Social Accounting as Valuation Mode: Historical Changes of Capital Accounting and Social Accounting in U.S
In: Korean Journal of Sociology, Band 53, Heft 2, S. 167-212
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In: Korean Journal of Sociology, Band 53, Heft 2, S. 167-212
In: Korean Journal of Sociology, Band 52, Heft 2, S. 37-84
In: Environment and behavior: eb ; publ. in coop. with the Environmental Design Research Association
ISSN: 1552-390X
The perception of safety significantly influences choices in outdoor activities, profoundly impacting overall well-being. While previous studies have highlighted urban trees' potential to reduce crime rates, the link between urban trees and perceived safety remains uncertain. This study investigates the relationship between urban trees and safety perception in Austin, Texas, USA, with a specific focus on the moderating role of neighborhood cleanliness and environmental justice considerations. Using multinomial logistic regression models, our analysis reveals a positive association between urban tree canopy coverage and safety perception, with a significant interaction between tree canopies and neighborhood cleanliness, further enhancing the sense of safety. Furthermore, we identified an optimal threshold of tree canopy that maximizes this effect. This highlights the crucial role of well-maintained urban green spaces, particularly tree canopies, in bolstering perceived safety. Such insights hold significance for evidence-based urban planning and community development, fostering well-being and safety for all residents.
In: Computers, environment and urban systems, Band 106, S. 102030
In: Environment and behavior: eb ; publ. in coop. with the Environmental Design Research Association, Band 54, Heft 1, S. 211-241
ISSN: 1552-390X
The built environment characteristics associated with walkability range from neighborhood-level urban form factors to street-level urban design factors. However, many existing walkability indices are based on neighborhood-level factors and lack consideration for street-level factors. Arguably, this omission is due to the lack of a scalable way to measure them. This paper uses computer vision to quantify street-level factors from street view images in Atlanta, Georgia, USA. Correlation analysis shows that some streetscape factors are highly correlated with neighborhood-level factors. Binary logistic regressions indicate that the streetscape factors can significantly contribute to explaining walking mode choice and that streetscape factors can have a greater association with walking mode choice than neighborhood-level factors. A potential explanation for the result is that the image-based streetscape factors may perform as proxies for some macroscale factors while representing the pedestrian experience as seen from eye-level.