Capturing spatial co-location patterns—subsets of two or more types of events that are geographically close—is one of the primary interests in spatial analysis because many phenomena are geographically related to each other. For example, in many central districts in cities across the world, different types of stores form clusters based on the benefits of spatial agglomeration. To analyze co-location, the cross K function has been used in many studies. However, this method and other existing methods are not likely to be appropriate for analyzing co-locations in a micro-scale space due to some limitations.
To precisely analyze the micro-scale co-location, this project develops a new statistical method named network dual K function. One of the important advantages of the method is that it deals with a network-constrained space. The proposed method will be applied to various types of stores in trendy districts in Tokyo to demonstrate the usefulness of the method for studies on economic geography, and to improve our understanding of urban agglomeration. In addition, as an application to health geography, this new approach will be also used to examine the relationships between the step counts of residents and the neighborhood built environments in Yokohama city, providing insights into living environments that affect human walkability.