Efficient Regionalization for Spatially Explicit Neighborhood Delineation

# Efficient Regionalization for Spatially Explicit Neighborhood Delineation ## AbstractNeighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches determine kendogenously. Existing approaches for neighborhood delineation that do not require pre-specification ofa k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency. ## Instructions The files in this archive demonstrate the code for the new max-p algorithm. To run the demonstration, please do the following: 1. extract the archive `unzip maxp.zip`2. Install [Anaconda python distribution](https://www.anaconda.com/distribution)3. `conda env create -f environment.yml`4. `conda activate maxp`5. `jupyter notebook`6. Select the notebook `demo.ipynb`

# 面向空间显式邻域划定的高效区域划分

## 摘要
城市社会科学研究中，邻域划定的应用愈发广泛，其核心目标是确定最适配的空间分析单元。此类问题中，邻域的真实数量（通常称为k参数）往往未知，因此分析人员通常需要通过算法手段实现内生确定。然而，现有无需预先指定k参数的邻域划定方法，要么不具备空间属性，要么会生成非连续或相互重叠的区域。本文提出将max-p-regions（最大p区域）用于邻域划定，从而可将地理空间划分为一系列同质性且地理上连续的邻域单元。此外，本文还开发了一种全新的高效算法，以解决求解max-p-regions时面临的计算挑战，使其能够应用于大规模邻域划定任务。该新算法已在开源Python空间分析库（Python Spatial Analysis Library, PySAL）中实现。本文基于模拟数据集与真实数据集开展了计算实验，实验结果验证了该算法的有效性与高效性。

## 使用说明
本归档文件包含了全新max-p算法的示例代码。如需运行该演示程序，请按以下步骤操作：
1. 解压归档文件：执行命令`unzip maxp.zip`
2. 安装[Anaconda Python发行版](https://www.anaconda.com/distribution)
3. 执行命令`conda env create -f environment.yml`创建运行环境
4. 执行命令`conda activate maxp`激活该环境
5. 执行命令`jupyter notebook`启动Jupyter Notebook
6. 选中并打开`demo.ipynb`笔记本文件