{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:59:38Z","timestamp":1778050778609,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.<\/jats:p>","DOI":"10.3390\/ijgi10080493","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T11:53:23Z","timestamp":1626868403000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6461-7243","authenticated-orcid":false,"given":"Waishan","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of City and Regional Planning, Cornell University, Ithaca, NY 14850, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-5837","authenticated-orcid":false,"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science, The University of Tokyo, Tokyo 113-8654, Japan"}]},{"given":"Xun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Architecture, University of Virginia, Charlottesville, VA 22904, USA"}]},{"given":"Xiaokai","family":"Huang","sequence":"additional","affiliation":[{"name":"Graduate School of Design, Harvard University, Cambridge, MA 02138, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dubey, A., Naik, N., Parikh, D., Raskar, R., and Hidalgo, C.A. (2016). Deep Learning the City: Quantifying Urban Perception at A Global Scale. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_12"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/13574800802451155","article-title":"Measuring the Unmeasurable: Urban Design Qualities Related to Walkability","volume":"14","author":"Ewing","year":"2009","journal-title":"J. Urban Des."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S1361-9209(97)00009-6","article-title":"Travel Demand and the 3Ds: Density, Diversity, and Design","volume":"2","author":"Cervero","year":"1997","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.apgeog.2016.09.024","article-title":"Measuring Visual Enclosure for Street Walkability: Using Machine Learning Algorithms and Google Street View Imagery","volume":"76","author":"Yin","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.landurbplan.2018.08.020","article-title":"Measuring Human Perceptions of a Large-Scale Urban Region Using Machine Learning","volume":"180","author":"Zhang","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.apgeog.2015.07.010","article-title":"\u2018Big Data\u2019 for Pedestrian Volume: Exploring the Use of Google Street View Images for Pedestrian Counts","volume":"63","author":"Yin","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.healthplace.2009.11.002","article-title":"Objective versus Subjective Measures of the Built Environment, Which Are Most Effective in Capturing Associations with Walking?","volume":"16","author":"Lin","year":"2010","journal-title":"Health Place"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Naik, N., Philipoom, J., Raskar, R., and Hidalgo, C. (2014, January 11\u201315). Streetscore\u2014Predicting the perceived safety of one million streetscapes. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.121"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e68400","DOI":"10.1371\/journal.pone.0068400","article-title":"The Collaborative Image of The City: Mapping the Inequality of Urban Perception","volume":"8","author":"Salesses","year":"2013","journal-title":"PLoS ONE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103086","DOI":"10.1016\/j.cities.2020.103086","article-title":"Measuring Human Perceptions of Streetscapes to Better Inform Urban Renewal: A Perspective of Scene Semantic Parsing","volume":"110","author":"Ma","year":"2021","journal-title":"Cities"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/01944361003766766","article-title":"Travel and the Built Environment: A Meta-Analysis","volume":"76","author":"Ewing","year":"2010","journal-title":"J. Am. Plann. Assoc."},{"key":"ref_12","unstructured":"Lynch, K. (1960). The Image of the City, The MIT Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.ufug.2015.06.006","article-title":"Assessing Street-Level Urban Greenery Using Google Street View and a Modified Green View Index","volume":"14","author":"Li","year":"2015","journal-title":"Urban For. Urban Green."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.amepre.2010.09.034","article-title":"Using Google Street View to Audit Neighborhood Environments","volume":"40","author":"Rundle","year":"2011","journal-title":"Am. J. Prev. Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, L., Yao, X., Liu, Y., Zhu, Y., Chen, W., Zhao, X., and Chi, T. (2020). Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data\u2014A Case Study of Shanghai, China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020106"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qiu, W., Li, W., Zhang, Z., Li, X., Liu, X., and Huang, X. (2021). Subjective and Objective Measures of Streetscape Perceptions: Relationships with Property Value in Shanghai. Preprints.","DOI":"10.20944\/preprints202103.0506.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1111\/ecin.12364","article-title":"Big data and big cities: The promises and limitations of improved measures of urban life: Big data and big cities","volume":"56","author":"Glaeser","year":"2018","journal-title":"Econ. Inq."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S223","DOI":"10.1123\/jpah.3.s1.s223","article-title":"Identifying and Measuring Urban Design Qualities Related to Walkability","volume":"3","author":"Ewing","year":"2006","journal-title":"J. Phys. Act. Health"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Herbrich, R., Minka, T., and Graepel, T. (2006, January 4\u20137). TrueSkill: A bayesian skill rating system. Proceedings of the 19th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.7551\/mitpress\/7503.003.0076"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fu, X., Jia, T., Zhang, X., Li, S., and Zhang, Y. (2019). Do Street-Level Scene Perceptions Affect Housing Prices in Chinese Megacities? An Analysis Using Open Access Datasets and Deep Learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217505"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (arXiv, 2016). Pyramid Scene Parsing Network, arXiv.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.buildenv.2018.02.042","article-title":"Mapping Sky, Tree, and Building View Factors of Street Canyons in a High-Density Urban Environment","volume":"134","author":"Gong","year":"2018","journal-title":"Build. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (arXiv, 2017). Mask R-CNN, arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/S0169-7161(82)02042-2","article-title":"39 Dimensionality and sample size considerations in pattern recognition practice","volume":"Volume 2","author":"Jain","year":"1982","journal-title":"Handbook of Statistics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","article-title":"The Revival of the Gini Importance?","volume":"34","author":"Nembrini","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_26","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Feng, H., Shu, Y., Wang, S., and Ma, M. (2006, January 11\u201315). SVM-Based Models for Predicting WLAN Traffic. Proceedings of the 2006 IEEE International Conference on Communications, Istanbul, Turkey.","DOI":"10.1109\/ICC.2006.254860"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102651","DOI":"10.1016\/j.trd.2020.102651","article-title":"Predicting Bicycling and Walking Traffic Using Street View Imagery and Destination Data","volume":"90","author":"Hankey","year":"2021","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"70080","DOI":"10.1109\/ACCESS.2021.3078117","article-title":"Empirical Analysis for Crime Prediction and Forecasting Using Machine Learning and Deep Learning Techniques","volume":"9","author":"Safat","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.cities.2017.11.017","article-title":"Place Quality in Innovation Clusters: An Empirical Analysis of Global Best Practices from Singapore, Helsinki, New York, and Sydney","volume":"74","author":"Esmaeilpoorarabi","year":"2018","journal-title":"Cities"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.landusepol.2018.02.027","article-title":"Evaluating Place Quality in Innovation Districts: A Delphic Hierarchy Process Approach","volume":"76","author":"Esmaeilpoorarabi","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120212","DOI":"10.1016\/j.jclepro.2020.120212","article-title":"Can the Improvement of Living Environment Stimulate Urban Innovation? Analysis of High-Quality Innovative Talents and Foreign Direct Investment Spillover Effect Mechanism","volume":"255","author":"Jiang","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_34","first-page":"43","article-title":"The Role of High-Tech Parks in China\u2019s Regional Economy: Empirical Evidence from the IC Industry in the Zhangjiang High-Tech Park, Shanghai","volume":"65","author":"Zeng","year":"2011","journal-title":"Arch. Sci. Geogr."},{"key":"ref_35","unstructured":"Calabr\u00f2, F., Della Spina, L., and Bevilacqua, C. (2019). Urban Planning and Innovation: The Strength Role of the Urban Transformation Demand. The Case of Kendall Square in Cambridge. New Metropolitan Perspectives, Springer International Publishing."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/13574809.2013.824366","article-title":"Urban Design Plans for Downtown San Francisco: A Paradigm Shift?","volume":"18","author":"Hu","year":"2013","journal-title":"J. Urban Des."},{"key":"ref_37","unstructured":"Bowles, J., and Giles, D. (2012). New Tech City, Center for an Urban Future."},{"key":"ref_38","unstructured":"O\u2019Mara, M. (2014). The Environmental Contradictions of High-Tech Urbanism. Now Urbanism, Routledge."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cooke, P. (2002). Knowledge Economies: Clusters, Learning and Cooperative Advantage, Routledge.","DOI":"10.4324\/9780203445402"},{"key":"ref_40","unstructured":"Bergquist, K., Raffo, J., and Fink, C. (2017). Identifying and Ranking the World\u2019s Largest Clusters of Inventive Activity, WIPO Economic Research Working Papers; WIPO."},{"key":"ref_41","first-page":"17","article-title":"Emerging Specialisations and Software Metropolitan Clusters\u2014A Comparative Network Analysis on San Francisco, New York and London","volume":"9","author":"Marra","year":"2017","journal-title":"Int. J. Technol. Learn. Innov. Dev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104187","DOI":"10.1016\/j.landusepol.2019.104187","article-title":"The Making of Smart Cities: Are Songdo, Masdar, Amsterdam, San Francisco and Brisbane the Best We Could Build?","volume":"88","author":"Yigitcanlar","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ye, Y., Xie, H., Fang, J., Jiang, H., and Wang, D. (2019). Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data. Sustainability, 11.","DOI":"10.3390\/su11061741"},{"key":"ref_44","unstructured":"Jansson, C. (2019). Factors Important to Street Users\u2019 Perceived Safety on a Main Street, Skolan F\u00f6r Arkitektur Och Samh\u00e4llsbyggnad."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.3390\/ijgi4031166","article-title":"Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset","volume":"4","author":"Li","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rozenberg, G., B\u00e4ck, T., and Kok, J.N. (2012). SVM Tutorial\u2014Classification, Regression and Ranking. Handbook of Natural Computing, Springer.","DOI":"10.1007\/978-3-540-92910-9"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pranckevi\u010dius, T., and Marcinkevicius, V. (2017). Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Balt J. Mod. Comput.","DOI":"10.22364\/bjmc.2017.5.2.05"},{"key":"ref_48","unstructured":"Jacobs, J. (1992). The Death and Life of Great American Cities, Vintage Books."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:32:48Z","timestamp":1760164368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["ijgi10080493"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10080493","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,21]]}}}