{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:27:05Z","timestamp":1784543225629,"version":"3.55.0"},"reference-count":78,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0505500"],"award-info":[{"award-number":["2018YFB0505500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0505503"],"award-info":[{"award-number":["2018YFB0505503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime.<\/jats:p>","DOI":"10.3390\/ijgi11030151","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T22:34:30Z","timestamp":1645569270000},"page":"151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Detecting People on the Street and the Streetscape Physical Environment from Baidu Street View Images and Their Effects on Community-Level Street Crime in a Chinese City"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5869-7424","authenticated-orcid":false,"given":"Han","family":"Yue","sequence":"first","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafang","family":"Xie","sequence":"additional","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7202-3418","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Chen","sequence":"additional","affiliation":[{"name":"Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"588","DOI":"10.2307\/2094589","article-title":"Social change and crime rate trends: A routine activity approach","volume":"44","author":"Cohen","year":"1979","journal-title":"Am. Sociol. Rev."},{"key":"ref_2","first-page":"7","article-title":"A theoretical model of crime hot spot generation","volume":"8","author":"Brantingham","year":"1999","journal-title":"Stud. Crime Crime Prev."},{"key":"ref_3","first-page":"197","article-title":"From criminals to criminal contexts: Reorienting criminal justice research and policy","volume":"10","author":"Weisburd","year":"2002","journal-title":"Adv. Criminol. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1057\/cpcs.2013.5","article-title":"Crime and residential security shutters in an Australian suburb: Exploring perceptions of \u2018eyes on the street\u2019, social interaction and personal safety","volume":"15","author":"Cozens","year":"2013","journal-title":"Crime Prev. Community Saf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.compenvurbsys.2017.08.001","article-title":"Built environment and violent crime: An environmental audit approach using Google Street View","volume":"66","author":"He","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.landurbplan.2012.08.006","article-title":"Does vegetation encourage or suppress urban crime? Evidence from Philadelphia, PA","volume":"108","author":"Wolfe","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, L., Lan, M., Yang, B., and Wang, Z. (2019). Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA. Remote Sens., 11.","DOI":"10.3390\/rs11171958"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.apgeog.2014.08.016","article-title":"Using remote sensing to assess the relationship between crime and the urban layout","volume":"55","author":"Patino","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hipp, J.R., Lee, S., Ki, D., and Kim, J.H. (2021). Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments. J. Quant. Criminol., 1\u201329.","DOI":"10.1007\/s10940-021-09506-9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s42001-021-00107-x","article-title":"Classifying crime places by neighborhood visual appearance and police geonarratives: A machine learning approach","volume":"4","author":"Amiruzzaman","year":"2021","journal-title":"J. Comput. Soc. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10940-021-09500-1","article-title":"Explaining Crime Diversity with Google Street View","volume":"37","author":"Khorshidi","year":"2021","journal-title":"J. Quant. Criminol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101631","DOI":"10.1016\/j.compenvurbsys.2021.101631","article-title":"Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery","volume":"88","author":"Zhou","year":"2021","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104003","DOI":"10.1016\/j.landurbplan.2020.104003","article-title":"\u201cPerception bias\u201d: Deciphering a mismatch between urban crime and perception of safety","volume":"207","author":"Zhang","year":"2021","journal-title":"Landsc. Urban Plan."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1177\/1748895819874868","article-title":"The influence of urban built environment on residential burglary in China: Testing the encounter and enclosure hypotheses","volume":"21","author":"Yue","year":"2019","journal-title":"Criminol. Crim. Justice"},{"key":"ref_16","unstructured":"Clarke, R.V., and Felson, M. (1993). Routine Activity and Rational Choice, Transaction."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2190\/R0G0-FRWY-100J-6KTB","article-title":"Bars, blocks, and crimes","volume":"11","author":"Roncek","year":"1981","journal-title":"J. Environ. Syst."},{"key":"ref_18","first-page":"17","article-title":"Intensity value analysis and the criminogenic effects of land use features on local crime patterns","volume":"2","author":"McCord","year":"2009","journal-title":"Crime Patterns Anal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1057\/sj.2011.1","article-title":"The role of neighborhood parks as crime generators","volume":"25","author":"Groff","year":"2012","journal-title":"Secur. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1111\/j.1745-9133.2011.00719.x","article-title":"Does fringe banking exacerbate neighborhood crime rates?","volume":"10","author":"Kubrin","year":"2011","journal-title":"Criminol. Public Policy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1177\/0011128720948037","article-title":"Modeling Crime Density with Population Dynamics in Space and Time: An Application of Assault in Gangnam, South Korea","volume":"68","author":"Jung","year":"2020","journal-title":"Crime Delinq."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Caminha, C., Furtado, V., Pequeno, T.H.C., Ponte, C., Melo, H.P.M., Oliveira, E.A., and Andrade, J.S. (2017). Human mobility in large cities as a proxy for crime. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0171609"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.apgeog.2018.04.016","article-title":"Routine activity, population(s) and crime: Spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link","volume":"95","author":"Boivin","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.dss.2018.07.003","article-title":"Improving crime count forecasts using Twitter and taxi data","volume":"113","author":"Vomfell","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_25","unstructured":"Felson, M., and Eckert, M. (2015). Crime and Everyday Life, Sage. [5th ed.]."},{"key":"ref_26","first-page":"1370","article-title":"Testing indicators of risk populations for theft from the person across space and time: The significance of mobility and outdoor activity","volume":"108","author":"Song","year":"2018","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_27","unstructured":"Wilcox, P., Land, K.C., and Hunt, S.A. (2003). Criminal Circumstance: A Dynamic, Multi-Contextual Criminal Opportunity Theory, Aldine de Gruyter."},{"key":"ref_28","unstructured":"Wortley, R., and Mazerolle, L. (2008). Crime Pattern Theory. Environmental Criminology and Crime Analysis, Willan Publishing."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/10683160008410831","article-title":"Examining burglars\u2019 target selection: Interview, experiment or ethnomethodology?","volume":"6","author":"Nee","year":"2000","journal-title":"Psychol. Crime Law"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/j.1468-2311.1979.tb00389.x","article-title":"Defensible space: The current status of a crime prevention theory","volume":"18","author":"Mayhew","year":"1979","journal-title":"Howard J. Crim. Justice"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yue, H., Zhu, X., Ye, X., and Guo, W. (2017). The Local Colocation Patterns of Crime and Land-Use Features in Wuhan, China. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6100307"},{"key":"ref_32","unstructured":"Jones, H.R. (1993). Crime and the Urban Environment, Avebury."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1057\/palgrave.udi.9000016","article-title":"Housing layout and crime vulnerability","volume":"5","author":"Shu","year":"2000","journal-title":"Urban Des. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.apgeog.2018.06.005","article-title":"Modelling the effects of street permeability on burglary in Wuhan, China","volume":"98","author":"Yue","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1057\/palgrave.udi.9000079","article-title":"Can streets be made safe?","volume":"9","author":"Hillier","year":"2004","journal-title":"Urban Des. Int."},{"key":"ref_36","unstructured":"Skogan, W.G., and Maxfield, M.G. (1981). Coping with Crime: Individual and Neighborhood Reactions, Sage Publications."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1080\/00330124.2014.970843","article-title":"The pen or the sword: A situated spatial analysis of graffiti and violent injury in Vancouver, British Columbia","volume":"67","author":"Walker","year":"2015","journal-title":"Prof. Geogr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/0047-2352(93)90033-J","article-title":"Abandoned buildings: Magnets for crime?","volume":"21","author":"Spelman","year":"1993","journal-title":"J. Crim. Justice"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s11205-012-0050-8","article-title":"Criminal victimization and crime risk perception: A multilevel longitudinal study","volume":"112","author":"Russo","year":"2013","journal-title":"Soc. Indic. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1057\/sj.2008.1","article-title":"Ambient populations and the calculation of crime rates and risk","volume":"23","author":"Andresen","year":"2008","journal-title":"Secur. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"160950","DOI":"10.1098\/rsos.160950","article-title":"Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models","volume":"4","author":"Panigutti","year":"2017","journal-title":"R. Soc. Open Sci."},{"key":"ref_42","first-page":"804","article-title":"Crime Risk Estimation with a Commuter-Harmonized Ambient Population","volume":"106","author":"Mburu","year":"2016","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1080\/07418825.2018.1445276","article-title":"Using Social Media to Measure Temporal Ambient Population: Does it Help Explain Local Crime Rates?","volume":"36","author":"Hipp","year":"2018","journal-title":"Justice Q."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10940-020-09487-1","article-title":"Who \u2018Tweets\u2019 Where and When, and How Does it Help Understand Crime Rates at Places? Measuring the Presence of Tourists and Commuters in Ambient Populations","volume":"37","author":"Tucker","year":"2021","journal-title":"J. Quant. Criminol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s10610-020-09446-3","article-title":"Cell Towers and the Ambient Population: A Spatial Analysis of Disaggregated Property Crime","volume":"27","author":"Johnson","year":"2020","journal-title":"Eur. J. Crim. Policy Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1177\/0265813516672454","article-title":"New insights on relationships between street crimes and ambient population: Use of hourly population data estimated from mobile phone users\u2019 locations","volume":"45","author":"Hanaoka","year":"2016","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1080\/15230406.2014.905756","article-title":"The impact of using social media data in crime rate calculations: Shifting hot spots and changing spatial patterns","volume":"42","author":"Malleson","year":"2014","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Algahtany, M., and Kumar, L. (2016). A Method for Exploring the Link between Urban Area Expansion over Time and the Opportunity for Crime in Saudi Arabia. Remote Sens., 8.","DOI":"10.3390\/rs8100863"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compenvurbsys.2018.05.005","article-title":"Representing place locales using scene elements","volume":"71","author":"Zhang","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Naik, N., Philipoom, J., Raskar, R., and Hidalgo, C. (2014, January 23\u201328). Streetscore\u2014Predicting the perceived safety of one million streetscapes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.121"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103096","DOI":"10.1016\/j.trd.2021.103096","article-title":"Spatial disparity of individual and collective walking behaviors: A new theoretical framework","volume":"101","author":"Jiang","year":"2021","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101481","DOI":"10.1016\/j.compenvurbsys.2020.101481","article-title":"Estimating pedestrian volume using Street View images: A large-scale validation test","volume":"81","author":"Chen","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1177\/0011128720910961","article-title":"Environmental Predictors of a Drug Offender Crime Script: A Systematic Social Observation of Google Street View Images and CCTV Footage","volume":"67","author":"Sytsma","year":"2020","journal-title":"Crime Delinq."},{"key":"ref_55","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.apgeog.2014.12.001","article-title":"Permeability, space syntax, and the patterning of residential burglaries in urban China","volume":"60","author":"Wu","year":"2015","journal-title":"Appl. Geogr."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Du, F., Liu, L., Jiang, C., Long, D., and Lan, M. (2019). Discerning the Effects of Rural to Urban Migrants on Burglaries in ZG City with Structural Equation Modeling. Sustainability, 11.","DOI":"10.3390\/su11030561"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"103471","DOI":"10.1016\/j.cities.2021.103471","article-title":"The roles of built environment and social disadvantage on the geography of property crime","volume":"121","author":"He","year":"2022","journal-title":"Cities"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1080\/07418829800093661","article-title":"Land use, physical deterioration, resident-based control, and calls for service on urban streetblocks","volume":"15","author":"Kurtz","year":"2006","journal-title":"Justice Q."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"101864","DOI":"10.1016\/j.jcrimjus.2021.101864","article-title":"Density, diversity, and design: Three measures of the built environment and the spatial patterns of crime in street segments","volume":"77","author":"Kim","year":"2021","journal-title":"J. Crim. Justice"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, J.G., Liu, L., Xiao, L.Z., Xu, C., and Long, D.P. (2020). Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9010060"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1177\/0022427810384135","article-title":"Robberies in Chicago: A block-level analysis of the influence of crime generators, crime attractors, and offender anchor points","volume":"48","author":"Bernasco","year":"2011","journal-title":"J. Res. Crime Delinq."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1111\/tgis.12580","article-title":"Examining the influences of air quality in China\u2019s cities using multi\u2014Scale geographically weighted regression","volume":"23","author":"Fotheringham","year":"2019","journal-title":"Trans. GIS"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1111\/j.1745-9125.1987.tb00826.x","article-title":"Understanding crime displacement: An application of rational choice theory","volume":"25","author":"Cornish","year":"1987","journal-title":"Criminology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/1524838015576412","article-title":"Green Space, Violence, and Crime: A Systematic Review","volume":"17","author":"Bogar","year":"2016","journal-title":"Trauma Violence Abus."},{"key":"ref_68","unstructured":"Blair, L. (2014). Community Gardens and Crime: Exploring the Roles of Criminal Opportunity and Informal Social Control, University of Cincinnati."},{"key":"ref_69","first-page":"343","article-title":"Environment and Crime in the Inner City","volume":"33","author":"Kuo","year":"2016","journal-title":"Environ. Behav."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fbuil.2018.00079","article-title":"Urban Gardens as a Space to Engender Biophilia: Evidence and Ways Forward","volume":"4","author":"Lin","year":"2018","journal-title":"Front. Built Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.3390\/ijerph10094086","article-title":"Green space and stress: Evidence from cortisol measures in deprived urban communities","volume":"10","author":"Roe","year":"2013","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1136\/injuryprev-2012-040439","article-title":"Greening vacant lots to reduce violent crime: A randomised controlled trial","volume":"19","author":"Garvin","year":"2013","journal-title":"Inj. Prev."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Talen, E. (2013). Charter of the New Urbanism, McGraw Hill Education.","DOI":"10.1007\/978-94-007-0753-5_3336"},{"key":"ref_74","unstructured":"Jacobs, J. (1961). The Death and Life of Great American Cities, Random House."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chalfin, A., Hansen, B., Lerner, J., and Parker, L. (2021). Reducing Crime Through Environmental Design: Evidence from a Randomized Experiment of Street Lighting in New York City. J. Quant. Criminol., 1\u201331.","DOI":"10.1007\/s10940-020-09490-6"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.apgeog.2016.12.011","article-title":"The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments","volume":"79","author":"Barnum","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1177\/2399808317735105","article-title":"Street crime prediction model based on the physical characteristics of a streetscape: Analysis of streets in low-rise housing areas in South Korea","volume":"46","author":"Lee","year":"2017","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_78","first-page":"350","article-title":"Agglomerative Effects of Crime Attractors and Generators on Street Robbery? An Assessment by Luojia 1-01 Satellite Nightlight","volume":"112","author":"Liu","year":"2021","journal-title":"Ann. Am. Assoc. Geogr."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/3\/151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:24:54Z","timestamp":1760135094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/3\/151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,22]]},"references-count":78,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["ijgi11030151"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11030151","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,22]]}}}