Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Keywords = Lishan Town

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 2027 KiB  
Article
The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China
by Xuesong Zhang, Maomao Zhang, Ju He, Quanxi Wang and Deshou Li
Sustainability 2019, 11(14), 3810; https://doi.org/10.3390/su11143810 - 11 Jul 2019
Cited by 27 | Viewed by 3794
Abstract
Cultivated land is a basic resource that is related to the sustainable development of the global economy and society. Studying the spatial and temporal distribution of cultivated land and its influential factors at the township scale is an important way to improve its [...] Read more.
Cultivated land is a basic resource that is related to the sustainable development of the global economy and society. Studying the spatial and temporal distribution of cultivated land and its influential factors at the township scale is an important way to improve its sustainable use. Based on the land use data in 2009 and 2015, this paper comprehensively uses kernel density estimation, spatial autocorrelation analysis, and the spatial autoregressive model to analyze the spatial distribution characteristics and influential factors of cultivated land. The results show that in 2009 and 2015, the maximum kernel density of cultivated land in Lishan Town was 31/km2 and 38/km2, respectively, and there is an increasing tendency for it in the future. The global spatial autocorrelation Moran’s I of the proportion of cultivated land area in the administrative villages of Lishan Town in 2009 and 2015 was 0.5251 and 0.3970, respectively. Cultivated land has significant spatial self-positive correlation agglomeration characteristics in spatial distribution. Based on spatial error model (SEM) analysis, the regression coefficients of the village were 0.236 and 0.196 in 2009 and 2015, respectively. The regression coefficients of the road were 0.632 and 0.630, respectively. The regression coefficients of the water system were 0.481 and 0.290, respectively. The regression coefficients of the topographic position index were −0.817 and −0.672, respectively. By comparing 2015 with 2009, the regression coefficients of each influential factor have been reduced to varying degrees. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
Show Figures

Figure 1

14 pages, 3888 KiB  
Article
Comparative Changes of Influence Factors of Rural Residential Area Based on Spatial Econometric Regression Model: A Case Study of Lishan Township, Hubei Province, China
by Xuesong Zhang, Ju He, Zhen Deng, Jiyue Ma, Guangping Chen, Maomao Zhang and Deshou Li
Sustainability 2018, 10(10), 3403; https://doi.org/10.3390/su10103403 - 25 Sep 2018
Cited by 29 | Viewed by 3169
Abstract
The influencing factors of rural residential areas have always been a key research direction in addressing rural problems in China. By introducing a spatial regression model combined with Kernel Density Estimation and Buffer Analysis, this study made a comparative study on the quantification [...] Read more.
The influencing factors of rural residential areas have always been a key research direction in addressing rural problems in China. By introducing a spatial regression model combined with Kernel Density Estimation and Buffer Analysis, this study made a comparative study on the quantification of the influencing factors of rural residential areas in 2009, 2012, and 2015 in Lishan Township, Hubei Province, China. The results showed that the elevation and slope of Lishan Township have always played a decisive role in the distribution of rural residential areas, that the influence of the water system is abnormal, and that the influence of roads and townships has been strengthened based on the spatial statistical analysis. Then, based on spatial econometric regression analysis, the coefficients of “Topographic indices” (CTI) were 0.666, 0.719, and 0.439 in 2009, 2012, and 2015, respectively. The coefficients of Road (CR) were 0.170, 0.112, and 0.108, respectively. The coefficients of Town (CT) were 0.120, 0.127, and 0.166, respectively. The coefficients of Water system (CWS) were 0.166, 0.124, and 0.173, respectively. With the change of time, the influence of road decreased and the influence of town increased gradually. Furthermore, the influence of the water system and topography showed volatility. Full article
Show Figures

Figure 1

Back to TopTop