Page 89 - JCAU-7-1

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

higher perceived safety and health quality. Sidewalks and First, volunteers conducted a comprehensive
streetlights had minimal influence on perceived scores, observation of each street scene, paying attention to the
which could relate to children’s limited street use at night presence of various elements listed in Table 1. Perceived
or in darker environments. safety and healthy environmental conditions were judged
based on the sufficient presence of combinations of various
In summary, open skies, abundant vegetation, clear
pedestrian routes, and good lighting conditions are indicators, as detailed as follows:
among the positive environmental factors influencing (i) Safety: Consider traffic safety facilities and community
safety measures. Check for adequate traffic signs and
scores. Natural landscapes (such as skies and vegetation) lights. Evaluate the adequacy and effectiveness of
and quality infrastructure (such as roads and sidewalks) street pedestrian safety features, such as sidewalks,
contribute positively to street environment ratings.
fences, and signals that enhance safety near fast-
5.2. Human validation of model predictions moving vehicles. Are these safety facilities adequate to
keep children safe?
Common methods for verifying machine learning model (ii) Health: Consider the environment of greenery and
accuracy include cross-validation, user counter-validation, street furniture, favoring natural elements. Assess green
accuracy metric calculation, or iterative refinement. coverage. Check for adequate street furniture, such as
Greene & Oliva (2009) noted that humans have a superior seating and shelter, and consider the extent to which
ability to recognize global properties in images. In this the sky is enclosed and the degree of air circulation in
project, to test the consistency between machine-simulated the street space. Determine if these elements add beauty
human ratings and actual human ratings, an additional and comfort to the environment. Are the environmental
set of manual ratings was collected from volunteers for facilities harmonious, adding to the street’s comfort?
comparison. The control group included two volunteers
with professional backgrounds in urban planning and After considering each category and its details,
design, who were also parents of a 1.5-year-old child. Using volunteers were asked to give an overall score based
their subjective first impressions, they rated 1,000 street on how well the streetscape met the criteria for an ideal
images from a predictive street view dataset via Jupyter urban environment. In this case, scoring focused on quick,
Web’s interactive computing platform (Figure 9). Ratings intuitive, global attribute identification rather than an
were assigned on a 1 – 7 scale to refine the recording of user in-depth analysis of every detail.
opinions while minimizing decision fatigue from excessive As shown in Table 3, a total of 291 SVIs received scores
choices. Volunteers typically took about 5 seconds to score ranging from 1 to 3, while 447 images were rated between
each image. The ratings were saved in a CSV file, and the 4 and 6. Forty-three images scored above 6. The mean
system stored SVIs in separate folders according to their manual rating was 4, indicating that, in general, the streets
scores for verification and comparison. in the Sham Shui Po district scored relatively low.

Figure 9. Manual rating system interface

Volume 7 Issue 1 (2025) 12 https://doi.org/10.36922/jcau.3578