Abstract
The techniques proposed by spatial econometrics are reaching greater dissemination nowadays, with special relevance in those sectors that are strongly related to their development in a specific geographic area. Generally, when a variable is affected by spatial autocorrelation, the latter needs to be treated using the techniques proposed to that end. The present study is focused on the exploratory analysis of a variable that is usually associated with tourism, i.e. the number of travellers, using the formal indices proposed by spatial statistics, which are Moran’s I and Getis & Ord G. The study analyses the distribution of this variable, concluding that it does not show a random pattern and that, therefore, subsequent confirmatory analyses or modelling of phenomena related to this variable will require the use of suitable techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anselin, L. (1980). Estimation methods for spatial autoregressive structures. Regional science dissertation and monograph Series, Ithaca, NY.
Anselin, L. (1988). Spatial econometrics: Methods and models. The Netherlands: Kluwer Academic Publisher.
Anselin, L. (1992). SpaceStat tutorial. A workbook for using SpaceStat in the analysis of spatial data (Technical Reports S-92-1). Santa Barbara: University of California; National Center for Geographic Information and Analysis.
Anselin, L. (1993). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. GISDATA Specialist Meeting on GIS and Spatial Analysis, Amsterdam, The Netherlands.
Anselin, L. (1995a). SpaceStat version 1.80: Users’ guide. Regional Research Institute West Virginia University: Morgantown.
Anselin, L. (1995b). Local indicators of spatial association (LISA). Geographical Analysis, 27(2), 93–115.
Anselin, L. (1999). The future of spatial analysis in the social sciences. Geographic Information Sciences, 5(2), 67–76.
Anselin, L. (2001). Spatial econometrics. In Baltagi (Ed.), A companion to theoretical econometrics (pp. 310–330). Oxford: Basil Blackwell.
Anselin, L., & Florax, R. (1995). New directions in spatial econometrics: Introduction. In L. Anselin & R. J. G. M. Florax (Eds.), New directions in spatial econometrics (pp. 3–18). Springer.
Anselin, L., & Rey, S. (1997). Introduction to the special issue on spatial econometrics. International Regional Science Review, 20, 1–8.
Arbia, G. (1989). Spatial data configuration in statistical analysis of regional economics and related problems. Dordrecht: Kluwer.
Barros, C. P., & Matías, A. (2007). Efficiency in a chain of small hotels with a stochastic production frontier model. In A. Matías, P. Nijkamp, & P. Neto (Eds.), Advances in modern tourism research: Economic perspectives (pp. 107–129). Heidelberg: Physica-Verlag.
Bloommestein, H. (1983). Specification and estimation of spatial econometric models: A discussion of alternative strategies for spatial economic modelling. Regional Science and Urban Economics, 13, 130–251.
Button, K., & Kulkarni, R. (2001). Spatial and distance statistics of the trucking and warehousing industries using GIS tools. 41th Congress of the European Association of Regional Science (ERSA) Zagreb.
Chasco, M. C. (2003). Econometría espacial aplicada a la predicción-extrapolación de datos microterritoriales. Madrid: Consejería de Economías e Innovación Tecnológica de la Comunidad de Madrid.
Chasco, C., & Vicens, J. (2000). EU-membership impacts in the Spanish province income convergence: A spatial autocorrelation perspective. Oviedo: Anales de Economía aplicada XIV Reunión de ASEPELT.
Cliff, A., & Ord, J. (1973). Spatial autocorrelation. London: Pion.
Cliff, A., & Ord, J. (1981). Spatial processes, models and applications. London: Pion.
Geary, R. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115–145.
Getis, A., & Ord, J. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206.
Kmenta, J. (1971). Elements of econometrics. New York: MacMillan.
López, F. A., Palacios, M. A., & Ruiz, M. (2001). Modelos explicativos del desempleo en términos de localización: una aplicación a las provincias españolas. Anales de Economía Aplicada, XV Reunión de ASEPELT. Santiago de Compostela.
Ma, T., Hong, T., & Zhang, H. (2015). Tourism spatial spillover effects and urban economic growth. Journal of Business Research, 68, 74–80.
Moran, P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, 10, 243–251.
Moreno, R., & Vayá, E. (2000). Técnicas econométricas para el tratamiento de datos espaciales: la econometría espacial 44. Universitat de Barcelona.
Moreno, R., & Vayá, E. (2004). Econometría Espacial: nuevas técnicas para el análisis regional. Una aplicación a las regiones europeas. Investigaciones regionales 1, 83–106.
Openshaw, S. (1993). Some suggestions concerning the development of artificial intelligence tools for spatial modelling and analysis in GIS. In M. M. Fisher & P. Nijkamp (Eds.), Geographic information system, spatial modelling and policy evaluation (pp. 17–33). Berlin: Springer Verlag.
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.
Paelinck, J. H. P., & Klaasen, L. H. (1979). Spatial econometrics. Saxon House.
Pavlyuk, D. (2010). Multi-tier spatial stochastic frontier model for competition and cooperation of European airports. Transport and Telecommunication, 11(3), 57–66.
Pavlyuk, D. (2013). Distinguishing between spatial heterogeneity and inefficiency: Spatial stochastic frontier analysis of European airports. Transport and Tecommunication, 14(1), 29–38.
Sánchez, M. (2008). Análisis Espacial de Datos y Turismo: Nuevas Técnicas para el Análisis Turístico. Una aplicación al caso extremeño. Revistas empresariales. Segunda Época, 2, 48–66.
Sánchez, J. M., Sánchez, M., & Rengifo, J. I. (2013). La evaluación del potencial para el desarrollo de turismo rural. Aplicación metodológica sobre la provincia de Cáceres. Geofocus, 13(1), 1–20.
Sánchez, J. M., Sánchez, M., & Rengifo, J. I. (2018). Patrones de distribución de la oferta turística mediante técnicas geoestadísticas en Extremadura (2004–2014). Boletín de la Asociación de Geógrafos Españoles, 76, 276–302.
Shoval, N., & Ahas, R. (2016). The use of tracking technologies in tourism research: The first decade. Tourism Geographies, 18(5), 587–606.
Student. (1914). The elimination of spurious correlation due to position in time or space. Biometrika, 10, 179–180.
Tiefelsdorf, M., & Boots, B. (1997). A note on the extremities of local Moran’s Ii and their impact on global Moran’s I. Geographical Analysis, 29(3), 249–257.
Vayá, E., & Suriñach, J. (1996). Contrastes de autocorrelación espacial: una aplicación al ámbito de las provincias españolas. X Reunión ASEPELT, Albacete (Castilla la Mancha).
Zhou, Y., Maumbe, K., Deng, J., & Selin, S. (2015). Resource-based destination competitiveness evaluation using a hybrid analytic hierarchy process (AHP): The case study of West Virginia. Tourism Management Perspectives, 15, 70–80.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rodríguez-Rangel, C., Sánchez-Rivero, M. (2019). Analysis of the Spatial Distribution Pattern of Tourist Activity: An Application to the Volume of Travellers in Extremadura. In: Artal-Tur, A., Kozak, M., Kozak, N. (eds) Trends in Tourist Behavior. Tourism, Hospitality & Event Management. Springer, Cham. https://doi.org/10.1007/978-3-030-11160-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-11160-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11159-5
Online ISBN: 978-3-030-11160-1
eBook Packages: Business and ManagementBusiness and Management (R0)