Transit Voronoi diagrams in multi-mode public transport networks

Chen, Bi Yu; Teng, Wenxin; Jia, Tao; Chen, Huiping; Liu, Xianglong

doi:10.1016/j.compenvurbsys.2022.101849

Cited by 5 publications

(1 citation statement)

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“…A variety of spatial measures have been used to delimit catchment areas and measure access coverage for urban locations. Popular methods include uniform euclidean 63,64 , network-based distance 65 , network voronois 66 , and spatial modelling approaches 9,39 . For node attributes, we adopt a uniform euclidean approach, as it provides a consistent, straightforward, and extensible basis for integrating (2023) 10:667 | https://doi.org/10.1038/s41597-023-02578-1 www.nature.com/scientificdata www.nature.com/scientificdata/ heterogeneous data sources across different network locations.…”

Section: Street View Imagery Street View Imagery (Svi) Provides a Sca...mentioning

confidence: 99%

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Yap,

Biljecki

2023

Sci Data

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Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset’s strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

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