摘要
时空大数据的出现给景观设计的发展带来了新的机遇,作为具有时空属性的大数据类型,研究其在景观规划中的创新应用具有重要意义。文章以桂林市独秀峰·王城景区为例,根据先前学者的理论研究,结合景区规划的特征,将多类景观时空大数据应用于微观尺度上的景观设计中,并尝试构建了其数据分析的基本框架;基于动态数据和静态数据分析了国庆期间王城景区的游客行为模式,包括游客构成分析、客流时空分析、关联目的地分析、游记信息情感分析,最后借鉴国内其他智慧景区的建设经验提出了王城景区的资源配置、业务管理、营销服务等规划建议。在桂林王城景区的规划中,灵活运用多类大数据对游客行为模式进行全面分析,是其规划设计的直接依据,以此制定了改善景区管理运营的多个策略,表明时空大数据的应用研究对于景观规划设计具有实际意义。
The emergence of spatio-temporal big data has brought new opportunities to the development of landscape design.As a type of big data with spatio-temporal attributes,it is of great significance to study its innovative applications in landscape planning.The article takes the Duxiufeng Wangcheng scenic spot in Guilin as an example.Based on the theoretical research of previous scholars and combined with the characteristics of scenic spot planning,this article applies multi-type landscape space-time big data to micro-scale landscape design,and tries to construct basic framework of its data analysis.Based on dynamic data and static data,it analyzes the tourist behavior patterns of the Wangcheng scenic spot during the National Day,including tourist composition analysis,passenger flow time and space analysis,associated destination analysis,travel information sentiment analysis,and finally draws on the construction experience of other domestic smart scenic spots.Planning suggestions for resource allocation,business management,and marketing services of the Wangcheng Scenic Area.In the planning of the Guilin Wangcheng City scenic spot,the flexible use of multiple types of big data to conduct a comprehensive analysis of tourist behavior patterns is the direct basis for its planning and design.This formulates multiple strategies to improve the management and operation of the scenic spot,indicating the application research of spatio-temporal big data,it has practical significance for landscape planning and design.
作者
梁燕敏
杨泽
黄智孟
LIANG Yan-min;YANG Ze;HUANG Zhi-meng(Guilin University of Technology,Guilin,Guangxi,541006;Guilin Tourism University,Guilin,Guangxi,541006)
关键词
时空大数据
景观规划
智慧景区
游客行为
Spatio-temporal Big Data
Landscape Planning
Smart Scenic Spot
Tourist Behavior