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CITATIONS & CREDITS

1. Citations

Caula, Sabina, et al. “Seasonal Variation in Species Composition of an Urban Bird Community in Mediterranean France.” Landscape and Urban Planning, vol. 87, no. 1, 2008, pp. 1–9. https://doi.org/10.1016/j.landurbplan.2008.03.006. Accessed 22 Apr. 2019.

 

Coron, L., et al. “The Suite of Lumped GR Hydrological Models in an R Package.” Environmental Modelling & Software, vol. 94, 2017, pp. 166–171. https://doi.org/10.1016/j.envsoft.2017.05.002. Accessed 6 Aug. 2020.

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Fischer, Jason D., et al. “Categorizing Wildlife Responses to Urbanization and Conservation Implications of Terminology.” Conservation Biology, vol. 29, no. 4, 7 Jan. 2015, pp. 1246–1248, https://doi.org/10.1111/cobi.12451.

 

Kemper, T., et al. Description of the Settlement Classification Projections 2015–2070 Public Release SMOD PROJ R2020 V1.0. European Commission, 2022.

 

Magurran, Anne E. Measuring Biological Diversity. Wiley, 2023. www.wiley.com/en-us/Measuring+Biological+Diversity-p-9780632056330. Accessed 27 May 2025.

 

Schiavina, Marcello, Michele Melchiorri, and Martino Pesaresi. GHS-SMOD R2023A: Application of the Degree of Urbanisation Methodology. European Commission, Joint Research Centre (JRC), 2023. http://data.europa.eu/89h/a0df7a6f-49de-46ea-9bde-563437a6e2ba. Accessed 27 May 2025.

2. Credit

Bird observation data provided by eBird (www.ebird.org) and the Cornell Lab of Ornithology.

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Built-up area data derived from the Global Human Settlement Layer (GHSL), produced by the European Commission Joint Research Centre (JRC) and accessed via Google Earth Engine.

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Tools and Libraries Used:

  • Programming language: Python 3

  • Data analysis and manipulation: pandas, numpy

  • Geospatial processing: geopandas, shapely

  • Visualization: plotly, lets-plot, folium

  • Web and API integration: requests, dotenv, wikipedia

  • Progress tracking and automation: tqdm

  • Database and storage: sqlite3, SQLAlchemy

  • Website deployment: GitHub Pages

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Website Template provided by Wix. 

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Thanks to all teachers from LSE Data Science Institute for providing detailed guide and help in lectures, classes, drop-in sessions and slack channel.

 

Special thanks to Jim Zhao for providing pictures in Introduction parts.

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