A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms

Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly...

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Bibliographic Details
Published in:Urban forestry & urban greening Vol. 56; p. 126871
Main Authors: Tang, Ziyi, Ye, Yu, Jiang, Zhidian, Fu, Chaowei, Huang, Rong, Yao, Dong
Format: Journal Article
Language:English
Published: Elsevier GmbH 01.12.2020
Subjects:
algorithms
analytical methods
artificial intelligence
case studies
China
Data-informed urban design
geographic information systems
green infrastructure
Greenway planning
Machine learning algorithms
Multi-sourced urban data
planning
urban areas
China
Multi-sourced urban data
Machine learning algorithms
Greenway planning
Data-informed urban design
ISSN:1618-8667, 1610-8167
Online Access:Get full text
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Summary:Urban greenways have been recognized as an important strategy to improve human-scale quality in high-density built environments. Nevertheless, current greenway suitability analysis mainly focuses on geographical and natural issues, failing to account for human-scale urban design factors. Accordingly, this study proposes a data-informed approach to planning urban greenway networks using a combination of classical urban design theories, multi-sourced urban data, and machine learning algorithms. Maoming City in China was used as a case study. Per classical urban design theories, specifically, Cervero and Ewing’s 5D variables, density, diversity, design, dimensions of destination accessibility, and distance-to-transit, were selected as key factors. A series of new urban data, including points of interest (PoIs), location-based service (LBS) positioning data, and street view images, were applied in conjunction with machine learning algorithms and geographical information system (GIS) tools to measure these key factors at a human-scale resolution and generate an optimized greenway suitability analysis. This analytical approach is an attempt to take human-scale concerns into account on a city-wide scale regarding greenway network generation. It also pushes the methodological boundaries of greenway planning by combining classical urban design thinking with new urban data and new techniques.
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ISSN:1618-8667
1610-8167
DOI:10.1016/j.ufug.2020.126871