spatiotemporal and environmental patterns of emergency ambulance calls in Malta between 2022 and 2024. Specifically, it sought to (1) identify spatial and temporal trends in EMS demand, (2) explore their interaction across geographic and temporal dimensions, and (3) assess the relationship between ambulance call volumes and daily maximum temperature. Design and Methods: A retrospective, observational, and quantitative design was employed. Anonymised ambulance dispatch data were obtained from the national Computer-Aided Dispatch (CAD) system and processed into a dataset of 132,500 unique calls. Descriptive and inferential statistics were performed using SPSS, while spatial and spatiotemporal mapping were conducted in ArcGIS Pro. Correlations with environmental variables were tested using Pearson’s r. Participants: All emergency ambulance calls originating from mainland Malta between 1 January 2022 and 31 December 2024 were included. Calls with incomplete locality data or representing outbound inter-facility transfers were excluded. Results: Ambulance demand increased by approximately 22% across the study period, with consistent mid-morning peaks (around 10:00am) and dual seasonal surges in summer (July– August) and winter (December). Spatially, the Northern Harbour and Southern Harbour districts accounted for over half of all calls. A moderate, statistically significant correlation was found between daily maximum temperature and call volume (r = 0.297, p < 0.001). Conclusions: Ambulance demand in Malta is shaped by identifiable spatial, temporal, and environmental patterns. These findings provide a foundation for predictive modelling, targeted resource allocation, and evidence-based EMS planning in small-island health systems.