Regionalization has emerged as a crucial research area for the past 50 years, including aggregating smaller areas into larger, contiguous, and/or homogeneous regions. Spatial optimization techniques are advantageous for solving regionalization problems, yet their nondeterministic polynomial-time (NP) hard nature leads to computational complexity and time consumption, especially with extensive datasets. Although regionalization studies play a pivotal role in defining boundaries for multi-scalar analysis and modeling complex socio-environmental systems (SES), current approaches lack integrated consideration of raster and vector data. We introduce a unified, structured framework integrating Geographic Information Systems (GIS), image segmentation, and regionalization to identify and characterize socio-environmental units accounting for various data models and types. We use a public geodatabase of the Rio Grande/Bravo basin, an SES covering diverse cultures, ecosystems, and economies, to demonstrate the functionality of our newly developed method, which effectively identifies spatial units for subsequent SES analysis and modeling. The delineation process accounts for various factors, including administrative boundaries, estimated total quantities, compactness, spatial contiguity, and similarity in socio-environmental characteristics. To make this work reproducible, replicable, and expandable, we developed the approach entirely based on open-source Python packages. Our method is easily transferable to other research using various data formats and spatial scales to delineate spatial unit boundaries effectively and efficiently.

中文翻译：

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在区域化中结合矢量和栅格数据：用于描绘社会环境系统分析的空间单元边界的统一框架

过去 50 年来，区域化已成为一个重要的研究领域，包括将较小的区域聚合成较大的、连续的和/或同质的区域。空间优化技术有利于解决区域化问题，但其非确定性多项式时间 (NP) 硬性导致计算复杂性和时间消耗，尤其是在数据集广泛的情况下。尽管区域化研究在定义多标量分析的边界和对复杂的社会环境系统（SES）建模方面发挥着关键作用，但当前的方法缺乏对栅格和矢量数据的综合考虑。我们引入了一个统一的、结构化的框架，集成了地理信息系统（GIS）、图像分割和区域化，以识别和描述考虑各种数据模型和类型的社会环境单元。我们使用里奥格兰德/布拉沃盆地的公共地理数据库（涵盖不同文化、生态系统和经济的 SES）来展示我们新开发的方法的功能，该方法可以有效地识别后续 SES 分析和建模的空间单元。划定过程考虑了各种因素，包括行政边界、估计总量、紧凑性、空间连续性和社会环境特征的相似性。为了使这项工作可重现、可复制和可扩展，我们完全基于开源 Python 包开发了该方法。我们的方法可以轻松地转移到使用各种数据格式和空间尺度的其他研究中，以有效且高效地描绘空间单元边界。