Enhancing Mobile Network Infrastructure Through Spatio-Temporal Analysis and Decision Support Systems by Geo-Decisional Prototype
Abstract
The telecommunications industry generates copious amounts of data, including geographic information vital for robust business forecasting. This study endeavours to develop a prototype Spatial Online Analytical Processing (SOLAP) system aimed at conducting multidimensional statistics and predicting the expansion of radio antenna parks. Six fundamental concepts underpin our approach: (1) Big Data management for chronological storage of business data, (2) Multidimensional modelling to structure data around ‘facts’ and ‘dimensions’, (3) OLAP for navigating Big Data and conducting multidimensional statistics, (4) GIS for representing spatial data on satellite and topographical backgrounds, (5) SOLAP, which merges GIS and OLAP properties for statistical analysis and forecast scenarios, and (6) Open Source tools for crafting an operational SOLAP prototype using available software. The prototype’s design encompasses four pivotal steps: (1) Multidimensional spatial database modelling, (2) Establishment of a data warehouse space dubbed ‘GouvData’ using PostgreSQL, (3) Development of the SOLAP system ‘GouvRes’ by integrating a GIS platform (QGIS) and an OLAP ‘cube’ developed in Python, and (4) Creation of a user interface to formulate statistical queries and urban planning scenarios. This study presents a holistic framework for leveraging spatial intelligence in mobile operator forecasting, with due consideration given to urban planning, thus contributing to strategic decision-making and fostering business development. As a result of this study, the capability to depict the magnitude of traffic across a region using interactive maps and satellite imagery has been developed. This capability allows for the effective monitoring and management of the extension of mobile network infrastructure.
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