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Measuring the neighbourhood effect to calibrate land use models
Affiliation:1. Tetra Tech, Raleigh, NC, USA;2. Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA;4. Near East University, Faculty of Civil and Environmental Engineering, Near East Boulevard, Nicosia, N. Cyprus, via Mersin 10, Turkey;1. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Granada, Spain;2. Department de Géographie, Aménagement et Environment, GEODE, Université de Toulouse Jean Jaurés, France;1. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China;2. School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;3. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;4. Department of GIS and Remote Sensing, Tarbiat Modares University, Tehran, Iran
Abstract:Many spatially explicit land use models include the neighbourhood effect as a driver of land use changes. The neighbourhood effect includes the inertia of land uses over time, the conversion from one land use to another, and the attraction or repulsion of surrounding land uses. The neighbourhood effect is expressed in the neighbourhood rules, but calibration of the neighbourhood rules is not straightforward. This paper aims to characterise the neighbourhood effect of observed land use changes and use this information to improve the calibration of land use models. We measured the over- and underrepresentation of land uses in the neighbourhood of observed land use changes using a modified version of the enrichment factor. Enrichment factors of observed land use changes in Germany between 1990 and 2000 indicate that the neighbourhood effect exists. This suggests that it is appropriate to use neighbourhood rules to simulate urban land use changes. Observed enrichment factors were used to calibrate a land use model for Germany from 1990 to 2000 and the obtained neighbourhood rules were validated independently from 2000 to 2006. The results show that both the allocation accuracy and the pattern accuracy of the land use model improved for the calibration period, as well as for the independent validation period. This indicates that enrichment factors can be used to improve the calibration of the neighbourhood rules in land use models.
Keywords:Land use change  Constrained cellular automata  Calibration  Neighbourhood rules  Enrichment factors
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