公共空间安全感研究:以上海城市街景感知为例
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摘要
在文献基础上梳理了街道安全感影响因素,并采用上海样本检验了"街道眼"等西方街道安全理论。邀请30位学生和30位市民对上海5个不同发展时期社区的300张百度街景图片进行安全感评定。实验发现绿视率、管理程度、车道数等都对安全感起着显著作用,并分别建立了单双车道和多车道街道空间的安全感回归模型。其中发现绿视率(单双车道相关系数R=0.728,p<0.01;多车道相关系数R=0.471,p<0.01)、管理程度(单双车道相关系数R=0.766,p<0.01;多车道相关系数R=0.450,p<0.01)、车道数量因素(相关系数R=0.502,p<0.01)对安全感均有显著的积极作用,界面透明度(单双车道相关系数R=0.222,p<0.01)、独立自行车道(相关系数R=0.309,p<0.01)及设计美感(相关系数R=0.432,p<0.01)等因素在单双车道空间中具有积极影响,而助动车与自行车(单双车道相关系数R=-0.327,p<0.01;多车道相关系数R=-0.281,p<0.01)在对安全感知评价具有消极影响,机动车(单双车道相关系数R=0.251,p<0.01;多车道相关系数R=-0.327,p<0.01)在单双车道与多车道空间中呈现相反的作用。
On the basis of literature, the paper sorts out factors influencing sense of security in streets. It also tests western street security theories, such as the Street Eye theory, with the Shanghai samples. It invites 30 students and 30 citizens to evaluate 300 Baidu street view pictures of Shanghai communities in five different development periods in terms of sense of security. The results showed that the green vision rate, management level and number of lanes leave significant impacts on sense of security, while different impact on perceived safety. It sets up security regression models for single-and double-lane and multi-lane street space, finding that the green vision rate(single-and double-lane correlation coefficient R=0.728, p<0.01; multi-lane correlation coefficient R=0.471, p<0.01), management level(single-and double-lane correlation coefficient R=0.766, p<0.01; multi-lane correlation coefficient R=0.450, p<0.01), and number of lanes(correlation coefficient R=0.502, p<0.01) have a significant positive effect on the sense of security. Interface transparency(single-and double-lane correlation coefficient R=0.222, p=0.010), independent bicycle lane(correlation coefficient R=0.309, p<0.01) and design aesthetics factors(correlation coefficient R=0.432, p<0.01) have a positive impact in the single and double lane space. Scooters and bicycles(single-and double-lane correlation coefficient R=-0.327, p<0.01; multi-lane correlation coefficient R=-0.281, p<0.01)have a negative impact on safety perception evaluation. Motor vehicles(single-and double-lane correlation coefficient R=0.251, p<0.01; multilane correlation coefficient R=-0.327, p<0.01) present opposite effect in single and double lanes, and multi-lane space.
On the basis of literature, the paper sorts out factors influencing sense of security in streets. It also tests western street security theories, such as the Street Eye theory, with the Shanghai samples. It invites 30 students and 30 citizens to evaluate 300 Baidu street view pictures of Shanghai communities in five different development periods in terms of sense of security. The results showed that the green vision rate, management level and number of lanes leave significant impacts on sense of security, while different impact on perceived safety. It sets up security regression models for single-and double-lane and multi-lane street space, finding that the green vision rate(single-and double-lane correlation coefficient R=0.728, p<0.01; multi-lane correlation coefficient R=0.471, p<0.01), management level(single-and double-lane correlation coefficient R=0.766, p<0.01; multi-lane correlation coefficient R=0.450, p<0.01), and number of lanes(correlation coefficient R=0.502, p<0.01) have a significant positive effect on the sense of security. Interface transparency(single-and double-lane correlation coefficient R=0.222, p=0.010), independent bicycle lane(correlation coefficient R=0.309, p<0.01) and design aesthetics factors(correlation coefficient R=0.432, p<0.01) have a positive impact in the single and double lane space. Scooters and bicycles(single-and double-lane correlation coefficient R=-0.327, p<0.01; multi-lane correlation coefficient R=-0.281, p<0.01)have a negative impact on safety perception evaluation. Motor vehicles(single-and double-lane correlation coefficient R=0.251, p<0.01; multilane correlation coefficient R=-0.327, p<0.01) present opposite effect in single and double lanes, and multi-lane space.
引文
[1]徐磊青.以环境设计犯罪研究与实践30年[J].新建筑,2003(6):4-7.XU Leiqing.Research and Practice of CPTED in Architectural Design and Community Planning[J].New Architecture,2003(6):4-7.
[2]徐磊青.社区安全与环境设计:在“可防卫空间”之后[J].同济大学学报(社会科学版),2002(1):34-39.XU Leiqing.Community Security and Environment Design:Behind the Defensible Space[J].Journal of Tongji University(Social Science Section),2002(1):34-39.
[3]简·雅各布斯.美国大城市的死与生[M].南京:译林出版社,2006.JACOBS J.The death and Life of Great American Cities[M].Nanjing:Yilin Press,2006.
[4]WILSON J Q,KELLING G L.Broken Windows[J].Atlantic Monthly,1982,249(3):29-38.
[5]SALESSES P,SCHECHTNER K,HIDALGO C A.The Collaborative Image of the City:Mapping the Inequality of Urban Perception[J].Plo S one,2013,8(7):e68400.
[6]NAIK N,RASKAR R,HIDALGO C A.Cities are Physical Too:Using Computer Vision to Measure the Quality and Impact of Urban Appearance[J].American Economic Review,2016,106(5):128-32.
[7]DUBEY A,NAIK N,PARIKH D,et al.Deep Learning the City:Quantifying Urban Perception at a Global Scale[C]//European Conference on Computer Vision.Springer,Cham,2016:196-212.
[8]HARVEY C,AULTMAN-HALL L,HURLEY S E,et al.Effects of Skeletal Streetscape Design on Perceived Safety[J].Landscape&Urban Planning,2015,142:18-28.
[9]REA M S,BULLOUGH J D,BRONS J A.Spectral Considerations for Outdoor Lighting:Designing for Perceived Scene Brightness[J].Lighting Research&Technology,2014,47(8):909-19.
[10]陈筝,刘悦来,刘颂.美国景观视觉资源规划设计方法评析及新技术改进潜力分析[J].风景园林,2015(5):111-117.CHEN Zheng,LIU Yuelai,LIU Song.A Review of Landscape Visual Resource Planning and Design Methods in the United States and Potential Improvements via New Techniques[J].Landscape Architecture.2015(5):111-117.
[11]徐磊青,孟若希,陈筝.迷人的街道:建筑界面与绿视率的影响[J].风景园林,2017(10):27-33.XU Leiqing,MENG Ruoxi,CHEN Zheng.Fascinating Streets:The Impact of Building Facades and Green View[J].Landscape Architecture,2017(10):27-33.
[12]DE N M,VIERIU R L,ZEN G,et al.Are Safer Looking Neighborhoods More Lively?:A Multimodal Investigation into Urban Life[C]//Proceedings of the 2016 ACM on Multimedia Conference.ACM,2016:1127-1135.
[13]PETERSON G L.A Model of Preference:Quantitative Analysis of the Perception of the Visual Appearance of Residential Neighborhoods[J].Journal of Regional Science,1967,7(1):19-31.
[14]QUERCIA D,O'HARE N K,CRAMER H.Aesthetic Capital:What Makes London Look Beautiful,Quiet,and Happy?[C]//Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work&Social Computing.ACM,2014:945-955.
[15]KEIZER K,LINDENBERG S,STEG L.The Spreading of Disorder[J].Science,2008,322(5908):1681-1685.
[16]勒·柯布西耶.走向新建筑[M].天津:天津科学技术出版社,1998.CORBUSIER L.Vers Une Architecture[M].Tianjin:Tianjin Science and Technology Press,1998.
[17]顾启良.上海老城厢风情录[M].上海:上海远东出版社,1992.GU Qiliang.The Cultural and Relics of Shanghai’s Old City Area[M].Shanghai:Shanghai Far East Publishers,1992.
(1)图1作者江文津自摄;图2、4、8、14作者江文津自绘;图3、5~7、图9~13源自百度地图开放平台。
(2)表1~5作者江文津自绘。
[2]徐磊青.社区安全与环境设计:在“可防卫空间”之后[J].同济大学学报(社会科学版),2002(1):34-39.XU Leiqing.Community Security and Environment Design:Behind the Defensible Space[J].Journal of Tongji University(Social Science Section),2002(1):34-39.
[3]简·雅各布斯.美国大城市的死与生[M].南京:译林出版社,2006.JACOBS J.The death and Life of Great American Cities[M].Nanjing:Yilin Press,2006.
[4]WILSON J Q,KELLING G L.Broken Windows[J].Atlantic Monthly,1982,249(3):29-38.
[5]SALESSES P,SCHECHTNER K,HIDALGO C A.The Collaborative Image of the City:Mapping the Inequality of Urban Perception[J].Plo S one,2013,8(7):e68400.
[6]NAIK N,RASKAR R,HIDALGO C A.Cities are Physical Too:Using Computer Vision to Measure the Quality and Impact of Urban Appearance[J].American Economic Review,2016,106(5):128-32.
[7]DUBEY A,NAIK N,PARIKH D,et al.Deep Learning the City:Quantifying Urban Perception at a Global Scale[C]//European Conference on Computer Vision.Springer,Cham,2016:196-212.
[8]HARVEY C,AULTMAN-HALL L,HURLEY S E,et al.Effects of Skeletal Streetscape Design on Perceived Safety[J].Landscape&Urban Planning,2015,142:18-28.
[9]REA M S,BULLOUGH J D,BRONS J A.Spectral Considerations for Outdoor Lighting:Designing for Perceived Scene Brightness[J].Lighting Research&Technology,2014,47(8):909-19.
[10]陈筝,刘悦来,刘颂.美国景观视觉资源规划设计方法评析及新技术改进潜力分析[J].风景园林,2015(5):111-117.CHEN Zheng,LIU Yuelai,LIU Song.A Review of Landscape Visual Resource Planning and Design Methods in the United States and Potential Improvements via New Techniques[J].Landscape Architecture.2015(5):111-117.
[11]徐磊青,孟若希,陈筝.迷人的街道:建筑界面与绿视率的影响[J].风景园林,2017(10):27-33.XU Leiqing,MENG Ruoxi,CHEN Zheng.Fascinating Streets:The Impact of Building Facades and Green View[J].Landscape Architecture,2017(10):27-33.
[12]DE N M,VIERIU R L,ZEN G,et al.Are Safer Looking Neighborhoods More Lively?:A Multimodal Investigation into Urban Life[C]//Proceedings of the 2016 ACM on Multimedia Conference.ACM,2016:1127-1135.
[13]PETERSON G L.A Model of Preference:Quantitative Analysis of the Perception of the Visual Appearance of Residential Neighborhoods[J].Journal of Regional Science,1967,7(1):19-31.
[14]QUERCIA D,O'HARE N K,CRAMER H.Aesthetic Capital:What Makes London Look Beautiful,Quiet,and Happy?[C]//Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work&Social Computing.ACM,2014:945-955.
[15]KEIZER K,LINDENBERG S,STEG L.The Spreading of Disorder[J].Science,2008,322(5908):1681-1685.
[16]勒·柯布西耶.走向新建筑[M].天津:天津科学技术出版社,1998.CORBUSIER L.Vers Une Architecture[M].Tianjin:Tianjin Science and Technology Press,1998.
[17]顾启良.上海老城厢风情录[M].上海:上海远东出版社,1992.GU Qiliang.The Cultural and Relics of Shanghai’s Old City Area[M].Shanghai:Shanghai Far East Publishers,1992.
(1)图1作者江文津自摄;图2、4、8、14作者江文津自绘;图3、5~7、图9~13源自百度地图开放平台。
(2)表1~5作者江文津自绘。