ABSTRACT

The raster data structure stores categorical and continuous field data for spatial analysis, environmental modeling, and resource planning. With rapidly advancing sensor networks, the spatial resolution of data is increasing, sometimes outpacing the optimum resolution for applications. Overcoming granularity differences between raw and “analysis ready” data often requires upscaling source data to a desired target map with the goal of maintaining the structure and spatial variance of the higher resolution data. Common strategies for resampling categorical data (nearest neighbor and majority rule) force users to choose between preserving map structure and map variety. A new method is presented here that integrates global and zonal class proportions to guide the optimal allocation of classified cells. This technique provides more representative maps with respect to variety and structure, better retains minority classes, and produces higher (or equal) levels of user’s and producer’s accuracy than the traditional methods. An R-based implementation is provided that has serviceable run times, and the performance of the algorithm is shown to be scalable, proving the tool widely usable.

摘要

栅格数据结构存储用于空间分析、环境建模和资源规划的分类和连续字段数据。随着传感器网络的快速发展，数据的空间分辨率不断提高，有时甚至超过了应用程序的最佳分辨率。克服原始数据和“分析就绪”数据之间的粒度差异通常需要将源数据放大到所需的目标地图，目的是保持更高分辨率数据的结构和空间变化。重采样分类数据的常用策略（最近邻和多数规则）迫使用户在保留地图结构和地图多样性之间进行选择。这里提出了一种新方法，该方法整合了全局和区域类比例，以指导分类单元的优化分配。与传统方法相比，该技术在多样性和结构方面提供了更具代表性的地图，更好地保留了少数类，并产生了更高（或相等）水平的用户和生产者的准确性。提供了一个基于 R 的实现，它具有可维护的运行时间，并且算法的性能被证明是可扩展的，证明该工具具有广泛的可用性。