1 to 10 of 23 Results
Feb 20, 2024
Spatial Data Lab, 2020, "New Technology, New Features, and New Directions", https://doi.org/10.7910/DVN/UHM36E, Harvard Dataverse, V2
This webinar introduced some recent development of China Data Online, the primary online China data service distributed by the China Data Institute, a long-term partner of the All China Marketing Research (ACMR). This talk discussed some new technology and new features of the Chi... |
Feb 20, 2024
Spatial Data Lab, 2020, "Webinar for "Resources for COVID-19 Study"", https://doi.org/10.7910/DVN/OTYQUY, Harvard Dataverse, V24
CDL webinars. |
Spatial Data Lab, 2021, "Webinar Series on COVID-19 Impact Analysis", https://doi.org/10.7910/DVN/TGORYN, Harvard Dataverse, V6
As a joint effort by scholars and professionals from the Center for Geographical Analysis at Harvard University, the Geo-Computation Center for Social Sciences at Wuhan University, the China Data Institute, the NSF Spatiotemporal Innovation Center, RMDS Lab, and some other instit... |
Spatial Data Lab, 2023, "Spatiotemporal Data Analysis with Codeless Visual Programming", https://doi.org/10.7910/DVN/I0AWAM, Harvard Dataverse, V1
This seminar will introduce the KNIME Analytics Platform and its Geospatial Analytics extension developed by the Spatial Data Lab (SDL) team at Harvard's Center for Geographic Analysis (CGA). The SDL team members will share the presentations, presenting the project's vision and d... |
Aug 2, 2022
Spatial Data Lab, 2022, "InstituteIntroduction to Weibo Mobility Index Data", https://doi.org/10.7910/DVN/QNV29K, Harvard Dataverse, V1
As one of the largest social media, Weibo is considered as the “Twitter” in China. Based on the geo-tagged information in the sampled Weibo data, we built the Weibo mobility index by provinces and prefecture cities in China. This webinar gives an introduction to this Weibo mobili... |
Aug 2, 2022
Spatial Data Lab, 2022, "Data Science Trainings on Analytical Workflows", https://doi.org/10.7910/DVN/BWTK2I, Harvard Dataverse, V1
Co-sponsored by the Center for Geographic Analysis of Harvard University, RMDS Lab and Future Data Lab, the workflow-based data analysis project aims to provide new approach for efficient data analysis and replicable, reproducible and expandable research. This year-round webinar... |
Aug 2, 2022
Spatial Data Lab, 2022, "Training Webinars on China Research Data: Sources, Tools and Applications", https://doi.org/10.7910/DVN/LN6OHH, Harvard Dataverse, V1
This webinar series introduce some research data with a focus on China and discuss the difference from the US data. Each webinar will cover the following topics: (1) data sources, data collection, data category, definition, description, and interpretation; (2) alternative data an... |
Jul 20, 2022
Yuxiang Zhong; Biyu Chen, 2022, "Monte Carlo Method and Application in Urban Traffic Simulation", https://doi.org/10.7910/DVN/ZCGGTK, Harvard Dataverse, V1
A KNIME-based case study demonstrates the Monte Carlo technique in simulating urban traffic flows, illustrating major steps in the traffic simulation modules: trip generation, trip distribution, mode choice, and trip assignment. The case study comes from the book, Quantitative Me... |
Jul 20, 2022
Lingjie Hu; Biyu Chen, 2022, "Linear Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers", https://doi.org/10.7910/DVN/YYIEGC, Harvard Dataverse, V1
A KNIME-based case study applied the linear programming(LP) method to examine the issue of wasteful commuting in Columbus, Ohio; an example of allocating healthcare providers in Baton Rouge, Louisiana to illustrate the implementation of a location-allocation problem. The case stu... |
Jul 20, 2022
Hanchen Yu; Wendy Guan; Shuming Bao, 2022, "The Workbench for Spatial Data Science", https://doi.org/10.7910/DVN/D2GWPZ, Harvard Dataverse, V1
This presentation explores methodologies and establishes protocols for developing workbenches for spatial data science in research, teaching, and business applications. The objectives of this workbench are to provide:(1) An easy, efficient and customizable toolkit for spatial dat... |
