Length L-function for Network-Constrained Point Data

TL;DR

This paper introduces a novel length L function for network-constrained point data to accurately detect aggregations by using a consistent neighborhood measure, improving upon existing network K functions.

Contribution

The study proposes a new length L function that uses a consistent neighborhood measure for better aggregation detection in network-constrained data.

Findings

The length L function outperforms the network K function in detecting true aggregation scales.

It successfully identifies aggregations in high-density network areas.

Application to taxi data revealed differences in demand patterns between weekdays and weekends.

Abstract

Network constrained points are referred to as points restricted to road networks, such as taxi pick up and drop off locations. A significant pattern of network constrained points is referred to as an aggregation; e.g., the aggregation of pick up points may indicate a high taxi demand in a particular area. Although the network K function using the shortest path network distance has been proposed to detect point aggregation, its statistical unit is still radius based. R neighborhood, in particular, has inconsistent network length owing to the complex configuration of road networks which cause unfair counts and identification errors in networks (e.g., the length of the r neighborhood located at an intersection is longer than that on straight roads, which may include more points). In this study, we derived the length L function for network constrained points to identify the aggregation by designing a novel neighborhood as the statistical unit; the total length of this is consistent throughout the network. Compared to the network K function, our method can detect a true to life aggregation scale, identify the aggregation with higher network density, as well as identify the aggregations that the network K function cannot. We validated our method using taxi trips pick up location data within Zhongguancun Area in Beijing, analyzing differences in maximal aggregation between workdays and weekends to understand taxi demand in the morning and evening peak.