Analysis of a Distribution of Point Events Using the Network‐Based Quadrat Method

Abstract

This study proposes a new quadrat method that can be applied to the study of point distributions in a network space. While the conventional planar quadrat method remains one of the most fundamental spatial analytical methods on a two-dimensional plane, its quadrats are usually identified by regular, square grids. However, assuming that they are observed along a network, points in a single quadrat are not necessarily close to each other in terms of their network distance. Using planar quadrats in such cases may distort the representation of the distribution pattern of points on a network. The network-based units used in this article, on the other hand, consist of subsets of the actual network, providing more accurate aggregation of the data points along the network. The performance of the network-based quadrat method is compared with that of the conventional quadrat method through a case study on a point distribution on a network. The w 2 statistic and Moran's I statistic of the two quadrat types indicate that (1) the conventional planar quadrat method tends to overestimate the overall degree of dispersion and (2) the network-based quadrat method derives a more accurate estimate on the local similarity. The article also performs sensitivity analysis on network and planar quadrats across different scales and with different spatial arrangements, in which the abovementioned statistical tendencies are also confirmed.

Computational Streetscapes

“…Now, data science is being increasingly trained on fusion across big data. For example, network science and GIS are leading the way in providing structure across unstructured big data that streetscapes often cast in the course of their everyday dynamics [420][421][422]. Indeed, these types of approaches may be what the community needs moving forward in an era of computational social science that is beginning to fuse a wealth of qualitative and observational work [21] with near-ubiquitous sensing [423] and big data capabilities [424,425].…”

The visualization and analysis of urban facility pois using network kernel density estimation constrained by multi-factors

“…However his study still used 2-D grid cells and the outcome is still mapped onto a 2-D Euclidean space. Shiode (2008) pointed out that using square grid in such cases may distort the representation of the distribution on a network. Hence a network-based quadrat method is proposed for a more accurate aggregation.…”

A Time-constrained Network Voronoi Construction and Accessibility Analysis in Location-based Service Technology

“…Essentially the ESPT-based method is vector-based, representing the network with set of nodes and segments, and establishing the related network topology with the classic Dijkstra's algorithm. Alternatively, Shiode (2008), Tan et al (2012), and Xie et al (2008) leaded another potential field in which they realized the discrete mode of network by dividing real-world network into equal-length linear units, and based on that mode to construct N-VDs and estimate kernel density of traffic accidents occurred on a street network. However, the method by Tan et al (2012) still calculated the network distance with the vector-based Dijkstra's algorithm, lacking of efficiency for large-scale data sets.…”