https://github.com/caimeng2/smartcityus

Code for "Linking smart cities and SDGs through descriptive analysis of US municipalities"

https://github.com/caimeng2/smartcityus

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
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  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
    Links to: nature.com
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  • Scientific vocabulary similarity
    Low similarity (7.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Code for "Linking smart cities and SDGs through descriptive analysis of US municipalities"

Basic Info
  • Host: GitHub
  • Owner: caimeng2
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 41.8 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

Code for "Linking smart cities and SDGs through descriptive analysis of US municipalities"

Cai, M., Decaminada, T., Li, Y., Durst, N. J., Kassens-Noor, E., & Wilson, M. (2025). Linking smart cities and SDGs through descriptive analysis of US municipalities. Nature Cities, 1–5. https://doi.org/10.1038/s44284-024-00192-9

System requirements

The code was executed on macOS version 14.5 using R version 4.2.2 and Python version 3.8.3.

Installation

Before running the code, please ensure that the packages in each .ipynb and .Rmd file are installed. Please visit each package's website for installation instructions. The typical installation time should not be more than a few minutes.

Data and code

The list of municipalities, their population sizes, locations, and the American Community Survey data can be downloaded from https://www.nhgis.org/. The website addresses of U.S. municipalities are available at https://doi.org/10.1177/23998083231190961. The datasets generated and analyzed during the study are deposited in the folder data. All code is available in the current directory.

The expected runtime for each code sheet is within a few minutes, except for 07_get_text.py, which was run on Michigan State University's High-Performance Computing Center and took a few hours.

Owner

  • Name: Meng Cai
  • Login: caimeng2
  • Kind: user

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