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The Research on Causes of Megacity Problem Based on Spatial Disequilibrium

QI Zi xiang, LV Yong qiang, WANG Ya xin

Article ID: 2605
Vol 3, Issue 2, 2023

DOI: https://doi.org/10.54517/vfc.v3i2.2605
VIEWS - 31 (Abstract)

Abstract

By using spatial models with lag and error and through many experiments, this paper uses distance threshold which can maximize spatial autocorrelation, instead of setting spatial weight matrix by adjacent boundaries, making measurement better suit the features of our country’s cities distribution to explore the reasons for big city disease empirically. It finds that distribution of public service distribution and spatial non-equilibrium are factors for excessive population agglomeration and therefore big city disease, and provides solutions for the factors mentioned above.

Keywords

Spatial disequilibrium; Spatial weights; Models with lag and error; Generalized spatial two stage least squares estimation.

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References

1. Paelinck P. L’Efficacité Des Mesures De Politique éConomique RéGionale [A]. L’ Efficacité Des Mesures De Politique éConomique RéGionale [M]. CERVNA, 1967.

2. Moran P. The Interpretation of Statistical Maps [J]. Journal of the Royal Statistical Society, 1947, 10(2): 243-251.

3. Geary R. C. The Contiguity Ratio and Statistical Mapping [J]. The Incorporated Statistician, the Incorporated Statistician 1954,5(3):115-145.

4. Cliff A., Ord J.K. Testing for Spatial Autocorrelation Among Regression Residuals [J]. Geographical Analysis, 1972,(4):267-284.

5. Ord J.K. Estimation Methods for Models of Spatial Interaction [J]. Journal of the American Statistical Association, 1975,70(349): 120-126.

6. Paelinck P., Klaassen L.H. Spatial Econometrics [M]. Saxon House,1979.

7. Anselin L. Spatial Econometrics: Methods and Models [M].Kluwer Academic Publishers, 1988.

8. Anselin L. Spatial Econometrics [A]. Palgrave Handbook of Econometrics [M]. Palgrave Macmillan, 2006.

9. Pace R.K., Barry R. Quick Computation of Spatial Autoregressive Estimators [J]. Geographical Analysis, 1997, 29(3):232-246.

10. Barry R., Pace R.K. LA Monte Carlo Estimator of the Log Determinant of Large Sparse Matrices [J]. Linear Algebra and Its Applications, 1999, 289(1): 41-54.

11. Pace R.K., Lesage J.P. Chebyshev. Approximation of Log-Determinants of Spatial Weights Matrices [J]. Computational Statistics and Data Analysis 2004, 45(2): 179-196.

12. Zhang Y., Leithead W.E. Approximate Implementation of Logarithm of Matrix Determinant in Gaussian Processes [J]. Journal of Statistical Computation and Simulation, 2007, 77(4): 329-348.

13. Bivand R., Hauke J., Kossowski T. Computing the Jacobian in Gaussian Spatial Autoregressive Models, an Illustrated Comparison of Available Methods [J]. Geographical Analysis 2013,45(2):150-179.

14. Anselin L. Estimation Methods for Spatial Autoregressive Structures [D]. Cornell University, 1980.

15. Kelejian H.H., Prucha I.R. A Generalized Spatial Two Stage Least Squ ares Procedures for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances [J]. Journal of Real Estate Finance and Economics 1998, 17(1): 99-121.

16. Kelejian H.H., Prucha I.R. A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model [J]. International Economic Review 1999, 40(2): 509-533.

17. Kelejian H.H., Prucha I.R. Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances [J]. Journal of Econometrics 2010,157(1): 53-67.

18. Drukker D., Egger P., Prucha I. On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbancesand Endogenous Regressors [J]. Econometric Reviews 2013, 32(5):686-733.

19. Hepple L. Bayesian Analysis of the Linear Model with Spatial Dependence [A]. Exploratory and Explanatory Statistical Analysis of Spatial Data [M]. Martinus Nijhoff, 1979.

20. Lesage J.P. Bayesian Estimation of Spatial Autoregressive Models [J]. International Regional Science Review 1997,20(2):113-129.

21. Wang X., Kockelman K.M., Lemp J.D. The Dynamic Spatial Multinomial Probit Model, Analysis of Land Use Change Using Parcel-Level Data [J]. Journal of Transport Geography, 2012,24(4):77-88.

22. Getis A. Spatial Filtering in a Regression Framework, Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures [A]. New Directions in Spatial Econometrics [M]. Springer-Verlag, 1995.

23. Anselin L., Kelejian H.H., Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors [J].International Regional Science Review, 1997, 20(1): 153-182.

24. Baltagi B.H., Li D. LM Tests for Functional Form and Spatial Error Correlation [J]. International Regional Science Review,2001, 24(2): 194-225.

25. Anselin L. Spatial Econometrics [A]. A Companion to Theoretical Econometrics [M]. Blackwell, 2001. Spatial Regression, Spatial.

26. Lauridsen J., Kosfeld R. A Test Strategy for Spurious Nonstationarity, and Spatial Cointegration [J]. Papers in Regional Science, 2006,85(3):363-377.

27. Kelejian H.H. A Spatial J-Test for Model Specification Against a Single or a Set of Non-Nested Alternatives [J]. Letters in Spatial and Resource Sciences, 2008, 1(1): 3-11.

28. Amaral P.V., Anselin L. Finite Sample Properties of Moran's I Test for Spatial Autocorrelation in Tobit Models [J]. Papers in Regional Science, 2014, 93(4): 773-781.

29. Anselin L., Florax R. J. G. M. New direction in spatial econometrics [M]. Springer-Verlag Berlin and Heidelberg GmbH& Co. K, 1995.

30. Fingleton B. Spurious spatial regression: Some Monte Carlo results with spatial unit root and spatial cointegration [J]. Journal of Regional Science, 1999, 39: 1-19.

31. Lauridsen J., Kosfeld R. A test strategy for spurious spatial regression, spatial nonstationarity, and spatial cointegration [J]. Papers in Regional Science, 2006, 85: 363-377.

32. Lee L. F., Yu J. H. Spatial nonstationarity and spurious regression: The case with a row-normalized spatial weights matrix[J]. Spatial Economic Analysis, 2009, 4: 301-327.

33. Shen Tiyan, Qi Zixiang, Wang Yanbo. Market design for orderly inter-regional transfer of industries in Beijing-Tianjin-Hebei – Based on bilateral matching algorithm[J]. Economist, 2016(4):42-52.