Spatial Data Analysis in Cyberspace Data, Methodology, Tools 在网络空间数据，空间数据分析的方法，工具_精品.doc

Spatial Data Analysis in Cyberspace: Data, Methodology, Tools and Applications Place: East China University of Science and Technology, Shanghai, China Date: July 1-3, 2013 /Learming/SeminarsContent.aspx?id=39 Cosponsors: The Lab for Urban and Regional Analysis, East China University of Science and Technology The State Key Laboratory of Information Engineering In Surveying, Mapping And Remote Sensing, Wuhan University China Data Center, the University of Michigan Instructors: Edward Chao, Data Numerica Institute, U.S.A. Xinyue Ye, Computational Social Science Lab and Department of Geography, Kent State University Shuming Bao, China Data Center, University of Michigan Yuming Wu, East China University of Science and Technology This workshop is designed to introduce some new tools for spatial data analysis in cyberspace, which will include the spatial analytical tools such as GeoStat and StatGL developed by Data Numerica Institute, PySAL build on Python developed by GeoDa Center for Geospatial Analysis and Computation at Arizona State University, and Urban and Regional Explorer, a web based system for spatial data analysis offered by the Lab for Urban and Regional Analysis of the East China University of Science and Technology. The workshop will discuss the methodology, functions, and data available for web based spatial data analysis. Some case studies will be demonstrated for applications in spatial economy and population, environmental assessment, religious studies, and disease mappings. Researchers from government agencies, research institutes, and universities will find the workshop particularly relevant. Topics (3 days) (Morning: 9:00-12:00; Afternoon: 2:00-5:00) Day 1: July 1 Introduction to Spatial Statistics and Computation Morning: Introduction to Spatial Statistics and Econometrics Lecturers: Yuming Wu Introduction to R Packages and StatGL for Spatial Data Analysis Lecturer: Edward Chao Afternoon: Introduction to Python and PySAL for Spatial Data Analys