[논문]Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data

Hua, Wang; Yuxin, Zhu; Mengyu, Wang; Jiqiang, Niu; Xueye, Chen; Yang, Zhang

doi:10.3837/tiis.2021.02.021

Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data 원문보기

KSII Transactions on internet and information systems : TIIS, v.15 no.2, 2021년, pp.767 - 785

Hua, Wang (Henan Key Laboratory of food safety data intelligence, Zhengzhou University of Light Industry) , Yuxin, Zhu (Henan Key Laboratory of food safety data intelligence, Zhengzhou University of Light Industry) , Mengyu, Wang (Henan Key Laboratory of food safety data intelligence, Zhengzhou University of Light Industry) , Jiqiang, Niu (Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution Xinyang Normal University) , Xueye, Chen (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources) , Yang, Zhang (College of Urban Economics and Public Administration, Capital University of Economics and Business)

Abstract ▼ AI-Helper

With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the 'high-high'-type area being concentrated in the border area directly north and northeast of Luoyang, while the 'low-low'-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the 'high-low'-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.

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표/그림 (15)

그림 Fig. 1. Location of Luoyang City
그림 Fig. 2. Partial Temporal and Spatial Data Maps
표 Table 1. Land-use Transfer Matrix
그림 Fig. 3. Transfer Ratio of Cultivated Land in Luoyang City from 2009 to 2014
그림 Fig. 4. Transfer Ratio of Cultivated Land in Luoyang City from 2009 to 2014
그림 Fig. 5. Cultivated Land Quantity Moran Scatter Plot in Luoyang City
표 Table 2. Global Moran's I Values and Test of Cultivated Land Quantity in Luoyang City
그림 Fig. 6. LISA Map of Cultivated Land Holdings in Luoyang City
그림 Fig. 7. Results of the Bivariate Spatial Autocorrelation
그림 Fig. 8. LISA Maps of the Bivariate Local Spatial Autocorrelation
표 Table 3. OLS Model of Cultivated Land Holdings Quantity in Luoyang City
표 Table 4. Spatial Correlation Test Results
표 Table 5. SLM Model of Cultivated Land Holdings Quantity in Luoyang City
표 Table 6. SEM Model of Cultivated Land Holdings Quantity in Luoyang City
그림 Fig. 9. Model Prediction Value Fitting Curve

AI 본문요약
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제안 방법

A spatial autocorrelation model was used to analyze the evolution of the spatial pattern of cultivated land at the township scale in Luoyang, and the spatial dependence between cultivated land and influencing factors was selected as an example. Finally, a spatial autoregressive model was chosen to analyze the driving mechanism of the spatial change of cultivated land in Luoyang, in order to provide support for the coordination of urbanization and cultivated land protection in the new era, and the scientific preparation of national land and spatial planning.
To address the shortcomings of current research, this work used Luoyang––a new sub- center city in China–as the study area, from the perspective of spatial clustering and dependency

대상 데이터

To address the shortcomings of current research, this work used Luoyang––a new sub- center city in China–as the study area, from the perspective of spatial clustering and dependency. The study was based on spatiotemporal big data of cultivated land use in Luoyang for the three years 2009, 2014 and 2019. A spatial autocorrelation model was used to analyze the evolution of the spatial pattern of cultivated land at the township scale in Luoyang, and the spatial dependence between cultivated land and influencing factors was selected as an example.

이론/모형

For this work, the raster data for 2009, 2014 and 2019 were obtained by preliminary processing in ArcGIS, in order to obtain the transfer matrices between cropland and other landscape types for 2009– 2014 and 2014–2019, using the Area Tabulation tool in Spatial Analysis, and shown in Fig
From the transfer matrix, the area changes in various land classes can be derived. In this study, we used the Spatial Analyst tool in ArcGIS to process the land-use data of Luoyang City in 2009, 2014 and 2019 in order to obtain the land-use transfer matrix. From the obtained matrix, the area of cultivated land converted to urban construction land, forestland and other land categories became apparent, as well as the area of other land categories converted to cultivated land between 2009 and 2014 and 2014 and 2019.
In fact, research data often have spatial autocorrelation. The spatial autoregressive model (SLM and SEM) incorporated this point into the regression equation, which makes up for the shortcomings of the classical measurement method in the statistics of spatial data.

성능/효과

(1) In terms of the transfer of cultivated land from the countryside, the main sources of cultivated land transferred out were urban(73.27%) industrial and mining(8.21%), and transport land(14.35%), while the main sources of land transferred in were grassland(44.41%), water and water conservancy facility(18.01%), and forest(10.98%).
(2) Calculation of the global Moran I index values(Moran’s I≈0.7134) showed that there was a strong spatial correlation between the amount of cultivated land holdings in Luoyang City
(3) Analysis of the 2014 spatial autoregressive model revealed that, slope and industrial output all having a significant effect on cultivated land holdings. The absolute values of the regression coefficients indicated the magnitude of the influence of the different factors on the amount of cultivated land holdings as being slope > elevation > gross industrial output.
The absolute values of the regression coefficients indicated the magnitude of the influence of the different factors on the amount of cultivated land holdings as being slope > elevation > gross industrial output
The low-low type of townships were concentrated in the south of Luoyang City, and were heavily influenced by topography and urbanization. The expansion of this type of area during the study period mainly occurred in the Luoyang City area, mostly influenced by spatial polarization and assimilation by the lower values in the high-low areas.
The absolute values of the regression coefficients indicated the magnitude of the influence of the different factors on the amount of cultivated land holdings as being slope > elevation > gross industrial output. Whereas elevation had a positive effect, slope and industrial output had a negative effect.

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