Page 148 - JCAU-7-2
P. 148
Journal of Chinese
Architecture and Urbanism Urban features of PRD in online image
References Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P.,
& Ratti, C. (2014). Geo-located Twitter as proxy for global
Appleyard, D. (1969). Why buildings are known. Environment mobility patterns. Cartography and Geographic Information
and Behavior, 1(2):131-166. Science, 41:260-271.
https://doi.org/10.1177/001391656900100202 https://doi.org/10.1080/15230406.2014.890072
Chen, Y. F., Xu, W. P., & Li, X. (2022). Village pictures AI He, N., & Li, G. (2021). Urban neighbourhood environment
contributes to rural construction evaluation. World assessment based on street view image processing: A review
Architecture, 389(11):119-120. of research trends. Environmental Challenges, 4:100090.
Cranshaw, J., Schwartz, R., Hong, J. I., & Sadeh, N. (2012). The https://doi.org/10.1016/j.envc.2021.100090
Livehoods project: Utilizing social media to understand the
dynamics of a city. Proceedings of the International AAAI He, Y. L., Zhang, L., & Li, R. X. (2020). A preliminary study on
Conference on Web and Social Media, 6:58-65. the constituent elements and cognitive characteristics of
spatial image in small towns: Case studies in Yantai City.
https://doi.org/10.1609/icwsm.v6i1.14278
Development of Small Cities & Towns, 35(8):52-60.
Dong, K., & Gao, J. S. (2011). Functional orientation model of Hillier, B. (1999). Space Is the Machine: A Configurational Theory
central towns and evaluation--taking Guangzhou city of Architecture. United Kingdom: Cambridge University
central town as an example. Journal of capital university of Press.
Economics and Business, 13(1):102-106.
https://doi.org/10.1068/a200339
Elwood, S., & Leszczynski, A. (2013). New spatial media, new
knowledge politics. Transactions of the Institute of British Hu, B. J. (2014). The collective memory in the age of internet. Social
Geographers, 38(4):544-559. Science in Chinese Colleges and Universities, (3):98-106.
https://doi.org/10.1111/j.1475-5661.2012.00543.x Hunter, W. C. (2008). A typology of photographic representations
for tourism: Depictions of groomed spaces. Tourism
Evans, G. W., Smith, C., & Pezdek, K. (1982). Cognitive maps and Management, 29:354-365.
urban form. Journal of the American Planning Association,
48(2):232-244. https://doi.org/10.1016/j.tourman.2007.03.008
https://doi.org/10.1080/01944368208976543 Kavaratzis, M., & Ashworth, G. J. (2005). City branding: An
effective assertion of identity or a transitory marketing
Fan, J. H., & Wang, L. (2010). Spatial analysis of rural landscape trick? Tijdschrift Voor Economische en Sociale Geografie,
image in the Pearl River Delta. Journal of Anhui Agriculture 96:506-514.
Science, 38(3):1579-1582.
https://doi.org/10.1111/j.1467-9663.2005.00482.x
Filomena, G., Verstegen, J. A., & Manley, E. D. (2019).
A computational approach to “The Image of the City”. Cities, Kong, D. (2020). Network media: A new way to reshape urban
89:14-25. space image. Jiangxi Social Sciences, 40(9):240-247.
https://doi.org/10.1016/j.cities.2019.01.006 Lee, Y., & Schmidt, C. G. (1988). Evolution of urban spatial
cognition: Patterns of change in Guangzhou. China
Francescato, D., & Mebane, W. (1973). How citizens view two Environment and PIanning, 20(3):339-351.
great cities: Milan and Rome. In: Image and Environment.
Chicago: Aldine. Li, L., Ru, Y. I., & Lin, Z. H.(2022). Spatial and temporal dynamics
of the new urbanization quality of central towns in
Gavric, K. D., Culibrk, D. R., Lugonja, P. I., Mirkovic, M. R., & Guangdong province and its influencing factors. Ecological
Crnojevic, V. S. (2011). Detecting Attractive Locations Economy, 38(7):121-131.
and Tourists Dynamics Using Geo-referenced Images. In:
2011 10 International Conference on Telecommunication in Li, X., & Xu, X, Q. (1993). A spatial analysis of the image of
th
Modern Satelite Cable and Broadcasting Services (TELSIKS). Guangzhou city. Human Geography, 8(3):27-35.
Belgrade.
https://doi.org/10.13959/j.issn.1003-2398.1993.03.001
Gu, C. L., & Song, G. C. (2001). Urban image space and main Li, X., Cai, Y., & Ratti, C. (2018). Using Street-Level Images and
factors in Beijing. Acta Geographica Sinica, 56(1):64-74. Deep Learning for Urban Landscape Analysis. Landscape
https://doi.org/10.11821/xb200101008 Architecture Frontiers, 6(2):20-29.
Han, X., Wang, L., Seo, S. H., He, J., & Jung, T. (2022). Measuring https://doi.org/10.15302/J-LAF-20180203
perceived psychological stress in urban built environments Liang, B., & Pan, S. K. (2015). A study of destination attention and
using Google street view and deep learning. Frontiers in co-occurrence effects based on tourism digital footprints--a
Public Health, 10:891736.
case study of Shanghai’s historic district. Journal of Tourism,
https://doi.org/10.3389/fpubh.2022.891736 30(7):80-90.
Volume 7 Issue 2 (2025) 14 https://doi.org/10.36922/jcau.5733

