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Journal of Chinese
Architecture and Urbanism Machine-simulated scoring of child-friendly streets

such as Hong Kong with high summer temperatures and By employing the latest machine learning technologies
humidity, intense sunlight can make urban environments for processing, analyzing, and interpreting online street
uncomfortable or even dangerous. Pedestrians generally view data, this project has demonstrated the potential of
prefer shaded areas that offer cooler and more pleasant deep learning methods for large-scale, systematic urban
conditions. environment analysis at the district scale. This approach

Although this project shows research potential, it also can assist urban scholars and city planners in conducting
has several limitations. First, the dataset size restricted comprehensive studies for child-friendly characteristics
the analysis’s breadth and depth; a small sample size may in various neighborhoods, identifying bottlenecks, and
not fully capture the complexities of urban environments implementing strategic urban improvement initiatives.
and children’s diverse needs. In addition, the study was Acknowledgments
constrained by practical conditions, including a limited
number of participants in the validation group and a None.
lack of variation, thereby introducing potential sample Funding
bias. Second, the study’s focus on specific urban areas
may limit the generalizability of the findings, as children None.
from different urban and cultural backgrounds may have
different experiences and needs. In addition, the indicators Conflict of interest
and predictive models used may not adequately capture The authors declare they have no competing interests.
children’s unique perspectives and needs, particularly
regarding perceptions of safety and social interaction Author contributions
spaces.
Conceptualization: All authors
7. Conclusion Investigation: All authors
Methodology: All authors
This project represents an initial attempt to use a machine- Writing – original draft: All authors
simulated human scoring model to replicate human Writing – review & editing: All authors
perception of street environments using deep-learning
models, with the goal of identifying metropolitan areas Ethics approval and consent to participate
suitable for children. We hope that once matured, this Not applicable.
method can serve as a valuable reference for the renewal
of older urban areas. Consent for publication
A machine learning-based model was developed to Not applicable.
analyze the quality of streets in the Sham Shui Po district of
Hong Kong SAR, China. The model focuses on evaluating Availability of data
various factors in the street environment and their impact The data for this study include three types: OpenStreetMap
on the perception of child-friendliness. Results emphasize data (Road network), Google Street View Images data,
the significance of natural elements (such as sky views and Hong Kong Geographic Information (from Hong Kong
vegetation), well-developed urban infrastructure (such as CSDI portal https://portal.csdi.gov.hk/csdi-webpage/)
sidewalks and streetlights), and reductions in population
density, building density, and vehicle traffic to enhance References
street environment quality and encourage a child-friendly
atmosphere. These factors improve the safety and health Anguelov, D., Dulong, C., Filip, D., Frueh, C., Lafon, S., Lyon, R.,
of streets, promoting a more liveable and inclusive urban et al. (2010). Google street view: Capturing the world at
street level. Computer, 43(6):32-38.
environment.
https://doi.org/10.1109/MC.2010.170
Through systematic analysis and assessment of
different street locations in Sham Shui Po, this research Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet:
confirmed that open sky views, ample green spaces, A deep convolutional encoder-decoder architecture for
and clear pedestrian paths positively impact street image segmentation. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 39(12):2481-2495.
environment scores. Conversely, overcrowding and heavy
traffic negatively affect the comfort and attractiveness of https://doi.org/10.1109/TPAMI.2016.2644615
the environment and pose potential threats to children’s Bain, L., Gray, B., & Rodgers, D. (2012). Living Streets: Strategies
physical and mental health and safety. for Crafting Public Space. United States: John Wiley & Sons.

Volume 7 Issue 1 (2025) 14 https://doi.org/10.36922/jcau.3578