新城市科学背景下的城市设计新可能
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摘要
本文面对智慧城市和新城市科学迅速发展的背景,立足新时期城市设计转型需求,针对新近涌现出的以机器学习、虚拟现实、计算机可视化等为代表的多种新技术和以街景数据、兴趣点数据、位置服务数据等为代表的多源城市数据开展讨论。继而对其所带来的城市设计新可能进行了探讨和展望,并对于相关典型工具和代表性案例进行了介绍。这些伴随新城市科学涌现的数据和技术不仅能够为城市设计在场地分析、设计开展和方案评估上带来工作方式的革新,而且有望进一步明确城市设计的专属技术体系,并以此推动城市设计的领域构建。
With the fast development of information and communication techniques, the availability of new urban data and new analytical tools triggers the generation of a new science of cities, i.e., data-intensive analysis and computational approaches of cities to pursue deeply quantitative understandings. Urban design as an effort focusing on the making of lively urban places is gradually transforming toward a more human-scale and quality-control direction,which requires in-depth understandings on spatial features of the city and corresponded socioeconomic performance. In this context, urban design that was mainly based on designer's experiences and intuition is facing new potentials generated from the new urban science.This paper, as a reflection of this trend, discusses new analytical tools such as machine learning, virtual reality, computer visualization, morphological analyses, and multi-sourced urban data such as street view images, points of interest, location-based service, based on the urban design transformation requirements. Specifically, the new analytical tools provide not only in-depth understandings on urban morphology but also algorithms and computational tools adaptive to complex built environment. The multi-sourced urban data provides not only fine-scale and quantitative data recording features of urban spaces but also human perception and behavior related to the spatial features. For instance, newly-proposed urban morphological tools like UNA and Form Syntax, are able to achieve a co-present of various kinds of urban morphological elements and their related performance. Newly-developed machine learning algorithms like SegNet and the availability of large amount of street view images make the measuring of streetscape features possible. In general, the integration of new urban data and new analytical tools leads to improvements on three directions that were hard to achieve: 1)fine-scale spatial data but also covering large areas at the same time, 2) quantitative description of urban morphological features, 3) socio-economic performance affected by built environment.These improvements were hard to achieve via classical methods decade ago, which might help promote the further construction of urban design through the development of exclusive technical basement.It then explores the new possibility of urban design and introduces the related typical tools and representative cases from the following three directions. Firstly, there are some design assistant tools, e.g., Urbane and Vitalvizor, utilizing fine-scale open data and computer visualization techniques to achieve a direct and clear 3 D illustration between spatial features and corresponded socio-economic performance. These real time and active analyses could assist urban design processes from site analysis, idea generation, proposal evaluation, etc.Secondly the combination of multi-sourced urban big data and computation algorithms in machine learning would assist urban designers and planners to measure the immeasurable in the past that includes many intangible issues like spatial quality and vitality. Improvements in this direction would assist smart and efficient design policy on the key points. By this way,small design improvements would lead to large outputs on public benefits. Improvements in this direction might help promote better quality and vitality making in design practices. Moreover, the utilization of new techniques like VR and wearable devices, and detailed urban data would assist the achievement of perceptual-based design evaluations. This kind of human-scale approach would be easier to achieve more accurate analyses,compared with intuitive feelings.In short, the emerging new techniques and new data in the context of new urban science is bringing new possibilities to seek in-depth understandings on urban design and regeneration for better place-making and smart decision making. Many urban design processes that were traditionally inspired by intuition can now be supported by a scientifically grounded analysis, combining creativity and rationality into one framework. Although these presented studies and cases are still exploratory, they move a step forward and supplement classical urban design theories with quantitative measurements. These studies also help to achieve an efficient large-scale analysis but also keep fine-scale spatial resolution. It is a response to the increasing interest in introducing new quantitative thinking into urban design. It indicates a transformation towards the paradigm shift of urban analytics-linking science, engineering, design and information technology. Endeavors in this direction would better connect design practices and researches, as a way to advance further development of research by design.
With the fast development of information and communication techniques, the availability of new urban data and new analytical tools triggers the generation of a new science of cities, i.e., data-intensive analysis and computational approaches of cities to pursue deeply quantitative understandings. Urban design as an effort focusing on the making of lively urban places is gradually transforming toward a more human-scale and quality-control direction,which requires in-depth understandings on spatial features of the city and corresponded socioeconomic performance. In this context, urban design that was mainly based on designer's experiences and intuition is facing new potentials generated from the new urban science.This paper, as a reflection of this trend, discusses new analytical tools such as machine learning, virtual reality, computer visualization, morphological analyses, and multi-sourced urban data such as street view images, points of interest, location-based service, based on the urban design transformation requirements. Specifically, the new analytical tools provide not only in-depth understandings on urban morphology but also algorithms and computational tools adaptive to complex built environment. The multi-sourced urban data provides not only fine-scale and quantitative data recording features of urban spaces but also human perception and behavior related to the spatial features. For instance, newly-proposed urban morphological tools like UNA and Form Syntax, are able to achieve a co-present of various kinds of urban morphological elements and their related performance. Newly-developed machine learning algorithms like SegNet and the availability of large amount of street view images make the measuring of streetscape features possible. In general, the integration of new urban data and new analytical tools leads to improvements on three directions that were hard to achieve: 1)fine-scale spatial data but also covering large areas at the same time, 2) quantitative description of urban morphological features, 3) socio-economic performance affected by built environment.These improvements were hard to achieve via classical methods decade ago, which might help promote the further construction of urban design through the development of exclusive technical basement.It then explores the new possibility of urban design and introduces the related typical tools and representative cases from the following three directions. Firstly, there are some design assistant tools, e.g., Urbane and Vitalvizor, utilizing fine-scale open data and computer visualization techniques to achieve a direct and clear 3 D illustration between spatial features and corresponded socio-economic performance. These real time and active analyses could assist urban design processes from site analysis, idea generation, proposal evaluation, etc.Secondly the combination of multi-sourced urban big data and computation algorithms in machine learning would assist urban designers and planners to measure the immeasurable in the past that includes many intangible issues like spatial quality and vitality. Improvements in this direction would assist smart and efficient design policy on the key points. By this way,small design improvements would lead to large outputs on public benefits. Improvements in this direction might help promote better quality and vitality making in design practices. Moreover, the utilization of new techniques like VR and wearable devices, and detailed urban data would assist the achievement of perceptual-based design evaluations. This kind of human-scale approach would be easier to achieve more accurate analyses,compared with intuitive feelings.In short, the emerging new techniques and new data in the context of new urban science is bringing new possibilities to seek in-depth understandings on urban design and regeneration for better place-making and smart decision making. Many urban design processes that were traditionally inspired by intuition can now be supported by a scientifically grounded analysis, combining creativity and rationality into one framework. Although these presented studies and cases are still exploratory, they move a step forward and supplement classical urban design theories with quantitative measurements. These studies also help to achieve an efficient large-scale analysis but also keep fine-scale spatial resolution. It is a response to the increasing interest in introducing new quantitative thinking into urban design. It indicates a transformation towards the paradigm shift of urban analytics-linking science, engineering, design and information technology. Endeavors in this direction would better connect design practices and researches, as a way to advance further development of research by design.
引文
[1] HOLLANDS R G. Will the Real Smart City Please Stand Up? Intelligent,Progressive or Entrepreneurial?[J].City, 2008, 12(3):303-320.
[2] PALMISANO S J. A Smarter Planet:The Next Leadership Agenda[J]. IBM,2008(6):1-8.
[3] BATTY M. The New Science ofCities[M]. Cambridge:MIT Press, 2013.
[4] TOWNSEND A. Cities of Data:Examining the New Urban Science[J].Public Culture, 2015, 27(2):201-212.
[5]卢济威.新时期城市设计的发展趋势[J].上海城市规划,2015(1):3-4.LU J W. The New Trends of Urban Design in the New Era[J]. Shanghai Urban Planning Review, 2015(1):3-4.
[6] ROWLEY A. Definitions of Urban Design:The Nature and Concerns of Urban Design[J]. Planning Practice and Research,1994, 9(3):179-197.
[7] BUCHANAN P. What City? A Plea for Place in the Public Realm[J].Architectural Review, 1988, 1101(11):31-41.
[8] CONZEN M R G. Thinking about Urban Form:Papers on Urban Morphology,1932-1998[M]. New York:Peter Lang,2004.
[9] YE Y, VAN NES A. Quantitative Tools in Urban Morphology:Combining Space Syntax, Spacematrix and Mixed-Use Index in A GIS Framework[J]. Urban Morphology,2014, 18(2):97-118.
[10]叶宇,戴晓玲.新技术与新数据条件下的空间感知与设计运用可能[J].时代建筑,2017(5):6-13.YE Y, DAI X L. Spatial Perception and Design Potentials in the Context of New Analytical Techniques and New Data[J].Time+Architecture, 2017(5):6-13.
[11] EWING R,CLEMENTE O. Measuring Urban Design:Metrics for Livable Places[M]. New York:Island Press,2013.
[12] GEHL J,KAEFER L J,REIGSTAD S. Close Encounters with Buildings[J].Urban Design International, 2006, 11(1):29-47.
[13]徐磊青,康琦.商业街的空间与界面特征对步行者停留活动的影响——以上海市南京西路为例[J].城市规划学刊,2014,29(3):104-111.XU L Q, KANG Q. The Relationshipbetween Pedestrian Behaviors and the Spatial Features along the Ground-floor Commercial Street:The Case of West Nanjing Road in Shanghai[J]. Urban Planning Forum, 2014(3):104-111.
[14]叶宇,庄宇,张灵珠.城市设计中活力营造的形态学探究——基于城市空间形态特征量化分析与居民活动检验[J].国际城市规划,2016(1):26-33.YE Y,ZHUANG Y,ZHANG L Z.Designing Urban Spatial Vitality from Morphological Perspective-A Study Based on Quantified Urban Morphology and Activities'Testing[J]. Urban Planning International, 2016(1):26-33.
[15]龙瀛,叶宇.人本尺度城市形态:测度,效应评估及规划设计响应[J].南方建筑,2016(5):41-47.LONG Y, YE Y. Human-Scale Urban Form:Measurements, Performances,and Urban Planning&Design Interventions[J]. South Architecture,2016(5):41-47.
[16]叶宇,庄宇.城市形态学中量化分析方法的涌现[J].城市设计,2016,4(6):56-65.YE Y, ZHUANG Y. The Raising of Quantitative Morphological Tools in Urban Morphology[J]. Urban Design,2016, 4(6):56-65.
[17] KARIMI K. A Configurational Approach To Analytical Urban Design:Space Syntax's Methodology[J]. Urban Design International, 2012, 17(4):297-318.
[18] BATTY M. Defining Geodesign(=GIS+design?)[J]. Environment and Planning B:Planning and Design, 2013,40(1):1-2.
[19] SEVTSUK A, MEKONNEN M. Urban Network Analysis:A New Toolbox for Measuring City Form in ArcGIS[C]//Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design. Society for Computer Simulation International, 2012:18.
[20] YE Y, YEH A, ZHUANG Y, et al."Form Syntax"as a Contribution to Geodesign:A Morphological Tool for Urbanity-Making in Urban Design[J].Urban Design International, 2017, 22(1):73-90.
[21] YE Y, RICHARDS D, LU Y, et al.Measuring Daily Accessed Street Greenery:A Human-Scale Approach for Informing Better Urban Planning Practices[J]. Landscape and Urban Planning, 2018. https://doi.org/10.1016/j.landurbplan.2018.08.028
[22] NAIK N, PHILIPOOM J, RASKAR R, et al. Streetscore-predicting the Perceived Safety of One Million Streetscapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2014:779-785.
[23] BADRINARAYANAN V,KENDALL A, CIPOLLA R. SegNet:A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation[J].IEEE Transactions on Pattern Analysis&Machine Intelligence, 2017(12):2481-2495.
[24] FERREIRA N, LAGE M,DORAISWAMY H, et al. Urbane:A 3d Framework to Support Data Driven Decision Making in Urban Development[C]//Visual Analytics Science and Technology(VAST), 2015IEEE Conference on. IEEE, 2015:97-104.
[25] ZENG W, YE Y. VitalVizor:A Visual Analytics System for Studying Urban Vitality[J]. IEEE Computer Graphics and Applications, 2018, 38(5):38-53.
[26] NAIK N, KOMINERS S D, RASKAR R, et al. Computer Vision Uncovers Predictors of Physical Urban Change[J].Proceedings of the National Academy of Sciences,2017, 114(29):7571-7576.
[27]叶宇,张灵珠,颜文涛,等.街道绿化品质的人本视角测度框架——基于百度街景数据和机器学习的大规模分析[J].风景园林,2018(8):24-29.YE Y, ZHANG L, YAN W T, et al. Measurement Framework of Street Greening Quality from Humanistic Perspective:A Large-scale Analysis Based on Baidu Street View Images and Machine Learning Algorithms[J].Landscape Architecture, 2018(8):24-29.
[28]叶宇,张昭希,曾伟,等.人本尺度的街道空间品质测——结合街景数据和新分析技术的大规模、高精度评价框架[J].国际城市规划,2019(1):18-27.YE Y, ZHANG Z X, ZENG W, et al.Human-scale Quality on Streets:A Large-scale and Efficient Analytical Approach Based on Street View Images and New Urban Analytical Tools[J].Urban Planning International, 2019(1):18-27.
[2] PALMISANO S J. A Smarter Planet:The Next Leadership Agenda[J]. IBM,2008(6):1-8.
[3] BATTY M. The New Science ofCities[M]. Cambridge:MIT Press, 2013.
[4] TOWNSEND A. Cities of Data:Examining the New Urban Science[J].Public Culture, 2015, 27(2):201-212.
[5]卢济威.新时期城市设计的发展趋势[J].上海城市规划,2015(1):3-4.LU J W. The New Trends of Urban Design in the New Era[J]. Shanghai Urban Planning Review, 2015(1):3-4.
[6] ROWLEY A. Definitions of Urban Design:The Nature and Concerns of Urban Design[J]. Planning Practice and Research,1994, 9(3):179-197.
[7] BUCHANAN P. What City? A Plea for Place in the Public Realm[J].Architectural Review, 1988, 1101(11):31-41.
[8] CONZEN M R G. Thinking about Urban Form:Papers on Urban Morphology,1932-1998[M]. New York:Peter Lang,2004.
[9] YE Y, VAN NES A. Quantitative Tools in Urban Morphology:Combining Space Syntax, Spacematrix and Mixed-Use Index in A GIS Framework[J]. Urban Morphology,2014, 18(2):97-118.
[10]叶宇,戴晓玲.新技术与新数据条件下的空间感知与设计运用可能[J].时代建筑,2017(5):6-13.YE Y, DAI X L. Spatial Perception and Design Potentials in the Context of New Analytical Techniques and New Data[J].Time+Architecture, 2017(5):6-13.
[11] EWING R,CLEMENTE O. Measuring Urban Design:Metrics for Livable Places[M]. New York:Island Press,2013.
[12] GEHL J,KAEFER L J,REIGSTAD S. Close Encounters with Buildings[J].Urban Design International, 2006, 11(1):29-47.
[13]徐磊青,康琦.商业街的空间与界面特征对步行者停留活动的影响——以上海市南京西路为例[J].城市规划学刊,2014,29(3):104-111.XU L Q, KANG Q. The Relationshipbetween Pedestrian Behaviors and the Spatial Features along the Ground-floor Commercial Street:The Case of West Nanjing Road in Shanghai[J]. Urban Planning Forum, 2014(3):104-111.
[14]叶宇,庄宇,张灵珠.城市设计中活力营造的形态学探究——基于城市空间形态特征量化分析与居民活动检验[J].国际城市规划,2016(1):26-33.YE Y,ZHUANG Y,ZHANG L Z.Designing Urban Spatial Vitality from Morphological Perspective-A Study Based on Quantified Urban Morphology and Activities'Testing[J]. Urban Planning International, 2016(1):26-33.
[15]龙瀛,叶宇.人本尺度城市形态:测度,效应评估及规划设计响应[J].南方建筑,2016(5):41-47.LONG Y, YE Y. Human-Scale Urban Form:Measurements, Performances,and Urban Planning&Design Interventions[J]. South Architecture,2016(5):41-47.
[16]叶宇,庄宇.城市形态学中量化分析方法的涌现[J].城市设计,2016,4(6):56-65.YE Y, ZHUANG Y. The Raising of Quantitative Morphological Tools in Urban Morphology[J]. Urban Design,2016, 4(6):56-65.
[17] KARIMI K. A Configurational Approach To Analytical Urban Design:Space Syntax's Methodology[J]. Urban Design International, 2012, 17(4):297-318.
[18] BATTY M. Defining Geodesign(=GIS+design?)[J]. Environment and Planning B:Planning and Design, 2013,40(1):1-2.
[19] SEVTSUK A, MEKONNEN M. Urban Network Analysis:A New Toolbox for Measuring City Form in ArcGIS[C]//Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design. Society for Computer Simulation International, 2012:18.
[20] YE Y, YEH A, ZHUANG Y, et al."Form Syntax"as a Contribution to Geodesign:A Morphological Tool for Urbanity-Making in Urban Design[J].Urban Design International, 2017, 22(1):73-90.
[21] YE Y, RICHARDS D, LU Y, et al.Measuring Daily Accessed Street Greenery:A Human-Scale Approach for Informing Better Urban Planning Practices[J]. Landscape and Urban Planning, 2018. https://doi.org/10.1016/j.landurbplan.2018.08.028
[22] NAIK N, PHILIPOOM J, RASKAR R, et al. Streetscore-predicting the Perceived Safety of One Million Streetscapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2014:779-785.
[23] BADRINARAYANAN V,KENDALL A, CIPOLLA R. SegNet:A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation[J].IEEE Transactions on Pattern Analysis&Machine Intelligence, 2017(12):2481-2495.
[24] FERREIRA N, LAGE M,DORAISWAMY H, et al. Urbane:A 3d Framework to Support Data Driven Decision Making in Urban Development[C]//Visual Analytics Science and Technology(VAST), 2015IEEE Conference on. IEEE, 2015:97-104.
[25] ZENG W, YE Y. VitalVizor:A Visual Analytics System for Studying Urban Vitality[J]. IEEE Computer Graphics and Applications, 2018, 38(5):38-53.
[26] NAIK N, KOMINERS S D, RASKAR R, et al. Computer Vision Uncovers Predictors of Physical Urban Change[J].Proceedings of the National Academy of Sciences,2017, 114(29):7571-7576.
[27]叶宇,张灵珠,颜文涛,等.街道绿化品质的人本视角测度框架——基于百度街景数据和机器学习的大规模分析[J].风景园林,2018(8):24-29.YE Y, ZHANG L, YAN W T, et al. Measurement Framework of Street Greening Quality from Humanistic Perspective:A Large-scale Analysis Based on Baidu Street View Images and Machine Learning Algorithms[J].Landscape Architecture, 2018(8):24-29.
[28]叶宇,张昭希,曾伟,等.人本尺度的街道空间品质测——结合街景数据和新分析技术的大规模、高精度评价框架[J].国际城市规划,2019(1):18-27.YE Y, ZHANG Z X, ZENG W, et al.Human-scale Quality on Streets:A Large-scale and Efficient Analytical Approach Based on Street View Images and New Urban Analytical Tools[J].Urban Planning International, 2019(1):18-27.