PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 29, 20260 citationsOpen Access

Urban Snake Displacement Risk Index

View Full Paper
ASAlameen Shuaib

Key Points

  • To develop and introduce the Urban Snake Displacement Risk Index (USDRI) for quantifying the risk of snake displacement in urban areas.
  • Developed a reproducible composite framework aggregating four displacement mechanisms: sighting density, habitat loss, urban expansion, and proximity.
  • Normalized each component to a range of 0-25 and summed them to produce a final USDRI score.
  • Implemented the framework as Boseman, which retrieves various location data for real-time USDRI computation.
  • Simulated analysis across six cities to illustrate score differentiation based on urbanization patterns.
  • The USDRI produced scores indicating varying levels of displacement risk corresponding to urbanization patterns in the tested cities.
  • Illustrative analysis showed that USDRI scores effectively reflect the underlying pressures driving snake appearances in urban settings.
  • For the first time, the tool offers a standardized measure to understand the risks associated with snake encounters.

Abstract

Rapid urbanization in Sub-Saharan Africa, South Asia, and Latin America issystematically destroying snake habitat and forcing species into human settlements,contributing to tens of thousands of snakebite deaths annually. Despite this documented crisis, no standardized quantitative tool exists to measure the underlying displacement pressure driving these encounters. This paper introduces the UrbanSnake Displacement Risk Index (USDRI), a reproducible composite framework thataggregates four independently documented displacement mechanisms—sighting density, habitat loss, urban expansion, and urban proximity—into a single score from 0 to 100, classified into four actionable risk bands. Each component is normalized toan equal 0–25 range and summed to produce the final index, ensuring transparency and reproducibility without requiring empirically unjustified weighting assumptions The framework is implemented as Boseman, an urban snake displacement search engine that retrieves real occurrence data from iNaturalist and GBIF, satellite habitat data from Google Earth Engine, urban land cover data from the Copernicus Urban Atlas, and geocoding from OpenStreetMap Nominatim, computing USDRI scores in real time for any geographic query. Simulated illustrative analysis across six cities Lagos, Nairobi, Accra, Mumbai, Bangkok, and São Paulo demonstrates that the framework produces differentiated scores corresponding to known urbanization and conflict patterns. USDRI fills a documented methodological gap in conservation science and urban public health by providing, for the first time, a quantitative tool that answers not only where snakes are appearing but why they are appearing andhow serious the underlying pressure driving those appearances is

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Alameen Shuaib (2026) studied this question.

synapsesocial.com/papers/69c8c3a8de0f0f753b39e968https://doi.org/10.5281/zenodo.19245786
Ask AI
Helpful
Bookmark
Share
View Full Paper