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基于RS与GIS的城市边缘空间扩张趋势预测仿真

Prediction and Simulation of Urban Fringe Space Expansion Trend Based on RS and GIS
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摘要 为明确区域扩张方向、避免城市发展中心偏移,提出一种基于RS与GIS的扩张趋势预测方法。利用RS和GIS采集城市边缘的空间数据,利用调节系数去除遥感图像中噪声,将城市边缘空间扩张,看作一个只有时间存在变动的线性变化现象,根据上述特点建立预测空间,通过历史输入值、输出值以及标准值,推导计算空间中信息响应域及数据流域分布情况,将实际待检测区域特征代入,推导响应域及数据流域拟合度最高的空间状态,最后凭借预测向量矩阵得出占地面积、预测面积以及房屋数据量等指标的预测输出。仿真结果证明,在占地面积、图像表达以及扩张方向三个指标对比中,所提方法预测准确率都是最高的,可为城市扩建提供决策基础。 In order to clarify the direction of regional expansion and avoid the deviation of urban development center,this paper proposed a method to predict expansion trend based on RS and GIS.At first,RS and GIS were used to collect data of urban fringe space.And then adjustment coefficients were adopted to remove the noise from remote sensing images.The expansion of urban fringe space was regarded as a kind of linear variation that only time changed.Based on this characteristic,the prediction space was constructed.Moreover,the information response domain and data basin distribution in the space were deduced and calculated through historical input values,output values and standard values.Furthermore,the spatial state with the highest fitting degree between the response domain and the data basin was derived by substituting the features of the actual area.Finally,the prediction outputs of indexes such as floor area,predicted area and house data size were obtained by prediction vector matrix.Simulation results prove that the proposed method has the highest prediction accuracy in the comparison of three indexes such as floor area,image expression and expansion direction,which can provide a decision-making basis for urban expansion.
作者 靳云龙 吕静 冯志佰 尹钰博 JIN Yun-long;LV Jing;FENG Zhi-bai;YIN Yu-bo(College of Architecture and Urban Planning,Jilin Jianzhu University,Changchun Jilin 130000,China;School of Geographical Sciences,Changchun Normal University,Changchun Jilin 130000,China)
出处 《计算机仿真》 北大核心 2023年第8期164-168,共5页 Computer Simulation
基金 吉林省社会科学基金项目(2020B231) 吉林省高教科研课题项目(JGJX2021D577)。
关键词 城市边缘空间扩张 遥感图像 预测向量矩阵 占地面积 Urban fringe space expansion Remote sensing image Prediction vector matrix Floor area
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