14 October 2009 Predicting methods of construction land demand and application in county's general land use planning
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Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 749219 (2009) https://doi.org/10.1117/12.837344
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China
Abstract
China faces a serious problem is that dramatic expansion of construction land cause largely reduction of cultivated land. To control the scale of construction land is the focus of land use and planning management work, whose core is land use control, and how to forecast the quantity of construction land scientifically, reasonably and correctly is an important content in general land use planning. In this paper, based on the field survey and statistic data of land changes in Changjiang Hainan province during 1996-2005, the gross construction land in this region was simulated and predicted using trend analysis method, exponent smoothing method, remnant GM(1,1) method, Markov model and multifactor optimal combination method, respectively. From the compare of the average relative error, GM(1,1) Method is better to predict, but its parameters c and p indicate this model can't be used to forecast long term data. From the result of Markov model, the average relative error is small, but its maxerror is 4.15%. By comparison of these models, Multifactor optimal combination method is more reliable and effective for policymakers of land management, and which is preferable for predicting construction land demand of county's general land-use planning.
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Xiaoyong Gao, Xiaoyong Gao, Yaolin Liu, Yaolin Liu, Lilan Su, Lilan Su, Yang Zhang, Yang Zhang, } "Predicting methods of construction land demand and application in county's general land use planning", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749219 (14 October 2009); doi: 10.1117/12.837344; https://doi.org/10.1117/12.837344
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