ABSTRACT Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings.

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局部模糊地理加权聚类：一种新的地理人口分割方法

摘要 模糊地理加权聚类已被提出作为一种在应用于地理人口统计分析时改进模糊 c 均值算法的方法。这种聚类方法允许一个空间实体属于多个不同程度的聚类，即隶属度值。尽管模糊地理加权聚类尝试创建具有地理意识的聚类，但它部分无法追踪空间依赖性和异质性，因为作为全局度量，成员值是跨整个空间实体集计算的。在这里，我们介绍了模糊地理加权聚类的第一个局部版本，“局部模糊地理加权聚类”。在局部模糊地理加权聚类中，空间实体的成员值仅根据其邻域内的空间实体的成员值更新，而不是根据全局度量最初提出的整个实体集更新。此外，我们应用粒子群优化元启发式来克服关于模糊 c 均值算法的随机初始化问题。为了评估我们的方法，我们使用纽约曼哈顿的癌症事件基准数据集将局部模糊地理加权聚类与全局模糊地理加权聚类进行了比较。结果表明，局部模糊地理加权聚类在所有实验设置中都优于全局版本。我们应用粒子群优化元启发式来克服关于模糊 c 均值算法的随机初始化问题。为了评估我们的方法，我们使用纽约曼哈顿的癌症事件基准数据集将局部模糊地理加权聚类与全局模糊地理加权聚类进行了比较。结果表明，局部模糊地理加权聚类在所有实验设置中都优于全局版本。我们应用粒子群优化元启发式来克服关于模糊 c 均值算法的随机初始化问题。为了评估我们的方法，我们使用纽约曼哈顿的癌症事件基准数据集将局部模糊地理加权聚类与全局模糊地理加权聚类进行了比较。结果表明，局部模糊地理加权聚类在所有实验设置中都优于全局版本。