An Optimization Model for Landscape Planning and Environmental Design of Smart Cities Based on Big Data Analysis

Abstract

This paper adopts “digital landscape design logic,” analyzes and researches smart city and digital landscape design, and builds a digital city based on design logic, design basis, environment analysis, and results in a presentation based on the dilemma of landscape garden planning and design at this stage and the development trend of the smart garden and digital landscape design. The optimization model of the landscape and environment design is constructed based on design logic, design basis, environment analysis, and result presentation. First, on the Hadoop distributed computing platform based on the MapReduce parallel processing framework, we implement the massive small file processing methods (Hadoop Archives, CombineFileInputFormat, and Sequence Files) to compensate for the inherent defects of Hadoop and experimentally compare the memory consumption and execution efficiency of the three methods to propose a choice. The memory consumption and execution efficiency of the three methods are experimentally compared to propose a selection strategy. Finally, based on the MR-PFP algorithm, we parallelize the frequent itemset in-cab trajectory big data to generate interesting strong association rules. The experimental results show that the MR-PFP algorithm has better speedup ratio performance and higher mining efficiency than the parallel frequent pattern (PFP) growth algorithm. The research and analysis focused on the digital implementation of the standalone environmental analysis, using Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The study explores the use of digital landscape design methods and technologies in the landscape design process. Using Rhino + Grasshopper parametric and visualization programming software, we built parametric analysis models around elevation, slope, slope direction, water catchment, and viewable area and used mapping and overlay techniques to quantify the urban space. Finally, the purpose of collecting, monitoring, analyzing, simulating, creating, and reproducing landscape information is achieved.