热带地理 ›› 2020, Vol. 40 ›› Issue (5): 919-929.doi: 10.13284/j.cnki.rddl.003272
收稿日期:
2020-01-05
修回日期:
2020-04-21
出版日期:
2020-09-28
发布日期:
2020-10-10
通讯作者:
袁媛
E-mail:liuy575@mail2.sysu.edu.cn;yyuanah@163.com
作者简介:
刘颖(1996―),女,江西吉安人,硕士研究生,主要研究方向为城市贫困,(E-mail)基金资助:
Ying Liu1(), Yuan Yuan1(
), Hanfa Xing2, Yuan Meng2, Tong Niu1
Received:
2020-01-05
Revised:
2020-04-21
Online:
2020-09-28
Published:
2020-10-10
Contact:
Yuan Yuan
E-mail:liuy575@mail2.sysu.edu.cn;yyuanah@163.com
摘要:
以广州市为例,选取中心4个区6 670个采样点(涵盖121个社区)的百度街景图片,从城市建成环境特征探讨了城市贫困识别的可能。首先,训练基于深度神经网络的街景图片分类模型后,对街景要素进行语义分割,并通过缓冲区分析统计社区尺度的街景指标;其次,经主成分分析法提取出建筑围合感、植被围合感、天空开阔感和道路开阔感4个街景主因子,并验证其与多重贫困指数(IMD)的相关性;最后,通过采用简单随机抽样法选取61个社区,构建街景预测的多元线性回归模型,对剩余60个社区进行贫困预测,验证街景指标测度城市贫困的度量精度。结果发现,案例社区的多重贫困指数(IMD)与建筑围合感呈正相关,与植被围合感、天空开阔感、道路开阔感呈负相关;从整体看来,街景预测结果与传统城市贫困测度的空间规律基本相符,而且结果通常比传统测度的城市贫困程度高。这是因为受测度内容、社区类型、街道属性等方面的影响,街景识别方式比较适用于判断建成环境较差的贫困社区。街景图片预测有利于刻画城市贫困人群真实的生活环境,便于对城市建成区进行及时监测,在一定程度上可以与传统城市贫困测度相互校正、弥补不足。
中图分类号:
刘颖, 袁媛, 邢汉发, 孟媛, 牛通. 街景图片识别城市贫困的适用性——基于广州市中心城区的验证[J]. 热带地理, 2020, 40(5): 919-929.
Ying Liu, Yuan Yuan, Hanfa Xing, Yuan Meng, Tong Niu. The Applicability of Street View Images to Identify Urban Poverty in the Central Urban Region of Guangzhou[J]. Tropical Geography, 2020, 40(5): 919-929.
表3
街景指标及主因子特征值、解释贡献、旋转后成份荷载矩阵"
域 | 指标 | 主因子 | |||
---|---|---|---|---|---|
一 | 二 | 三 | 四 | ||
天空开敞度 | 0°水平视角 | -0.451 | -0.294 | 0.819 | -0.106 |
20°仰角 | -0.373 | -0.334 | 0.843 | -0.161 | |
绿视率 | 0°水平视角 | -0.157 | 0.939 | -0.173 | -0.019 |
20°仰角 | -0.022 | 0.921 | -0.287 | -0.142 | |
路面占比 | 0°水平视角 | -0.159 | -0.063 | 0.599 | 0.731 |
20°仰角 | -0.175 | -0.142 | 0.031 | 0.952 | |
建筑占比 | 0°水平视角 | 0.827 | -0.433 | -0.247 | -0.193 |
20°仰角 | 0.829 | -0.468 | -0.219 | -0.071 | |
界面围合度 | 0°水平视角 | 0.782 | 0.323 | -0.418 | -0.237 |
20°仰角 | 0.693 | 0.461 | -0.452 | -0.181 | |
主因子特征值 | 2.58 | 2.43 | 1.61 | 1.52 | |
主因子贡献值/% | 48.01 | 29.42 | 13.01 | 4.88 | |
主因子累计贡献值/% | 48.01 | 77.43 | 90.44 | 95.32 |
Arietta S M, Efros A A and Ramamoorthi R.2014.City Forensics: Using Visual Elements to Predict Non-Visual City Attributes.IEEE Transactions on Visualization and Computer Graphics, 20(12): 2624-2633. | |
Burgess E W.2008.The Growth of the City: An Introduction to a Research Project.Urban Ecology, 18: 71-78. | |
曹哲静,龙瀛.2017. 数据自适应城市设计的方法与实践——以上海衡复历史街区慢行系统设计为例. 城市规划学刊,(4):47-55. [Cao Zhejing and Long Ying.2017.Methodology and Practice of Data Adaptive Urban Design: Case Study of Slow Traffic System Design in Hengfu Historical District of Shanghai.Urban Planning Forum, (4): 47-55.] | |
柴子为,王帅磊,乔纪纲.2015. 基于夜间灯光数据的珠三角地区镇级GDP估算. 热带地理,35(3):379-385. [Chai Ziwei, Wang Shuailei and Qiao Jigang. 2015.The Pearl River Delta Region's Town-Level GDP Estimate Based on Night-Time Lighting Data.Tropical Geography, 35(3): 379-385. ] | |
Chen G , Gu C and Wu F.2006.Urban Poverty in the Transitional Economy: a Case of Nanjing, China.Habitat International, 30(1): 1-26. | |
Chen Y M, Liu X P, Li X, Liu Y L and Xu X C.2016.Mapping the Fine-Scale Spatial Pattern of Housing Rent in the Metropolitan Area by Using Online Rental Listings and Ensemble Learning.Applied Geography, 75: 200-212. | |
Duque J C, Patino J E, Ruiz L A and Pardo-Pascual J E.2015.Measuring Intra-Urban Poverty Using Land Cover and Texture Metrics Derived from Remote Sensing Data.Landscape and Urban Planning, 135: 11-21. | |
Huang Youqin.2004. Housing Markets, Government Behaviors, and Housing Choice: A Case Study of Three Cities in China. Environment and Planning A, 36(1): 45-68. | |
Kang K and Wang X.2014. Fully Convolutional Neural Networks for Crowd Segmentation. Computer Science, 49(1): 25-30. | |
Kelly C M, Wilson J S, Baker E A, Miller D K and Schootman M.2013.Using Google Street View to Audit the Built Environment: Inter-Rater Reliability Results.Annals of Behavioral Medicine, 45(1): 108-112. | |
Kendall A, Badrinarayanan V and Cipolla R.2015.Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding.Computer Science, 11: 1-11. | |
Li X, Zhang C and Li W. 2015.Who Lives in Greener Neighborhoods? The Distribution of Street Greenery and Its Association with Residents' Socioeconomic Conditions in Hartford, Connecticut, USA.Urban Forestry & Urban Greening, 14(4): 751-759. | |
Liang C, Sensen C and Wenwen Z. 2017. Use of Tencent Street View Imagery for Visual Perception of Streets. ISPRS International Journal of Geo-Information, 6(9): 265. | |
刘玉亭. 2005. 转型期中国城市贫困的社会空间. 北京:科学出版社. [Liu Yuting. 2005.Social Space of Urban Poverty in China During the Transition Period.Beijing: Science Press.] | |
Long Y and Shen Z.2015.Geospatial Analysis to Support Urban Planning in Beijing. [2020-09-12]. https://www.springer.com/gp/book/9783319193410. | |
Long Y and Liu L.2017. How Green Are the Streets? An Analysis for Central Areas of Chinese Cities Using Tencent Street View. PLoS ONE, 12(2): e0171110. | |
马蓓蓓,李海玲,魏也华,薛东前,江军.2018. 西安市贫困空间结构特征与发生机理. 地理学报,73(6):1018-1032. [Ma Beibei, Li Hailing, Wei Yihua, Xue Dongqian and Jiang Jun.2018.Spatial Structure and Mechanism of Urban Poverty in Xi'An City.Acta Geographica Sinica, 73(6): 1018-1032.] | |
Mingione E.1996.Urban Poverty and the Underclass: A Reader.Oxford: Urban Research Publications. | |
Naik N, Kominers S D and Raskar R.2015.Do People Shape Cities, or Do Cities Shape People? The Co-Evolution of Physical, Social and Economic Change in Five Major US Cities.NBER Working Papers, 61: 1-38. | |
Noor A M, Alegana V A, Gething P W, Tatem A J and Snow R W.2008.Using Remotely Sensed Night-Time Light as a Proxy for Poverty in Africa.Population Health Metrics, 6(1): 5. | |
谌丽,张文忠,党云晓,余建辉.2012. 北京市低收入人群的居住空间分布、演变与聚居类型. 地理研究,31(4):720-732. [Shen Li, Zhang Wenzhong, Party Yunxiao and Yu Jianhui.2012.The Spatial Distribution, Transition and Residential Pattern of Low-Income Residents in Beijing.Geographical Research, 31(4): 720-732.] | |
Small M L and Mcdermott M.2006.The Presence of Organizational Resources in Poor Urban Neighborhoods: An Analysis of Average and Contextual Effects.Social Forces, 84(3): 1697-1724. | |
唐婧娴,龙瀛. 2017.特大城市中心区街道空间品质的测度——以北京二三环和上海内环为例. 规划师,33(2):68-73. [Tang Jingxian and Long Ying.2017.Metropolitan Street Space Quality Evaluation: Second and Third Ring of Beijing, Inner Ring of Shanghai.Planners, 33(2): 68-73.] | |
Venerandi A, Quattrone G and Capra L.2015.Measuring Urban Deprivation from User Generated Content.Computer Science, 19: 254-264. | |
Wojna Z, Gorban A N and Lee D S.2017.Attention-Based Extraction of Structured Information from Street View Imagery.Annals of Behavioral Medicine, 1: 844-850. | |
Wang R , Lu Y and Zhang J.2019. The Relationship between Visual Enclosure for Neighbourhood Street Walkability and Elders' Mental Health in China: Using Street View Images. Journal of Transport & Health, 13: 90-102. | |
Wilson W J. 2010. Why Both Social Structure and Culture Matter in a Holistic Analysis of Inner-City Poverty. Annals of the American Academy of Political & Social Science, 629(1): 200-219. | |
Wu F.2010. Urban Poverty and Marginalization under Market Transition: The Case of Chinese Cities. International Journal of Urban & Regional Research, 28(2): 401-423. | |
袁媛,吴缚龙,许学强.2009. 转型期中国城市贫困和剥夺的空间模式. 地理学报,64(6):753-763. [Yuan Yuan, Wu Fulong and Xu Xueqiang.2009.The Spatial Pattern of Poverty and Deprivation in Transitional Chinese City: Analysis of Area-Based Indicators and Individual Data.Acta Geographica Sinica, 64(6): 753-763.] | |
袁媛.2011. 社会空间重构背景下的贫困空间固化研究. 现代城市研究,(3):14-18. [Yuan Yuan.2011.Research on Stabilization of Spatiality of Urban Poverty against the Background of Social-Spatial Reconstruction.Modern Urban Research, (3): 14-18.] | |
Yuan Y and Wu F.2014. The Development of the Index of Multiple Deprivations from Small-Area Population Census in the City of Guangzhou, PRC. Habitat International, 41: 142-149. | |
袁媛,刘菁,陈逸敏,尤智扬.2018.基于遥感影像及在线房租数据的城市内部贫困空间测度研究——以广州市内城核心区为例.人文地理,33(3):60-67. [Yuan Yuan, Liu Jing, Chen Yimin and You Zhiyang.2018.Poverty Measurement of Urban Internal Space Based on Remote Sensing Images and Online Rental Information: A Case Study of the City Core of Guangzhou.Human Geography, 33(3): 60-67.] | |
Yuan Y, Xu M and Cao X. 2018. Exploring Urban-Rural Disparity of the Multiple Deprivation Index in Guangzhou City from 2000 to 2010. Cities, 79: 1-11. | |
张祚,李江风,陈双,刘艳中. 2011. 经济适用住房在城市中的空间分布——基于DEM的武汉市实例分析. 地理学报,66(10):1309-1320. [Zhang Wei, Li Jiangfeng, Chen Shuang and Liu Yanzhong. 2011.Spatial Distribution of Affordable Houses in Cities:A Case Study of Wuhan Based on DEM.Acta Geographica Sinica, 66(10): 1309-1320.] |
[1] | 朱洪平, 朱文涛, 郑荣宝. 地铁对服务业集聚的影响——以广州市为例[J]. 热带地理, 2021, 41(1): 114-123. |
[2] | 林琳, 严程棋, 杨莹, 范艺馨, 吴箐. 广州老年人小病慢病“旁路”就医行为及其影响因素[J]. 热带地理, 2020, 40(6): 993-1003. |
[3] | 边艳, 周春山, 胡锦灿. 基于住房需求意愿的广州市中产阶层住房区位选择[J]. 热带地理, 2020, 40(5): 832-842. |
[4] | 贺辰戋, 欧阳婷萍, 彭莎莎. 广州市表层土壤磁学性质的空间插值方法比较[J]. 热带地理, 2020, 40(5): 903-918. |
[5] | 张晨, 肖大威, 黄翼. 广州市美丽乡村空间分异特征及其影响因素[J]. 热带地理, 2020, 40(3): 551-561. |
[6] | 林勋媛, 王广兴, 胡月明. 基于开放大数据的广州市中心城区职住平衡特征研究[J]. 热带地理, 2020, 40(2): 254-265. |
[7] | 张晨, 周霞, 李勇, 杨骥, 李林. 半封闭大棚实验下广州市植被滞尘光谱特征分析[J]. 热带地理, 2020, 40(2): 266-277. |
[8] | 邓清华, 薛德升, 龚建周. 网购时代广州市居民购买食品行为特征[J]. 热带地理, 2019, 39(5): 780-789. |
[9] | 范诗彤,李立勋,符天蓝. 流动摊贩疏导区的实践效应和挑战 ——以广州市荔湾区源溪社区疏导区为例[J]. 热带地理, 2019, 39(1): 81-90. |
[10] | 王长建,张虹鸥,汪菲,叶玉瑶,吴康敏,徐茜,杜志威. 城市能源消费碳排放特征及其机理分析 ——以广州市为例[J]. 热带地理, 2018, 38(6): 759-770. |
[11] | 吴康敏,张虹鸥,王洋,叶玉瑶,金利霞,吴旗韬. 广州市零售业态空间分异特征与机制[J]. 热带地理, 2018, 38(2): 196-207. |
[12] | 李家会,董玉祥. 大都市区不同土地利用系统转换时空分异特征——以广州市为例[J]. 热带地理, 2018, 38(2): 264-273. |
[13] | 王珏晗,周春山. 广州市商业型健身房空间分布及其影响因素[J]. 热带地理, 2018, 38(1): 120-130. |
[14] | 叶晓琪,宋小青,谭子安,吴志峰. 大都市镇域耕地功能格局及其成因 ——以广州市为例[J]. 热带地理, 2017, 37(6): 862-873. |
[15] | 王洋,张虹鸥,叶玉瑶,吴旗韬,金利霞. 广州市社会空间质量的综合评价与分布格局[J]. 热带地理, 2017, 37(1): 25-32. |
|