Abstract
The investigation and modeling of land use dynamics can be conducted at different scales based on the objective of the study. However, few studies have looked at comparing various scale aspects, such as spatial resolution and the related neighborhood effect, for practical case study applications. In this chapter, we contribute to this under-explored area with a detailed study of how changes in the data preparation procedures and the scale decisions made in setting up a land use model can affect its performance. For these purposes we used a Cellular Automata (CA) based land use model, which we applied to the Madrid region in Spain. In order to discover the most appropriate method for preparing input data, different vector-to-raster conversion and resampling strategies were tested with reference to 4 statistics. For vector-to-raster conversion, the cell center method was found to give the best results across all of the statistics. Furthermore, direct conversion from the original vector map to raster format at the desired cell size was found to give better results than resampling to the desired cell size from a different cell size. We also tested the effect of changing spatial resolution and cell neighborhood distance on a model’s goodness-of-fit to real data using a range of location and pattern metrics. Although differences were noted in the simulations, all the applications fitted the data satisfactorily. Nevertheless, the 50 × 50 m cell resolution applications were visually much more realistic, perhaps because this resolution was used in the initial calibration of the model. The results indicate that data conversion issues have a major effect on the quality of the input data. Additionally, models of this type appear to be much less sensitive to scale changes, either through cell resolution changes, neighborhood changes, or both, than is usually suggested by the literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barredo JI, Demicheli L, Lavalle C, Kasanko M, McCormick N (2004) Modelling future urban scenarios in developing countries: an application case study in Lagos, Nigeria. Environ Plann B 31(1):65–84
Carver S, Brunsdon C (1994) Vector to raster conversion error and feature complexity: an empir-ical study using simulated data. Int J Geogr Inf Syst 8(3):261–270
De Lucio RL (2011) Transformaciones territoriales recientes en la región urbana de Madrid. Urban 8:124–161
De Koning GHJ, Veldkamp A, Fresco LO (1998) Land use in Ecuador: a statistical analysis at different aggregation levels. Agric Ecosyst Environ 70:231–247
Díaz-Pacheco J, Gutiérrez J (2013) Exploring the limitations of CORINE land cover for monitoring urban land use dynamics in metropolitan areas. J Land Use Sci 9(3):243–259. doi:10.1080/1747423X.2012.761736
EEA (2014) Report of Content of CORINE Land Cover.European Environmental Agency. http://www.eea.europa.eu/publications/COR0-landcover
ESRI (2010) Manual ArcGIS [software GIS]. Versión 10.0. Redlands, CA: Environmental Sys-tems Research Institute, Inc
Gardner RH, Milne BT, Turnei MG, O’Neill RV (1987) Neutral models for the analysis of broad-scale landscape pattern. Landsc Ecol 1(1):19–28
Hewitt R, Escobar F (2011) The territorial dynamics of fast-growing regions: unsustainable land use change and future policy challenges in Madrid, Spain. Appl Geogr 31(2):650–667
Hewitt R, Van Delden H, Escobar F (2014) Participatory land use modelling, pathways to an integrated approach. Environ Model Softw 52:149–165
Hewitt R, Díaz-Pacheco J (2017) Stable models for metastable systems? Lessons from sensitivity analysis of a Cellular Automata urban land use model. Comput Environ Urban Syst 62:113–124.
Jantz C, Goetz SJ (2005) Analysis of scale dependencies in an urban land‐use‐change model. Int J Geogr Inf Sci 19(2):217–241
Jenerette GD, Wu J (2001) Analysis and simulation of land-use change in the central Arizona-Phoenix region, USA. Landsc Ecol 16(7):611–626
Kok K, Veldkamp A (2001) Evaluating impact of spatial scales on land use pattern analysis in Central America. Agr Ecosyst Environ 85(1):205–221
Lajoie G, Hagen-Zanker AH (2007) Modeling urban sprawl on the island La Réunion: contribu-tion of the Metronamica® cellular automata for territorial prospect. Cybergeo 405
Lam SN, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. The Professional Geographer 44(1):88–98
Longley P, Batty M (2003) Advanced spatial analysis: the CASA book of GIS. ESRI, Inc
McGarigal K, Marks BF (1994) Spatial pattern analysis program for quantifying landscape structure reference manual. Forest Science Department, Oregon State University, Corvallis Oregon
Ménard A, Marceau DJ (2005) Exploration of spatial scale sensitivity in geographic cellular automata. Environ Plan 32(5):693–714
Openshaw S, Taylor P (1979) A million or so correlation coefficients: three experiments on the modifiable areal unit problem. In: Wrigley N (ed) Statistical applications in the spatial sciences. Pion, London, pp 127–144
Openshaw S (1983) The modifiable areal unit problem, vol 38. In: Geo books, Norwich
Plata Rocha W, Gómez Delgado M, Bosque Sendra J (2009) Cambios de usos del suelo y expansión urbana en la Comunidad de Madrid (1990–2000). Scripta Nova: revistaelectrónica de geografía y cienciassociales 13
Pontius JRG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32(15):4407–4429
RIKS (2014) Metronamica Documentation.Research Institute for Knowledge Systems, The Netherlands. http://www.riks.nl/
Samat N (2006) Characterizing the scale sensitivity of the cellular automata simulated urban growth: A case study of the SeberangPerai Region, Penang State, Malaysia. Computers, environment and urban systems 30(6):905–920
Switzer P (1975) Estimation of the accuracy of qualitative maps. Wiley, London
Theobald DM, Hobbs NT (1998) Forecasting rural land-use change: a comparison of regression-and spatial transition-based models. Geogr Environ Modell 2:65–82
Tobler W (1988) Resolution, resampling, and all that. Building databases for global science, vol 12, pp 9–137
Van Delden H, Escudero JC, Uljee I, Engelen G (2005) METRONAMICA: A dynamic spatial land use model applied to Vitoria-Gasteiz. In: Virtual Seminar of the MILES Project. Centro de Estudios Ambientales, Vitoria-Gasteiz
Van Delden H, Stuczynski T, Ciaian P, Paracchini ML, Hurkens J, Lopatka A, Shi Y, Gomez Prieto O, Calvo S, Van Vliet J, Vanhout R (2010) Integrated assessment of agricultural poli-cies with dynamic land use change modelling. Ecol Model 221(18):2153–2166
Van Delden H, Hurkens J (2011) A generic integrated spatial decision support system for urban and regional planning. Keynote presented at MODSIM11 international congress on modelling and simulation, Perth, Australia
Van Delden H, Van Vliet J, Rutledge DT, Kirkby MJ (2011) Comparison of scale and scaling issues in integrated land-use models for policy support. Agric Ecosyst Environ 142(1):18–28
Van Delden H, Díaz-Pacheco J, Shi Y, Van Vliet J (2012) Calibration of cellular automata based land use models: lessons learnt from practical experience, CAMUSS (Cellular Automata Modeling for Urban and Spatial Systems) conference, November 8-10. Oporto, Portugal
Van Vliet J, White R, Dragicevic S (2009) Modeling urban growth using a variable grid cellular automaton. Comput Environ Urban Syst 33(1):35–43
Van Vliet J, Bregt AK, Hagen-Zanker A (2011) Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol Modell 222(8):1367–1375
Visser H, De Nijs T (2006) The map comparison kit. Environ Model Softw 21(3):346–358
White R (2006) Pattern based map comparisons. J Geogr Syst 8(2):145–164
White R, Engelen G (1993) Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land-use patterns. Environ Plann A 25:1175
White R, Engelen G (2000) High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput Environ Urban Syst 24(5):383–400
Woodcock CE, Strahler AH (1987) The factor of scale in remote sensing. Remote Sens Environ 21(3):311–332
Acknowledgements
This work has been supported in part by project SIGEOMOD-II, BIA2013-43462-P (Spanish Ministry of Economy and Competitivity and European Regional Development Fund FEDER). The authors also gratefully acknowledge funding received under remit of the EU FP7 project COMPLEX (project no. 308601). The MLU geodatabase was built by researchers from the Department of Human Geography, Universidad Complutense de Madrid with financial support from the Ministerio de Ciencia e Innovación (project TRA2008-06682). Finally, we thank the anonymous reviewers for their helpful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Díaz-Pacheco, J., van Delden, H., Hewitt, R. (2018). The Importance of Scale in Land Use Models: Experiments in Data Conversion, Data Resampling, Resolution and Neighborhood Extent. In: Camacho Olmedo, M., Paegelow, M., Mas, JF., Escobar, F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-60801-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-60801-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60800-6
Online ISBN: 978-3-319-60801-3
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)