Geographical information science: GeoComputation and nonstationarity
نویسنده
چکیده
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منابع مشابه
Advances in geocomputation (1996-2011)
1. What is geocomputation? The first international conference on 'Geocomputation', hosted by the School of Geography at the University of Leeds in 1996, launched a new research agenda in geographical analysis and modelling (Openshaw & Abrahart, 1996): ''The art and science of solving complex spatial problems with computers'' (GeoComputation, 2012). The interest generated in this field was estab...
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A strategy for raster-based geocomputation under different parallel computing platforms Cheng-Zhi Qin, Li-Jun Zhan, A-Xing Zhu & Cheng-Hu Zhou a State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China b Department of Geography, University of Wisconsin Madison, M...
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Lecturer in Geographical Information Science (University of Nottingham: UK). Elected Chair of RGS-IBG Geographical Information Science Research Group; Associate Editor for Water Resources Research; Co-Editor of "GeoComputation" (Taylor & Francis, 2000 ) and "Neural Networks for Hydrological Modelling" (A.A. Balkema Publishers, 2004); Principal research interests include GIS and Neu ral Network ...
متن کاملSpatial nonstationarity and the scale of species-environment relationships in the Mojave Desert, California, USA
Species distribution models (SDM) are used to quantify the relationship between some measure of species’ importance and environmental factors. They have become a fertile area of research interest at the confluence of spatial ecology and GIScience and have been used to study a wide range of biogeographical phenomena, ranging from invasive species, vector-borne diseases, and biodiversity. One iss...
متن کاملAn Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing
This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. ...
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