区域人口迁移时空溢出效应与动力机制分析
Space-time Spillover Effects and Driving Forces of Regional Migration Process
查看参考文献45篇
|
文摘
|
区域人口迁移流的规模不仅取决于迁出地与目的地的"双边"要素,也与前期迁移流和周边迁移流息息相关。传统重力模型揭示了区域人口迁移过程的"推-拉"机制,但受制于对时空维度的忽视,无法有效表达迁移流之间的时空依赖关系,因而难以度量区域要素变化对迁移流产生的时空溢出效应。本文引入多种形式的时空依赖结构,构建迁移流时空重力模型,并采用贝叶斯马尔可夫链蒙特卡洛(MCMC)方法进行估计。在此基础上,结合时空效应框架量化区域要素对迁移流的影响,定量分析人口迁移过程的时空溢出效应与动力机制。本文以1985-2015年中国省际人口迁移为例,通过与非空间的动态重力模型估计结果比较,初步表明时间依赖、空间依赖以及时空扩散依赖在区域人口迁移过程中不容忽视;时空维度上,区域要素变化在初期对迁移网络的溢出效应超过对该区域迁移流的直接影响;逐渐衰减的时空溢出效应维持了区域人口迁移规模发展的相对稳定,与动态重力模型估计结果形成了鲜明对比。区域人口规模、人均GDP水平及其时空溢出效应共同驱动中国省际人口迁移系统的发展。耦合时空维度依赖关系的时空重力模型能更好地理解区域人口迁移过程的演化特征,为促进区域人口均衡发展提供科学的决策依据。 |
|
其他语种文摘
|
Interregional migration is a significant component of regional population growth as well as a major driving force in urbanization process. The evolution of migration flows is not only related to the characteristics of origin and destination regions, but also the past and surrounding migration flows. Most empirical migration studies based on traditional gravity models have failed to capture space-time spillover effects during the migration process due to ignoring time or spatial dependence among migration flows. By introducing several space-time interaction effects, this paper constructed the space-time gravity model of interprovincial migration flows in China over the period of 1985-2015 and estimated the model using Bayesian Markov Chain Monte Carlo (MCMC) method. The space-time spillover effects evaluation framework further explained the space and time dynamics in the evolution of interprovincial migration associated with changes in regional GDP per capita and population size. The preliminary results are as follow: firstly, the estimates of time, spatial and space-time diffusion dependence are all significant, which can provide powerful means for exploring complex and systematic behaviors among regional migration flows. Secondly, regional population size dominates the Chinese interprovincial migration process more than twice the influence of regional GDP per capita. Thirdly, the spillover effects of regional socio-economic factors play a quite significant role during regional migration process, which are greater than the corresponding origin and destination effects in the short term. More importantly, the decaying spillover effects through the whole space-time network will help the migration system stay at an equilibrium state over the long term. All in all, the coupled space-time gravity model contributes to capture the space-time spillover effects and driving forces during the regional migration process, which provides a scientific basis for predicting future migration trends and promoting balanced regional population development. |
|
来源
|
地球信息科学学报
,2018,20(6):817-826 【核心库】
|
|
DOI
|
10.12082/dqxxkx.2018.180015
|
|
关键词
|
区域人口迁移
;
时空重力模型
;
网络系统
;
时空溢出效应
;
中国
|
|
地址
|
1.
南京大学地理与海洋科学学院, 南京, 210023
2.
江苏省地理信息技术重点实验室, 江苏省地理信息技术重点实验室, 南京, 210023
3.
江苏省地理信息资源开发与利用协同创新中心, 江苏省地理信息资源开发与利用协同创新中心, 南京, 210023
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1560-8999 |
|
基金
|
国家自然科学基金项目
;
江苏高校优势学科建设工程
;
江苏省地理信息资源开发与利用协同创新中心资助项目
|
|
文献收藏号
|
CSCD:6258647
|
参考文献 共
45
共3页
|
|
1.
Bijak J.
Forecasting international migration in Europe: A Bayesian view,2010
|
被引
1
次
|
|
|
|
|
2.
White M J.
International handbook of migration and population distribution,2016
|
被引
2
次
|
|
|
|
|
3.
Le Sage J P. Spatial econometric modeling of origin-destination flows.
Journal of Regional Science,2008,48(5):941-967
|
被引
37
次
|
|
|
|
|
4.
Le Sage J P. Interpreting Spatial econometric origin-destination flow models.
Journal of Regional Science,2015,55(2):188-208
|
被引
7
次
|
|
|
|
|
5.
Sen A.
Gravity models of spatial interaction behavior,1995
|
被引
5
次
|
|
|
|
|
6.
王亚平. 基于空间OD模型的中国省际人口迁移机制分析.
西北师范大学学报(自然科学版),2015,51(3):89-97
|
被引
1
次
|
|
|
|
|
7.
Etzo I. The determinants of the recent interregional migration flows in Italy: A panel data analysis.
Journal of Regional Science,2011,51(5):948-966
|
被引
6
次
|
|
|
|
|
8.
Mitze T. On the mutual dynamics of interregional gross migration flows in space and time.
Spatial Econometric Interaction Modelling,2016:415-440
|
被引
1
次
|
|
|
|
|
9.
Rainer H. Social networks in determining migration and labour market outcomes: Evidence from the German Reunification.
Econ Transit,2009,17(4):739-767
|
被引
1
次
|
|
|
|
|
10.
Chun Y. 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
|
被引
11
次
|
|
|
|
|
11.
Black W R. Network autocorrelation in transport network and flow systems.
Geographical Analysis,1992,24(3):207-222
|
被引
7
次
|
|
|
|
|
12.
Griffith D A. Explorations into the relationships between spatial structure and spatial interaction.
Environment and Planning A,1980,12:187-201
|
被引
4
次
|
|
|
|
|
13.
蒲英霞. 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
|
被引
25
次
|
|
|
|
|
14.
Fan C C. Modeling interprovincial migration in China, 1985-2000.
Eurasian Geography & Economics,2005,46(3):165-184
|
被引
17
次
|
|
|
|
|
15.
王桂新. 中国人口迁移分布的顽健性与胡焕庸线.
中国人口科学,2016(1):2-13
|
被引
12
次
|
|
|
|
|
16.
Elhorst J P.
Spatial econometrics: From cross-sectional data to spatial panels,2014
|
被引
33
次
|
|
|
|
|
17.
Chun Y. Modeling network autocorrelation within migration flows by eigenvector spatial filtering.
Journal of Geographical Systems,2008,10(4):317-344
|
被引
12
次
|
|
|
|
|
18.
Le Sage J P. A Spatial interaction model with spatial structure origin and destination effects.
Journal of Regional Science,2013,15(3):265-289
|
被引
1
次
|
|
|
|
|
19.
Bhargava A. Estimating dynamic random effects models from panel data covering short time periods.
Econometrica,1983,51:1635-1659
|
被引
3
次
|
|
|
|
|
20.
Parent O. A space-time filter for panel data models containing random effects.
Computational Statistics & Data Analysis,2011,55(1):475-490
|
被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问