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The Study On Prediction Of Construction Land Demand Of Dongying City

Posted on:2010-01-17Degree:MasterType:Thesis
Country:ChinaCandidate:J H HaoFull Text:PDF
GTID:2189360278960917Subject:Industrial Engineering
Abstract/Summary:PDF Full Text Request
Land is the fundamental resource for the society development of human beings. Built-upland is a type of land which always be used in construction. With the development ofcivilization, people's demand became more and more. Land, as an important product stuff, theintensity of land use became higher and higher. As the result,the quantity of farmland isdecreasing; average area of tilth is deducing; urban is expanding; in land structure table, builtupland is ascending, farmland and non-used land is declining. During the nation'smodernization, for meeting the demand of built-up land from industrialization andurbanization, supplying an appropriate quantity of built-up land is necessary.It is an important part in an overall plan of utilization of land that construction landdemand, it must carry on comparatively accurate prediction to construction land demand towork out the overall plan of utilization of land scientifically and rationally.The paper utilizes the general land use planning as research object, combines Dongyingcity's variational data of land utilizing and the statistic data from society economy, and usescorrelate analysis and grey relation analysis to filtrate influence factor of the construction landdemand's change based on analyzing the research status of domestic and overseas constructionland demand prediction, and then constructs the construction land demand changes predictingmodel of regression and uses model to predict the construction land demand of the intending13 years. The model of construction land demand prediction has some value both in theory andin practice and it provides profitable reference for farther study of construction land demandprediction and government decision-making.
Keywords/Search Tags:construction land, demand, prediction, Dongying city, relevant analysis, grey relation analysis, multiple linear regression model
PDF Full Text Request
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