Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Future Internet Pub Date : 2024-01-30 DOI:10.3390/fi16020047
Andreas F. Gkontzis, S. Kotsiantis, G. Feretzakis, V. Verykios
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引用次数: 1

Abstract

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.
增强城市复原力:街区层面的智能城市数据分析、预测和数字孪生技术
智慧城市利用先进的数据分析、预测模型和数字孪生技术,为可持续城市发展提供了一种变革模式。预测分析对于前瞻性规划至关重要,使城市能够适应不断变化的挑战。同时,数字孪生技术提供了城市环境的虚拟复制品,促进了对城市系统的实时监控、模拟和分析。本研究强调了对城市系统进行实时监控、模拟和分析的重要性,以支持测试场景,找出瓶颈,提高智慧城市效率。本文深入探讨了市民报告分析、预测和数字孪生技术在社区层面的关键作用。该研究整合了提取、转换、加载(ETL)流程、人工智能(AI)技术和数字孪生方法,以处理和解释从市民与城市基于坐标的问题映射平台的互动中获得的城市数据流。利用数字孪生方法中的交互式地理数据框架,动态实体可促进基于各种情景的模拟,使用户能够可视化、分析和预测街区层面的城市系统响应。这种方法揭示了每个城市区域物理层面的先行和预测模式、趋势和相关性,从而改善城市功能、复原力和居民生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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