Developing and Sequencing Community Engagement and Experiential Education: A Case Study of Urban Geography Teaching and Research

Community engagement curricula and course design can provide substantial experiences for both community members and participating students. Using a case study approach, this research focuses on four steps in this process: initial community relationship forming, engaging in community service, transitioning to civic engagement, and developing a community-based research program. Narrative examples from student course evaluations position these community-based experiences as transformative for multiple parties. Institutional structures are presented as helpful entrees to engagement for students, while noting that community relationships provide contextualized, powerful, and meaningful relationships, supporting recommendations for emerging and existing community engagement programs.     

Measuring Community and University Impacts of Critical Civic Geography: Insights from Chicago

Geographers have increasingly adopted community-based learning and research into their teaching and scholarly activities since Bunge and Harvey called for an applied public geography that is both useful and challenges societal inequalities. With few exceptions, however, there has been little discussion of methods for measuring this work. Many published assessments focus on the impacts of projects on students but overlook the impacts on community partners. Impacts on faculty and the larger university community are also often ignored. This article discusses literature on the evaluation of community–university research and service learning from a critical perspective. A discussion of service learning and community-based research (CBR) projects at two Chicago universities, DePaul and Chicago State, is presented. In both cases challenges were encountered to achieve full evaluation of projects, yet both included an evaluation of university and community partners that allowed for assessment of the projects’ value to all partners.     

Carbon pricing in practice: a review of existing emissions trading systems

This article analyses the implementation of emissions trading systems (ETSs) in eight jurisdictions: the EU, Switzerland, the Regional Greenhouse Gas Initiative (RGGI) and California in the US, Québec in Canada, New Zealand, the Republic of Korea and pilot schemes in China. The article clarifies what is working, what isn’t and why, when it comes to the practice of implementing an ETS. The eight ETSs are evaluated against five main criteria: environmental effectiveness, economic efficiency, market management, revenue management and stakeholder engagement. Within each of these categories, ETS attributes ? including abatement cost, stringency of the cap, improved allocation practices over time and the trajectory of price stability ? are assessed for each system. Institutional learning, administrative prudence, appropriate carbon revenue management and stakeholder engagement are identified as key ingredients for successful ETS regimes. Recent implementation of ETSs in regions including California, Québec and South Korea indicates significant institutional learning from prior systems, especially the EU ETS, with these regions implementing more robust administrative and regulatory structures suitable for handling unique national and sub-national opportunities and constraints. The analysis also shows that there is potential for a ‘double dividend’ in emissions reductions even with a modest carbon price, provided the cap tightens over time and a portion of the auctioned revenues are reinvested in other emissions-reduction activities. Knowledge gaps exist in understanding the interaction of pricing instruments with other climate policy instruments and how governments manage these policies to achieve optimum emissions reductions with lower administrative costs.

Key policy insights

• Countries are learning from each other on ETS implementation.

• Administrative and regulatory structures of ETS jurisdictions appear to evolve and become more robust in every ETS analysed.

• A ‘double dividend’ for emissions reductions may also exist in cases where mitigation occurs as a result of the ETS policy and when auction revenues are reinvested in other emissions-reduction activities.

     

基于智能物联网技术的天津城市积水监测预警系统

通过智能物联网技术实时获取积水监测实况数据,利用天津市气象精细化格点预报产品和城市自动雨量观测站实况数据,以机器学习、神经网络模型和天津市城市内涝风险等级划分原理为基础,研究基于用户实时位置的城市内涝预报预警技术,研发天津市城市自动化积水监测预警系统。结果表明,该系统具备一定的城市内涝风险监测预警预报能力,并在2018—2020年多次重大天气过程中应用,积水深度预报结果与监测结果基本一致,应用数据表明验证结果良好,系统可以为政府防灾减灾决策、指挥调度提供精准、及时的气象数据支撑。     

基于e-Learning的测绘专业教学训练系统的设计与实现

介绍了e-Learning的概念及体系结构,结合测绘专业教育的实际需求,在e-Learning三层体系结构的基础上,给出e-Learning环境下测绘专业教学训练的系统设计及实现方案。     

自组织网络在遥感土地覆盖分类中应用研究

设计完成和比较了自组织网络的几种算法在遥感土地覆盖分类中的应用,结果表明非监督和监督学习结合方法进行遥感土地覆盖分类,各算法在分类性能上无显著差异,因此可采用算法和较简单的单竞争学习网络,根据最邻近原则进行非参数分类。     

基于ST-ConvLSTM的南海海表面CO2分压的空间和时间序列预测

海表面二氧化碳分压(pCO₂)的未来变化趋势,对统计评估全球碳收支以及理解全球气候变化背景下的海洋酸化现象至关重要。目前传统的海面pCO₂预测方法大部分基于有限的实测数据,然而实测数据存在着时间和地理方面的制约,且计算成本较高。近年来,随着时空观测数据的爆炸性增长,基于深度学习的数据驱动模型在海表面pCO₂预测方面中表现出良好的潜力。然而,由于多种环境因素与海表面pCO₂之间的关系错综复杂,到目前为止尚无十分简单有效的相关模型来对海表面pCO₂进行预测。为应对这一挑战,利用时空卷积长短时记忆神经网络(ST-ConvLSTM)模型,通过海面温度(sea surface temperature, SST)、海面盐度(sea surface salinity, SSS)、叶绿素a浓度(chl a)和海面pCO₂数据,预测南海的海面pCO₂,并将2019年1～12月的数据作为测试集对模型的表现进行了验证。结果显示, ST-ConvLSTM模型...     

基于深度学习的倾斜摄影建筑物表面损毁信息提取

建筑物受损信息是地震受灾程度评估的基础,针对传统建筑物表面信息识别人工成本高、效率低等问题,受深度学习提取建筑物影像的启发,提出利用无人机倾斜摄影模型与深度学习相结合的方法提取震后建筑物表面破损信息。以2019年长宁6.0级地震为例,选用双河镇震后倾斜摄影模型切片图为数据源,对比分析面向对象分类方法、VGG-16模型和DeeplabV3+模型对建筑物表面损毁信息的提取结果。分析结果表明,针对建筑物表面破损信息的提取,尤其是细小裂缝的提取,语义分割网络DeeplabV3+模型具有较强的优势(准确率96.93%、召回率96.85%、总体精度96.89%),可实现建筑物表面破损信息的有效提取,具有较强的应用价值。     

基于迁移学习的地球物理测井储层参数预测方法研究

随着大数据和机器学习的成熟和推广应用,人工神经网络在地球物理测井预测储层参数中得到重视.本文引入迁移学习进行测井储层参数预测,以孔隙度预测神经网络模型和孔隙度含水饱和度联合预测神经网络模型为基础模型,分别以渗透率及含水饱和度预测作为目标任务进行迁移学习,以提升储层参数预测效果和效率.文中详细阐述了基于迁移学习的测井储层...