Note: All meeting links will be sent out one day before the event.

Co-sponsored by the Center for Geographic Analysis of Harvard University, RMDS Lab, Future Data Lab, and China Data Institute, the workflow-based data analysis project aims to provide a new approach for efficient data analysis and replicable, reproducible, and expandable research.

The training webinars will discuss the following topics: (1) the objectives of workflow-based data analysis; (2) the comparison of workflow tools for data analysis; (3) the implementation of data analysis workflows for selected case studies; (4) potential extensions and future directions.

What does the price include?

• synchronous webinars on workflow-based data analysis
• complimentary FREE access to recorded webinars and PPTs
• datasets and workflows for case studies of each topic
• complimentary FREE access to the statistical database on China Data Online (which usually cost around $500 for each dataset)

The registration fee is $68-$98 for each webinar, which will offer (1) one-month complimentary access to recorded webinars and PPTs; (2) data and workflows for case studies; and (3) one-month complimentary access to Statistical Database on China Data Online, or $680-$980 for the year-long program, which will offer (1) one-year complimentary access to those recorded monthly training webinars and PPTs; (2) data and workflows for case studies; and (3) one-year complimentary access to Statistical Database on China Data Online.

Leading Speakers

• Dr. Tao Hu, Center for Geographic Analysis at Harvard University.
• Alicia Wei, RMDS Lab

Schedule

1. Statistical Data Analysis With Workflows

Thursday, October 1st, 2020 at 8:00-9:00am, PST

Topics:

• The objectives of data analysis workflows;
• The comparison of workflow tools for data analysis;
• The implementation of data analysis workflows for selected case studies with the gov’t statistics of China, which include population and environment, urban and rural development, green energy transition, county-level GDP estimates with nighttime light data;
• Potential extensions and future directions.

2. Analysis of Population Census Data & Demographic Change

Thursday, November 6th, 2020, 8:00-9:00am, PST

Topics:

• The spatial-temporal hierarchy of regional inequality of China
• Unconditional convergence of Chinese provinces (1952-2017): Some statistical analysis results
• Quantitative Methods for the Comparative Analysis of Cities in History
• Growth and development in prefecture-level cities in China

3. Analysis of Economic Census Data & Industrial Change

Thursday, December 3rd, 2020, 8:00-9:00am, PST

Topics:

• Agglomeration and location choice of foreign financial institutions in China
• Determinants of industrial innovation in China Evidence from its recent economic census
• Export sophistication and economic growth: Evidence from China
• The impact of human and physical capital accumulation on Chinese growth after 1994: A spatial econometric approach

4. The Integration of Data and Maps for Spatial Analysis

Thursday, January 7th, 2021, 8:00-9:00am, PST

Topics:

• A Spatial data analysis of regional development in Greater Beijing, China, in a GIS environment
• Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data
• Estimation of Urban Population Dynamics Using DMSP-OLS Night-Time Lights Time Series Sensors Data
• Does FDI Drive Economic Growth? Evidence from City Data in China

5. Spatial Analysis of Patent Data

Thursday, January 28th, 2021, 8:00-9:00am, PST

Topics:

• A comparison of the spatial distribution of innovative activities in China and the US
• Spillover effects of FDI on innovation in China Evidence from provincial data
• Pattern of patent-based environmental technology innovation in China
• Spatial Distribution of Patents in China

6. Spatial Analysis of Health with Statistics and GIS Data

Thursday, February 25th, 2021, 8:00-9:00am, PST

Topics:

• Spatial Equity of Multilevel Healthcare in the Metropolis of Chengdu, China: A New Assessment Approach
• Temporal and Spatial Analysis of Neural Tube Defects and Detection of Geographical Factors in Shanxi Province, China
• Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis
• A Multivariate Spatiotemporal Model of COVID-19 Epidemic Using Ensemble of ConvLSTM Networks

7. Spatial Analysis of Environment with Statistics and GIS Data

Thursday, March 25th, 2021, 8:00-9:00am, PST

Topics:

• Lee, S., & Oh, D. W. (2015). Economic growth and the environment in China: Empirical evidence using prefecture level data. China Economic Review, 36, 73-85..
• Wang, Y. (2010). The analysis of the impacts of energy consumption on environment and public health in China. Energy, 35(11), 4473-4479.
• You, H. (2016). Characterizing the inequalities in urban public green space provision in Shenzhen, China. Habitat International, 56, 176-180.
• Zheng, S., & Kahn, M. E. (2017). A new era of pollution progress in urban China? Journal of Economic Perspectives, 31(1), 71-92.

8. Spatiotemporal Analysis of Urban Development

Thursday, April 29th, 2021, 8:00-9:00am, PST

• Spatiotemporal analysis of housing prices in China: a big data perspective.
• Spatio-temporal analysis of urban spatial interaction in globalizing China—A case study of Beijing-Shanghai Corridor.
• Spatio-Temporal Patterns of Urban-Rural Development and Transformation in East of the Hu Huanyong Line, China.
• Polycentric urban development in China: A multi-scale analysis.

9. Spatiotemporal Analysis of Rural Development

Thursday, May 27th, 2021, 8:00-9:00am, PST

• Spatio-temporal pattern of China's rural development: A rurality index perspective
• Differentiation of rural development driven by industrialization and urbanization in eastern coastal China
• Spatiotemporal Decoupling between Population and Construction Land in Urban and Rural Hubei Province
• Analysis of rural transformation development in China since the turn of the new millennium

10. Human Mobility in Space and Time

Thursday, June 24th, 2021, 8:00-9:00am, PST

• Guo, Y., Yu, H., Zhang, G., & Ma, D. T. (2021). Exploring the impacts of travel-implied policy factors on COVID-19 spread within communities based on multi-source data interpretations. Health & Place, 69, 102538.
• Nielsen, F., Marti, G., Ray, S., & Pyne, S. (2021). Clustering patterns connecting COVID-19 dynamics and Human mobility using optimal transport. Sankhya B, 83(1), 167-184.
• Wang, S., Liu, Y., & Hu, T. (2020). Examining the change of human mobility adherent to social restriction policies and its effect on COVID-19 cases in Australia. International journal of environmental research and public health, 17(21), 7930.
• Chan, K. W. (2012). Migration and development in China: trends, geography and current issues. Migration and Development, 1(2), 187-205.

11. Spatiotemporal Analysis of Culture and Society

Thursday, July 22nd, 2021, 8:00-9:00am, PST

12. Future Data Analysis: New Development and Directions

Thursday, August 19th, 2021, 8:00-9:00am, PST

About RMDS Lab

Global Association for Research Methods and Data Science is the leading community-centered data science research organization. Our global platform enables people to meet, learn, and collaborate with fellow data science professionals to solve problems. RMDS Lab was founded in 2009 by IBM Chief Data Scientist Alex Liu and is headquartered in Pasadena, CA with partners worldwide. It serves more than 35,000 members and affiliates around the world. In December 2019, RMDS has successfully held “IM Data” annual conference in the Pasadena Convention Center with 2000+ attendees from all over the world. For more information, visit www.grmds.org.

About Future Data Lab

Future Data Lab (http://futuredatalab.org) is designed for providing a new generation of data services with cutting-edge methodology and technology. Its missions are: to support research in the human and natural components of local, regional and global change; to promote quantitative-research on spatial evolution and global change; to promote collaborative research in spatial and global studies; and to promote the use and sharing of new data sources in teaching and research.

The Future Data Lab is partnering with different academic and business partners for data, tools and data case studies.