Incorporating Spatial Autocorrelation with Neural Networks in Empirical Land-Use Change Models

Hone-Jay Chu, Chen-Fa Wu and Yu-Pin Lin
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Hone-Jay Chu: Department of Geomatics, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, Republic of China
Chen-Fa Wu: Department of Horticulture, National Chung Hsing University, 250, Kuo Kuang Road, Taichung 402, Taiwan, Republic of China
Yu-Pin Lin: Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Road, Da-an District, Taipei City 106, Taiwan, Republic of China

Environment and Planning B, 2013, vol. 40, issue 3, 384-404

Abstract: Land-use data can accurately reflect spatial pattern dependence (ie, spatial autocorrelation) and a nonlinear relationship with driving variables. In this study land-use dynamics in the Paochiao Watershed, Taiwan are forcast for the next fifteen years by incorporating artificial neural networks with spatial autocorrelation (Auto-ANNs) into the conversion of land use and its effects (CLUE-s) model. In addition to spatial autocorrelations of land use, Auto-ANNs-CLUE-s considers the nonlinear relationships between driving factors and land-use patterns. Results of a three-map comparison indicate that the Auto-ANNs-CLUE-s model has a better overall performance than Auto-logistic-CLUE-s. The Auto-ANNs-CLUE-s is highly applicable for all resolutions from multiresolution validation. The results of landscape metrics demonstrate the prevalence of urban sprawl in the study area. The proposed model is an alternative means of improving land use and environmental planning.

Keywords: land-use change; spatial autocorrelation; ANNs; CLUE-s; landscape metrics; three-map comparison (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:40:y:2013:i:3:p:384-404

DOI: 10.1068/b37116

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