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- Xiaoqing Yang https://ror.org/04mw2ty13College of Art and Design, Anhui Institute of Information Technology, 241199, Wuhu, Anhui, China
https://ror.org/04mw2ty13College of Art and Design, Anhui Institute of Information Technology, 241199, Wuhu, Anhui, China
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- Roopesh Sitharan https://ror.org/04zrbnc33Faculty of Creative Multimedia, Multimedia University, 63100, Selangor, Malaysia
https://ror.org/04zrbnc33Faculty of Creative Multimedia, Multimedia University, 63100, Selangor, Malaysia
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- Elyna Amir Sharji https://ror.org/04zrbnc33Faculty of Creative Multimedia, Multimedia University, 63100, Selangor, Malaysia
https://ror.org/04zrbnc33Faculty of Creative Multimedia, Multimedia University, 63100, Selangor, Malaysia
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- He Feng https://ror.org/007cx7r28College of Arts and Education, Chizhou University, 247000, Chizhou, Anhui, China
https://ror.org/007cx7r28College of Arts and Education, Chizhou University, 247000, Chizhou, Anhui, China
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Soft Computing - A Fusion of Foundations, Methodologies and ApplicationsVolume 28Issue 3Feb 2024pp 1971–1988https://doi.org/10.1007/s00500-023-09570-2
Published:08 January 2024Publication History
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See AlsoWhat is Data Visualization and Why is It Important? - GeeksforGeeksHow can data visualization improve your organization's growth and expansion?What are the best data visualization tools for diagnosing performance issues?Understanding Data Visualization: Importance, Types, Tools & Future TrendsCreate a New Binder
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Soft Computing - A Fusion of Foundations, Methodologies and Applications
Volume 28, Issue 3
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Abstract
Abstract
The exponential growth of urban data presents significant challenges in efficiently analyzing and gaining actionable insights for urban planning and design. This paper proposes a big data analytics framework using MapReduce-based parallel FP-growth (MP-PFP) algorithm leveraging tools like Hadoop, MapReduce, and distributed crawlers to uncover patterns and trends from large-scale, heterogeneous urban datasets. A key contribution is the integration of diverse data types, from socio-economic datasets to environmental parameters, into a consistent analysis framework. The methodology employs frequent pattern mining algorithms on a scalable analytics platform to process behavior data and derive planning directives. Additionally, data visualization and parametric analysis techniques transform raw statistics into interactive 3D landscape representations that expose environmental site attributes. Specifically, the MapReduce capabilities enable distributed parallel processing of vast urban data volumes, ensuring speed and efficiency. The data visualization module creates immersive VR representations of urban landscapes, allowing interactive modifications. Advanced simulation techniques are incorporated to model the impact of planning directives on multiple landscape attributes. The framework is designed as a scalable, customizable solution that can integrate diverse urban data sources with customizable analytics, modeling and visualization modules through APIs. Comparative evaluations demonstrate a classification accuracy improvement from 68 to 93% over prevailing approaches. The framework has proven superior in data integration, real-time responsiveness, and accurately modeling the dynamic complexities of urban landscapes. The quantifiable simulations empower designers to make more informed planning decisions aligned with community needs. Despite ongoing data accuracy and privacy concerns, the methodology shows promising capabilities in harnessing urban big data to drive intelligent, sustainable urban development through its integration of data-driven insights, computational analysis, and interactive visualization. It brings impactful innovations to the future of urban informatics and planning.
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Index Terms
Exploring the integration of big data analytics in landscape visualization and interaction design
Computing methodologies
Human-centered computing
Visualization
Visualization application domains
Information systems
Information systems applications
Spatial-temporal systems
Index terms have been assigned to the content through auto-classification.
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Published in
Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 28, Issue 3
Feb 2024
909 pages
ISSN:1432-7643
EISSN:1433-7479
Issue’s Table of Contents
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
- Published: 8 January 2024
- Accepted: 8 December 2023
Author Tags
- Big data analytics
- Landscape design
- Data visualization
- Urban planning
- Hadoop
- MapReduce
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