Detection of desirable areas for urban growth through GIS and OWA: the case of Culiacan and Navolato

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José Jesús Uriarte Adrián
Wenseslao Plata Rocha
Rosendo Romero Andrade
Gabriela Corrales Barraza
José C. Beltrán González
Ricardo Remond Noa


Due to an accelerated growth of artificial surfaces, an increasing interest in the changes on land use has been detected in the last decade, making it necessary to propose models able to create plans of optimal and sustainable development. The goal of this work is to identify the potentially most suitable spaces for urban growth, through geospatial simulations which integrate the Geographic Information Systems and OWA (Ordered Weighted Average). We propose a sensitivity analysis methodology to the results in order to assess their robustness with respect to the input values’ weights variability, based on histograms and distances classification. Finally, the selection proposal shows a considerable percentage according to the planned area. 

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URIARTE ADRIÁN, José Jesús et al. Detection of desirable areas for urban growth through GIS and OWA: the case of Culiacan and Navolato. CIENCIA ergo-sum, [S.l.], v. 27, n. 2, jun. 2020. ISSN 2395-8782. Disponible en: <>. Fecha de acceso: 17 oct. 2021 doi:
Ciencias de la tierra y de la atmósfera


Al-Shalabi, M. A., Mansor, S. B., Ahmed, N. B., & Shiriff, R. (2006). GIS based multicriterio approach-es to housing site suitability assessment. XXIII International FIG Congress. Munich, Germany.

Anon. (2010). INEGI. Retrevied from

Barredo, J. I., & Gómez, M. D. (2008). Towards a set of IPCC SRES urban land use scenarios: Modelling urban land use in the Madrid region. In Modelling Environmental Dynamics (pp. 363-385). Springer, Berlin, Heidelberg.

Barredo, J., & Bosque-Sendra, J. (2009). Comparison of multi-criteria evaluation methods integrated in geographical information systems to allocate urban areas. Departamento de Geografía, Universidad de Alcalá de Henares, España. Financiado por la Comisión Interministerial de Ciencia y Tecnología (Proyecto no. AMB 94-1017).

Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers and Geosciences, 32, 399-401.

Chang, N. B., Parvathinathan, G., & Breeden, J. B. (2008). Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region. Journal of Environmental Management, 87(1), 139-153.

Comisión Europea. (1999). Estrategia territorial europea. Hacia un desarrollo equilibrado y sostenible de la UE. Retrieved form

Corrales Barraza, G., Rocha Plata, W., Monjardin Armenta, S. A., Uriarte Adrián, J. D, & Beltrán González, J. C. (2017). Diseño de un modelo de demanda de superficie para la simulación geoespacial de usos de suelo en Novolato y Culiacán, Sinaloa, México. Persona y Sociedad, 31(1), 9-26.

Corrales Barraza, G. (2013). Análisis de cambio de uso de suelo para el estado de Sinaloa utilizando Sistemas de Información Geográfica (tesis de maestría). México: Universidad Autónoma de Sinaloa.

Drobne, S., & Lisec, A. (2009). Multi-attribute decision analysis in GIS: Weighted linear combination and ordered weighted averaging. Informatics, 33(4).

Eastman, J. R. (2012). Idrisi Selva Manual 17. Clark University.

Feng, Y., Liu, M., Chen, L., & Liu, Y. (2016). Simulation of dynamic urban growth with partial least squares regressionbased cellular automata in a GIS environment. ISPRS International Journal of Geo-Information, 5(12), 243.

Furtado, B. A., & Eberhardt, I. D. R. (2015). A simple agent-based spatial model of the economy: Tools for policy. arXiv preprint arXiv:1510.04967.

Gómez, M., & Barredo, J. (2005). Sistemas de Información Geográfica y evaluación multicriterio en la ordenación del territorio (segunda edición). RA-MA.

Gómez-Delgado, M., Aguilera Benavente, F., Barreira González, P., Bosque Sendra, J., & Rodríguez Espinosa, V. M. (2014). Simulación prospectiva del crecimiento urbano en la Comunidad Autónoma de Madrid a partir de modelos basados en autómatas celulares y modelos basados en EMC.

Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667-688.

IMPLAN. (2015). IMPLAN. Sinaloa, México. Retrevied from

INEGI. (2017). Uso de suelo y vegetación. México.

INEGI. (2014). Instituto Nacional de Estadística y Geografía (INEGI). México. Retrevied from http://www.

Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7,

Jiang, H. y Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173-184.

Malczewski, J. (2002). Fuzzy screening for land suitability analysis. Geographical & Environmental Modelling, 6(1), 27-39.

Malczewski, J., Chapman, T., Flegel, C., Walters, D., Shrubsole, D., & Healy, M. A. (2003). GIS–multicriteria evaluation with ordered weighted averaging (OWA): Case study of developing watershed management strategies. Environment and Planning A, 35(10), 1769-1784.

Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in planning, 62(1), 3-65.

Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal
of Geographical Information Science, 20(7), 703-726.

Mogaji, K. A., Lim, H. S., & Abdullah, K. (2014). Modeling groundwater vulnerability prediction using geographic information system (GIS)-based ordered weighted average (OWA) method and DRASTIC model theory hybrid approach. Arabian Journal of Geosciences, 7(12), 5409-5429.

Naghdizadegan, M., Behifar, M., & Mirbagheri, B. (2013). Spatial deforestation modeling using cellular automata (Case study: Central Zagros Forests). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1.

Pearson, K. (1920). Notes on the history of correlation. Biometrika, 13(1), 25-45.

Phua, M. H., & Minowa, M. (2005). A GIS-based multi-criteria decision-making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia. Landscape and Urban Planning, 71(2).

Plata, W., Gómez, M., & Bosque, J. (2010). Desarrollo de modelos de crecimiento urbano óptimo para la Comunidad de Madrid. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica, 10, 103-134.

Plata Rocha, W., Gómez-Delgado, M., Bosque-Sendra, J., Aguilar, J. M. (2013). Análisis de sensibilidad para un modelo de simulación de crecimiento urbano. Propuesta metodológica explícitamente especial. GeoFocus,
13(2), 158-178.

Qureshi, M. E., Harrison, S. R., & Wegener, M. K. (1999). Validation of multicriteria analysis models. Agricultural Systems, 62(2), 105-116.

Roldán López, H., (2006). La urbanización metropolitana de Culiacán. México: Gobierno de Sinaloa, Fontamara.

Roura-Pascual, N., Reiner, M. K., Richardson, D. M., & Hui, C. (2010). Spatially-explicit sensitivity analysis for conservation management: Exploring the influence of decisions in invasive alien plant management. Diversity
and Distributions, 16(3), 426-438.

Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical modelling, 9(3-5), 161-176.

Tan, R., Liu, Y., Zhou, K., Jiao, L., & Tang, W. (2015). A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers, Environment and Urban Systems, 49, 15-29.

Van der Heijden, K. (2005). Scenarios: The art of strategic conversation (2nd ed.). Chichester: John Wiley & Sons.