中文摘要：

通过与现有方法进行对比，提出了一种新的基于k阶数据场的城镇土地定级模型。在要素分层上，模型提供了更灵活的分层策略，解决了不同类要素之间空间分布关联关系的表达，并且提供了灵活的权重归一化处理机制；在作用域的分割上，以k阶Voronoi多边形“最近邻近”划分构建了女元影响环境作用域；在评估点作用分的演算方面，提出了与k元影响环境相适应的多要素分值演算策略，其中主要以三种情形的拟合来说明作用分的演算，模型中还可以采用其他更多形式的多要素演算规则。

英文摘要：

By comparing with current methods, we propose forward a new urban land classification model based on k-order data field point to point-factors. This model has three key techniques links： on the layer＇s classification, the model provides more flexible policy and expresses the spatial distribution correlation of different class factors. Moreover, we desert the old classification method purely according to property. On the effect area division, we build k-factor influencing environmental effect area which has an advantage in no crack and no cover by k-order Voronoi polygon nearest neighborhood division. With k-factor influencing effect area, we can control model precision and meet the needs of Urban Land Classification in complicated conditions. On the calculation of assessing point effect value, we present a factor value calculating strategy corresponding to k-factor influencing environment , especially in three kinds of approximation as well as more kinds of multi-factor calculating rules.