Research on the spatial structure of desert tourism flow based on ecological digital footprints: a case study of Ningxia

Based on the analysis of tourists’ digital footprints from 2018 to 2022, this paper examines the desert tourism in the Upper Reaches of the Yellow River in Ningxia, China. By combining social network analysis and ArcGIS methods, it visually presents the spatial network structure characteristics of tourism flow in Ningxia’s desert destinations in recent years. The results indicate that (1) the high rate of visitors in Ningxia is closely related to the grade value of the scenic spots and the popularity of cities, and the most popular scenic spots are concentrated in the Ningxia section of the Yellow River Basin. (2) Hot desert scenic spots with historical, cultural, and urban development characteristics have greater advantages and competitiveness; these spots can better connect with less popular tourism nodes and serve as breakthrough points for the rational allocation of tourism resources. (3) The tourism flow network among scenic spots in Ningxia is relatively loose, with an imbalance in the tourism network among these spots. (4) The spatial tourism flow presents a pattern of superposition and coincidence of multiple triangular structures. Among them, Zhongwei, Yinchuan, and Shizuishan are more attractive to tourists than Wuzhong and Guyuan.

Authors

Institutions

Publication Details

Journal
Frontiers in Environmental Science
Published
2025-03-27
DOI
https://doi.org/10.3389/fenvs.2025.1544890
Citations
1
Primary Topic
Diverse Aspects of Tourism Research
Type
article
Field-Weighted Citation Impact
4.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the spatial structure of desert tourism flow based on ecological digital footprints: a case study of Ningxia

Qiang Luo, Baoling Dong, Xiaowei Guo
1 citations
Frontiers in Environmental Science
Diverse Aspects of Tourism Research
4.20
article

Research on the spatial structure of desert tourism flow based on ecological digital footprints: a case study of Ningxia

Qiang Luo, Baoling Dong, Xiaowei Guo
article en
1 citations

Abstract

Based on the analysis of tourists’ digital footprints from 2018 to 2022, this paper examines the desert tourism in the Upper Reaches of the Yellow River in Ningxia, China. By combining social network analysis and ArcGIS methods, it visually presents the spatial network structure characteristics of tourism flow in Ningxia’s desert destinations in recent years. The results indicate that (1) the high rate of visitors in Ningxia is closely related to the grade value of the scenic spots and the popularity of cities, and the most popular scenic spots are concentrated in the Ningxia section of the Yellow River Basin. (2) Hot desert scenic spots with historical, cultural, and urban development characteristics have greater advantages and competitiveness; these spots can better connect with less popular tourism nodes and serve as breakthrough points for the rational allocation of tourism resources. (3) The tourism flow network among scenic spots in Ningxia is relatively loose, with an imbalance in the tourism network among these spots. (4) The spatial tourism flow presents a pattern of superposition and coincidence of multiple triangular structures. Among them, Zhongwei, Yinchuan, and Shizuishan are more attractive to tourists than Wuzhong and Guyuan.

Frontiers in Environmental ScienceVol. 13
Guizhou Normal University (CN), Guizhou University of Finance and Economics (CN), Guizhou Academy of Sciences (CN), Shaanxi Normal University (CN)
Openalex Percentile: Top 9%
Diverse Aspects of Tourism Research
4.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.