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

袁媛, 刘菁, 陈逸敏, 尤智扬

人文地理 ›› 2018, Vol. 33 ›› Issue (3) : 60-67.

PDF(2198 KB)
PDF(2198 KB)
人文地理 ›› 2018, Vol. 33 ›› Issue (3) : 60-67. DOI: 10.13959/j.issn.1003-2398.2018.03.008
经济

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

  • 袁媛1, 刘菁1, 陈逸敏2, 尤智扬3
作者信息 +

POVERTY MEASUREMENT OF URBAN INTERNAL SPACE BASED ON REMOTE SENSING IMAGES AND ONLINE RENTAL INFORMATION: A CASE STUDY OF THE CITY CORE OF GUANGZHOU

  • YUAN Yuan1, LIU Jing1, CHEN Yi-min2, YOU Zhi-yang3
Author information +
文章历史 +

摘要

学界以社会经济指标(人口普查、问卷调查等)为主导测度城市内部贫困空间,取得较好的研究成果;但是普查数据周期长、贫困群体社会经济数据可获得性低,如何制定全覆盖、多方位、易获取的测度指标是该项研究的主要挑战之一。本文尝试使用遥感影像、在线房租等公众可获取的数据,采用FETEX2.0、WEKA等软件,利用三个指标(土地覆盖指数、复杂度、单位房租)建构基于大数据的贫困指数,测度广州市内城核心区718个居委会的贫困得分。再将测度结果与利用第六次全国人口普查数据测度的贫困空间对比分析,探究两种测度方法的区别和适用性。结果显示:①两者的测度结果具有较强的一致性,尤其对前5%最贫困的居委会具有较高重合度;②本文建构的大数据贫困指数对城中村、传统工业区等典型贫困空间识别效果更好。本研究使用易获取、更新周期短的数据,有利于城市贫困空间分布的实时监测,对引导精准分配扶贫资源、有效实施贫困社区更新规划具有重要意义。

Abstract

Academia has achieved remarkable results in poverty measurement mainly based on socioeconomic statistical data, such as census data and survey data. The development of wide coverage and multi-dimensionalpoverty indicators is one of the main challenges in urban poverty studies. By integratingonline rental information and remote sensing images from Google Earth and Landsat8, thisstudyproposesa BDPI (Big data poverty index), which is composed of three indicesin cluding land cover index, surface texture index and unit rent, to measure the spatial distribution of poverty in the city core of Guangzhou. 718 communities were included and a series of software such as FETEX2.0 and WEKA was applied in processes. Then the paper compares the result with the spatial distribution of multiple deprivation based on the Sixth national population census data, and study the differences between the two methods. The result shows that there is a high consistency between the two results, especially for the top 5% communities of poverty.

关键词

贫困空间测度 / 城市贫困 / 内城核心区 / 广州市 / 大数据

Key words

poverty measurement of urban internal space / urban poverty / city core / Guangzhou / big data

引用本文

导出引用
袁媛, 刘菁, 陈逸敏, 尤智扬. 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[J]. 人文地理. 2018, 33(3): 60-67 https://doi.org/10.13959/j.issn.1003-2398.2018.03.008
YUAN Yuan, LIU Jing, CHEN Yi-min, YOU Zhi-yang. POVERTY MEASUREMENT OF URBAN INTERNAL SPACE BASED ON REMOTE SENSING IMAGES AND ONLINE RENTAL INFORMATION: A CASE STUDY OF THE CITY CORE OF GUANGZHOU[J]. HUMAN GEOGRAPHY. 2018, 33(3): 60-67 https://doi.org/10.13959/j.issn.1003-2398.2018.03.008
中图分类号: C912.8   

参考文献

[1] UNDP. Human development report 1998[EB/OL].(2018-03-09)[20-18-03-09]. http://hdr.undp.org/en/content/human-development-report-1998.

[2] UNDP. Human development report 2011[EB/OL].(2018-03-09)[20-18-03-09]. http://hdr.undp.org/en/content/human-development-report-2011.

[3] Townsend P. Deprivation[J]. Journal of Social Policy, 1987,16(1):125-146.

[4] Langlois A, Kitchen P. Identifying and measuring dimensions of urban deprivation in Montreal:An analysis of the 1996 census data[J]. Urban Studies, 2001,38(1):119-139.

[5] Apparicio P, Cloutier M S, Shearmur R. The case of Montréal's missing food deserts:Evaluation of accessibility to food supermarkets[J]. International Journal of Health Geographics, 2007,6(1):4.

[6] Taubenbock H, WurmM, Setiadi N, et al. Integrating remote sensing and social science[C/OL]. (2009-06-26)[2018-03-09]. Joint Urban Remote Sensing Event, Shanghai, 2009:1-7. doi:10.1109/URS.-2009.5137506. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5137506&isnumber=5137466.

[7] Anderson W G. Concrete and poverty, vegetation and wealth? A counterexample from remote sensing of socioeconomic indicators on the U.S.-Mexico border[J]. Professional Geographer, 2015,66(2):166-179.

[8] Jain S. Remote sensing application for property tax evaluation[J]. International Journal of Applied Earth Observation & Geoinformation, 2008,10(1):109-121.

[9] Imran M, Stein A, Zurita-Milla R. Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products[J]. International Journal of Applied Earth Observation & Geoinformation, 2014,26(1):322-334.

[10] Hall G B, Malcolm N W, Piwowar J M. Integration of remote sensing and GIS to detect pockets of urban poverty:The case of Rosario, Argentina[J]. Transactions in GIS, 2001,5(3):235-253.

[11] Niebergall S, Loew A, Mauser W. Object-oriented analysis of very high-resolution quickbird data for mega city research in Delhi/India[C/OL]. (2007-06-18)[2018-03-09]. doi:10.1109/UR S.2007.371836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4234435&isnumber=4234359.

[12] Duque J C, Patino J E, Ruiz L A, et al. Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data[J]. Landscape & Urban Planning, 2015,135:11-21.

[13] Jean N, Burke M, Xie M, et al. Combining satellite imagery and machine learning to predict poverty[J]. Science, 2016,353(6301):790-794.

[14] Blumenstock J E. Fighting poverty with data[J]. Science, 2016,353(6301):753-754.

[15] 袁媛,古叶恒,陈志灏.中国城市贫困的空间差异特征[J].地理科学进展,2016,35(2):195-203.[Yuan Yuan, Gu Yeheng, Chen Zhihao. Spatial differentiation of urban poverty of Chinese cities[J]. Progress in Geography, 2016,35(2):195-203.]

[16] 刘艳华,徐勇.中国农村多维贫困地理识别及类型划分[J].地理学报,2015,70(6):993-1007.[Liu Yanhua, Xu Yong. Geographical identification and classification of multi-dimensional poverty in rural China[J]. Acta Geographica Sinica, 2015,70(6):993-1007.]

[17] 陈烨烽,王艳慧,王小林.中国贫困村测度与空间分布特征分析[J]. 地理研究,2016,35(12):2298-2308.[Chen Yefeng, Wang Yanhui, Wang Xiaolin. Measurement and spatial analysis of poverty-stricken villages in China[J]. Geographical Research, 2016,35(12):2298-2308.]

[18] 王艳慧,钱乐毅,段福洲.县级多维贫困度量及其空间分布格局研究——以连片特困区扶贫重点县为例[J].地理科学,2013,33(12):1489-1497.[Wang Yanhui, Qian Leyi, Duan Fuzhou. Multidimensional poverty measurement and spatial distribution pattern at the country scale:A case study on key country from national contiguous special poverty-stricken areas[J]. Scientia Geographica Sinica, 2013,33(12):1489-1497.]

[19] 袁媛,王仰麟,马晶,等.河北省县域贫困度多维评估[J].地理科学进展,2014,33(1):124-133.[Yuan Yuan, Wang Yanglin, Ma Jing, et al. Multidimensional evaluation of county poverty degree in Hebei province[J]. Progress in Geography, 2014,33(1):124-133.]

[20] 曾永明,张果.基于GIS和BP神经网络的区域农村贫困空间模拟分析——一种区域贫困程度测度新方法[J].地理与地理信息科学,2011,27(2):70-75.[Zeng Yongming, Zhang Guo, Spatial simulating in regional rural poverty based on GIS and BP neural network:A new appraisement method on regional rural poverty[J]. Geography and Geo-Information Science, 2011,27(2):70-75.]

[21] 许月卿,李双成,蔡运龙.基于GIS和人工神经网络的区域贫困化空间模拟分析——以贵州省猫跳河流域为例[J].地理科学进展, 2006,25(3):79-85.[Xu Yueqing, Li Shuangcheng, Cai Yunlong. Spatial simulation using for regional poverty GIS and artificial neural network:A case study of Maotiaohe watershed, Guizhou province[J]. Progress in Geography, 2006,25(3):79-85.]

[22] 顾朝林,克斯特洛德·C.北京社会极化与空间分异研究[J].地理学报,1997,52(5):385-393.[Gu Chaolin, Kesteloot C. Social polarisation and segregation phenomenon in Beijing[J]. Acta Geographica Sinica, 1997,52(5):385-393.]

[23] 何春阳,史培军,李景刚,等.基于DMSP/OLS夜间灯光数据和统计数据的中国大陆20世纪90年代城市化空间过程重建研究[J].科学通报,2006,51(7):856-861.[He Chunyang, Shi Peijun, Li Jinggang, et al. Study on the reconstruction of urbanization space in mainland China in the 1990s by using DMSP/OLS data and statistical data[J]. Chinese Science Bulletin, 2006,51(7):856-861.]

[24] 王琪,袁涛,郑新奇.基于夜间灯光数据的中国省域GDP总量分析[J].城市发展研究,2013,20(7):44-48.[Wang Qi, Yuan Tao, Zheng Xinqi. GDP gross analysis at province-level in China based on night-time lightsatellite imagery[J]. Urban Studies, 2013,20(7):44-48.]

[25] 陈果,顾朝林,吴缚龙.南京城市贫困空间调查与分析[J].地理科学, 2004,24(5):542-548.[Chen Guo, Gu Chaolin, Wu Fulong. Spatial analysis of urban poverty in Nanjing[J]. Scientia Geographica Sinica, 2004,24(5):542-548.]

[26] 袁媛,吴缚龙,许学强.转型期中国城市贫困和剥夺的空间模式[J]. 地理学报,2009,64(6):753-763.[Yuan Yuan, Wu Fulong, Xu Xueqiang. The spatial pattern of poverty and deprivation in transitional Chinese city:Analysis of area-based indicators and individual data[J]. Acta Geographica Sinica, 2009,64(6):753-763.]

[27] 何深静,刘玉亭,吴缚龙.南京市不同社会群体的贫困集聚度、贫困特征及其决定因素[J].地理研究,2010,29(4):703-715.[He Shenjing, Liu Yuting, Wu Fulong. Poverty profiles and poverty determinants of different social groups in Nanjing[J]. Geographical Research, 2010,29(4):703-715.]

[28] 袁媛,李珊.大城市低收入邻里社会贫困的测度差异与成因[J].地理学报,2012,67(10):59-67.[Yuan Yuan, Li Shan. The measurement, spatial differentiation and driving forces of social deprivation in low-income neighborhoods in Chinese large cities[J]. Acta Geographica Sinica, 2012,67(10):59-67.]

[29] Yuan Yuan, Wu Fulong. The development of the index of multiple deprivations from small-area population census in the city of Guangzhou, PRC[J]. Habitat International, 2014,41(1):142-149.

[30] Chen Yimin, Liu Xiaoping, Li Xia, et al. Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning[J]. Applied Geography, 2016,75:200-212.

基金

国家自然科学基金项目(51678577);广东省科技创新青年拔尖人才项目;高校基本科研业务费中山大学重大项目培育(15lgjc38)


PDF(2198 KB)

Accesses

Citation

Detail

段落导航
相关文章

/