Evidence map›Paper›PMID 40455821›Full record

ArticlePloS one2025

Exploring structural heterogeneity and organizational patterns of tourist flow networks among motivational subgroups.

Xumei Pan, Ting Liu, Lixia Yan

Abstract read
In one paragraph

Article in PloS one, 2025. 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

3 authors.

Xumei PanSchool of Geographical Science, Shanxi Normal University, Taiyuan, Shanxi, China.ORCID 0000-0002-6568-3207
Ting LiuSchool of Geographical Science, Shanxi Normal University, Taiyuan, Shanxi, China.
Lixia YanSchool of Geographical Science, Shanxi Normal University, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tourists' motivation is a key factor influencing their spatiotemporal behavior patterns. However, studies analyzing the spatial movement patterns of tourists with different motives are scant. In this study, the travel diaries of 4,306 tourists were employed as the primary data source, and the Louvain algorithm was applied to identify tourist flow networks with different motives; moreover, the complex network method was utilized to analyze the hierarchical structure and heterogeneity of the organizational patterns of tourist flow networks with different motives. The analysis revealed the following main conclusions. (1) There are three types of tourism flow networks with different motivations: exploring nature, exploring culture and nature-culture motivations. (2) All types of motivated tourist flow networks exhibit significant node hierarchies. The core nodes in natural network are the most controllable, whereas those in natural-cultural network are less controllable and more evenly connected. (3) Node connectivity strength decays significantly with distance. Geographic constraints exert the greatest impact on natural network. (4) Different-motivated tourist flow networks present a Multi-scale Hierarchical Nested Pattern. Tourists gather in multi-core cultural networks and multi-core natural-cultural networks, using them as a base to spread to neighboring regions. They shift between core nodes and use new cores as a base for tours. This study enriches the theoretical system of tourist behavior research and provides effective practical recommendations for the Shanxi tourism industry to improve tourists' travel experience.

Indexed as

MotivationTourismTravelAdultAlgorithmsFemaleHumansMale

Identifiers

PMID40455821
PMCPMC12129182

What OpenQuestion holds

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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.