Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

Land, Journal Year: 2024, Volume and Issue: 13(8), P. 1161 - 1161

Published: July 29, 2024

Urban space constitutes a complex system, the quality of which directly impacts life for residents. In high-density cities, factors such as green coverage in street spaces, color richness, and accessibility services are crucial elements affecting daily life. Moreover, application advanced technologies, deep learning combined with view image analysis, has certain limitations, especially context urban streets. This study focuses on within fabric Macau Peninsula, exploring characteristics environments. By leveraging imagery multi-source data, this research employs principal component analysis (PCA) deep-learning techniques to conduct comprehensive evaluation key indicators quality. Utilizing semantic segmentation ArcGIS technology, quantifies 16 indicators. The findings reveal significant variations service-related DLS, ALS, DCE, MFD, reflecting uneven distribution service facilities. index richness index, along other indicators, notably influenced by tourism commercial activities. Correlation indicates presence land-use conflicts between spaces facilities settings. Principal uncovers diversity complexity cluster categorizing them into four distinct groups, representing different combinations spatial characteristics. innovatively provides quantitative assessment quality, emphasizing importance considering multiple achieve coordinated development enhance results offer new perspectives methodologies

Language: Английский

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Spatiotemporal Dynamics and Driving Factors of Urban Green Space in Texas (2001–2021): A Multi-Source Geospatial Analysis

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 1166 - 1166

Published: April 2, 2025

This study investigates the changes in urban green space coverage across 254 counties of varying types Texas from 2001 to 2021, aiming explore spatial patterns transformation and its socioeconomic driving factors. By analyzing Landsat remote sensing data building type datasets, combined with land use transition matrices, GIS statistics tools, regression analysis population GDP data, this comprehensively examines change different types. The results indicated significant differences cities: (1) Urban areas higher populations rankings, as well their surrounding regions, show a more pronounced trend converting into built-up areas, particularly expansion medium low-density areas. (2) In contrast, smaller cities rural occur at slower pace. Further reveals that spaces is primarily driven by residential development, about 39% high-density over 65% being replaced land. (3) indicate growth are main factors for changes, explaining up 86% 84% respectively. These findings provide important theoretical support practical guidelines conservation, planning, sustainable development policies.

Language: Английский

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Evolution Model, Mechanism, and Performance of Urban Park Green Areas in the Grand Canal of China

Land, Journal Year: 2023, Volume and Issue: 13(1), P. 42 - 42

Published: Dec. 30, 2023

Urban park green areas are part of territorial space planning, shouldering the mission providing residents with high-quality ecological products and public space. Using a combination several measurement models such as BCG (Boston Consulting Group) matrix, ESDA (Exploratory Spatial Data Analysis), MLR (Machine Learning Regression), GWR (Geographically Weighted GeoDetector, this paper presents an empirical study on changes in Park Green Areas (UPGAs) Grand Canal China. By quantitatively measuring spatio–temporal evolution patterns UPGAs, reveals driving mechanisms behind them proposes policy recommendations for planning management based performance evaluation. The UPGAs their China’s characterized by significant spatial heterogeneity correlation, diversified development HH (High-scale–High-growth), HL (High-scale–Low-growth), LH (Low-scale–High-growth), LL (Low-scale–Low-growth) emerging. is dominated positive oversupply equilibrium, where undersupply coexists oversupply. Therefore, recommends implementation zoning strategy future areas, urban parks, infrastructure. It also recommended to design differentiated construction strategies policies each area, while promoting inter-city mutual cooperation joint preparation integrated symbiosis planning. Furthermore, China influenced many factors very complex dynamic mechanisms, there differences nature, intensity, effects, interaction effects between different factors. infrastructure, it necessary interconnect enhance synergies population, aging, industry economy, civilization maximize performance.

Language: Английский

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Distribution Of Active Urban Park Visits Based on Range of Services in Semarang

IOP Conference Series Earth and Environmental Science, Journal Year: 2024, Volume and Issue: 1418(1), P. 012080 - 012080

Published: Dec. 1, 2024

Abstract Urban parks are green oases in the middle of hustle and bustle urban, where people can do activities socialize. Not only they beautiful, but urban also designed to facilitate various activities, such as recreation sports. The aim this research is determine distribution active based on service coverage Semarang. So, it will be able provide guidance users when visiting parks. scope area Semarang, which observations objects focuses 51 This a continuation previous research. In research, results mapping range park services Semarang were obtained using buffer process, so uses mapping. Analysis was carried out ensure all houses had visit directions. done because there still that not included Generally, describe completely even. There districts have directions choices for parks, Selatan District, Timur District. However, visit, specifically Tugu Therefore, public advised nearest first visit.

Language: Английский

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