多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用

doi:10.18306/dlkxjz.2016.12.006

Abstract

Abstract:

Population distribution data are essential for socioeconomic and environmental studies, such as population estimation, spread of disease, natural disaster relief, and environmental protection. Existing research has proved that spatialized population grid data can precisely delineate the spatial pattern of population distribution, while model selection and size of grids may influence the accuracy of population distribution modeling. It is therefore important to estimate population distribution using appropriate models and at a proper spatial scale. This study mainly focused on the spatialization modeling of Anhui Province county-level population census data in 2010 at three grid scales. Anhui Province was selected for the study due to its complex landforms and significant difference of population distribution within its area. Population regionalization was carried out as a preprocessing step: 78 counties in Anhui Province were divided into four groups. Combining with land-use data and nighttime light (DMSP/OLS), urban residential areas were reclassified to reflect regional differences. Based on the population regionalization, multi-factor linear regression (MFLR) and geographically weighted regression (GWR) models were employed to integrate the reclassified urban residential land-use data with the rural residential land-use data. This study established three population spatial datasets at 1 km, 5 km, and 10 km gird scales. Comparing the two models’ precision at each scale, the results show that the modeling and grid scale have much influence on the accuracy of the spatialization result, which increased with the grid scale by using the MFLR model and the highest accuracy was achieved in the 10 km grid datasets. For the GWR model, the accuracy decreased as the grid scale increased, and the highest model accuracy was obtained at the 1 km scale. Overall, the GWR model had a higher accuracy (22.31%) than the MFLR model when taking into account the geographic location and local modeling. This study may provide a scientific basis for the production and application of population spatial data and provide a reference of spatialization for other types of statistical data in the future.

Key words: population distribution, spatialization, multi-scales, multi-factor linear regression, Geographically Weighted Regression (GWR), Anhui Province

Kejing WANG, Hongyan CAI, Xiaohuan YANG. Multiple scale spatialization of demographic data with multi-factor linear regression and geographically weighted regression models[J].PROGRESS IN GEOGRAPHY, 2016, 35(12): 1494-1505.

Figures/Tables 12

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References 25

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数据名称	数据时相	数据类型	空间数据比例尺/分辨率	数据来源
县级人口统计数据	2010年	表格	/	2010年人口普查资料
县级GDP统计数据	2010年	表格	/	安徽省社会经济统计年鉴
县级行政边界数据	2010年	矢量	1:100万	中国科学院资源环境科学数据中心
河流、交通数据	2008年	矢量	1:100万	中国科学院资源环境科学数据中心
地貌数据	2009年	矢量	1:100万	中国科学院资源环境科学数据中心
ASTER-DEM	1999年	栅格	30 m	中国科学院资源环境科学数据中心
土地利用数据	2010年	栅格	1 km×1 km	中国科学院资源环境科学数据中心
DMSP/OLS(F182010)	2010年	栅格	1 km×1 km(重采样后)	http://www.ngdc.noaa.gov

分区序号	分区名称	县(市、区)个数	人口密度/(人/km²)	GDP密度/(万元/km²)	河流密度/(km/km²)	坡度/°	高程/m	耕地面积比例/%	城镇建设用地面积比例/%
1	人口密度高值市辖区	8	2414	13506	1.02	3	28	48	30
2	中北部平原较高人口密度地区	28	531	783	0.98	2	23	78	15
3	中部台地较低人口密度地区	25	334	607	0.87	4	58	59	7
4	南部山地低人口密度地区	17	155	291	0.58	15	345	19	2

分区	基于多元统计回归方法的R²			Sig.	基于GWR方法的R²			Sig.
分区	1 km		5 km	Sig.	10 km	1 km	5 km	10 km
分区1	0.98	0.98	0.98	0.00	0.82	0.83	0.81	0.00
分区2	0.93	0.94	0.93	0.00
分区3	0.90	0.90	0.91	0.00
分区4	0.83	0.84	0.82	0.00

分区	基于多元统计回归方法的误差(RE)/%				基于GWR方法的误差(RE)/%
分区	1 km		5 km	10 km	1 km	5 km	10 km
分区1	18.52	17.15	14.51		17.71	12.00	18.09
分区2	19.94	19.40	22.10		16.89	16.43	18.30
分区3	27.20	26.57	20.27		24.02	24.18	22.18
分区4	29.46	29.92	29.67		30.86	34.55	35.86
全省均值	24.20	23.76	23.38		22.31	22.41	23.35

模型方法	尺度	误差范围/%	县个数	占总数比例/%
基于夜间灯光数据再分类的多元统计回归方法	1 km	<10	24	30.77
		10~20	21	26.92
		20~30	9	11.54
		>30	24	30.77
	5 km	<10	21	26.92
		10~20	24	30.77
		20~30	13	16.67
		>30	20	25.64
	10 km	<10	26	33.33
		10~20	22	28.21
		20~30	11	14.10
		>30	19	24.36
基于夜间灯光数据再分类的 GWR方法	1 km	<10	26	33.33
		10~20	19	24.36
		20~30	13	16.67
		>30	20	25.64
	5 km	<10	24	30.76
		10~20	22	28.21
		20~30	10	12.82
		>30	22	28.21
	10 km	<10	22	28.21
		10~20	19	24.36
		20~30	16	20.51
		>30	21	26.92

Multiple scale spatialization of demographic data with multi-factor linear regression and geographically weighted regression models

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	分级阈值 /(m/°)	县数量 /个	多元统计方法误差百分比/%				GWR方法误差百分比/%
	分级阈值 /(m/°)	县数量 /个	1 km		5 km	10 km	1 km	5 km	10 km
高程	0~50	49	18.78	17.24	19.83		16.60	15.15	16.98
	50~150	11	34.78	36.68	18.83		30.55	32.84	29.38
	>150	18	32.34	33.52	32.25		32.94	35.97	37.42
坡度	0~3	35	20.40	17.96	23.73		17.63	15.83	17.41
	3~6	19	20.77	21.75	15.06	17.15		16.80	18.58
	>6	24	32.44	33.85	26.23	33.27		36.45	35.79

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