Urban Green Space Planning and Design Based on Big Data Analysis and BDA-UGSPD Model

Abstract

Green cities are described as the environmental influences by expanding recycling, decreasing waste, increasing housing density, lowering emissions while intensifying open space, and boosting sustainable local businesses. Green infrastructures (GI) are progressively related to urban water management for long-term transitions and immediate solutions towards sustainability. Urban green spaces (UGS) play a vital role in conserving urban environment sustainability by giving various ecology services. In this study, big data analytics-based urban green space planning design (BDA-UGSPD) has been introduced. Luohe city and the Shali River area have been chosen as the study area owing to the high number and a considerable assortment of UGS. Monitoring has been conducted in the Shali river to evaluate water quality for irrigation for agriculture. The Master Plan Scenario had a compact green space system, and the urban land use layout has been categorized by systematization and networking, and it did not consider the service capacity of green spaces. The Planning Guidance Scenario initialized constraint states, which provide more rigorous and effective urban spaces. It enhanced the service functions of the green space model layout. The simulation findings illustrate that the proposed BDA-UGSPD model enhances the land-use classification accuracy ratio by 92.0%, probability ratio by 90.6%, decision-making ratio by 95.0%, climate change adaptation ratio by 94.5%, water quality assessment ratio by 95.9%, and reduces the root mean square error ratio by 9.7% compared to other popular approaches.