Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China

Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial...

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Main Authors: Yingxi Chen, Yan Xu, Nannan Ye
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:ISPRS International Journal of Geo-Information
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Online Access:https://www.mdpi.com/2220-9964/14/4/174
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author Yingxi Chen
Yan Xu
Nannan Ye
author_facet Yingxi Chen
Yan Xu
Nannan Ye
author_sort Yingxi Chen
collection DOAJ
description Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification.
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spelling doaj-art-506f49da259447b3b0791a4177d08e1d2025-08-20T02:28:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114417410.3390/ijgi14040174Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, ChinaYingxi Chen0Yan Xu1Nannan Ye2School of Environmental Science and Engineering, Suzhou University of Science and Technology, No. 99 Xuefu Road, Huqiu District, Suzhou 215009, ChinaSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, No. 99 Xuefu Road, Huqiu District, Suzhou 215009, ChinaSchool of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, No. 99 Xuefu Road, Huqiu District, Suzhou 215009, ChinaBalancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification.https://www.mdpi.com/2220-9964/14/4/174MGWRspatial autocorrelationregional disparitiesgrain productionrural areas
spellingShingle Yingxi Chen
Yan Xu
Nannan Ye
Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
ISPRS International Journal of Geo-Information
MGWR
spatial autocorrelation
regional disparities
grain production
rural areas
title Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
title_full Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
title_fullStr Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
title_full_unstemmed Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
title_short Geospatial Analysis of Regional Disparities in Non-Grain Cultivation: Spatiotemporal Patterns and Driving Mechanisms in Jiangsu, China
title_sort geospatial analysis of regional disparities in non grain cultivation spatiotemporal patterns and driving mechanisms in jiangsu china
topic MGWR
spatial autocorrelation
regional disparities
grain production
rural areas
url https://www.mdpi.com/2220-9964/14/4/174
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AT yanxu geospatialanalysisofregionaldisparitiesinnongraincultivationspatiotemporalpatternsanddrivingmechanismsinjiangsuchina
AT nannanye geospatialanalysisofregionaldisparitiesinnongraincultivationspatiotemporalpatternsanddrivingmechanismsinjiangsuchina