Spatially-weighted Network Dual K Function: An Extended Statistical Method for Analyzing Co-location on a Street Network
Summary
Capturing spatial co-location patterns—subsets of two or more groups of events that are geographically close—is one of the primary interests in spatial analysis because of its versatility. To reveal the spatial clustering of different types of point events on street networks using an exact statistical formula, the network dual K function method has been recently developed. However, the method has still some limitations. One of the major drawbacks is to assume a uniform distribution of events as a null hypothesis against which to test the degree of co-location. While this uniformity assumption is one of the most frequently encountered null hypotheses in spatial statistics, the actual distribution is quite likely to be uneven because of the biased density of consumers and urban establishments. To overcome this limitation, i.e., to take into account the inhomogeneous distribution, we propose an extended method, which is called the spatially-weighted network dual K function. The objectives are 1) to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network considering inhomogeneous distribution; 2) to implement this statistical method in computational procedures using free GIS software packages; 3) to apply the method to different types of healthy and unhealthy stores in a trendy district in Tokyo. The results show that the method is useful for measuring spatial proximity among various kinds of stores and revealing locational tendencies, such as relatively healthy and unhealthy food environments.
Speaker’s information
Wataru Morioka is a PhD candidate at the Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign. He earned a BA in Cultural and Creative Studies from Aoyama Gakuin University and an MS in Environmental Sciences from University of Tokyo. Wataru’s current research interest is space-time analysis using GIScience. Through these methods, he is aiming to evaluate people’s living environment such as accessibility to shopping facilities and other public amenities in order to enhance public life. He has been publishing research papers in major academic journals on GIScience and spatial analysis (e.g., International Journal of Geographical Information Science, Transactions in GIS, and Geographical Analysis).
This seminar series is co-organized by CHUD (Center for Housing & Urban Development), GeoSAT (Center for Geospatial Sciences, Applications and Technology), and TAMIDS-DAL (Design and Analytics Lab for Urban Artificial Intelligence @ Texas A&M Institute of Data Science).
Time: 10:00-10:30 a.m. US Central Time (Thursday, April 13, 2023)
Zoom Meeting ID: 931 9502 5171 Passcode: 217139
Direct Link: https://tamu.zoom.us/j/93195025171?pwd=a3M0U2pCUmV4Z1AyVURFblIzMVB4QT09
Host: Jiaxin Du, Data Science Ambassador@TAMIDS, PhD candidate@LAUP, TAMU
recording: https://youtu.be/oe0l0Lm6MxI