以中国手足口病疫情平发年2017年为例,探讨气象因素对中国手足口病(hand foot and mouth disease,HFMD)发病的时空影响,并对各省、自治区、直辖市(以下称各地)中心城市手足口病发病类型进行划分。本研究采用聚类分析、空间自相关等方法...以中国手足口病疫情平发年2017年为例,探讨气象因素对中国手足口病(hand foot and mouth disease,HFMD)发病的时空影响,并对各省、自治区、直辖市(以下称各地)中心城市手足口病发病类型进行划分。本研究采用聚类分析、空间自相关等方法分析2017中国各地中心城市气象(气温、降水)因素对手足口病发病影响及其时空分异。结果表明:(1)在时间上,2017年中国各地中心城市手足口病发病有明显的季节差异,年内有单峰发病模式和双峰(高低峰、双高峰)发病模式;(2)2017年中国各地中心城市手足口病月发病率分别与年均降水量、年均温,呈二次函数关系(R^(2)=0.6623)和指数函数关系(R^(2)=0.6469);(3)基于各地中心城市2017年手足口病发病率数据,用系统聚类法将上述城市的手足口病发病聚为8类;(4)在空间上,2017年中国各省、市手足口病发病表现为东南各省市发病率高,西北各省市发病率低的特点,并随降水量由东南向西北呈现递减趋势,且2017年2月、4月、12月各地中心城市手足口病发病率有显著的空间相关。气象因素对中国手足口病发病存在影响,中国手足口病发病在时间和空间上均存在显著差异,系统聚类分析结果在宏观尺度上可为手足口病防控提供参考。展开更多
"全球多源对地观测数据集成研究"收集了1981年以来全球10多种卫星原始数据、6种卫星高级数据产品、气象生态台站网络和各种地面观测数据,并在此基础上对所收集的数据进行整理和格式转换,该题经统计、收集、整理和汇编总量近60..."全球多源对地观测数据集成研究"收集了1981年以来全球10多种卫星原始数据、6种卫星高级数据产品、气象生态台站网络和各种地面观测数据,并在此基础上对所收集的数据进行整理和格式转换,该题经统计、收集、整理和汇编总量近600TB的卫星遥感数据。采用时间序列方法,辅以时空先验知识和时空连续性,实现全球多年MODIS和AVHRR地表反射率数据云雪监测和时空滤波,生成多年时空连续的高质量地表反射率数据。针对全球陆表特征参量产品生产的原始数据、预处理数据、中间产品数据和产品数据,客观地进行数据的分级和编目,构建全球陆表特征参量数据集成平台,实现数据全生产链路有效性检测和数据完整性检测。建立陆表特征参量产品在线分发服务系统,并于2012年12月22日在地球观测组织(Group on Earth Observations,GEO)第9次全会上面向全球用户公开发布。同时通过北京师范大学全球变化与数据处理中心和美国马里兰大学的Global Land Cover Facility向全球用户提供免费的数据查询、检索和下载服务。展开更多
Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development...Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development".The selection of the target location of resettlement sites for poverty-stricken villages is of critical importance to the success of resettlement projects,yet the selection process is challenged by the need for analyzing a variety of contributing factors,and the need for many rounds of tedious data processing.So in this paper we present an in-depth analysis of the major factors and data processing model concerning mountainous povertystricken villages,which also takes a major part of China's poor villages.Our analysis shows the following factors bear the most importance in resettlement selection:1) topography:candidate areas should have slope less than 25 degrees and altitude less than 2400 meters.2) accessibility:close to market conventions places and transportation facilities.3) farming resources:with abundant land and water resources.4) non-intrusiveness:interests of receiving villages should be considered and negative impact minimized.A simple measure could be having the candidate area 1000 m away from the receiving residents.5) Minimal ecological and political footprint:candidate areas shall not conflict with nature conservation areas or nationally planned key land use projects.6) Social and cultural compatibility:residents will better off if relocated in the same county,considering language,religion,ethnic culture and other factors.Taking Makuadi,Lushui County of Nujiang Prefecture as a case study,we demonstrate how GIS analysis and modeling tools can be used in the selection process of resettlement projects in mountainous areas.展开更多
文摘以中国手足口病疫情平发年2017年为例,探讨气象因素对中国手足口病(hand foot and mouth disease,HFMD)发病的时空影响,并对各省、自治区、直辖市(以下称各地)中心城市手足口病发病类型进行划分。本研究采用聚类分析、空间自相关等方法分析2017中国各地中心城市气象(气温、降水)因素对手足口病发病影响及其时空分异。结果表明:(1)在时间上,2017年中国各地中心城市手足口病发病有明显的季节差异,年内有单峰发病模式和双峰(高低峰、双高峰)发病模式;(2)2017年中国各地中心城市手足口病月发病率分别与年均降水量、年均温,呈二次函数关系(R^(2)=0.6623)和指数函数关系(R^(2)=0.6469);(3)基于各地中心城市2017年手足口病发病率数据,用系统聚类法将上述城市的手足口病发病聚为8类;(4)在空间上,2017年中国各省、市手足口病发病表现为东南各省市发病率高,西北各省市发病率低的特点,并随降水量由东南向西北呈现递减趋势,且2017年2月、4月、12月各地中心城市手足口病发病率有显著的空间相关。气象因素对中国手足口病发病存在影响,中国手足口病发病在时间和空间上均存在显著差异,系统聚类分析结果在宏观尺度上可为手足口病防控提供参考。
文摘"全球多源对地观测数据集成研究"收集了1981年以来全球10多种卫星原始数据、6种卫星高级数据产品、气象生态台站网络和各种地面观测数据,并在此基础上对所收集的数据进行整理和格式转换,该题经统计、收集、整理和汇编总量近600TB的卫星遥感数据。采用时间序列方法,辅以时空先验知识和时空连续性,实现全球多年MODIS和AVHRR地表反射率数据云雪监测和时空滤波,生成多年时空连续的高质量地表反射率数据。针对全球陆表特征参量产品生产的原始数据、预处理数据、中间产品数据和产品数据,客观地进行数据的分级和编目,构建全球陆表特征参量数据集成平台,实现数据全生产链路有效性检测和数据完整性检测。建立陆表特征参量产品在线分发服务系统,并于2012年12月22日在地球观测组织(Group on Earth Observations,GEO)第9次全会上面向全球用户公开发布。同时通过北京师范大学全球变化与数据处理中心和美国马里兰大学的Global Land Cover Facility向全球用户提供免费的数据查询、检索和下载服务。
基金supported by the National Natural Science Foundation of China (Grant No.40761019)National Natural Science Foundation of Yunnan (Grant No.2007D157M)
文摘Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development".The selection of the target location of resettlement sites for poverty-stricken villages is of critical importance to the success of resettlement projects,yet the selection process is challenged by the need for analyzing a variety of contributing factors,and the need for many rounds of tedious data processing.So in this paper we present an in-depth analysis of the major factors and data processing model concerning mountainous povertystricken villages,which also takes a major part of China's poor villages.Our analysis shows the following factors bear the most importance in resettlement selection:1) topography:candidate areas should have slope less than 25 degrees and altitude less than 2400 meters.2) accessibility:close to market conventions places and transportation facilities.3) farming resources:with abundant land and water resources.4) non-intrusiveness:interests of receiving villages should be considered and negative impact minimized.A simple measure could be having the candidate area 1000 m away from the receiving residents.5) Minimal ecological and political footprint:candidate areas shall not conflict with nature conservation areas or nationally planned key land use projects.6) Social and cultural compatibility:residents will better off if relocated in the same county,considering language,religion,ethnic culture and other factors.Taking Makuadi,Lushui County of Nujiang Prefecture as a case study,we demonstrate how GIS analysis and modeling tools can be used in the selection process of resettlement projects in mountainous areas.
