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重庆粮食产量时空格局演变研究
引用本文:高勇.重庆粮食产量时空格局演变研究[J].绵阳师范高等专科学校学报,2014(5):88-92,97.
作者姓名:高勇
作者单位:四川师范大学地理与资源科学学院,四川成都610066
摘    要:为揭示重庆粮食生产时空演变特征,采用ESDA方法与GIS技术,以人均粮食产量为测度指标,对重庆2003-2011年县域粮食产量的时空格局演变特征进行分析.研究结果表明:重庆人均粮食产量的总体特征表现为:区域绝对差异和相对差异均在增大,全局空间自相关Moran指数均大于0.55,表明空间聚集特征十分显著,并有不断增强的趋势;Moran散点图中落入HH象限和LL象限的县明显多于HL和LH象限的数量,三个典型年份分别占县域总数的78.95%、81.58%、84.21%,表明有很强的空间聚集格局;LISA聚集图显示重庆人均粮食产量形成以华蓥山以西和渝东南翼为中心的高产聚集区以及以主城区为中心的低产聚集区.

关 键 词:人均粮食产量  时空演变  空间自相关  空间聚集性

Spatial and Temporal Evolution of Grain Yield Patterns in Chongqing
GAO Yong.Spatial and Temporal Evolution of Grain Yield Patterns in Chongqing[J].Journal of Mianyang Teachers College,2014(5):88-92,97.
Authors:GAO Yong
Institution:GAO Yong (School of Geography & Resource Science, Sichuan Normal University, Chengdu, Sichuan 610066)
Abstract:In order to reveal the spatial and temporal evolution characteristics of grain yield in Chongqing, using the method of ESDA and GIS technology, with the per capita grain yield as the measuring index, this article analyzes the temporal and spatial evolution characteristics of Chongqing's annual grain production in 2003 -2011. The result shows that the overall characteristics of Chongqing's per capita grain yield production are : both the absolute and relative differences are increasing, the global spatial autoeorrelation Moran index is over 0.55, which shows that the spatial agglomeration characteristics are obvious, with growing trend; Moran dispersion the counties in HH quadrant and LL quadrant is more than that in HL and LH quadrant of the Moran dispersion. And the three typical years accounted for 78.95% , 81.58% , and84.21% of the total counties respectively, which shows that there is strong spatial pattern aggregation; and the LISA cluster shows that Chongqing's per capita grain yield forms the high -yielding gathering area taking the west Huaying Mountain and southeast Chongqing as the center, and the low -yielding gathering area taking the main urban area as the center.
Keywords:per capita grain yield  spatial and temporal evolution  spatial autocorrelation  spatial aggregation
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