Un modelo de interacción espacial para el flujo de pasajeros -entre terminales aéreas de Colombia en los años 2004 a 2015.
| dc.contributor.advisor | Giraldo Henao, Ramón | spa |
| dc.contributor.author | Santana Alfonso, Adrían Alberto | spa |
| dc.date.accessioned | 2020-02-25T20:38:13Z | spa |
| dc.date.available | 2020-02-25T20:38:13Z | spa |
| dc.date.issued | 2020-02-25 | spa |
| dc.description.abstract | Se hace un análisis de información acerca del flujo de pasajeros en Colombia para los años 2004 a 2015 con diferentes variantes del modelo de gravedad. También es usada para evaluar la bondad de ajuste de otras estrategias estadísticas, que aunque conocidas, no han sido aplicadas en ese contexto, como por ejemplo los modelos mixtos. Para identificar posibles no linealidades e involucrar información espacio temporal que ayude en la descripción y predicción del flujo de pasajeros, se hace uso de las técnicas de regresión no paramétrica. Por ultimo, se evalúan técnicas de modelación funcional , con el propósito de involucrar información espacio temporal de los flujos dentro el modelo de gravedad. El ajuste de todos los modelos considerados, se realiza en el software R. El documento está organizado en dos partes. En la primera se presenta un marco teórico que incluye modelos de interacción espacial (modelo de gravedad y de dependencia espacial), modelos mixtos, no paramétricos y funcionales. En segunda instancia, con base en los métodos mencionados, se hace un análisis de información correspondiente al flujo de pasajeros aéreos en Colombia en el periodo 2004-2015. Finalmente, se dan conclusiones específicas respecto a los datos estudiados y sobre las alternativas de modelación que podrían considerarse a futuro. | spa |
| dc.description.additional | Maestria en Ciencias | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.format.extent | 58 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/75742 | |
| dc.language.iso | spa | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.department | Departamento de Estadística | spa |
| dc.relation.references | Agresti, A. (2003). Categorical Data Analysis, Wiley Series in Probability and Statistics, Wiley, New York. Akaike, H. (1974). A new look at the statistical model identification, IEEE Transactions on Automatic Control 19(6): 716–723. Avila, H. (2017). El modelo de gravedad y los determinantes del comercio entre Colombia y sus principales socios economicos, Revista Civilizar de Empresa y Economia 7: 89. Bailey, T. & Gatrell, A. (1995). Interactive Spatial Data Analysis, Longman Scientific & Technical, New York. Bivand, R. & Piras, G. (2015). Comparing implementations of estimation methods for spatial econometrics, Journal of Statistical Software, Articles 63(18): 1–36. Bivand, R. & Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association, TEST 27(3): 716–748. Bowman, A. & Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations, Oxford Statistical Science Series, OUP Oxford. Boyle, P. J., Flowerdew, R. & Shen, J. (1998). Modelling inter-ward migration in hereford and worcester: The importance of housing growth and tenure, Regional Studies 32(2): 113–132. Breusch, T. & Pagan, A. (1979). A simple test for heteroscedasticity and random coefficient variation, Econometrica 47(5): 1287–94. Bult, J. (1993). Semiparametric versus parametric classification models: An application to direct marketing, Journal of Marketing Research 30(3): 380–390. Burger, M., Van Oort, F. & Linders, G.-J. (2009). On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation, Spatial Economic Analysis 4(2): 167–190. Cameron, A. C., Hammond, P., Holly, A. & Trivedi, P. K. (2013). Regression Analysis of Count Data, Cambridge University Press, Cambridge. Cameron, A. C. & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications, Cambridge University Press. Cardenas, M. & Garcia, C. (2004). El modelo gravitacional y el TLC entre Colombia y Estados Unidos, Working papers series. documentos de trabajo, Fedesarrollo. Carey, H. (1877). Principles of Social Science, number v. 2 in Principles of Social Science, J.B. Lippincott & Company. Chambers, J. & Hastie, T. (1992). Statistical Models in S, Wadsworth & Brooks/Cole computer science series, CRC Press, Inc. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots, Journal of the American statistical association 74(368): 829–836. Cleveland, W. S. & Devlin, S. J. (1988). Locally weighted regression: An approach to regression analysis by local fitting, Journal of the American Statistical Association 83: 596–610. Cleveland, William S.and Loader, C. (1996). Smoothing by local regression: Principles and methods, in M. G. Hardle, Wolfgangand Schimek (ed.), Statistical Theory and Computational Aspects of Smoothing, Physica-Verlag HD. Correa, J. & Salazar, J. (2016). Introduccion a los Modelos Mixtos, Universidad Nacional de Colombia, Sede- Medellin. Curry, L. (1972). A spatial analysis of gravity flows, Regional Studies 6(2): 131–147. Davis, R. A., Lii, K.-S. & Politis, D. N. (2011). Remarks on Some Nonparametric Estimates of a Density Function, Springer New York, New York, NY, pp. 95–100. De Boor, C. (2001). A practical guide to splines; rev. ed., Applied mathematical sciences, Springer, Berlin. de Boor, C., Lyche, T. & Schumaker, L. L. (1976). On Calculating with B-Splines II. Integration, Birkhauser Basel. Delicado, P. (2008). Curso de modelos no parametricos, Universidad Politecnica de Catalunya. Deming, W. E. & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known, The Annals of Mathematical Statistics 11(4): 427–444. Dennett, A. (2012). Estimating flows between geographical locations:“get me started in spatial interaction modelling”, Technical report . Dierckx, P. (1995). Curve and surface fitting with splines, Oxford University Press. Dobson, A. & Barnett, A. (2008). An Introduction to Generalized Linear Models, Chapman & Hall/CRC Texts in Statistical Science, CRC Press. Doucet, R., Margaretic, P., Thomas-Agnan, C., Villotta, Q. et al. (2014). Spatial dependence in (origin-destination) air passenger flows, ITEA Annual Conference and Summer School on Transportation Economics (Kuhmo Nectar) . Draper, N. & Smith, H. (2014). Applied Regression Analysis, Wiley series in probability and mathematical statistics, Wiley, New York. Dubin, R. (2003). Robustness of spatial autocorrelation specifications: some monte carlo evidence, Journal of Regional Science 43(2): 221–248. Durbin, J. & Watson, G. (1951). Testing for Serial Correlation in Least Squares Regression, University of Cambridge. Department of Applied Economics. Reprint series, University of Cambridge. Eilers, P. H. & Marx, B. D. (1996). Flexible smoothing with b splines and penalties, Statistical Science pp. 89–102. Fischer, M. & Getis, A. (2009). Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer Berlin Heidelberg. Fischer, M. M. & Griffith, D. A. (2008). Modeling spatial autocorrelation in spatial interaction data: an application to patent citation data in the european union, Journal of Regional Science 48(5): 969–989. Fischer, M. M. & Reggiani, A. (2004). Spatial interaction models: From the gravity to the neural network approach, Contributions to Economic Analysis 266: 317–346. Fischer, M. M., Reismann, M. & Scherngell, T. (2010). Spatial interaction and spatial autocorrelation, Perspectives on spatial data analysis, Springer, pp. 61–79. Fischer, M. & Wang, J. (2011). Spatial Data Analysis: Models, Methods and Techniques, SpringerBriefs in Regional Science, Springer Berlin Heidelberg. Fitzmaurice, G., Ware, G., Laird, N. & Ware, J. (2004). Applied Longitudinal Analysis, Wiley Series in Probability and Statistics - Applied Probability and Statistics Section Series, Wiley, New York. Flowerdew, R. (2010). Modelling migration with Poisson regression, Technologies for Migration and Commuting Analysis: Spatial Interaction Data Applications pp. 261– 279. Flowerdew, R. & Aitkin, M. (1982). A method of fitting the gravity model based on the Poisson distribution, Journal of Regional Science 22(2): 191–202. Flowerdew, R. & Lovett, A. (1988). Fitting constrained Poisson regression models to interurban migration flows, Geographical Analysis 20(4): 297–307. Fotheringham, A. & O’Kelly, M. (1989). Spatial Interaction Models: Formulations and Applications, Ispra Courses on Remote Sensing, Springer. Fox, J. (2008). Applied Regression Analysis and Generalized Linear Models, SAGE Publications. Green, P. J. & Silverman, B. W. (1993a). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach, CRC Press. Green, P. J. & Silverman, B. W. (1993b). Nonparametric regression and generalized linear models: a roughness penalty approach, Chapman and Hall/CRC. Griffith, D. A. (2007). Spatial structure and spatial interaction: 25 years later, The Review of Regional Studies 37(1): 28. Griffith, D. A. & Jones, K. G. (1980). Explorations into the relationship between spatial structure and spatial interaction, Environment and Planning A: Economy and Space 12(2): 187–201. Gujarati, D. (2003). Basic econometrics, Economic series, McGraw Hill. H¨ardle, W., Liang, H. & Gao, J. (2012). Partially Linear Models, Springer Science & Business Media. Hartig, F. (2019). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.2.4. URL: https://CRAN.R-project.org/package=DHARMa Harville, D. (1976). Extension of the gauss-markov theorem to include the estimation of random effects, The Annals of Statistics 4(2): 384–395. Hastie, T. & Tibshirani, R. (1990). Generalized Additive Models, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis. Hayes, K. & Haslett, J. (1999). Simplifying general least squares, The American Statistician 53(4): 376–381. Hayfield, T. & Racine, J. S. (2008). Nonparametric econometrics: The np package, Journal of Statistical Software 27(5). URL: http://www.jstatsoft.org/v27/i05/ Henao, R., Carolina, G., Viloria, A. & Mercedes, G. (2016). Intra-industry Trade in Colombia (1974-2014). John, O., Lyhagen, J. & Reggiani, A. (2016). A new way of determining distance decay ¨ parameters in spatial interaction models with application to job accessibility analysis in sweden, European Journal of Transport and Infrastructure Research 16(2): 344– 362. Ke, C. & Wang, Y. (2001). Semiparametric nonlinear mixed-effects models and their applications, Journal of the American Statistical Association 96(456): 1272–1298. Keele, L. & Keele, L. (2008). Semiparametric regression for the social sciences, Wiley Online Library. King, G. (1988). Statistical models for political science event counts: Bias in conventional procedures and evidence for the exponential Poisson regression model, American Journal of Political Science 32: 838–863. Kleiber, C. & Zeileis, A. (2008). Applied Econometrics with R, Springer-Verlag, New York. URL: https://CRAN.R-project.org/package=AER Laird, N. M. & Ware, J. H. (1982). Random-effects models for longitudinal data, Biometrics 38(4): 963–974. Landau, L. D. & Lifshitz, E. M. (2013). Statistical Physics, Elsevier Science. LeSage, J. P. & Pace, R. K. (2008). Spatial econometric modeling of origin-destination flows, Journal of Regional Science 48(5): 941–967. LeSage, J. & Pace, R. (2009). Introduction to Spatial Econometrics, Statistics: A Series of Textbooks and Monographs, CRC Press. Li, Q. & Racine, J. S. (2007). Nonparametric Econometrics: Theory and Practice, Princeton University Press. Lin, X. & Zhang, D. (1999). Inference in generalized additive mixed models by using smoothing splines, Journal of the Royal Statistical Society Series B 61(2): 381–400. Lowry, I. (1966). Migration and metropolitan growth: two analytical models, UCLA Institute of Government and Public Affairs series, Chandler Pub. Co. Margaretic, P., Thomas-Agnan, C. & Doucet, R. (2017). Spatial dependence in (origindestination) air passenger flows, Papers in Regional Science 96(2): 357–380. Martinussen, T. & Scheike, T. H. (1999). A semiparametric additive regression model for longitudinal data, Biometrika 86(3): 691–702. Marx, B. D. & Eilers, P. H. (1998). Direct generalized additive modeling with penalized likelihood, Computational Statistics & Data Analysis 28(2): 193–209. McCulloch, C. E. & Neuhaus, J. M. (2014). Generalized Linear Mixed Models, Wiley, New York . Moran, P. A. (1950). Notes on continuous stochastic phenomena, Biometrika 37(1/2): 17– 23. Nadaraya, E. A. (1964). On estimating regression, Theory of Probability and its Applications 9: 141–142. Newton, I. (1833). Philosophiae Naturalis Principia Mathematica, Vol. 1, G. Brookman. Ntzoufras, I. (2011). Bayesian modeling using WinBUGS, Vol. 698, John Wiley & Sons. OKelly, M. E. & Lee, W. (2005). Disaggregate journey-to-work data: implications for excess commuting and jobs–housing balance, Environment and Planning A 37(12): 2233–2252. OKelly, M. E., Niedzielski, M. A. & Gleeson, J. (2012). Spatial interaction models from irish commuting data: variations in trip length by occupation and gender, Journal of Geographical Systems 14(4): 357–387. Olariaga, O., Bolivar, N. & Galeana, O. (2017). Gravitational analysis of the colombian air transport network, Universidad Santo Tomas . Oshan, T. M. (2016). A primer for working with the spatial interaction modeling (SpInt) module in the python spatial analysis library ( PySAL), Region 3(2): R11–R23. Pathak, M. (2014). Beginning Data Science with R, EBL-Schweitzer, Springer International Publishing. Patterson, H. D. & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal, Biometrika 58: 545–554. Pinheiro, J. & Bates, D. (2006). . Mixed-Effects Models in S and S-PLUS, Statistics and Computing, Springer, New York. Plane, D. A. (1982). An information theoretic approach to the estimation of migration flows, Journal of Regional Science 22(4): 441–456. R Development Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. URL: http://www.R-project.org Ramsay, J. O., Heckman, N. & Silverman, B. W. (1997). Spline smoothing with modelbased penalties, Behavior Research Methods, Instruments, & Computers 29(1): 99–106. Ramsay, J. O., Hooker, G. & Graves, S. (2009). Functional Data Analysis with R and MATLAB, 1st edn, Springer Publishing Company, Incorporated. Ramsay, J. O., Wickham, H., Graves, S. & Hooker, G. (2018). fda: Functional Data Analysis. R package version 2.4.8. URL: https://CRAN.R-project.org/package=fda Ramsay, J., Ramsay, J., Silverman, B., Silverman, H. & Media, S. S. (2005). Functional Data Analysis, Springer Series in Statistics, Springer. Rao, C. R., Rao, C. R., Statistiker, M., Rao, C. R. & Rao, C. R. (1973). Linear Statistical Inference and Its Applications, Vol. 2, Wiley. Raymer, J. (2007). The estimation of international migration flows: A general technique focused on the origin-destination association structure, Environment and Planning A 39(4): 985–995. Robinson, G. K. et al. (1991). That blup is a good thing: the estimation of random effects, Statistical Science 6(1): 15–32. Ruppert, D., Wand, M. P. & Carroll, R. J. (2003). Semiparametric Regression, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press. Schabenberger, O. & Gotway, C. (2004). Statistical Methods for Spatial Data Analysis, Chapman & Hall/CRC Texts in Statistical Science, Taylor & Francis. Schumaker, L. (1981). Spline functions: basic theory, Pure and applied mathematics, Wiley. Sen, A. & Smith, T. E. (2012). Gravity models of spatial interaction behavior, Springer Science & Business Media. Sen, A. & Soot, S. (1981). Selected procedures for calibrating the generalized gravity model, Papers in Regional Science 48(1): 165–176. Shannon, C. E. (2001). A mathematical theory of communication, ACM SIGMOBILE mobile computing and communications review 5(1): 3–55. Shapiro, S. S. & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples), Biometrika 52: 591–611. Snickars, F. & Weibull, J. W. (1977). A minimum information principle: Theory and practice, Regional science and urban economics 7(1-2): 137–168. Speed, T. (1991). That blup is a good thing: the estimation of random effects: Comment, Statistical Science 6(1): 42–44. Stillwell, J. (1978). Interzonal migration: some historical tests of spatial-interaction models, Environment and Planning A 10(10): 1187–1200. Stirling, J. (1764). Methodus differentialis, sive Tractatus de summatione et interpolatione serierum infinitarum. Auctore Jacobo Stirling, RSS, prostat apud J. Whiston & B. White, in Fleet-street. Taylor, P. J. & Openshaw, S. (1975). Distance decay in spatial interactions, Concepts and Techniques in Modern Geography. Tiefelsdorf, M. & Boots, B. (1995). The specification of constrained interaction models using the spss loglinear procedure, Geographical Systems 2: 21–38. Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region, Economic Geography 46(sup1): 234–240. Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S, fourth edn, Springer, New York. Verbyla, A. P., Cullis, B. R., Kenward, M. G. & Welham, S. J. (1999). The analysis of designed experiments and longitudinal data by using smoothing splines, Journal of the Royal Statistical Society: Series C (Applied Statistics) 48(3): 269–311. Wahba, G. (1976). A Survey of Some Smoothing Problems and the Method of the Generalized Cross-validation for Solving Them, Department of Statistics : technical report, University of Wisconsin. Wand, M. & Jones, M. (1994a). Kernel Smoothing, Taylor & Francis. Wand, M. & Jones, M. (1994b). Kernel Smoothing, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis. Watson, G. S. (1964). Smooth Regression Analysis, Sankhy¯a Ser. 26: 359–372. Willekens, F. (1983). Log-linear modelling of spatial interaction, Papers in Regional Science 52(1): 187–205. Wilson, A. (2010). Entropy in urban and regional modelling: Retrospect and prospect, Geographical Analysis 42(4): 364–394. Wilson, A. G. (1967). A statistical theory of spatial distribution models, Transportation Research 1(3): 253–269. Wilson, A. G. (1971). A family of spatial interaction models, and associated developments, Environment and Planning A 3(1): 1–32. Wood, S. (2006a). Generalized Additive Models: An Introduction with R, Chapman & Hall/CRC Texts in Statistical Science, Taylor & Francis. Wood, S. N. (2006b). Low-rank scale-invariant tensor product smooths for generalized additive mixed models, Biometrics 62(4): 1025–1036. Wu, H. & Zhang, J. (2006). Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches, Wiley, New York. Young, E. (1924). The Movement of Farm Population, Bulletin (Cornell University. Agricultural Experiment Station), Cornell University Agricultural Experiment Station. Zeger, S. L. & Diggle, P. J. (1994). Semiparametric models for longitudinal data with application, Biometrics pp. 689–699. Zhang, D., Lin, X., Raz, J. & Sowers, M. (1998). Semiparametric stochastic mixed models for longitudinal data, Journal of the American Statistical Association 93(442): 710–719. Zuur, A., Ieno, E. N., Walker, N., Saveiliev, A. A. & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R, Springer, New York. | spa |
| dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.spa | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.proposal | Terminales Aéreas | spa |
| dc.subject.proposal | Colombia | spa |
| dc.subject.proposal | flujos de pasajeros | spa |
| dc.title | Un modelo de interacción espacial para el flujo de pasajeros -entre terminales aéreas de Colombia en los años 2004 a 2015. | spa |
| dc.type | Trabajo de grado - Doctorado | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_db06 | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/doctoralThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |