以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马...以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马蹄”形结构,黔东、南和西部地区高而中部及北部较低。②基于CART模型的贵州省贫困影响因素重要性的排序为平均隔离度>路网密度>水域比例>平均偏远度>NDVI>年均降水。③根据CART模型决策规则,对贵州省扶贫攻坚提出以下对策建议:首先,应采取更加“精准”的易地扶贫和村镇体系规划降低居民点隔离度,确保居民点之间平均隔离度小于4847 m。其次,在居民点距离确定的基础上,应科学改善区域的生产生活用水条件,将水域面积比例尽可能提升至0.8%以上,保障生活用水和生产灌溉,提升水资源承载能力。最后,在确保居民点隔离度改善,水资源丰度提升的前提下,应重视喀斯特石漠化地区的生态保护修复,将县域的NDVI提升至0.45以上,提高区域生态资产,提升贫困社区韧性,将生态保护与脱贫攻坚相结合,促进区域人地关系和谐发展。展开更多
本文认为将might, could, should, would 不看作may, can, shall, will 的过去时态形式而当作独立于它们的词位的观点是错误的说情态助动词没有现在时态和过去时态即没有时态语法范畴的观点是错误的文章重申过去时态实质是距离性(易仲良...本文认为将might, could, should, would 不看作may, can, shall, will 的过去时态形式而当作独立于它们的词位的观点是错误的说情态助动词没有现在时态和过去时态即没有时态语法范畴的观点是错误的文章重申过去时态实质是距离性(易仲良 1987展开更多
Numerous domestic scholars have argued that a remote location is the major factor preventing the transformation and sustainable development of resource-exhausted cities. Research to date, however, has not presented re...Numerous domestic scholars have argued that a remote location is the major factor preventing the transformation and sustainable development of resource-exhausted cities. Research to date, however, has not presented relevant evidence to support this hypothesis or explained how to identify the concept of ‘remoteness'. Resource-exhausted cities designated by the State Council of China were examined in this study alongside the provincial capital cities that contain such entities and three regional central cities that are closely connected to this phenomenon: Beijing, Shanghai, and Guangzhou. Spatial and temporal distances are used to calculate and evaluate the location remoteness degrees(LRDs) of resource-exhausted cities, in terms of both resource types and regions. The results indicate that resource-exhausted cities are indeed remote from the overall samples. Based on spatial distances, the LRDs are α_1 = 1.36(i.e., distance to provincial capital city) and β_1 = 1.14(i.e., distance to regional central city), but when based on temporal distances, α_2 = 2.02(i.e., distance to provincial capital city) and β_2 = 1.44(i.e., distance to regional central city). Clear differences are found in the LRDs between different regions and resource types, with those in western China and forest industrial cities the most obviously remote. Finally, the numbers of very remote resource-exhausted cities based on spatial and temporal distances(i.e.,α > 1.5 ∩β> 1.5) are 14 and 19, respectively, encompassing 17.9% and 24.4% of the total sampled. Similarly, 25 and 30 not remote resource-exhausted cities based on spatial and temporal distances(i.e.,α≤1.0 ∩β≤ 1.0) encompass 32.1% and 38.5% of the total, respectively. This study provided supporting information for the future development and policy making for resource-exhausted cities given different LRDs.展开更多
文摘以贫困形势严峻和地理环境空间异质性显著的贵州省为案例,将分类与回归树(Classification and Regression Tree,CART)模型引入贫困研究,分析了贫困空间格局影响因素并制定了相关对策。结论表明:①贵州省的贫困格局呈现出典型的敞口“马蹄”形结构,黔东、南和西部地区高而中部及北部较低。②基于CART模型的贵州省贫困影响因素重要性的排序为平均隔离度>路网密度>水域比例>平均偏远度>NDVI>年均降水。③根据CART模型决策规则,对贵州省扶贫攻坚提出以下对策建议:首先,应采取更加“精准”的易地扶贫和村镇体系规划降低居民点隔离度,确保居民点之间平均隔离度小于4847 m。其次,在居民点距离确定的基础上,应科学改善区域的生产生活用水条件,将水域面积比例尽可能提升至0.8%以上,保障生活用水和生产灌溉,提升水资源承载能力。最后,在确保居民点隔离度改善,水资源丰度提升的前提下,应重视喀斯特石漠化地区的生态保护修复,将县域的NDVI提升至0.45以上,提高区域生态资产,提升贫困社区韧性,将生态保护与脱贫攻坚相结合,促进区域人地关系和谐发展。
基金National Natural Science Foundation of China,No.40701044
文摘Numerous domestic scholars have argued that a remote location is the major factor preventing the transformation and sustainable development of resource-exhausted cities. Research to date, however, has not presented relevant evidence to support this hypothesis or explained how to identify the concept of ‘remoteness'. Resource-exhausted cities designated by the State Council of China were examined in this study alongside the provincial capital cities that contain such entities and three regional central cities that are closely connected to this phenomenon: Beijing, Shanghai, and Guangzhou. Spatial and temporal distances are used to calculate and evaluate the location remoteness degrees(LRDs) of resource-exhausted cities, in terms of both resource types and regions. The results indicate that resource-exhausted cities are indeed remote from the overall samples. Based on spatial distances, the LRDs are α_1 = 1.36(i.e., distance to provincial capital city) and β_1 = 1.14(i.e., distance to regional central city), but when based on temporal distances, α_2 = 2.02(i.e., distance to provincial capital city) and β_2 = 1.44(i.e., distance to regional central city). Clear differences are found in the LRDs between different regions and resource types, with those in western China and forest industrial cities the most obviously remote. Finally, the numbers of very remote resource-exhausted cities based on spatial and temporal distances(i.e.,α > 1.5 ∩β> 1.5) are 14 and 19, respectively, encompassing 17.9% and 24.4% of the total sampled. Similarly, 25 and 30 not remote resource-exhausted cities based on spatial and temporal distances(i.e.,α≤1.0 ∩β≤ 1.0) encompass 32.1% and 38.5% of the total, respectively. This study provided supporting information for the future development and policy making for resource-exhausted cities given different LRDs.
