基于多源数据的疫情下中美两国热点话题变化分析

目的/意义:探讨多源数据对于全球性热点话题变化分析的准确性提升。方法/过程:使用TFIDF提取后微博特征词和TNM方法处理后的GDELT数据融合对中、美民众的关注热点和变化趋势进行分析。结果/结论:使用多源数据融合的方法能够更好地表现不同区域、国家在同一全球突发事件时的真实关注点,能较全面地体现出区域间的关注热点和变化趋势的差异性。数据库的收录范围和使用受众限制了数据的普适性,并不能很好地表现全部人群的关注热点。使用多源数据融合的方法可以较为全面地对全球化突发事件进行区域或国家间的舆情对比分析。

基于Google Earth Engine的遥感大数据分析研究进展与应用

随着地理信息数据规模不断增长,传统的空间分析模式受限于软硬件的性能已经不能对大数据多尺度研究提供较好的支持。以GEE平台为基础,通过算法原理阐述平台架构体系以及分析GEE的技术特点,论述其在遥感大数据分析领域的研究进展、人工智能技术在地理信息分析领域的应用,探索地理空间数据分析的智能化方向。最后,结合交叉学科前沿探索GEE在未来的热点研究领域中的应用模式,并展望GEE作为云端空间信息服务平台的未来发展以及和下一代互联网技术的协同演进。

Detection of spatial hot spots and variation for the neon flying squid Ommastrephes bartramii resources in the northwest Pacific Ocean

With the increasing effects of global climate change and fishing activities,the spatial distribution of the neon flying squid(Ommastrephes bartramii) is changing in the traditional fishing ground of 150°-160°E and 38°-45°N in the northwest Pacific Ocean.This research aims to identify the spatial hot and cold spots(i.e.spatial clusters) of O.bartramii to reveal its spatial structure using commercial fishery data from2007 to 2010 collected by Chinese mainland squid-j igging fleets.A relatively strongly-clustered distribution for O.bartramii was observed using an exploratory spatial data analysis(ESDA) method.The results show two hot spots and one cold spot in 2007 while only one hot and one cold spots were identified each year from2008 to 2010.The hot and cold spots in 2007 occupied 8.2%and 5.6%of the study area,respectively;these percentages for hot and cold spot areas were 5.8%and 3.1%in 2008,10.2%and 2.9%in 2009,and 16.4%and 11.9%in 2010,respectively.Nearly half(>45%) of the squid from 2007 to 2009 reported by Chinese fleets were caught in hot spot areas while this percentage reached its peak at 68.8%in 2010,indicating that the hot spot areas are central fishing grounds.A further change analysis shows the area centered at156°E/43.5°N was persistent as a hot spot over the whole period from 2007 to 2010.Furthermore,the hot spots were mainly identified in areas with sea surface temperature(SST) in the range of 15-20℃ around warm Kuroshio Currents as well as with the chlorophyll-a(chl-a) concentration above 0.3 mg/m^3.The outcome of this research improves our understanding of spatiotemporal hotspots and its variation for O.bartramii and is useful for sustainable exploitation,assessment,and management of this squid.

Carbon Productivity Analysis to Address Global Climate Change

Developing low-carbon economy and enhancing carbon productivity are basic approaches to coordinating economic development and protecting global environment, which are also the major ways to address climate change under the framework of sustainable development. In this paper, the authors analyze the annual rate of carbon productivity growth, the differences of carbon productivity of different countries, and the factors for enhancing carbon productivity. Consequently, the authors clarify their viewpoint that the annual rate of carbon productivity growth can be used to weigh the efforts that a country takes to address climate change, and propose policies and suggestions on promoting carbon production.

Industrial Carbon Emissions Status and Analysis Based on Energy Consumption

With frequent disastrous weathers and increasingly prominent GHG effects in recent years, normal existence and development of mankind are facing unprecedented threats and challenges. GHG emissions mitigation for the global climate changes has been the focus of concern of the world. As the biggest developing country and the second largest country of carbon-emission, China attaches importance to the carbon emission reduction. The major GHG component is carbon dioxide and in China, the emis- sion of carbon dioxide is mainly from industrial production. In the paper, the status and trend of Coz emission from industrial departments, high-carbon emission and its specific industries are shown in statistics. Meanwhile, the policy environment, industrial organization structure and technology of carbon high emission are all discussed based on practical situations in these departments and industries. At the end, through the analysis of gray correlation, correlativity is explored for both fossil energy consumption and total carbon emission, and also for the production value and carbon emission of each industrial sector. Some policy proposals for the establishment of low-carbon industries and transition of economic development pattern are set forth.

A Simulation of the Upper-Tropospheric Temperature Pattern in BCC_CSM1.1

A simulation of the upper-tropospheric temperature (UTT) by the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1) model is evaluated through a comparison with NCEP/NCAR reanalysis data. It is shown that this model has the ability to simulate the climate pattern of the UTT in all four seasons. The spatial correlation on the climatological distribution between the simulation and the observation is 0.92, 0.93, 0.90, and 0.93 for spring, summer, autumn, and winter, respectively. The first leading mode of the UTT in the simulation agrees with that in the observation, except that the simulated second leading mode corresponds to the observed first leading mode in spring. The standard deviation distribution of the simulation is also roughly consistent with the observation, with a pattern coefficient of 0.82, 0.78, 0.82, and 0.82 in spring, summer, autumn, and winter, respectively. The potential UTT change in the second half of the 21st century under the Representative Concentration Pathway 8.5 (RCP8.5) scenario is examined. The prominent change is that the summer UTT will increase over Eurasia and decrease over the North Pacific compared with the present, indicating that the zonal thermal contrast between Asia and the North Pacific will be strengthened within the context of future global warming. The intensity of the interannual variability of the UTT over the Asian-Pacific region is also generally increased. The zonal thermal contrast between Asia and the North Pacific will tend to be enhanced in winter, concurrent with the intensified interannual variability.

Reconstructing provincial cropland area in eastern China during the early Yuan Dynasty(AD1271–1294)

Reconstructing historical land use and land cover change(LUCC) at the regional scale is an important component of global environmental change studies and of improving global historical land use datasets. By analyzing data in historical documents, including military-oriented cropland(hereafter M-cropland) area, the number of households engaged in M-cropland(hereafter M-household) reclamation, cropland area, and the number of households, we propose a conversion relationship between M-cropland area and cropland area reclaimed by each household. A provincial cropland area estimation method for the Yuan Dynasty is described and used to reconstruct the provincial cropland area for AD1290. Major findings are as follows.(1) Both the M-cropland and cropland areas of each household were high in the north and low in the south during the Yuan Dynasty, which resulted from different natural conditions and planting practices. Based on this observation, the government-allocated M-cropland reclamation area to each household was based on the cropland area reclaimed by each household.(2) The conversion relationship between M-cropland and cropland areas per household showed conversion coefficients of 1.23 and 0.65 for the south and north, respectively.(3) The cropland area in the entire study area in AD1290 was 535.4×106 mu(Chinese area unit, 1 mu=666.7 m^2), 57.8% in the north and 42.2% in the south. The fractional cropland areas for the entire study area, north, and south were 6.8%, 6.6%, and 7.1%, respectively and the per capita cropland areas for the whole study area, north, and south were 6.7, 15.6, and 4.1 mu, respectively.(4) Cropland was mainly distributed in the middle and lower reaches of the Yellow River(including the Fuli area), Huaihe River Basin(including Henan Province), and middle and lower reaches of the Yangtze River(including Jiangzhe, Jiangxi, and Huguang provinces).