中文摘要：

经济发展和城市扩张使包括耕地在内的大量开放空间被不断侵蚀,随着人们对耕地非市场价值和生态功能认识的不断加深,如何合理高效可持续地利用耕地被广泛关注。采用空间自相关分析方法从乡镇尺度定量研究了武汉市耕地非农化的空间关系,以探讨快速发展时期平原城市耕地非农化空间关系的变化及其原因。研究表明：（1）全局Morans I显示武汉市耕地非农化在1990-2011年的4个时期均呈现出显著的全局空间自相关,其聚集强度以2005年为分界点,前3个时期不断加强后有所下降;（2）通过EB修正的武汉市耕地非农化的LISA图显示武汉市耕地非农化的聚集区域由开始处于区域外围的LL型聚集为主导逐步转变为处于城乡交错区域的LH＋HH型聚集为主导;（3）通过绘制不同时期不同聚集类型的重心移动路径可以发现LH＋HH型聚集均出现先向东南再向东北移动的趋势,这种耕地非农化趋势与区域的产业布局有密切关系。通过空间自相关分析掌握武汉市耕地非农化的空间特征是开展耕地保护和实现耕地合理利用的基石,根据研究结果可以确定重点和优先管制区域以提升土地利用管制效果,协调好城市建设和耕地资源保护间的关系。

英文摘要：

A lot of open space including farmland is being invaded and occupied because of economic development and city expansion. Sustainable use of farmland is increasingly concerned for the ecological functions, and non-market value of farmland has been recognized and accepted widely. The key issue of rational use of farmland is characterizing the spatial distribution pattern of farmland conversion and identifying the locations of hotspots and cold spots of farmland conversion. In this paper, farmland conversion data were extracted by using spatial statistics and spatial analyst functions in ArcGIS based on the land use data between 1990 and 2011. To explore the characteristics of spatial distribution and moving route of spatial agglomeration districts of farmland conversion, spatial autocorrelation analysis is applied to different periods of farmland conversion in the Wuhan City. Based on the analysis, the main conclusions are as follows： Firstly, the global Moran＇s I index indicated that the farmland conversion in the Wuhan City showed significant global spatial autocorrelation during the all four periods from 1990 to 2011, and the aggregation intensity displayed an increasing-decreasing tendency. The aggregation intensity trend reflected that the farmland conversion in the Wuhan City presented spread and out of order in space in the period from 2005 to 2011. Secondly, Empirical Bayes methods could increase the stationarity of variables while the local Moran＇s I statistic is computed for rates or proportions. So we used the local indicators of spatial association cluster map with the Empirical Bayes methods to show the location of cluster areas of farmland conversion in the Wuhan City and the spatial distribution of different types of aggregation （e.g., low-low, low-high and high-high）. The areas presenting high-high type clustering are exactly the hotspots of farmland conversion. The conversion between the types of aggregations indicates that low-high type clustering is a potential high-high type clustering