Evidence map›Paper›PMID 35162332›Full record

ArticleInternational journal of environmental research and public health2022

Selection of the Most Scenic Viewpoints on an Island Based on Space-Time Perception: The Case of Nan'ao Island, China.

Tongyan Zhang, Shengrui Zhang, Yingjie Wang, Hu Yu, Hongrun Ju, Hanyun Xue

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tongyan ZhangManagement College, Ocean University of China, Qingdao 266100, China.
Shengrui ZhangManagement College, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-4393-0561
Yingjie WangInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Hu YuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0003-3159-8183
Hongrun JuSchool of Tourism and Geography Science, Qingdao University, Qingdao 266071, China.
Hanyun XueManagement College, Ocean University of China, Qingdao 266100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Selecting the most scenic viewpoints in an island forest park can provide a scientific basis for island ecotourism planning. In this paper, considering the influence of climatic conditions on sightseeing, landscape factors, accessibility factors, and seasonal change factors are selected from the perspective of time and space to analyze the landscape spatiotemporal characteristics, and the construction of a landscape spatiotemporal perception evaluation model and the analysis of influencing factors are carried out. The results show that the evaluation model for landscape spatiotemporal perception factors can quantitatively describe tourists' comprehensive perception of a landscape in different regions and time periods in ecotourism areas, and can identify the spatiotemporal characteristics of landscape perception. Case studies preliminarily prove the feasibility of the model and calculation process. This method provides a strong reference for the development and planning of island tourism, especially providing new ideas and methods for the design of sightseeing routes in the development and planning of small-scale scenic viewpoints, which can enrich island tourism planning.

Indexed as

Time PerceptionChinaForestsTourismChinaGIS methodisland tourismscenic viewpoint evaluationspace–time perception

Identifiers

PMID35162332
PMCPMC8835557

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.