DataCenter-NASA-hackathon

What exactly does it do:

• Kartta is a dashboard that helps urban planners, city governments, and policymakers understand the environmental and social risks associated with the construction and operation of data centers in cities.
• By combining NASA Earth Observation data with urban and socioeconomic datasets, Kartta generates an interactive, data-driven decision map that supports evidence-based urban planning. The platform is not about preventing construction, but about helping cities make smarter decisions, identifying the most suitable locations for new data centers, and suggesting mitigation strategies for existing ones. This combination allows users to see and quantify how data centers affect the environment and nearby communities, and to understand what actions can be taken to reduce negative impacts while promoting sustainable digital growth in cities and human settlements.

How does it work:

• We are a data-driven platform that uses a wide range of environmental and urban datasets to generate a comprehensive overview of cities and human settlements. By combining NASA satellite observations with local information (water resources, energy supply, population density, and household consumption patterns), Kartta creates an integrated picture of how urban areas function and evolve.
• This data fusion allows planners and decision-makers to make more accurate, evidence-based decisions about where to locate new data centers and how to improve existing urban areas affected by current ones. In short, we turn complex, multi-source data into clear insights for sustainable urban planning related to data center constructions and maintenance.

Resources:

Data Source:

• Center for International Earth Science Information Network (CIESIN), Columbia University.
• (2018). Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 [Data set].
• NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW
• City of Chicago. (2025).
• Chicago Energy Benchmarking [Data set].
• Chicago Data Portal. Retrieved October 5, 2025, from https://data.cityofchicago.org/Environment-Sustainable-Development/Chicago-Energy-Benchmarking/xq83-jr8c/about_data
• NASA Langley Research Center (LaRC) POWER Project. (2025). NASA Prediction of Worldwide Energy Resources (Version 9.0.1) [Data set]. NASA. Retrieved October 5, 2025, from https://power.larc.nasa.gov/data-access-viewer/
• NASA Near Real-Time Capability for Earth Observation (LANCE). (2024). VIIRS Land Near Real-Time Data [Data set]. NASA Earthdata. Retrieved October 4, 2025, from https://www.earthdata.nasa.gov/data/instruments/viirs/land-near-real-time-data
• Natural Earth. (2023). Admin 0 – Countries (Version 5.1.1) [Data set]. ttps://www.naturalearthdata.com/downloads/10m-cultural-vectors/
• Natural Earth. (2023). Admin 1 – States, Provinces (Version 5.1.1) [Data set]. https://www.naturalearthdata.com/downloads/10m-cultural-vectors/
• The MOPITT Science Team. (2021). MOPITT Level 3 Carbon Monoxide (CO) Gridded Monthly Averages (MOP03M) (Version 9) [Data set]. NASA Langley Atmospheric Science Data Center DAAC. https://doi.org/10.5067/TERRA/MOPITT/MOP03M.00
• U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA). (2022). Landsat 8–9 Collection 2 Level-2 Surface Reflectance Code (LaSRC) Product.
• NASA Earth Science Data Systems (ESDS) Program, Land Processes Distributed Active Archive Center (LP DAAC). https://doi.org/10.5066/P9OGBGM6

Software & Libraries:

• Figma, Inc. (2025). Figma [Computer software]. https://www.figma.com
• Plotly Technologies Inc. (2023). Plotly Python Open Source Graphing Library (Version 5.18.0) [Computer software]. https://plotly.com/python/
• QGIS Development Team. (2025). QGIS Geographic Information System (Version 3.38 'Prizren') [Computer software]. https://qgis.org
• Streamlit Inc. (2023). Streamlit: The fastest way to build and share data apps (Version 1.29.0) [Computer software]. https://streamlit.io/

Citation Reference List:

• Guidi, G., Dominici, F., Gilmour, J., Butler, K., Bell, E., Delaney, S., & Bargagli-Stoffi, F. J. (2024). Environmental Burden of United States Data Centers in the Artificial Intelligence Era. [Preprint]. arXiv:2411.09786v1. https://arxiv.org/abs/2411.09786
• Ngata, W., Bashir, N., Westerlaken, M., Liote, L., Chandio, Y., & Olivetti, E. (2025). The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities. [Preprint]. arXiv:2506.03367. https://arxiv.org/abs/2506.03367

The team:

• Cristal Rivera, Ligia Anjos, Deniz Yener, Myrnelle Cinco, Venus Schwidorowski