Abstract. This study introduces a scalable, cloud-native, no-code Geographic Information System (GIS) platform tailored for large-scale spatial decision-making in logistics operations. The platform aims to enable non-technical users such as field operators and branch managers—to design, deploy, and visualize spatial queries and dashboards without requiring software development skills. Its architecture integrates a multi-layered microservices framework with a PostGIS backend and supports dynamic visualization of millions of spatial points in real time. Advanced optimization modules, such as clustering (k-means, DBSCAN) and locationrouting algorithms (p-median, set covering), are encapsulated as configurable plug-ins within the platform. Designed and tested in a national courier network, the system addresses major technical challenges such as query performance on fragmented big data, visual responsiveness under heavy load, and inter-database integration via engines like Trino. Experimental evaluations demonstrate sub-second response times for complex spatial aggregations over datasets exceeding 1.5 million records. The platform fosters improved operational visibility, reduced IT dependency, and faster decision loops in dynamic geospatial environments. The architecture and findings of this work present a practical contribution to enterprise-grade GIS systems and open new research directions in user-centric geospatial analytics.
Sarı et al. (Tue,) studied this question.