Evidence map›Paper›PMID 38961968›Full record

ArticleHeliyon2024

Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China.

Yuehua Jiang, Wei Huang, Xinxing Xiong, Boyang Shu, Jixin Yang, Minglong Li, Xufeng Cui

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Yuehua JiangSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Wei HuangSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Xinxing XiongSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Boyang ShuSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Jixin YangSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Minglong LiSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
Xufeng CuiSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of "overall dispersion, partial concentration", with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

Indexed as

GeoDetectorSpatial patternsStandard deviation ellipseTourist attractions

Identifiers

PMID38961968
PMCPMC11219313

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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.