基于广义回归神经网络的全球表层海水1°×1°二氧化碳分压数据推演

表层海水二氧化碳分压是评估海洋碳源汇强度的关键参数,但其实测数据较少、时空分布极不均匀,导致二氧化碳交换通量的估算有很大的不确定性,海洋源汇特征就不能确切获取。为了解决这个难题,在收集的表层大洋二氧化碳地图(Surface Ocean CO₂ Atlas,SOCAT)实测数据集基础上,运用广义回归神经网络建立二氧化碳分压与经纬度、时间、温度、盐度和叶绿素浓度间的非线性关系,构建了1998?2018年间全球1°×1°经纬度的表层海水二氧化碳分压格点数据,其标准误差为16.93 μatm,平均相对误差为2.97%,优于现有研究中的前反馈神经网络、自组织映射神经网络和机器学习算法等方法。根据构建的数据所绘制的全球表层海水二氧化碳分压的分布与现有研究有较好的一致性。     

一种利用浮标站资料改进海浪模式有效波高预报的方法

基于浮标站海浪历史数据,利用回归分析方法建立了海浪数值模式有效波高预报产品的一元二次回归方程订正统计模型。通过2017年7月1日-2018年10月10日期间业务试运行结果发现:订正方程能有效改善有效波高数值预报产品的预报精度,且预报时效越短订正效果越显著。其中,第6~11 h预报时效内的订正前后平均绝对误差值减小0.17~0. 241 m,第6~18 h预报时效内订正前后均方根误差减小幅度为0.103~0. 28 m。这说明应用订正统计模型对海浪模式输出产品进行订正,也是改进海浪模式预报准确率的一种有效途径。     

基于SVR和LSTM算法反演散射计下有效波高研究

有效波高反演对于海洋工程及海洋环境安全具有重要意义.我国海洋二号(HY-2A)卫星载有散射计和高度计等获取海洋要素的仪器.散射计可获取海洋风场数据但无法直接获取有效波高数据,高度计可获取海洋有效波高数据但覆盖区域狭小.本文将散射计与高度计各自优势结合,利用支持向量回归(SVR)和长短期记忆(LSTM)智能算法反演散射计...     

中国城市流动人口居留意愿影响因素的空间分异特征

流动人口居留意愿的影响因素往往存在显著的空间分异特征,然而既有研究对此关注较少。基于2015年中国流动人口动态监测数据,运用半参数地理加权回归（SGWR）模型,结合k均值聚类法,对中国城市流动人口居留意愿影响因素的空间分异特征展开研究。结论如下：① 流动人口居留意愿主要受流动人口群体自身特征的影响,受流入地的影响相对较弱;② 社会经济因素与流动人口居留意愿的关系紧密,人口、家庭及流动特征因素同样产生影响,收入、婚姻、跨省流动等因素对流动人口居留意愿产生抑制作用,而住房支出、参保率、子女个数等因素则产生促进作用;③ 各影响因素总体上呈现带状的空间分异模式,可概括为“E-W”“N-S”“NE-SW”“SE-NW”4种。民族、家庭等因素的正向影响自北向南递减,参保率、未婚率、二产就业等因素的影响自西北向东南递减,子女个数及人均GDP的影响自东北向西南递减。东部经济较发达地区高收入流动人口的居留意愿相对更弱,华南地区住房支出较高的流动人口居留意愿相对更强。④ 全国可划分为四大影响区,其中,华北地区、华中地区、华东地区受多因素共同影响;西北地区及部分西南地区主要受人口及社会因素的影响;东北三省及内蒙古东部地区主要受经济及家庭因素的影响;华南地区及部分中、东、西南部地区,除住房支出外,受大部分因素的影响相对最小。最后,本文对中国流动人口的服务与管理提出相应建议。     

Swelter in the city: Urban greenery and its effects on temperature in Guwahati,India

Changing urban landscape with multistoried high rises, roads and pavements is continuously reducing urban green space. These structures result in high surface temperature variation within cities. To explore the relationship between surface temperature and normalized difference vegetation index (NDVI), this study estimates two models—geographically weighted regression (GWR) and a fixed effect panel data model in relation to the Guwahati Metropolitan Area (GMA), a secondary city in north east India. The results indicate the superiority of GWR regression in presence of spatial dependence. Panel data analysis shows that the densely populated urban areas in the GMA with less than 10 per cent greenery are 1°C warmer than the sub-urban areas with 50 per cent greenery.     

海南省连片贫困地区农户致贫风险分析

农村贫困与减贫是世界性难题,也是中国各级政府高度重视并着力解决的重大民生问题。基于农户及致贫风险的文献梳理,从区位、社会和劳动力3个要素维度构建了农户致贫风险分析的二元Logistic回归模型。采用484户农户问卷调查数据,分析了海南省连片贫困地区农户的致贫风险,提出有效减贫和持续发展对策。研究发现:① 海南连片贫困地区生态环境良好但贫困发生率较高,家庭劳动力较充裕但受教育水平较低,子女教育支出负担重,因病因残致贫比例较高,女性务工人口较多,农户自身脱贫致富的发展动力不足。② 海拔高度200 m以下、男性户主、拥有残疾或患病成员、务工人口比例低、女性务工人员占比高、以及单位劳动力供养学生数高的农户具有更大的致贫风险。③ 研究未发现女性户主、少数民族、低受教育水平户主、大型规模家庭有更高的致贫风险,女性成员比例、抚养比等因素对农户贫困影响较小。激发农户内生动力、大力发展特色化和规模化农业、增加农户就业机会、加强针对农民工、女性务工人员和病残群体的社会保障等减贫政策制定实施是实现脱贫攻坚目标的重要途径。     

基于地理探测器和GWR模型的中国重点镇布局定量归因

重点镇是小城镇发展的龙头,形成科学合理的重点镇布局对优化中国城市化战略格局有重要意义。论文以2004年和2014年分别公布的1887个和3675个全国重点镇为样本,对其分布及效应的变动特征进行探究,进而在地级尺度对重点镇布局的影响因子及其作用进行地理探测和局部空间回归。结果表明：① 经增补调整,中国重点镇布局及建设效应的均衡性增强,主要集聚区西移北扩,冷热点的分布突破“胡焕庸线”,经济辐射效应的分化程度减弱,体现出政策因素的有力影响。除县际均衡和区域倾斜政策外,重点镇的分布还受到海拔高度、公路网密度、常住人口城镇化率等因子的显著作用。② 因子探测器、GWR模型和交互作用探测器的结合能更精准地刻画影响因子的作用方式、方向、路径和强度。中国重点镇的布局不是5个显著性因子均匀、独立、直接作用的结果,而是影响均具空间异质性的各因子两两交互作用后增效的产物。③ 县际均衡政策与其他因子的协同作用是形成现有重点镇分布格局的主导力量;区域倾斜政策的效果总体较好,但目标区域还需更准确。     

全球粮食安全格局的时空演化

Food security is the primary prerequisite for achieving other Millennium Development Goals(MDGs).Given that the MDG of“halving the proportion of hungers by 2015”was not realized as scheduled,it will be more pressing and challenging to reach the goal of zero hunger by 2030.So there is high urgency to find the pattern and mechanism of global food security from the perspective of spatio-temporal evolution.In this paper,based on the analysis of database by using a multi-index evaluation method and radar map area model,the global food security level for 172 countries from 2000 to 2014 were assessed;and then spatial autocorrelation analysis was conducted to depict the spatial patterns and changing characteristics of global food security;then,multi-nonlinear regression methods were employed to identify the factors affecting the food security patterns.The results show:1)The global food security pattern can be summarized as“high-high aggregation,low-low aggregation”.The most secure countries are mainly distributed in Western Europe,North America,Oceania and parts of East Asia.The least secure countries are mainly distributed in sub-Saharan Africa,South Asia and West Asia,and parts of Southeast Asia.2)Europe and sub-Saharan Africa are hot and cold spots of the global food security pattern respectively,while in non-aggregation areas,Haiti,North Korea,Tajikistan and Afghanistan have long-historical food insecurity problems.3)The pattern of global food security is generally stable,but the internal fluctuations in the extremely insecure groups were significant.The countries with the highest food insecurity are also the countries with the most fluctuated levels of food security.4)The annual average temperature,per capita GDP,proportion of people accessible to clean water,political stability and non-violence levels are the main factors influencing the global food security pattern.Research shows that the status of global food security has improved since the year 2000,yet there are still many challenges such as unstable global food security and acute regional food security issues.It will be difficult to understand these differences from a single factor,especially the annual average temperature and annual precipitation.The abnormal performance of the above factors indicates that appropriate natural conditions alone do not absolutely guarantee food security,while the levels of agricultural development,the purchasing power of residents,regional accessibility,as well as political and economic stability have more direct influence.     

Theory and measurement model of administrative region potential from a perspective of administrative division adjustment: Taking Chongqing city as a case study

Journal of Geographical Sciences - There is a lack of basic theory and method to examine the effect of administrative division (AD) adjustment on the regional development. Based on the theory and...     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.