2018年7月3日至7月13日,上海财经大学城市与区域科学学院/财经研究所将邀请英国研究院院士、社会科学院院士、皇家学会会员、伦敦大学学院规划学Michael Batty教授,伦敦大学Paul Longley教授,美国东密歇根大学地理空间信息科学与教育研究所(IGRE)所长及创始人谢一春教授,日本东北大学的曾道智教授和北卡罗来纳大学教堂山分校的Yan Song教授,在上海财经大学开设为期11天的《空间科学前沿:大数据应用与空间经济理论》国际暑期课程。
本次暑期课程将着眼于理论与数据两方面,理论方面将为大家讲解空间经济学最前沿的问题,数据方面将为大家介绍空间大数据、遥感大数据和城市大数据及其应用。Michael Batty教授会为大家详解大数据与智慧城市相关研究;Paul Longley教授为大家讲解地理信息学中的核心知识和消费者大数据;Yan Song教授为大家讲解城市空间结构的度量、经济分析以及大数据在城市空间结构与城市经济活力研究中的应用;谢一春教授主要为大家讲解空间遥感的基础知识、最新技术以及耦合人-自然系统的研究中遥感和社会经济大数据的整合;曾道智教授将为大家系统的讲解空间经济学的建模方法、基本理论模型,并为广大学子厘清空间经济学的前沿问题。
主讲教授
Michael Batty
伦敦大学学院规划学教授、高级空间分析中心(CASA)主席。1970年来一直致力于城市的计算机模型及其可视化分析,曾任威尔士大学卡的夫分校城市规划教授、院长,纽约州立大学布法罗分校国家地理信息分析中心主任;现任英国研究院院士、社会科学院院士、皇家学会会员;2004年女皇生日被授予大英帝国司令勋章,2013年被授予Lauréat Prix International de Géographie Vautrin Lud(被誉为地理学界的诺贝尔奖 ),2015年由于在城市科学上的杰出工作被地理科学皇家协会被授予“创始人奖章”。
Paul Longley
Professor of Geographic Information Science
Director, Consumer Data Research Centre
Department of Geography, University College London
Pearson Building, Gower Street, London, WC1E 6BT
Research Themes:Data Science、Digital Humanities、
Genetics、Global Health、
London、Migration、Populations & Lifelong、Health、Risk & Security、Sustainable Cities.
W: http://www.ucl.ac.uk
谢一春
博士,终身教授,美国东密歇根大学地理空间研究和教育研究所(IGRE)所长及创始人(始于1998年),中国科学院百人计划-海外杰出研究员,中国科学院地理科学与资源研究所高级客座研究员,美国科学促进会(AAAS)会员,美国地球物理联盟(AGU)会员,美国摄影测量和遥感学会(ASPRS)会员,美国地理学家协会(AAG)会员,美国地理信息科学与技术咨询委员会成员。谢一春教授长期从事地理信息系统与遥感的理论与方法研究,及其与城市扩张研究与时空模型,草地生态系统以及环境变化与人类活动影响,资源环境,土地利用和土地覆盖变化的交叉研究。他发表了6本专著和120多篇同行专家公审的学术论文,他领导的地理空间研究和教育研究所在2016年获得美国ESRI Special Achievement Award in GIS (地理信息系统特殊成就奖),谢教授也曾获美国科学基金会的科学?技术?工程?数学?教育科研杰出奖(2010)、美国地理学家协会的杰出学者奖 (2004)、中国科学院的百人计划海外杰出学者奖(2002-2005)。
Zeng,Dao-Zhi (曾道智)
日本东北大学信息科学研究科,博士生导师。1996年在日本京都大学取得工学博士后转入经济学领域,从事空间经济学的研究,其成果涉及城市经济学、区域经济学、国际贸易、环境经济学、实验经济学等多个学科,有40多篇论文发表在这些专业的有代表性的(SSCI)学术期刊上。他曾获得日本应用区域学会的坂下奖,电子情报通信学会的论文奖,美国 Western Regional Science Association的论文奖。现在担任学术期刊Journal of Systems Science and Complexity, Papers in Regional Science, Interdisciplinary Information Sciences, Asian Journal of Management Science and Applications的编辑委员。他还是日本应用区域学会、中国区域科学学会的理事。
Yan Song, Ph.D.Director, Program on Chinese CitiesProfessor, Department of City and Regional Planning
Specialization: Land Use and Environmental Planning
Song’s research interests includes low carbon and green cities, plan evaluation, land use development and regulations, spatial analysis of urban spatial structure and urban form, land use and transportation integration and how to accommodate research in the above fields by using planning supporting systems such as GIS and other computer-aided planning tools.
课程安排
时 间:2018年7月3日-7月4日
主讲人:Yan Song教授
题 目:Urban Spatial Structureand Urban Sustainable Development
1
课程简介: Cities are different in the spatial organization of urban activities, functions, and land uses and transportation network, thus forming different spatial structures. Urban Spatial Structure has profound implications for a range of urban outcomes such as economic efficiency, vibrancy, commuting behavior, and health performances of residents, etc. Studying the quantification of urban spatial structure and correlating urban form with outcomes is essential to provide better pathways to sustainable cities and regions.
2
课程大纲:
Lecture 1 城市空间结构之解析与度量 Quantifying urban spatial structure
Lecture 2 城市空间结构之经济分析 Economic analysis of urban form and urban spatial structure
Lecture 3 大数据在城市空间结构与城市经济活力研究中的应用 Big data and its application in the research on urban form and vibrancy
Lecture 4 城市空间结构与城市健康研究 1 Urban spatial structure and air quality
Lecture 5 城市空间结构与城市健康研究 2 Urban spatial structure and health
Lecture 6 新型可持续性发展城市之治理政策 Policies on urban governance and management for urban sustainability
时 间:2018年7月4日-7月5日
主讲人:谢一春教授
题 目:Big Data of Remote Sensing & GIS in Coupled Human-Natural System Research and Urban Studies
课程简介: One effort of advancing science and engineering for sustainability research is to assess how ecosystem services respond to and interact with various policy and management regimes across gradients of climate and land-use over an area of interest. From the perspective of analytical analysis, the studies of human adaptations and policy interventions involve data such as household surveys, socioeconomic statistics and policy assessments at multi-levels of administrative units. On the other hand, the examinations of climate and land-use changes require field samples at unique sites and along typical transects reflecting both natural and man-made gradients.
In this lecture series, I will give a brief overview of RS fundamentals, RS Big Data challenges, current techniques, and integration of RS and socioeconomic Big Data in coupled human-natural system research. Eight one-hour lectures will be presented over two or three days.
Hour 1: Fundamentals of Remote Sensing (RS)
Hour 2: RS Big Data Challenges
Hour 3: How to Classify Historical RS Big Data
Hour 4: How to Process Time-Series RS Big Data
Hours 5+6: How to integrate RS and socioeconomic Big Data to support coupled human-natural system research
Hour 7: How to handle incomplete information in RS Big Data
Hour 8: Future Direction of RS Big Data Research and Application
时 间:2018年7月7日-7月8日
主讲人:Paul Longley教授
题 目:Representation, Geographic Information Science and Consumer Data Research
课程简介: Increasing real shares of all of the data that are collected about us as citizens are acquired by businesses and other customer facing organisations. Alone or in combination with other consumer, administrative and conventional statistical sources, they hold the prospect of richer, more granular and more frequently updated measures of what is going on in contemporary society. At the same time, improvements in geographic information technologies are improving our abilities to contextualise short term (diurnal) activities in improved geographies of residence, workplace and lifestyle.
But these developments in Big Data bring new challenges and pitfalls. Few if any customer facing organisations have a monopoly of provision of goods and services to consumers, and the nature of their market shares is likely to exhibit bias – the source and operation of which is unknown. Consumer Big Data thus threaten to compromise the science of inference from ‘samples’ (Big Data that are in practice a by-product of market operations) to populations that are in practice not clearly defined.
课程大纲:This series of lectures considers the core organising principles and concepts of representation in geographic information science. It then considers a gallery of applications developed by the Consumer Data Research Centre that illustrate both the potential and pitfalls of consumer Big Data in better understanding residential mobility and population churn, demographic change, the changing role of retail areas and changes in the relationship between online and offline behaviour.
The core GISc material will be based upon
Longley P A, Goodchild M F, Maguire D J, RHind D W 2015. Geographic Information Science and Systems (Fourth Edition). Hoboken NJ, Wiley (Previous editions of this book are available in Mandarin, Czech, Korean, Polish and Portuguese.)
The case studies will be based upon Longley P A, Cheshire J A, Singleton A D (eds) 2018. Consumer Data Research. London, UCL Press.
The Nature of Geographic Data (1)
The Nature of Geographic Data (2)
Representing Geographic Variability
Uncertainty in Representation (1)
Uncertainty in Representation (2)
Introduction to Consumer Big Data
A Gallery of Consumer Data Applications (1)
A Gallery of Consumer Data Applications (2)
主讲人:Michael Batty教授
题 目:Big Data in Urban Analysis, Application and Planning
课程简介: Big data represents the information exhaust from real time sensing that are being rapidly deployed in cities to control many routine functions. In this sense, big data is often associated with automation in cities that is the focus of the smart cities movement and assuch, is changing our focus on cities from the long term to the short term,from the evolution of the city over years and decades to change in the city over the daily cycle of 24 hours. In these lectures, we will first explain the context to the smart city and illustrate how these added layers of new technology based on real time sensing are generating much more complex and intricate cities than we have ever had before.
课程大纲:We begin by defining what big means interms of size and then introduce the ‘three Vs’ of big data – volume, velocityand variety – which relate to various other characteristics of such data sets. Big data, in fact, is changing our focus on what is important in cities and focusing our attention on much smaller time spans over which cities change. We describe how such data is captured and generated and extend our argument to traditional data which can also become big under certain conditions were it to be disaggregated or combined with other data. We illustrate different types of big data and then illustrate its features using three examples involving movement and social media. We use these arguments as a prelude to the substance and the method of typical applications world wide which focus on different aspects of big data and the wider context in which the data and models are developed as part of the smart cities movement. We explore the way big data can be visualised in 2, 3 and 4D and how we can use visualisation as in integrating platform for routine and long term data sets, for open data and real time sensed data as that produced by crowdsourcing.
时 间:2018年7月9日-7月13日
主讲人:Zeng,Dao-Zhi (曾道智)教授
题 目:Research Frontiers of Spatial Economics
课程简介: Given the unevenness in the spatial distribution of natural resources, climate differences, proximity to coasts and rivers, we observe a lot of agglomerations of economic activities in the world. Spatial economics brings location, distance, and land into economics to explain where economic activities locate. It has become one of the main economic fields that can be used to understand how the new map of economic activities is being redrawn.
Spatial economics is a discipline covering at least international trade and regional economics, which differ in labor mobility. International trade mainly considers the production and consumption flows when labor is immobile while reginal economics focuses on industrial location when labor is mobile across regions. Trade theory will be the first half of our course, followed by regional economics.
1a. Background of Spatial Economics
1b. Dixit-Stiglitz model
2a. Krugman-type one-factor models
2b. Footloose capital models
3a. Melitz model
3b. Core-Periphery model
4a. Quasi-linear utility and pro-competitive effect
4b. Re-dispersion
5a. Welfare measurement
5b. VES utility / Applications
一点说明
本课程针对本校(院)研究生授课,部分教室可能有少量空位,感兴趣的同学与老师可以留言或联系后台,CCRL可以代为联系。此外,本课程不收取任何费用,交通、食宿等费用自理,组织方不承担任何责任性义务。
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