基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析

doi:10.12082/dqxxkx.2019.180483

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (3): 455-466.doi: 10.12082/dqxxkx.2019.180483

基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析

卢秀^1,^2,³(), 李佳^1,^2,^*(), 段平^1,², 李晨^1,², 王金亮^1,²

1. 云南师范大学旅游与地理科学学院, 昆明 650500
2. 云南省高校资源与环境遥感重点实验室, 昆明 650500
3. 南京师范大学虚拟地理环境教育部重点实验室,南京 210023

收稿日期:2018-09-24 修回日期:2019-01-02 出版日期:2019-03-15 发布日期:2019-04-01
通讯作者: 李佳 E-mail:lx_rsgis@163.com;keguigiser@163.com
作者简介:
作者简介：卢秀（1992- ）,女,江苏淮安人,博士生,主要从事资源环境遥感、夜间灯光遥感研究。E-mail: lx_rsgis@163.com
基金资助:
国家自然科学基金项目（41561048）

Spatial Difference of GDP in Yunnan Border Area based on Nighttime Light and Land Use Data

Xiu LU^1,^2,³(), Jia LI^1,^2,^*(), Ping DUAN^1,², Chen LI^1,², Jinliang WANG^1,²

1. College of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China
2. Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming 650500, China
3. Key Laboratory of Virtual Geographic Environ-ment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China

Received:2018-09-24 Revised:2019-01-02 Online:2019-03-15 Published:2019-04-01
Contact: Jia LI E-mail:lx_rsgis@163.com;keguigiser@163.com
Supported by:
National Natural Science Foundation of China, No.41561048.

摘要/Abstract

摘要：

云南沿边地区包括8个地州,共56个县,其中有25个县市与老挝、缅甸和越南直接毗邻,具有重要的地缘位置。本研究利用土地利用数据和夜间灯光数据在实现云南沿边地区GDP空间化的基础上,对GDP的空间分布格局进行深入探讨,这对缩小区域经济差异及促进地区共同发展具有一定的指导意义。采用土地利用数据对国内生产总值（Gross Domestic Product, GDP）数据的第一产业进行空间化拟合,采用DMSP/OLS夜间灯光数据对GDP的第二、三产业进行拟合,将第一产业和第二、三产业空间化拟合的结果相加,实现云南沿边地区1992-2013年的GDP的空间化拟合。在此基础上对云南沿边地区GDP空间分布差异进行分析。结果表明：① 土地利用数据对第一产业建模的效果较好,拟合的多期数据的相对误差均低于1.12%,采用夜间灯光数据,基于“分类回归”方法对第二、三产业拟合相对误差最大仅为6.404%,最终二者之和拟合的GDP拟合精度都较好,相对误差最大仅为4.241%;② 22期GDP数据在空间分布上均呈现正的相关性,且均为显著集聚;③ GDP空间分布局部集聚的高值-高值区域集中在开远、蒙自等县域,低值-低值地区集中在绿春、西蒙等地区;④ 云南沿边地区县域之间的经济差异在1992-1996年逐渐增强,1996年之后,经济差异波动缩小,空间关联效应呈现波动式的增强和减弱;⑤ 云南沿边地区的三维插值结果均呈现出西北至东南一线的“洼地-丘陵-平地-高峰”地势变化格局,沿边地区的东南角地区即红河州的建水、个旧和开远等县市的GDP最高,“丘陵”地势主要集中在腾冲、保山市以及最南部的景洪地区,“洼地-平地”地势主要分布在沿边地区西北角的贡山和福贡等县域、西南角的西蒙和孟连等县及中部区域的绿春和江城县等地区。

关键词: GDP, 空间差异性, 云南沿边地区, DMSP/OLS夜间灯光数据, 土地利用数据

Abstract:

Yunnan border area is an important geographic location. It is composed of 56 counties in 8 municipalities. Among which, 25 counties are adjacent to Laos, Myanmar, and Vietnam. Land use and nighttime light data were used in this study to explore the spatial pattern of GDP based on the spatialization of GDP in the Yunnan border area. This study was expected to inform policy on reducing economic gaps between regions and promoting regional common development. The land use data was used to spatially fit the Gross Domestic Product (GDP) from the first industry, and the DMSP/OLS nighttime light data was used to fit GDP from the second and third industries. The fitting results were summed up to realize the spatialization of total GDP in the border area of Yunnan province from 1992 to 2013. Based on this, the spatial difference of GDP in the Yunnan border area was analyzed. The results showed that: (1) The land use data could be well used to model the GDP from the first industry, with goodness of fit (R²) being greater than 0.82 in each year and overall relative error being less than 1.12%. The nighttime light data and the classification regression method were used to fit the GDP from the second and third industries. The maximum relative error of fitting was 6.404%, and the fitting accuracy of the sum of the two industries was satisfactory with the maximum relative error being only 4.241%; (2) The 22-phase GDP data of the Yunnan border area was positively correlated in space, presenting an obvious clusters; (3) The distribution of GDP cluster in the county was characterized by High-High values (HH) and Low-Low values (LL). The distribution of Low-High and High-Low values was scattered with no regularity. The clustered high values of GDP were concentrated in Kaiyuan, Mengzi, and other counties, while the clustered low values of GDP were concentrated in Luchun, Ximeng, and other counties; (4) The economic gap between counties in the Yunnan border area gradually increased from 1992 to 1996 followed by a decrease trend afterward. The spatial correlation effect showed a fluctuation of increase and decrease; (5) Results of three-dimensional interpolation in the Yunnan border area presented a topographical pattern of “depression-hill-flat-peak” from the northwest to the southeast. The counties in the southeast corner of the border area such as Jianshui, Gejiu and Kaiyuan and other counties in the Honghe municipality, had the highest GDP. The “hill” terrain was mainly concentrated in Tengchong, Baoshan city, and the southernmost Jinghong area. The terrain of “depression-flat” was mainly distributed in the counties such as Gongshan and Fugong in the northwest corner of the border area, the counties in the southwest corner of Ximen and Menglian, and in the central area, such as Luchun and Jiangcheng counties.

Key words: GDP, spatial difference, Yunnan border area, DMSP/OLS nighttime light data, land use data

卢秀, 李佳, 段平, 李晨, 王金亮. 基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析[J]. 地球信息科学学报, 2019, 21(3): 455-466.

Xiu LU, Jia LI, Ping DUAN, Chen LI, Jinliang WANG. Spatial Difference of GDP in Yunnan Border Area based on Nighttime Light and Land Use Data[J]. Journal of Geo-information Science, 2019, 21(3): 455-466.

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年份	第一产业			第二、三产业			GDP
年份	模型R²	残差/亿元	相对误差/%	模型R²	残差/亿元	相对误差/%	残差/亿元	相对误差/%
1992	0.854	-0.536	0.784	0.953	-1.463	1.872	-1.999	1.365
1993	0.872	-0.845	1.116	0.948	-5.090	5.155	-5.934	3.403
1994	0.834	-0.422	0.432	0.945	-5.662	4.305	-6.084	2.654
1995	0.827	-0.148	0.120	0.920	-3.235	1.983	-3.384	1.180
1996	0.823	-0.210	0.144	0.922	-9.200	4.786	-9.410	2.786
1997	0.842	0.490	-0.308	0.913	-9.840	4.451	-9.349	2.459
1998	0.867	-0.230	0.140	0.950	-15.989	6.382	-16.219	3.912
1999	0.870	-0.930	0.553	0.957	11.576	-4.252	10.646	-2.418
2000	0.873	-0.822	0.476	0.963	11.307	-3.737	10.485	-2.205
2001	0.877	-0.824	0.469	0.904	22.212	-6.349	21.389	-4.070
2002	0.875	-0.899	0.492	0.886	25.604	-6.404	24.704	-4.241
2003	0.868	-1.173	0.592	0.978	5.669	-1.266	4.497	-0.696
2004	0.883	-0.329	0.147	0.931	28.009	-5.087	27.680	-3.575
2005	0.883	-1.411	0.529	0.915	11.260	-1.688	9.849	-1.055
2006	0.882	-2.610	0.882	0.946	3.208	-0.398	0.598	-0.054
2007	0.882	-2.904	0.835	0.939	-7.293	0.750	-10.197	0.772
2008	0.884	-0.376	0.094	0.933	-27.034	2.364	-27.411	1.774
2009	0.886	-0.565	0.126	0.950	-33.884	2.594	-34.448	1.964
2010	0.882	-2.141	0.432	0.947	-30.572	1.963	-32.714	1.593
2011	0.888	-0.924	0.153	0.934	11.459	-0.600	10.535	-0.419
2012	0.897	4.461	-0.601	0.915	17.417	-0.765	21.878	-0.725
2013	0.902	6.340	-0.745	0.903	48.677	-1.858	55.017	-1.585

年份	Moran's I值	Z值	P值
1992	0.1583	2.6365	0.0084
1993	0.1623	2.6784	0.0074
1994	0.1404	2.3618	0.0182
1995	0.1466	2.4527	0.0142
1996	0.1580	2.6122	0.0090
1997	0.1592	2.6310	0.0085
1998	0.1519	2.5200	0.0117
1999	0.1617	2.6754	0.0075
2000	0.1594	2.6452	0.0082
2001	0.1531	2.6903	0.0071
2002	0.1395	2.6226	0.0087
2003	0.1490	2.7461	0.0060
2004	0.1713	2.9983	0.0027
2005	0.1889	3.1890	0.0014
2006	0.1839	3.0619	0.0022
2007	0.1932	3.1743	0.0015
2008	0.2213	3.5947	0.0003
2009	0.2272	3.6522	0.0002
2010	0.2308	3.6996	0.0002
2011	0.2232	3.5772	0.0003
2012	0.2107	3.3908	0.0007
2013	0.2041	3.2864	0.0010

年份/类型	HH		LL		LH		HL
年份/类型	数量	占比/%	数量	占比/%	数量	占比/%	数量	占比/%
1992	4	7.14	2	3.57	0	0.00	1	1.79
1993	5	8.93	2	3.57	1	1.79	1	1.79
1994	4	7.14	4	7.14	0	0.00	1	1.79
1995	4	7.14	4	7.14	0	0.00	1	1.79
1996	4	7.14	4	7.14	0	0.00	1	1.79
1997	4	7.14	5	8.93	0	0.00	1	1.79
1998	4	7.14	6	10.71	1	1.79	1	1.79
1999	4	7.14	5	8.93	1	1.79	1	1.79
2000	4	7.14	5	8.93	1	1.79	1	1.79
2001	2	3.57	6	10.71	1	1.79	0	0.00
2002	2	3.57	6	10.71	1	1.79	0	0.00
2003	2	3.57	6	10.71	1	1.79	0	0.00
2004	3	5.36	6	10.71	1	1.79	0	0.00
2005	3	5.36	7	12.50	2	3.57	0	0.00
2006	2	3.57	7	12.50	2	3.57	0	0.00
2007	3	5.36	7	12.50	2	3.57	0	0.00
2008	3	5.36	7	12.50	2	3.57	0	0.00
2009	4	7.14	7	12.50	2	3.57	0	0.00
2010	4	7.14	7	12.50	2	3.57	0	0.00
2011	5	8.93	7	12.50	1	1.79	0	0.00
2012	4	7.14	5	8.93	2	3.57	0	0.00
2013	4	7.14	5	8.93	1	1.79	0	0.00

年份	变程a	块金值C₀	基台值C₀+C	变差系数C₀/(C₀+C)	拟合模型	R²
1992	104 442.664	0.001	0.463	0.002 16	高斯	0.797
1993	110 158.431	0.024	0.508	0.047 24	高斯	0.828
1994	146 500.000	0.001	0.553	0.001 81	球状	0.855
1995	155 000.000	0.001	0.592	0.001 69	球状	0.856
1996	152 500.000	0.001	0.626	0.001 60	球状	0.850
1997	159 300.000	0.001	0.616	0.001 62	球状	0.867
1998	153 700.000	0.001	0.610	0.001 64	球状	0.857
1999	154 300.000	0.001	0.597	0.001 68	球状	0.857
2000	155 600.000	0.001	0.584	0.001 71	球状	0.866
2001	118 299.000	0.028	0.596	0.046 98	高斯	0.853
2002	112 756.508	0.021	0.610	0.034 43	高斯	0.831
2003	110 158.431	0.011	0.591	0.018 61	高斯	0.834
2004	100 805.357	0.004	0.560	0.007 14	高斯	0.804
2005	134 700.000	0.002	0.553	0.003 62	球状	0.771
2006	164 400.000	0.001	0.567	0.001 76	指数	0.745
2007	154 200.000	0.001	0.568	0.001 76	指数	0.711
2008	153 600.000	0.001	0.576	0.001 74	指数	0.705
2009	140 700.000	0.062	0.556	0.111 51	球状	0.716
2010	146 700.000	0.001	0.565	0.001 77	指数	0.693
2011	145 800.000	0.001	0.569	0.001 76	指数	0.681
2012	147 900.000	0.007	0.556	0.012 59	指数	0.667
2013	151 200.000	0.001	0.540	0.001 85	指数	0.687

基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析

Spatial Difference of GDP in Yunnan Border Area based on Nighttime Light and Land Use Data

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