Tourist Landscape Preferences in a Historic Block Based on Spatiotemporal Big Data-A Case Study of Fuzhou, China

Int J Environ Res Public Health. 2022 Dec 21;20(1):83. doi: 10.3390/ijerph20010083.

Abstract

Historic blocks are valuable architectural and landscape heritage, and it is important to explore the distribution characteristics of tourists to historic blocks and their landscape preferences to realize the scientific construction and conservation of historic blocks and promote their sustainable development. At present, few studies combine the analysis of tourist distribution characteristics with landscape preferences. This study takes the historic block of Three Lanes and Seven Alleys in Fuzhou as an example, combines field research and questionnaires to construct a landscape preference evaluation indicator system for the historic block, measures the distribution characteristics of tourists in the block through the heat value of tourist flow obtained from the Tencent regional heat map, and analyses the influence of landscape preference indicators on the heat value of tourist flow in the block through stepwise multiple linear regression. The research shows that: (1) the spatial and temporal variation in the heat value of tourist flow tends to be consistent throughout the block, from 7 a.m. to 6 p.m., showing a "rising, slightly fluctuating and then stabilizing" state, both on weekdays and on weekends. (2) The factors influencing the heat value of tourist flow in the different spatial samples are various, with commercial atmosphere, plant landscape, accessibility of the road space, architecture, and the surrounding environment having a significant impact on the heat value of tourist flow. Based on the analysis of the landscape preferences of tourists in the historic block, a landscape optimization strategy is proposed to provide a reference for the management and construction of the block.

Keywords: big data; heat map; historic blocks; landscape planning; landscape preference.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Big Data*
  • China
  • Hot Temperature
  • Surveys and Questionnaires
  • Sustainable Development*

Grants and funding

This study was funded by (1) Fujian Social Science Planning Project “Restorative Outcomes of Urban Waterfront Space Based on Aquaphilia Perception”, grant number FJ2021C038; (2) Fujian First-class Undergraduate Program “Virtual Simulation Experiment of Zero-carbon Parking Lot”, grant number 111902108; (3) National Park Ecological Protection and High Quality Development Research project, grant number KLE21013A; (4) Forest Park Engineering Technology Research Center of State Forestry Administration, grant number PTJH15002.