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| 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 |
1. School of Geography and Planning, Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Sun Yat-sen University, Guangzhou 510275, China;
2. School of Geographic Sciences, Key Lab. of Geographic Information Science(Ministry of Education), East China Normal University, Shanghai 200241, China;
3. Baidu International Technology(Shenzhen) Co., Ltd, Shenzhen 518000, China |
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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.
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Received: 06 December 2017
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|
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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.]
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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.]
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2018, Vol. 33 
