Data Selection Strategies and Visualization Design Based on UI/UX in Smart City Platforms: A Case Study of the Anyang Smart City Information Platform

This study examines the optimization of data selection and visualization in smart city information platforms, focusing on the Anyang Smart City Information Platform. Smart cities generate vast amounts of data to address urban challenges and improve citizen-centered services. The effective management of this data is essential for prioritizing critical information and ensuring its usability in real-time decision-making and policy planning. Smart city platforms integrate diverse data sources, including IoT sensors, geospatial data, and public service records. However, the volume of available data necessitates a structured approach to filter out redundant or irrelevant information. Prioritization mechanisms based on factors such as urgency, reliability, and applicability ensure that decision-makers and monitoring personnel access the most relevant data when needed. This research aims to establish a framework for prioritizing real-time and static data using the Analytic Hierarchy Process (AHP), considering factors such as reliability, timeliness, and relevance. Additionally, it seeks to design UI/UX strategies that improve data accessibility for key stakeholders, including monitoring staff and policymakers, while implementing visualization techniques that allow complex datasets to be interpreted more intuitively. Findings indicate that strategic data selection enhances platform efficiency by ensuring that critical urban data is highlighted according to predefined priorities, reducing information overload and enabling more effective decision-making. The Anyang case offers a replicable framework for other municipalities seeking to enhance their data-driven decision-making capabilities. Future research could explore the integration of AI-driven predictive analytics to further refine data selection processes and enhance proactive urban planning.