Research and Reflection on Transit Oriented Development:a Case study of Shijiazhuang City,Hebei Province,China

Over the years,transit-oriented development (TOD) has provided many countries with valuable theoretical methods and experience to deal with urban traffic congestion,environmental pollution,energy consumption and other problems.After decades of rapid urbanization,many cities in China are facing more complex challenges in transportation than ever before.This paper introduces and critically analyzes the practical application of TOD under different backgrounds and modes.Taking Shijiazhuang City,Hebei Province,China as the main case,this paper discusses and evaluates the intervention role of TOD in China’s urban transportation planning,and critically reflects on the deficiency of TOD’s transformation from theory to practice.

Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland

High-Speed Rail(HSR)has increasingly become an important mode of inter-city transportation between large cities.Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development.Quantifying the impact of HSR’s interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making.We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact.This paper adopts two types of spatial information:toponyms from social media posts,and the geographical location information embedded in social media posts.The framework highlights the asymmetric nature of social interaction among cities,and proposes a series of metrics to quantify such impact from multiple perspectives-including interaction strength,spatial decay,and channel effect.The results show that HSRs not only greatly expand the uneven distribution of inter-city connections,but also significantly reshape the interactions that occur along HSR routes through the channel effect.

三维城市景观生态研究

城市景观元素的三维分析是城市景观研究发展的重要趋势之一。城市形态的结构特征与功能的空间作用不仅体现在二维空间中,同时在三维向度上表现出城市社会经济发展态势与空间演变的相互作用,因此在城市空间格局、过程与功能的讨论中,不能局限于二维平面。首先由三维视角提出城市景观格局及功能网络特征,并结合对航空摄影测量、卫星遥测、机载激光扫描等三维信息获取方法的认识,介绍目前城市景观三维信息提取的相关方法;其次,在三维城市景观生态研究内容方面,重点介绍城市景观要素提取、格局特征及其动态变化、生态环境效应和城市景观功能等相关研究,探讨城市内部的三维建成景观以及绿地景观要素提取与应用,并在传统景观格局分析方法基础上,提出了三维城市景观格局特征测量方法、动态变化监测方法以及动态模拟等相关内容,进一步对三维城市景观在生物多样性、局部气候变化等生态环境效应方面的研究进行了归纳总结;最后借助构建三维景观功能网络的思路,以作为三维城市景观功能优化研究的切入点。

Modeling and assessing international climate financing

资助的气候是在气候保护的当前的协商的一个关键问题。这研究建立基于施主国家为资助的气候和接受者国家使用在建立了资金的机制资助模型的气候专门为碳排放的资金减小。分享负担的原则基于 GDP，历史的排出物，和 consumptionbased 排出物。用这个模型，我们发展并且分析一系列情形模拟，包括在 Cancun 气候变化会议(2010 ) 达成的一个资助节目和几个随后的节目。结果证明资助的那持续气候能帮助与全球气候作斗争变化。然而， Cancun 协议被投射导致减小仅仅 0.01 ? 鷸吗？？

Geographic information science in the era of geospatial big data: A cyberspace perspective

The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost LIDAR-camera integrated device

Simultaneous Localization and Mapping(SLAM)has been widely used in emergency response,self-driving and city-scale 3D mapping and navigation.Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges(e.g.extreme lighting)while the traditional extractors are struggling.In this paper,we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework.In this thread,our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device.The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively.The scale estimation of the monocular visual SLAM is also significantly improved(0.01 versus 0.98).In addition,a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping.This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors.

What We Measure Matters:The Case of the Missing Development Data in Sendai Framework for Disaster Risk Reduction Monitoring

The Sendai Framework for Disaster Risk Reduction 2015−2030’s(SFDRR)framing moved away from disaster risk as a natural phenomenon to the examination of the inequality and injustice at the root of human vulnerability to hazards and disasters.Yet,its achievements have not seriously challenged the long-established capitalist systems of oppression that hinder the development leading to disaster risk creation.This article is an exploratory mapping exercise of and a collective reflection on Sustainable Development Goals(SDGs)and SFDRR indicators-and their use in measuring progress towards disaster risk reduction(DRR).We highlight that despite the rhetoric of vulnerability,the measurement of progress towards DRR remains event/hazard-centric.We argue that the measurement of disaster risk could be greatly enhanced by the integration of development data in future iterations of global DRR frameworks for action.

全球城市交通出行方式和出行目的地选择对新型冠状病毒传播的影响

城市交通出行方式和出行目的地的选择与新型冠状病毒的传播具有关联性.本研究基于2020年2~12月期间,苹果和谷歌公司记录的全球58个城市的交通出行报告,通过多变量时间序列分析,研究了城市交通出行行为对每日确诊病例的影响.研究发现,在全球尺度,与出行目的地相关的行为活动对每日确诊病例的贡献最大,其次是传染源和出行方式.在三类出行方式中,开车是最安全的通勤方式.而在一些大城市,步行的风险显著提高.虽然公众对乘坐公共交通比较担心,但该出行方式在部分城市反而是相对安全的.在六类出行目的地中,与车站和工作地相关的活动对疫情的传播贡献最大,而与药店和公园相关的活动对疫情传播的贡献最小.同时发现,建城区人口密度和口罩使用率在不同时期对病毒传播的影响具有差异性.该研究有助于为交通出行政策的制定提供依据,有助于市政在控制和预防病毒流行的同时保持城市开放.