Places for play: Understanding human perception of playability in cities using street view images and deep learning

Aarts, 2012, Outdoor play among children in relation to neighborhood characteristics: A cross-sectional neighborhood observation study, International Journal of Behavioral Nutrition and Physical Activity, 9, 98, 10.1186/1479-5868-9-98 Almanza, 2012, A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data, Health & Place, 18, 46, 10.1016/j.healthplace.2011.09.003 Bell, 2008, Neighborhood greenness and 2-year changes in body mass index of children and youth, American Journal of Preventive Medicine, 35, 547, 10.1016/j.amepre.2008.07.006 Booth, 2005, Obesity and the built environment, Journal of the American Dietetic Association, 105, 110, 10.1016/j.jada.2005.02.045 Brussoni, 2020, A qualitative investigation of unsupervised outdoor activities for 10-to 13-year-old children: “I like adventuring but I don’t like adventuring without being careful”, Journal of Environmental Psychology, 70, 101460, 10.1016/j.jenvp.2020.101460 Dadvand, 2014, Risks and benefits of green spaces for children: A cross-sectional study of associations with sedentary behavior, obesity, asthma, and allergy, Environmental Health Perspectives, 122, 1329, 10.1289/ehp.1308038 De Nadai, 2016, Are safer looking neighborhoods more lively? A multimodal investigation into urban life, 1127 Duarte, 2021 Dyment, 2008, Grounds for movement: Green school grounds as sites for promoting physical activity, Health Education Research, 23, 952, 10.1093/her/cym059 Fjørtoft, 2010, Schoolyard physical activity in 14-year-old adolescents assessed by mobile gps and heart rate monitoring analysed by gis, Scandinavian Journal of Public Health, 38, 28, 10.1177/1403494810384909 Fotheringham, 2003 Fotheringham, 2017, Multiscale geographically weighted regression (mgwr), Annals of the American Association of Geographers, 107, 1247, 10.1080/24694452.2017.1352480 Gao, 2017, A data-synthesis-driven method for detecting and extracting vague cognitive regions, International Journal of Geographical Information Science, 31, 1245 Gebru, 2017, Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the United States, Proceedings of the National Academy of Sciences, 114, 13108, 10.1073/pnas.1700035114 Gilliland, 2006, Environmental equity is child’s play: Mapping public provision of recreation opportunities in urban neighbourhoods, Vulnerable Children and Youth Studies, 1, 256, 10.1080/17450120600914522 Ginsburg, 2007, The importance of play in promoting healthy child development and maintaining strong parent-child bonds, Pediatrics, 119, 182, 10.1542/peds.2006-2697 Grigsby-Toussaint, 2011, Where they live, how they play: Neighborhood greenness and outdoor physical activity among preschoolers, International Journal of Health Geographics, 10, 66, 10.1186/1476-072X-10-66 He, 2016, Deep residual learning for image recognition, 770 Helbich, 2019, Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China, Environment International, 126, 107, 10.1016/j.envint.2019.02.013 Henry, 1990, More than just play: The significance of mutually directed adult-child activity, Early Child Development and Care, 60, 35, 10.1080/0300443900600104 Hitlin, 2016, 437 Hu, 2021, A framework to detect and understand thematic places of a city using geospatial data, Cities, 109, 103012, 10.1016/j.cities.2020.103012 Huang, 2020, Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study, Environment and Planning B: Urban Analytics and City Science, 47, 1543 Hurvich, 1993, A corrected akaike information criterion for vector autoregressive model selection, Journal of Time Series Analysis, 14, 271, 10.1111/j.1467-9892.1993.tb00144.x Jacobs, 2016 Kalogirou, 2020 Kang, 2020, A review of urban physical environment sensing using street view imagery in public health studies, Annals of GIS, 26, 261, 10.1080/19475683.2020.1791954 Kang, 2020, Understanding house price appreciation using multi-source big geo-data and machine learning, Land Use Policy, 104919 Katzmarzyk, 2018, Results from the United States 2018 report card on physical activity for children and youth, Journal of Physical Activity and Health, 15, S422, 10.1123/jpah.2018-0476 Kim, 2016, Urban natural environments, obesity, and health-related quality of life among hispanic children living in inner-city neighborhoods, International Journal of Environmental Research and Public Health, 13, 121, 10.3390/ijerph13010121 Lambert, 2019, What is the relationship between the neighbourhood built environment and time spent in outdoor play? A systematic review, International Journal of Environmental Research and Public Health, 16, 3840, 10.3390/ijerph16203840 Lämmle, 2013, Four classes of physical fitness in german children and adolescents: Only differences in performance or at-risk groups?, International Journal of Public Health, 58, 187, 10.1007/s00038-012-0427-0 Laxer, 2013, The proportion of youths’ physical inactivity attributable to neighbourhoodbuilt environment features, International Journal of Health Geographics, 12, 31, 10.1186/1476-072X-12-31 Lee, 2019, Proximity to parks and natural areas as an environmental determinant to spatial disparities in obesity prevalence, Applied Geography, 112, 102074, 10.1016/j.apgeog.2019.102074 Li, 2015, Does the visibility of greenery increase perceived safety in urban areas? Evidence from the Place Pulse 1.0 dataset, ISPRS International Journal of Geo-Information, 4, 1166, 10.3390/ijgi4031166 Loebach, 2016, Free range kids? Using GPS-derived activity spaces to examine children’s neighborhood activity and mobility, Environment and Behavior, 48, 421, 10.1177/0013916514543177 McCurdy, 2010, Using nature and outdoor activity to improve children’s health, Current Problems in Pediatric and Adolescent Health Care, 40, 102, 10.1016/j.cppeds.2010.02.003 Middel, 2019, Urban form and composition of street canyons: A human-centric big data and deep learning approach, Landscape and Urban Planning, 183, 122, 10.1016/j.landurbplan.2018.12.001 Montello, 2003, Where’s downtown?: Behavioral methods for determining referents of vague spatial queries, Spatial Cognition and Computation, 3, 185, 10.1207/S15427633SCC032&3_06 Nguyen, 2018, Pedestrian traffic safety and outdoor active play among 10–13 year olds living in a mid-sized city, Preventive Medicine Reports, 10, 304, 10.1016/j.pmedr.2018.04.010 Oshan, 2019, mgwr: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale, ISPRS International Journal of Geo-Information, 8, 269, 10.3390/ijgi8060269 Pedregosa, 2011, Scikit-learn: Machine learning in python, Journal of Machine Learning Research, 12, 2825 Quercia, 2014, Aesthetic capital: What makes London look beautiful, quiet, and happy?, 945 Richards, 2010 Seabold, 2010, Statsmodels: Econometric and statistical modeling with python, vol. 57, 61, 10.25080/Majora-92bf1922-011 Spencer, 2006 Stevenson, 2015, Preventing child pedestrian injury: A guide for practitioners, American Journal of Lifestyle Medicine, 9, 442, 10.1177/1559827615569699 Summerbell, 2005, Interventions for preventing obesity in children, Cochrane Database of Systematic Reviews, 3, 10.1002/14651858.CD001871.pub2 Tamis-LeMonda, 2004, Fathers and mothers at play with their 2-and 3-year-olds: Contributions to language and cognitive development, Child Development, 75, 1806, 10.1111/j.1467-8624.2004.00818.x W.H.O, 2015 Whitzman, 2012, Creating child-friendly high-rise environments: Beyond wastelands and glasshouses, Urban Policy and Research, 30, 233, 10.1080/08111146.2012.663729 Wridt, 2010, A qualitative gis approach to mapping urban neighborhoods with children to promote physical activity and child-friendly community planning, Environment and Planning. B, Planning & Design, 37, 129, 10.1068/b35002 Yan, 2017, From ITDL to Place2Vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts, 1 Yang, 2017, Big data and cloud computing: Innovation opportunities and challenges, International Journal of Digital Earth, 10, 13, 10.1080/17538947.2016.1239771 Yao, 2019, A human-machine adversarial scoring framework for urban perception assessment using street-view images, International Journal of Geographical Information Science, 33, 2363, 10.1080/13658816.2019.1643024 Yeh, 2020, Big data, urban analytics and the planning of smart cities, vol. 0, 179 Zhang, 2021, “Perception bias”: Deciphering a mismatch between urban crime and perception of safety, Landscape and Urban Planning, 207, 104003, 10.1016/j.landurbplan.2020.104003 Zhang, 2018, Representing place locales using scene elements, Computers, Environment and Urban Systems, 71, 153, 10.1016/j.compenvurbsys.2018.05.005 Zhang, 2018, Measuring human perceptions of a large-scale urban region using machine learning, Landscape and Urban Planning, 180, 148, 10.1016/j.landurbplan.2018.08.020 Zhou, 2019, Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning, Sustainable Cities and Society, 50, 101605, 10.1016/j.scs.2019.101605