基于夜间灯光的中国房屋空置的空间分异格局
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摘要
以中国295个地级行政单元为研究对象,基于NPP-VIIRS夜间灯光数据、土地覆被数据和国家基础地理信息数据,构建"鬼城"指数模型,并采用空间统计分析方法,揭示中国地级行政单元"鬼城"现象的空间分异格局。结果表明:经济发展较快的一些东部沿海城市和地区"鬼城"现象较为鲜见,资源枯竭型城市、地处山区的一些城市以及经济发展较为落后的地区和城市则是"鬼城"现象的典型代表;从全国范围看,"鬼城"指数呈现出中部>西部>东部,北方>中部>南方的趋势;"鬼城"指数较高的地级单元集聚性个体差异较小,"鬼城"指数较低的单元集聚性个体差异则较大;"鬼城"的热点区域形成"丁"字格局,冷点分布则比较破碎;随着城市等级的降低,"鬼城"指数呈现出逐渐上升的态势,且指数的分布区间由离散态向集中态逼近。
NPP-VIIRS nighttime lights data, land cover data and national fundamental geographic information data were used to build"ghost towns"index model to reveal spatial difference pattern of"ghost towns"phenomenon in China at prefecture level by taking both 295 prefecture level units and spatial statistics analysis methods into consideration. The results showed that the"ghost towns"of eastern coastal cities and regions with rapid development of economy were rarely appear. Resource-exhausted cities, hilly cities and lagging economic development cities and regions were typical representative of"ghost towns". The"ghost towns"index showing a trend of the central China>the western China>the eastern China, and the North>the Central>the South from all over the country. Individual difference of prefecture units cluster with higher"ghost towns"index were small, the lower prefecture units cluster were large. Hot spots formed a pattern of"ding"word, the distribution of cold spots were more fragmentation. As the city level lowering, the"ghost towns"index showing a rising trend, and the distribution interval of index from discrete to concentrate.
NPP-VIIRS nighttime lights data, land cover data and national fundamental geographic information data were used to build"ghost towns"index model to reveal spatial difference pattern of"ghost towns"phenomenon in China at prefecture level by taking both 295 prefecture level units and spatial statistics analysis methods into consideration. The results showed that the"ghost towns"of eastern coastal cities and regions with rapid development of economy were rarely appear. Resource-exhausted cities, hilly cities and lagging economic development cities and regions were typical representative of"ghost towns". The"ghost towns"index showing a trend of the central China>the western China>the eastern China, and the North>the Central>the South from all over the country. Individual difference of prefecture units cluster with higher"ghost towns"index were small, the lower prefecture units cluster were large. Hot spots formed a pattern of"ding"word, the distribution of cold spots were more fragmentation. As the city level lowering, the"ghost towns"index showing a rising trend, and the distribution interval of index from discrete to concentrate.
引文
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[2]白璐.“鬼城”:一个城市的误读——鄂尔多斯城市形象传播与重塑研究[D].兰州:兰州大学,2016.
[3]梁倩.国土部:应以“用地极限”控制城镇化规模[N].经济参考报,2013-04-30(1).
[4]陶然,曹广忠.“空间城镇化”、“人口城镇化”的不匹配与政策组合应对[J].改革,2008(10):83-88.
[5]聂翔宇,刘新静.城市化进程中“鬼城”的类型分析及其治理研究[J].南通大学学报:社会科学版,2013(4):111-117.
[6]陈远宏.“鬼城”探秘——揭露“鬼城”成因,抑制房地产泡沫蔓延[J].山西高等学校社会科学学报,2013,25(12):31-35.
[7]张耀宇,陈利根,陈会广.“土地城市化”向“人口城市化”转变——一个分析框架及其政策含义[J].中国人口·资源与环境,2016,26(3):127-135.
[8]Weinberg D H.How the United States measures well-being in household surveys[J].Journal of Official Statistics,2006,22(1):113-136.
[9]Chen Z,Yu B,Hu Y,et al.Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS Nighttime Light Composite Data[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2015,8(5):1-10.
[10]Zheng Q,Zeng Y,Deng J,et al.“Ghost cities”identification using multi-source remote sensing datasets:A case study in Yangtze River Delta[J].Applied Geography,2017,80:112-121.
[11]卓莉,陈晋,史培军,等.基于夜间灯光数据的中国人口密度模拟[J].地理学报,2005,60(2):266-276.
[12]潘竟虎,李俊峰.基于夜间灯光影像的中国电力消耗量估算及时空动态[J].地理研究,2016,35(4):627-638.
[13]Shi K,Yu B,Huang Y,et al.Evaluating the Ability of NPPVIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales:A Comparison with DMSP-OLS Data[J].Remote Sensing,2014,6(2):1 705-1 724.
[14]潘竟虎,胡艳兴.基于夜间灯光数据的中国多维贫困空间识别[J].经济地理,2016,36(11):124-131.
[15]Elvidge C D,Baugh K E,Zhizhin M,et al.Why VIIRS data are superior to DMSP for mapping nighttime lights[J].Proceedings of the Asia-Pacific Advanced Network,2013,35:62-69.
[16]俞乐,王杰,李雪草,等.基于多源数据集成的多分辨率全球地表覆盖制图[J].中国科学:地球科学,44(8):1 646-1 660.
[17]Li X,Xu H,Chen X,et al.Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China[J].Remote Sensing,2013,5(6):3 057-3 081.
[18]徐康宁,陈丰龙,刘修岩.中国经济增长的真实性:基于全球夜间灯光数据的检验[J].经济研究,2015(9):17-29.
[19]Letu H,Hara M,Yagi H,et al.Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects[J].International Journal of Remote Sensing,2010,31(16):4443-4458.
[20]Zhou Y,Smith S J,Elvidge C D,et al.A cluster-based method to map urban area from DMSP/OLS nightlights[J].Remote Sensing of Environment,2014,147(18):173-185.
[21]Anselin L,Syabri I,Kho Y.Geo Da:An Introduction to Spatial Data Analysis[J].Geographical Analysis,2006,38(1):5-22.
[22]张衍阁.中国城市分级[J].第一财经周刊,2013(43):1-8.