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Exploring the integration of big data analytics in landscape visualization and interaction design

  • Data analytics and machine learning
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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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Funding

This study was funded bythe Key Research Project of Philosophy and Social Sciences in Anhui Universities in 2022: Study on Digital Protection and Utilization of Red Revolution Sites in Rural Areas of Southern Anhui from the Perspective of Spatial Narrative (Project number: 2022AH051881) and Intangible Cultural Heritage research team.

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Authors and Affiliations

  1. College of Art and Design, Anhui Institute of Information Technology, Wuhu, 241199, Anhui, China

    Xiaoqing Yang

  2. Faculty of Creative Multimedia, Multimedia University, 63100, Selangor, Malaysia

    Roopesh Sitharan & Elyna Amir Sharji

  3. College of Arts and Education, Chizhou University, Chizhou, 247000, Anhui, China

    He Feng

Authors
  1. Xiaoqing Yang
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  2. Roopesh Sitharan
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  3. Elyna Amir Sharji
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  4. He Feng
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Correspondence to Xiaoqing Yang.

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Yang, X., Sitharan, R., Sharji, E.A. et al. Exploring the integration of big data analytics in landscape visualization and interaction design. Soft Comput 28, 1971–1988 (2024). https://doi.org/10.1007/s00500-023-09570-2

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