Artificial neural network for ecological-economic zoning as a tool for spatial planning

Abstract: The objective of this work was to analyze social and environmental information through an artificial neural network-self-organizing map (ANN-SOM), in order to provide subsidy to ecological-economic zoning (EEZ) as a tool to reduce the subjectivity of the process. The study area comprises 16 municipalities in the northeast of the state of Pará, Brazil, representative of the agricultural development in the state. Data processing involved three steps: preparation of the data in a geographic information system (GIS) environment; mathematical processing (ANN-SOM) of the data; and visualization and interpretation of the processing results, allowing the spatial planning of northeastern Pará. The results comprised 13 classes, regrouped according to behavioral similarity criteria into four categories, which represent the main areas of sustainability proposed for the state of Pará, according to existing EEZ. The proposed methodology allows individualizing areas in the region that EEZ had not defined, mainly due to the greater possibility of combining and integrating a large number of physical, social, and economic variables through the SOM.

摘要：本研究旨在通过人工神经网络-自组织映射（ANN-SOM）分析社会与环境信息，将其作为辅助工具应用于生态经济区划（EEZ）工作，以降低该区划流程的主观性。本研究的区域涵盖巴西帕拉州东北部的16个市镇，该区域是该州农业发展的典型代表。数据处理共包含三个环节：在地理信息系统（GIS）环境中完成数据预处理；采用ANN-SOM开展数据数学建模分析；对处理结果进行可视化展示与解译，可为帕拉州东北部的空间规划提供支撑。本研究最终得到13个聚类类别，依据行为相似性准则将其归为四大类别，结合现有EEZ框架，这些类别对应帕拉州拟划定的主要可持续发展区域。本研究提出的方法可实现EEZ未覆盖区域的精准划分，其核心优势在于通过自组织映射（SOM）能够灵活整合海量自然、社会与经济变量，实现多维度数据的协同融合。