基于空间滤波方法的中国省际人口迁移驱动因素
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摘要
人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波(ESF)负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明:(1)省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。(2)省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。(3)网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素:区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的"拉力"因素。(4)与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。
According to previous studies, not only does the conditional gravity model based on ordinary least squares often bring about poor fitting of migration flows in reality, but also there exists overdispersion in the extended Poisson gravity model. Simultaneously, network autocorrelation usually exists in population migration data(e.g., the spatial interaction among migration flows). The problems mentioned above result in biased estimation. In order to capture network autocorrelation and deal with the issue of overdispersion, we build an eigenvector spatial filtering negative binomial gravity model(ESF NBGM) based on the data of 1% national population sample survey in 2015, to analyze the driving mechanism of interprovincial population migration flows in China. The results are as follows:(1) Positive spatial spillover effect exists in interprovincial population migration flows, and ESF can capture network autocorrelation in data, so as to reduce the estimated deviation of the model.Furthermore, eigenvectors ranking top 1.4% can properly interpret the spatial pattern of high network autocorrelation in data.(2) There exists overdispersion in China's interprovincial migration flows. Considering this problem, a negative binomial regression model is more suitable for the estimation of driving mechanism for population migration, together with statistical enhancement.(3) Network autocorrelation leads to overestimation of distance variables and underestimation of non-distance variables. The results of the improved model reveal that: chief factors the affect driving mechanism are regional population characters, social network, economic development and education level. Meanwhile, living environment and road network gradually become one of the most crucial pulling factors that influence migration flows.(4) Compared to previous studies, social network(i.e. migration stock) plays a more significant role in population migration flows, while the impact of spatial distance keeps weakening.
According to previous studies, not only does the conditional gravity model based on ordinary least squares often bring about poor fitting of migration flows in reality, but also there exists overdispersion in the extended Poisson gravity model. Simultaneously, network autocorrelation usually exists in population migration data(e.g., the spatial interaction among migration flows). The problems mentioned above result in biased estimation. In order to capture network autocorrelation and deal with the issue of overdispersion, we build an eigenvector spatial filtering negative binomial gravity model(ESF NBGM) based on the data of 1% national population sample survey in 2015, to analyze the driving mechanism of interprovincial population migration flows in China. The results are as follows:(1) Positive spatial spillover effect exists in interprovincial population migration flows, and ESF can capture network autocorrelation in data, so as to reduce the estimated deviation of the model.Furthermore, eigenvectors ranking top 1.4% can properly interpret the spatial pattern of high network autocorrelation in data.(2) There exists overdispersion in China's interprovincial migration flows. Considering this problem, a negative binomial regression model is more suitable for the estimation of driving mechanism for population migration, together with statistical enhancement.(3) Network autocorrelation leads to overestimation of distance variables and underestimation of non-distance variables. The results of the improved model reveal that: chief factors the affect driving mechanism are regional population characters, social network, economic development and education level. Meanwhile, living environment and road network gradually become one of the most crucial pulling factors that influence migration flows.(4) Compared to previous studies, social network(i.e. migration stock) plays a more significant role in population migration flows, while the impact of spatial distance keeps weakening.
引文
[1] Pu Yingxia, Han Hongling, Ge Ying, et al. Multilateral mechanism analysis of interprovincial migration flows in China.Acta Geographica Sinica, 2016, 71(2):205-216.[蒲英霞,韩洪凌,葛莹,等.中国省际人口迁移的多边效应机制分析.地理学报, 2016, 71(2):205-216.]
[2] Shen J. Explaining interregional migration changes in China, 1985-2000, using a decomposition approach. Regional Studies, 2015, 49(7):1176-1192.
[3] Liu Y, Shen J. Modelling skilled and less-skilled interregional migrations in China, 2000-2005. Population, Space and Place, 2017. Doi:10.1002/psp.2027.
[4] Griffith D A. Spatial structure and spatial interaction:25 years later. The Review of Regional Studies, 2007, 37(1):28-38.
[5] Lesage J P, Pace R K. Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 2008, 48(5):941-967.
[6] Griffith D A, Jones K G. Explorations into the relationship between spatial structure and spatial interaction.Environment&Planning A, 1980, 12(2):187-201.
[7] Griffith D A. Modeling spatial autocorrelation in spatial interaction data:Empirical evidence from 2002 Germany journey-to-work flows. Journal of Geographical Systems, 2009, 11(2):117-140.
[8] Chun Y, Griffith D A. Modeling network autocorrelation in space-time migration flow data:An eigenvector spatial filtering approach. Annals of the Association of American Geographers, 2011, 101(3):523-536.
[9] Zhu Y, Chen W. The settlement intention of China's floating population in the cities:Recent changes and multifaceted individual-level determinants. Population Space&Place, 2010, 16(4):253-267.
[10] Ding Jinhong, Liu Zhenyu, Cheng Danming, et al. Areal differentiation of inter-provincial migration in China and characteristics of the flow field. Acta Geographica Sinica, 2005, 60(1):106-114.[丁金宏,刘振宇,程丹明,等.中国人口迁移的区域差异与流场特征.地理学报, 2005, 60(1):106-114.]
[11] Cai F, Wang D. Migration as marketization:What can we learn from China's 2000 census data? China Review, 2003, 3(2):73-93.
[12] Fan C C. Interprovincial migration, population redistribution, and regional development in China:1990 and 2000census comparisons. Professional Geographer, 2005, 57(2):295-311.
[13] Zhang Hongli, Liang Yinhe, Yang Weiqiong. Market potential, expected wages and interprovincial migration:Based on spatial econometrics model. Journal of Applied Statistics and Management, 2016, 35(5):868-880.[张红历,梁银鹤,杨维琼.市场潜能、预期收入与跨省人口流动:基于空间计量模型的分析.数理统计与管理, 2016, 35(5):868-880.]
[14] Lian Lei. An empirical study on the spatial effects in the migration. Population&Economics, 2016(2):30-39.[连蕾.我国人口迁移过程中的空间效应实证研究.人口与经济, 2016(2):30-39.]
[15] 1%Population Sample Survey Office of the State Council, Population Statistics Department of National Bureau of Statistics. China's 2015 1%Population Sample Survey Data. Beijing:China Statistics Press, 2017.[国务院全国1%人口抽样调查领导小组办公室,国家统计局人口和就业统计司. 2015年全国1%人口抽样调查资料.北京:中国统计出版社, 2017.]
[16] Hu Angang, Li Chunbo. New poverty during the new century:Knowledge poverty. Social Sciences in China, 2001(3):70-81.[胡鞍钢,李春波.新世纪的新贫困:知识贫困.中国社会科学, 2001(3):70-81.]
[17] China Communications Press. China Traffic Atlas. Beijing:China Communications Press, 2016.[人民交通出版社.中国交通地图册.北京:人民交通出版社, 2016.]
[18] National Bureau of Statistics(NBS). China Statistical Yearbook(2016). Beijing:China Statistics Press, 2016. http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm.[国家统计局.中国统计年鉴(2016).北京:中国统计出版社, 2016. http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm.]
[19] Population Census Office under the State Council, Department of Population and Employment Statistics under the National Bureau of Statistics(NBS). Tabulation on the 2010 Population Census of the People's Republic of China(Book I). Beijing:China Statistics Press, 2012.[国务院人口普查办公室,国家统计局人口和就业统计司.中国2010年人口普查资料(上).北京:中国统计出版社, 2012.]
[20] Shen J. Changing patterns and determinants of interprovincial migration in China 1985-2000. Population Space&Place, 2012, 18(3):384-402.
[21] Flowerdew R, Aitkin M. A method of fitting the gravity model based on the Poisson distribution. Journal of Regional Science, 1982, 22(2):191-202.
[22] Chun Y. Modeling network autocorrelation within migration flows by eigenvector spatial filtering. Journal of Geographical Systems, 2008, 10(4):317-344.
[23] Tiefelsdorf M, Boots B. The exact distribution of Moran's I. Environment and Planning A, 1995, 27(6):985-999.
[24] Getis A, Griffith D A. Comparative spatial filtering in regression analysis. Geographical Analysis, 2002, 34(2):130-140.
[25] Tao Wei, Gu Hengyu, Chen Hannan. Guangzhou's spatial distribution under the effect of the urban road network on the hotel industry. Tourism Tribune, 2015, 30(10):99-108.[陶伟,古恒宇,陈昊楠.路网形态对城市酒店业空间布局的影响研究广州案例.旅游学刊, 2015, 30(10):99-108.]
[26] Gu Hengyu, Shen Tiyan, Zhou Lin, et al. Measuring street layout's spatio-temporal effects on housing price based on GWR and s DNA model:The case study of Guangzhou. Economic Geography, 2018, 38(3):82-91.[古恒宇,沈体雁,周麟,等.基于GWR和sDNA模型的广州市路网形态对住宅价格影响的时空分析.经济地理, 2018, 38(3):82-91.]
[27] Hilbe J. Modeling Count Data. New York:Cambridge University Press, 2014.
[28] Liu Yanling, Feng Jian. Characteristics and impact factors of migration in China:Based on the analysis of the sixth census data. Human Geography, 2014, 29(2):129-137.[刘晏伶,冯健.中国人口迁移特征及其影响因素:基于第六次人口普查数据的分析.人文地理, 2014, 29(2):129-137.]
[29] Cai Fang, Wang Dewen. Population migration and mobility as marketization:Based on the fifth census data. Chinese Journal of Population Science, 2003(5):11-19.[蔡昉,王德文.作为市场化的人口流动:第五次全国人口普查数据分析.中国人口科学, 2003(5):11-19.]
[30] Ma Wei, Wang Yahua, Liu Shenglong. Transportation infrastructure and population migration in China:An analysis based on gravity model. China Soft Science, 2012(3):69-78.[马伟,王亚华,刘生龙.交通基础设施与中国人口迁移:基于引力模型分析.中国软科学, 2012(3):69-78.]
[31] Lao Xin, Shen Tiyan. An exploration into China's urban system evolution forecast based on intercity migration.Population&Economics, 2016(6):35-47.[劳昕,沈体雁.基于人口迁移的中国城市体系演化预测研究.人口与经济,2016(6):35-47.]
[32] Wang Guixin, Pan Zehan, Lu Yanqiu. China's inter-provincial migration patterns and influential factors:Evidence from year 2000 and 2010 population census of China. Chinese Journal of Population Science, 2012(5):2-13.[王桂新,潘泽瀚,陆燕秋.中国省际人口迁移区域模式变化及其影响因素:基于2000和2010年人口普查资料的分析.中国人口科学, 2012(5):2-13.]
[33] Wang Jinfeng, Xu Chengdong. Geodetector:Principle and prospective. Acta Geographica Sinica, 2017, 72(1):116-134.[王劲峰,徐成东.地理探测器:原理与展望.地理学报, 2017, 72(1):116-134.]
[34] Li Jiaming, Lu Dadao, Xu Chengdong, et al. Spatial heterogeneity and its changes of population on the two sides of Hu Line. Acta Geographica Sinica, 2017, 72(1):148-160.[李佳洺,陆大道,徐成东,等.胡焕庸线两侧人口的空间分异性及其变化.地理学报, 2017, 72(1):148-160.]
(1)平均受教育年限=(大专以上人口数×16+高中人口数×12+初中人口数×9+小学人口数×6)/总人口数
[2] Shen J. Explaining interregional migration changes in China, 1985-2000, using a decomposition approach. Regional Studies, 2015, 49(7):1176-1192.
[3] Liu Y, Shen J. Modelling skilled and less-skilled interregional migrations in China, 2000-2005. Population, Space and Place, 2017. Doi:10.1002/psp.2027.
[4] Griffith D A. Spatial structure and spatial interaction:25 years later. The Review of Regional Studies, 2007, 37(1):28-38.
[5] Lesage J P, Pace R K. Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 2008, 48(5):941-967.
[6] Griffith D A, Jones K G. Explorations into the relationship between spatial structure and spatial interaction.Environment&Planning A, 1980, 12(2):187-201.
[7] Griffith D A. Modeling spatial autocorrelation in spatial interaction data:Empirical evidence from 2002 Germany journey-to-work flows. Journal of Geographical Systems, 2009, 11(2):117-140.
[8] Chun Y, Griffith D A. Modeling network autocorrelation in space-time migration flow data:An eigenvector spatial filtering approach. Annals of the Association of American Geographers, 2011, 101(3):523-536.
[9] Zhu Y, Chen W. The settlement intention of China's floating population in the cities:Recent changes and multifaceted individual-level determinants. Population Space&Place, 2010, 16(4):253-267.
[10] Ding Jinhong, Liu Zhenyu, Cheng Danming, et al. Areal differentiation of inter-provincial migration in China and characteristics of the flow field. Acta Geographica Sinica, 2005, 60(1):106-114.[丁金宏,刘振宇,程丹明,等.中国人口迁移的区域差异与流场特征.地理学报, 2005, 60(1):106-114.]
[11] Cai F, Wang D. Migration as marketization:What can we learn from China's 2000 census data? China Review, 2003, 3(2):73-93.
[12] Fan C C. Interprovincial migration, population redistribution, and regional development in China:1990 and 2000census comparisons. Professional Geographer, 2005, 57(2):295-311.
[13] Zhang Hongli, Liang Yinhe, Yang Weiqiong. Market potential, expected wages and interprovincial migration:Based on spatial econometrics model. Journal of Applied Statistics and Management, 2016, 35(5):868-880.[张红历,梁银鹤,杨维琼.市场潜能、预期收入与跨省人口流动:基于空间计量模型的分析.数理统计与管理, 2016, 35(5):868-880.]
[14] Lian Lei. An empirical study on the spatial effects in the migration. Population&Economics, 2016(2):30-39.[连蕾.我国人口迁移过程中的空间效应实证研究.人口与经济, 2016(2):30-39.]
[15] 1%Population Sample Survey Office of the State Council, Population Statistics Department of National Bureau of Statistics. China's 2015 1%Population Sample Survey Data. Beijing:China Statistics Press, 2017.[国务院全国1%人口抽样调查领导小组办公室,国家统计局人口和就业统计司. 2015年全国1%人口抽样调查资料.北京:中国统计出版社, 2017.]
[16] Hu Angang, Li Chunbo. New poverty during the new century:Knowledge poverty. Social Sciences in China, 2001(3):70-81.[胡鞍钢,李春波.新世纪的新贫困:知识贫困.中国社会科学, 2001(3):70-81.]
[17] China Communications Press. China Traffic Atlas. Beijing:China Communications Press, 2016.[人民交通出版社.中国交通地图册.北京:人民交通出版社, 2016.]
[18] National Bureau of Statistics(NBS). China Statistical Yearbook(2016). Beijing:China Statistics Press, 2016. http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm.[国家统计局.中国统计年鉴(2016).北京:中国统计出版社, 2016. http://www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm.]
[19] Population Census Office under the State Council, Department of Population and Employment Statistics under the National Bureau of Statistics(NBS). Tabulation on the 2010 Population Census of the People's Republic of China(Book I). Beijing:China Statistics Press, 2012.[国务院人口普查办公室,国家统计局人口和就业统计司.中国2010年人口普查资料(上).北京:中国统计出版社, 2012.]
[20] Shen J. Changing patterns and determinants of interprovincial migration in China 1985-2000. Population Space&Place, 2012, 18(3):384-402.
[21] Flowerdew R, Aitkin M. A method of fitting the gravity model based on the Poisson distribution. Journal of Regional Science, 1982, 22(2):191-202.
[22] Chun Y. Modeling network autocorrelation within migration flows by eigenvector spatial filtering. Journal of Geographical Systems, 2008, 10(4):317-344.
[23] Tiefelsdorf M, Boots B. The exact distribution of Moran's I. Environment and Planning A, 1995, 27(6):985-999.
[24] Getis A, Griffith D A. Comparative spatial filtering in regression analysis. Geographical Analysis, 2002, 34(2):130-140.
[25] Tao Wei, Gu Hengyu, Chen Hannan. Guangzhou's spatial distribution under the effect of the urban road network on the hotel industry. Tourism Tribune, 2015, 30(10):99-108.[陶伟,古恒宇,陈昊楠.路网形态对城市酒店业空间布局的影响研究广州案例.旅游学刊, 2015, 30(10):99-108.]
[26] Gu Hengyu, Shen Tiyan, Zhou Lin, et al. Measuring street layout's spatio-temporal effects on housing price based on GWR and s DNA model:The case study of Guangzhou. Economic Geography, 2018, 38(3):82-91.[古恒宇,沈体雁,周麟,等.基于GWR和sDNA模型的广州市路网形态对住宅价格影响的时空分析.经济地理, 2018, 38(3):82-91.]
[27] Hilbe J. Modeling Count Data. New York:Cambridge University Press, 2014.
[28] Liu Yanling, Feng Jian. Characteristics and impact factors of migration in China:Based on the analysis of the sixth census data. Human Geography, 2014, 29(2):129-137.[刘晏伶,冯健.中国人口迁移特征及其影响因素:基于第六次人口普查数据的分析.人文地理, 2014, 29(2):129-137.]
[29] Cai Fang, Wang Dewen. Population migration and mobility as marketization:Based on the fifth census data. Chinese Journal of Population Science, 2003(5):11-19.[蔡昉,王德文.作为市场化的人口流动:第五次全国人口普查数据分析.中国人口科学, 2003(5):11-19.]
[30] Ma Wei, Wang Yahua, Liu Shenglong. Transportation infrastructure and population migration in China:An analysis based on gravity model. China Soft Science, 2012(3):69-78.[马伟,王亚华,刘生龙.交通基础设施与中国人口迁移:基于引力模型分析.中国软科学, 2012(3):69-78.]
[31] Lao Xin, Shen Tiyan. An exploration into China's urban system evolution forecast based on intercity migration.Population&Economics, 2016(6):35-47.[劳昕,沈体雁.基于人口迁移的中国城市体系演化预测研究.人口与经济,2016(6):35-47.]
[32] Wang Guixin, Pan Zehan, Lu Yanqiu. China's inter-provincial migration patterns and influential factors:Evidence from year 2000 and 2010 population census of China. Chinese Journal of Population Science, 2012(5):2-13.[王桂新,潘泽瀚,陆燕秋.中国省际人口迁移区域模式变化及其影响因素:基于2000和2010年人口普查资料的分析.中国人口科学, 2012(5):2-13.]
[33] Wang Jinfeng, Xu Chengdong. Geodetector:Principle and prospective. Acta Geographica Sinica, 2017, 72(1):116-134.[王劲峰,徐成东.地理探测器:原理与展望.地理学报, 2017, 72(1):116-134.]
[34] Li Jiaming, Lu Dadao, Xu Chengdong, et al. Spatial heterogeneity and its changes of population on the two sides of Hu Line. Acta Geographica Sinica, 2017, 72(1):148-160.[李佳洺,陆大道,徐成东,等.胡焕庸线两侧人口的空间分异性及其变化.地理学报, 2017, 72(1):148-160.]
(1)平均受教育年限=(大专以上人口数×16+高中人口数×12+初中人口数×9+小学人口数×6)/总人口数