Unveiling the Terrain of Fraud: Exploring the Association Between Physical Topography and Consumer Fraud Victimization

ABSTRACT

The widespread accessibility of the internet has facilitated social connections across geographical boundaries, but it has also led to an increase in consumer fraud victimization. This study aims to elucidate the geographical variations in fraud victimization by examining the influence of physical topography. Employing a two-stage conceptual framework of consumer fraud victimization, we used a nationwide survey in mainland China (n = 36,066) to explore the impact of mountainousness indicators on fraud exposure and fraud victimization. Our findings indicate a negative correlation between mean elevation and standard deviation in elevation with fraud exposure, while displaying a positive correlation with fraud victimization. Utilizing machine learning analyses, we discovered that mountainousness indicators outperformed factors such as income, physical condition, sex, education, and employment in predicting fraud victimization. Notably, physical topography played a more prominent role in the recognition model of fraud victimization compared to fraud exposure. These findings shed light on the fact that individuals residing in mountainous areas are less prone to fraud exposure but are at a heightened risk of becoming victims once exposed. This study offers valuable insights for the development of effective anti-fraud strategies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval and consent

This is a retrospective analysis of publicly available data. The Zhejiang University of Technology’s Research Ethics Committee has confirmed that no ethical approval is required. All the data collection and analysis methods were carried out in accordance with relevant guidelines and regulations.

Data

The data is accessible at https://chfser.swufe.edu.cn/datas/Products/Datas/DataList and https://asterweb.jpl.nasa.gov/gdem.asp

Materials and code availability

The materials and code can be made available upon reasonable request from the corresponding author.

Authors’ contribution statements

Conceptualization: [Liang Xu], [Hongting Li]; Methodology: [Liang Xu], [Liuchang Xu]; Formal analysis and investigation: [Liang Xu], [Liuchang Xu]; Writing – original draft preparation: [Liang Xu], [Liuchang Xu], [Min Xu]; Writing – review and editing: [Zaoyi Sun], [Hongting Li]; Funding acquisition: [Liang Xu], [Zaoyi Sun], [Hongting Li]; Supervision: [Hongting Li].

Additional information

Funding

This work was supported by the Humanities and Social Sciences Foundation of Zhejiang University of Technology [Grant number SKY-ZX-20240008], the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China [Grant number 22YJC840026], and the National Natural Science Foundation of the People’s Republic of China [Grant number 72371228].

Notes on contributors

Liang Xu

Liang Xu obtained his BS in applied psychology and PhD in psychology from Zhejiang University, in 2015 and 2020, respectively. He is currently a lecturer at Zhejiang University of Technology, with research interests spanning anti-fruad psychology, behavioral psychology, and personality psychology.

Min Xu

Min Xu is a master’s graduate student in Psychology at Zhejiang University of Technology. Her research interests lie in the field of applied psychology.

Zaoyi Sun

Zaoyi Sun received the BS degree in psychology from the Zhejiang University of Technology, China, in 2014, and the PhD degree in psychology from Zhejiang University, China, in 2019. She is currently a Lecturer with Zhejiang University of Technology. Her research interests include big-data-based behavioral analysis and user experience.

Liuchang Xu

Liuchang Xu received the B.S. degree in applied psychology and the Ph.D. degree in remote sensing and geographic information system from Zhejiang University, China, in 2015 and 2020, respectively. He is currently a Lecturer with the College of Mathematics and Computer Science, Zhejiang A&F University, China. His research interests include spatio-temporal analytics, articial intelligence, urban computing, signal processing, and big data mining.

Hongting Li

Hongting Li is a professor at the Department of Psychology, Zhejiang University of Technology. His research interests include applied psychology, human–computer interaction, ergonomics, user experience, augmented reality, and virtual reality.