1992～2010年中国积雪时空变化分析

积雪是地表覆盖的重要组成部分,也是气候变化的良好指示器,监测积雪变化对研究全球水循环、气候变化有着极为重要的意义.本文利用"正-背景数据学习"算法,即Presence and Background Learning Model(PBL模型)对中国积雪进行估测,得到Special Sensor Microwave Imager 5 days(SSM/I 5 d)中国积雪分布图,并利用Mann-Kendall趋势检验法对1992~2010年中国积雪进行了时空变化分析,具体探讨了年积雪日数变化、积雪年季分布与变化特征、稳定积雪区变化.结果表明,PBL模型在中国地区积雪估测精度表现良好,在与Interactive Multisensor Snow and Ice Mapping System(IMS),MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid(MOD10A2)等积雪产品的比较中取得了较高的一致性,年平均总体精度达到0.88.1992~2010年间中国三大主要积雪区积雪日数都有显著下降趋势,但青藏高原西北部则有显著上升趋势.前者是由气温上升所引起,后者则是由降水增多所引起.在积雪的季节分布上,新疆与东北-内蒙古地区积雪面积都是冬季最大,春、秋季相对较小;青藏高原积雪面积则是春、秋、冬季都很大,积雪的季节变化规律远不如其他两大积雪区明显.研究期间,中国稳定积雪区面积无显著变化趋势,仅青藏高原年降雪日数波动较大.

Changes of Population, Built-up Land, and Cropland Exposure to Natural Hazards in China from 1995 to 2015

By using the latest China population grid and land-use data,we assess the changing exposure of China’s population and land uses to the hazards of storm surges,droughts,earthquakes,floods,and landslides from 1995 to2015.We found that the single-hazard areas and the multihazard areas covered 43%and 26%of China’s territory,respectively.Population grew faster in the hazard-prone areas than in the non-hazard areas.Built-up area expanded more rapidly in the areas prone to earthquakes and landslides.Cropland changed rapidly in many hazard-prone areas.The hazard-prone areas affected by floods featured the highest cropland loss rates,while the areas prone to earthquakes and landslides featured the highest cropland growth rates.We detected areas with significant exposure changes by using hot spot analysis.It was found that population and built-up land in the Pearl River Basin were increasingly exposed to storm surges,floods,and landslides.The Haihe River Basin and Huaihe River Basin also showed a consistent increase of population and built-up land exposure to droughts and earthquakes.These findings can provide a foundation for the design and implementation of protection and adaptation strategies to improve the resilience of Chinese society to natural hazards.

Characterizing the urban spatial structure using taxi trip big data and implications for urban planning

Urban spatial structure is an important feature for assessing the effects of urban planning.Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments.Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures.However,these methods cannot efficiently reflect the influence of human activities.With the wide application of big data,analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning.In this study,we constructed a human-activity space network using the taxi trip big data.Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning.This method was applied to a case study based on one-month taxi trip data of Dongguan City.Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020,which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan.We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure.The analysis demonstrated that the taxi trip data are important big data on social spatial perception,and taxi data should be used for evaluating spatial structures in future urban planning.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata:a case study on city of Toronto

The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes.Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy,leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types.To overcome these limita-tions,an Accessibility-interacted Vector-based Cellular Automata(A-VCA)model was proposed for the better simulation of realistic land use change among different urban functional types.The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process.The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto,Canada,during 2012-2016.A vector-based CA without considering the driving factor of accessibility(VCA)and a popular grid-based CA model(Future Land Use Simulation,FLUS)were also implemented for compar-isons.The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature(kappa=0.907,figure of merit=0.283,and cumulative producer’s accuracy=72.83%±1.535%).The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models,suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy.The proposed model provides new tools for a better simula-tion of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

Offshoring,Wages,and Skill Premiums:Firm-level Evidence from China

Using detailed Chinese manufacturing firm production and trade data from 2000 to 2006,this study finds that offshoring significantly increases firms’average wages.First,using the quasi-natural experiment of China's accession to the World Trade Organization,we investigate how a reduction in offshoring costs affects the manufacturing firm's wages and find that a productivity effect and a job-relocation effect are two possible channels.Second,the dynamic decomposition of industry-level wages indicates that the within-firm effect is 0.547,accounting for 31.5 percent of the total variation.Finally,a Mincer-type regression shows that offshoring also increases within-firm skill premiums.Our findings have strong implications for the government related to framing appropriate industrial policies to raise wages and reduce income inequality.