空间功能视角下的公共服务对房价的影响——以成都市为例

房价的快速上涨和城市内部房价的巨大差异引起社会广泛关注,调控房价,防止局部区域房价过热势在必行。本文从城市空间功能的视角出发,以成都市2016年房价为例,基于地理探测器分析公共服务对房价的影响。结果表明：成都平均房价为8480元/m²,并从市中心沿交通环线和放射状干线同时向郊区递减,形成圈层加放射格局,总体上呈现西高东低、南高北低的特点。公共服务(主要包括医疗、金融和教育服务)对房价的影响以第三圈层和西南方最为显著,且高于城市层面上整体的影响。公共服务对房价的影响显著受不同区域的空间功能差异的影响,公共服务和空间功能差异会加剧房价的分异格局,并推动局部房价过热。因此,显著地受到在房价调控中,不仅要有传统的金融、经济政策,还要注重空间功能和公共服务的优化。     

中国流动人口的城市宜居性感知及其对定居意愿的影响

流动人口是中国城镇化进程的重要参与者,加强其定居意愿特征分析对制定中国新型城镇化发展政策具有重要参考价值。基于中国40个主要城市的宜居城市问卷调查数据,运用描述统计和地理探测器方法,揭示中国流动人口的城市宜居性感知特征及其对定居意愿的影响。研究结果表明：①中国流动人口的城市宜居性感知评价分异明显,呈现出“公共服务设施便利性>自然环境舒适性>社会人文环境舒适性>环境健康性>交通便捷性>城市安全性”的递减特征;②中国流动人口的公共服务设施便利性、社会人文环境舒适性、环境健康性和城市安全性等4个维度城市宜居性感知对其定居意愿具有显著影响,且影响强度依次递减,但二者关系并不完全呈简单的线性特征。③相对他人收入满意度、生活满意度、住房价格感知和地理区位等参照变量,以及年龄、性别和职业等个体与家庭属性也是影响中国流动人口定居意愿的重要因素。     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

Characterization of Black Carbon in the Ambient Air of Agra,India:Seasonal Variation and Meteorological Influence

This study characterizes the black carbon in Agra, India home to the Taj Mahal—and situated in the Indo-Gangetic basin.The mean black carbon concentration is 9.5 μg m~(-3)and, owing to excessive biomass/fossil fuel combustion and automobile emissions, the concentration varies considerably. Seasonally, the black carbon mass concentration is highest in winter, probably due to the increased fossil fuel consumption for heating and cooking, apart from a low boundary layer. The nocturnal peak rises prominently in winter, when the use of domestic heating is excessive. Meanwhile, the concentration is lowest during the monsoon season because of the turbulent atmospheric conditions and the process of washout by precipitation. The ratio of black carbon to brown carbon is less than unity during the entire study period, except in winter(December). This may be because that biomass combustion and diesel exhaust are major black carbon contributors in this region, while a higher ratio in winter may be due to the increased consumption of fossil fuel and wood for heating purposes. ANOVA reveals significant monthly variation in the concentration of black carbon; plus, it is negatively correlated with wind speed and temperature. A high black carbon mass concentration is observed at moderate(1–2 m s~(-1)) wind speed, as compared to calm or turbulent atmospheric conditions.     

Why Was the Strengthening of Rainfall in Summer over the Yangtze River Valley in 2016 Less Pronounced than that in 1998 under Similar Preceding El Niño Events?——Role of Midlatitude Circulation in August

It is widely recognized that rainfall over the Yangtze River valley (YRV) strengthens considerably during the decaying summer of El Niño, as demonstrated by the catastrophic flooding suffered in the summer of 1998. Nevertheless, the rainfall over the YRV in the summer of 2016 was much weaker than that in 1998, despite the intensity of the 2016 El Niño having been as strong as that in 1998. A thorough comparison of the YRV summer rainfall anomaly between 2016 and 1998 suggests that the difference was caused by the sub-seasonal variation in the YRV rainfall anomaly between these two years, principally in August. The precipitation anomaly was negative in August 2016——different to the positive anomaly of 1998. Further analysis suggests that the weaker YRV rainfall in August 2016 could be attributable to the distinct circulation anomalies over the midlatitudes. The intensified "Silk Road Pattern" and upper-tropospheric geopotential height over the Urals region, both at their strongest since 1980, resulted in an anticyclonic circulation anomaly over midlatitude East Asia with anomalous easterly flow over the middle-to-lower reaches of the YRV in the lower troposphere. This easterly flow reduced the climatological wind, weakened the water vapor transport, and induced the weaker YRV rainfall in August 2016, as compared to that in 1998. Given the unique sub-seasonal variation of the YRV rainfall in summer 2016, more attention should be paid to midlatitude circulation——besides the signal in the tropics——to further our understanding of the predictability and variation of YRV summer rainfall.     

南京及周边地区雷达气候学分析

为了研究南京及周边地区暖季（6-9月）对流风暴的活动分布规律,利用2009-2013年6-9月长时间序列的南京多普勒天气雷达数据识别对流回波并格点化,统计并分析了南京及周边地区对流风暴的气候学分布特征,结果表明:（1）在暖季,南京及周边地区对流风暴具有明显的区域分布特征,其中7与8月为对流风暴活动高峰期,对流风暴频数分布大值中心位于南京东部沿江地区;（2）不同尺度和伸展高度对流风暴的分布特征各不相同,较大较深对流的分布大值中心更加明显;（3）对流风暴的垂直结构因月份不同而有所差异,7与8月对流风暴强度最大;（4）不同尺度和伸展高度对流风暴频数存在明显的日变化特征,呈多峰分布,主峰值区位于午后,同样,各月份对流风暴频数的日变化特征也非常明显,呈单峰或者多峰分布。      

华南前汛期持续性暴雨年代际变化特征及成因

应用福建、广东、广西243个气象站1961-2012年逐日降水资料,构建华南前汛期暴雨强度指数,揭示了前汛期持续性暴雨年代际变化特征及其可能机理。研究表明:前汛期持续性暴雨经历了多发（1961-1972年）-少发（1973-1991年）-多发（1992-2012年）3个阶段,目前仍处于多发期,具有持续时间较长且强度增强的特点;由于前汛期降水的低频振荡受热带低频信号北传的调制,因此,导致这种显著年代际变化的可能成因是热带低频信号北传的周期和强度的年代际差异,当热带低频信号北传至华南时低频周期长（短）且强度强（弱）,则前汛期易出现持续时间长（短）且强度强（弱）的持续性暴雨。     

北半球平流层极涡崩溃早晚的环流特征及其对5月南亚降水的影响

本文采用1979—2014年NCEP/NCAR月平均再分析资料、CMAP和GPCP月平均降水资料,分析了北半球平流层极涡崩溃早晚的环流特征及其与南亚降水的关系。结果表明,北半球平流层极涡崩溃时间存在明显的年际变化特征。极涡崩溃偏早(偏晚)年,自3月开始异常信号从平流层向下传播,之后的4月,从平流层到对流层高层极区温度异常偏高(偏低),极涡异常偏弱(偏强),极夜急流异常偏弱(偏强)。结果还表明,5月南亚降水异常与平流层极涡崩溃时间的早晚存在显著相关,5月南亚降水异常与平流层极涡崩溃早晚年平流层异常信号的下传有关。当平流层极涡崩溃偏晚年,4月平流层极区表现为位势高度异常偏低,而中纬度则位势高度场异常偏高,并伴随位势高度异常场的向下传播,5月该位势高度异常场下传至阿拉伯海北部大陆上空对流层顶,形成有利于降水的环流场,导致南亚降水偏多。反之,则相反。     

南亚高压上下高原时间及其与高原季风建立早晚的关系

本文利用1948—2013年NCEP/NCAR逐日再分析资料,定义了南亚高压动态特征指数,讨论了南亚高压上下高原的时间以及与高原季风建立早晚的关系。研究表明,南亚高压北界位置在4月初开始北移,5月迅速北抬,最北可达到55°N,9月开始南撤,西伸脊点在5—10月移动较稳定,5—7月向西移动到青藏高原上空,8—10月向东移动撤离高原,11月—次年4月东西摆动剧烈。南亚高压初上高原大致为6月第3候(33候),而撤离约为10月第4候(58候)。南亚高压移上高原的时间较高原夏季风建立晚73 d左右。南亚高压撤离高原时间较高原冬季风建立约早5 d。高原夏季风的建立和南亚高压初上高原是青藏高原热力作用在不同阶段的结果,反映在了高原的高低层上。     

四川省持续性暴雨定义及时空分布特征

采用1961—2014年四川省158个气象站的逐日降水量资料,定义了四川省单站和区域持续性暴雨的标准,分析了近54年四川省持续性暴雨的时空分布特征。结果表明,盆地单站持续性暴雨多发生在9月,主要出现在盆地西北部、西南部和东北部,一般持续3天,一次持续性暴雨事件降水量一般可达150~200mm。而攀西地区单站持续性暴雨发生的次数一般为1~3次,6月发生范围最大,最长持续时间为4天,主要发生在攀西地区东部。区域持续性暴雨多发生在7月,降水中心主要分布在盆地西部沿山一带及盆地东北部,这与单站持续性暴雨频次高值区的分布基本一致。区域持续性暴雨在2001年后发生频次较前期频繁,特别是持续3天的持续性暴雨事件发生频率较高,但是强度略有减弱。