Data and Software

Hsu, A. Wang, X., Tan, J., Toh, W., and N. Goyal. (2022). Replication data for: Predicting European Cities’ Climate Mitigation Performance using Machine Learning Draft.  https://dataverse.unc.edu/dataset.xhtml?persistentId=doi%3A10.15139%2FS3%2FNRJ5ZO&version=DRAFT. Data-Driven EnviroLab Dataverse.

To help students, teachers, and researchers visualize the heat data collected by PocketLab sensors during their Urban Heat study, we developed Heat Data Visualization for PocketLab Sensor — a fast and light google Colab notebook. This visualization is available under a Creative Commons License: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Citation: Hua, H. and Wang, X (2022). Heat Data Visualization for PocketLab Sensor. Available: Data-Driven EnviroLab.https://datadrivenlab.org/data-and-software/

• Heat Data Visualization for PocketLab Sensor Colab Notebook
• Tutorial 

In partnership with Oxford University, The Energy and Climate Intelligence Unit, and the NewClimate Institute, we developed a dashboard that tracks and evaluates net-zero pledges around the world.

• Interactive web platform
• Datasets (xlsx)

Data-Driven Envirolab has developed the largest global database of city and regional government participation in climate action. Citation: Hsu, A., Yeo, Z.Y., Rauber, R. et al. ClimActor, harmonized transnational data on climate network participation by city and regional governments. Sci Data 7, 374 (2020). https://doi.org/10.1038/s41597-020-00682-0

• Datasets (CSV) via or our figshare or GitHub page
• ClimActor R package – data loads as an RData object when the package is loaded (devtools::install_github("datadrivenenvirolab/climactor", build_vignettes = TRUE)
• Video tutorial on package use

Replication data for: Thomas, R., A. Hsu, and A. Weinfurter. Sustainable and inclusive – Evaluating urban sustainability indicators’ suitability for measuring progress towards SDG-11. Environment and Planning B: Urban Analytics and City Science (2020). https://doi.org/10.1177/2399808320975404. Citation: 

• Access on Harvard Dataverse.

This platform displays census-tract level surface urban heat island (SUHI) intensities for US urbanized areas as well as socioeconomic information at the same level of aggregation. Citation: Chakraborty, T., Hsu, A., Manya, D., & Sheriff, G. (2020). A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications., ISPRS Journal of Photogrammetry and Remote Sensing.

Hsu, A., Tan, J., Ng, Y.M., Vanda, R. and N. Goyal. (2020). Replication data for: Performance determinants show European cities are delivering on climate mitigation. https://doi.org/10.7910/DVN/IJ4DDI, Harvard Dataverse, V1.

Hsu, A., N. Alexandre, J. Brandt, T. Chakraborty, S. Comess, A. Feierman, T. Huang, S. Janaskie, D. Manya, M. Moroney, N. Moyo, R. Rauber, G. Sherriff, R. Thomas, J. Tong, Y. Xie, A. Weinfurter, Z. Yeo (in alpha order), et al. (2018). The Urban Environment and Social Inclusion Index. New Haven, CT: Yale University. Available: datadrivenlab.org/urban.

All data are available for download here: https://datadrivenlab.org/urban/about/downloads/.

The 2016 Environmental Performance Index (EPI) ranks 180 countries on air quality, water and sanitation, fisheries, forestry, biodiversity and habitat, and climate change and energy. For more information see: www.epi2016.yale.edu.

In partnership with the Connecticut Green Bank, Data-Driven Yale developed scorecards ranking residential solar PV in each of the state’s 69 towns. See the website and final report here: http://www.ctsolarscoreboard.com/.