Academic Journal
A Spatial Analysis of Urban Space Albedo According to Urban Surfaces Color.
| Τίτλος: | A Spatial Analysis of Urban Space Albedo According to Urban Surfaces Color. |
|---|---|
| Συγγραφείς: | Gholami, Zahra1 (AUTHOR), Jalilisadrabad, Samaneh1 (AUTHOR) |
| Πηγή: | Journal of Urban Planning & Development. Sep2026, Vol. 152 Issue 3, p1-17. 17p. |
| Θεματικοί όροι: | *Albedo, *Reflectance measurement, *Urban climatology, *Python programming language, *Urban heat islands, *Chromaticity, *Geographic spatial analysis, *Sustainable urban development |
| Γεωγραφικοί όροι: | Kowloon (China), Hong Kong (China) |
| Περίληψη: | The color of urban surfaces is a crucial physical factor that influences the urban microclimate. Dark-colored surfaces tend to absorb more solar radiation than light-colored ones, thereby intensifying the urban heat island (UHI) effect. In many cities today, the lack of color regulation in urban planning and the rapid pace of construction have led to the predominance of darker surfaces. Traditional methods for measuring urban albedo have often been limited by their high cost, time consumption, and small-scale applicability. This study introduces a Python program (version 3.10) to estimate urban surface albedo using street view imagery. The approach is based on identifying the dominant color of urban spaces, which serves as a proxy for surface reflectance. The estimated albedo values are derived by associating dominant colors with their corresponding reflectance properties. Applied to the Kowloon District in Hong Kong, a densely urbanized area with deep street canyons, the results show that the southern parts of Kowloon exhibit lower albedo values due to a higher concentration of dark-colored surfaces. This research highlights the potential of using color-based analysis as a scalable and cost-effective tool for assessing urban albedo, informing sustainable urban planning strategies aimed at mitigating UHI impacts. Practical Applications: This research presents a simple and scalable method for estimating the reflectivity of urban surfaces, commonly known as albedo, by analyzing the dominant colors visible in street-level images. Since darker surfaces absorb more sunlight and cause higher temperatures in cities, identifying and mapping these areas is important for managing heat in dense urban environments. The method uses a Python-based program to extract the most common surface colors from public street view imagery and associates them with their typical reflectance levels. This approach enables planners and designers to evaluate and monitor urban surface brightness without the need for costly tools or time-consuming surveys. It highlights the role of color not just as a visual or cultural element but as a factor that directly influences energy use and comfort in cities. This technique can help inform better urban design decisions and policies, especially in response to rising temperatures and the need for more climate-friendly spaces. The method proposed in this paper classifies cities based on their urban surface albedo levels. This classification can be used to support the development of building regulations that promote a favorable microclimate. It can also encourage developers to consider climatic comfort when choosing colors for urban constructions. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
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