A Geographically Weighted Regression–Compute Unified Device Architecture Approach to Explore the Spatial Agglomeration and Heterogeneity in Arable Land Consumption in Southwest China

Chang Liu, Tingting Xu (), Letao Han, Sapu Du and Aohua Tian
Additional contact information
Chang Liu: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Tingting Xu: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Letao Han: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Sapu Du: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Aohua Tian: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Agriculture, 2024, vol. 14, issue 10, 1-23

Abstract: Arable land loss has become a critical issue in China because of rapid urbanization, industrial expansion, and unsustainable agricultural practices. While previous studies have explored the factors contributing to this loss, they often fall short in addressing the challenges of spatial heterogeneity and large-scale dataset analysis. This research introduces an innovative approach to geographically weighted regression (GWR) for assessing arable land loss in China, effectively addressing these challenges. Focusing on Chongqing, Guizhou, and Yunnan Provinces over the past two decades, it examines spatial autocorrelation with R-squared values exceeding 0.6 and residuals. Eight factors, including environmental elements (rain, evaporation, slope, digital elevation model) and human activities (distance to city, distance to roads, population, GDP), were analyzed. By visualizing and analyzing R² spatial patterns, the results reveal a clear spatial agglomeration distribution, primarily in urban areas with industries, highly urbanized cities, and flat terrains near rivers, influenced by GDP, population, rain, and slope. The novelty of this study is that it significantly enhances GWR computational capabilities for handling extensive datasets by utilizing Compute Unified Device Architecture (CUDA) on a high-performance GPU cloud server. Simultaneously, it conducts comprehensive analyses of the GWR model’s local results through visualization and spatial autocorrelation tools, enhancing the interpretability of the GWR model. Through spatial clustering analysis of local results, this study enables targeted exploration of factors influencing arable land changes in various temporal and spatial dimensions while also evaluating the reliability of the model results.

Keywords: GWR-CUDA; parallel computation; spatial agglomeration and heterogeneity; arable land change (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1675/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1675/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1675-:d:1485559

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().