A GIS Toolbox for Measuring and Mapping Person‐Based Space‐Time Accessibility

Abstract

This article presents a toolbox to compute and map person-based accessibility indicators, based on classical time geography concepts. The intent is to provide GIS and urban planning practitioners with a userfriendly and easily customizable tool. While it relies on well-known concepts, the toolbox implements a major innovation in person-based accessibility assessment by taking into account opening hours when measuring the accessibility of urban facilities. The toolbox can be downloaded from http://bit.ly/ 1h6yg5Z.

Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks

“…Similar to the existing NTP-Dij algorithm [6,31], a straightforward algorithm for constructing a NTP is to apply two shortest path searches using the link-based one-all Dijkstra's algorithm [33]. The forward search is used to calculate the least travel time t oj R from origin o to every link a ij (i.e., head node j).…”

“…At the core of time geography is the Space-time prism (STP) model, which depicts Space-time extents that can be physically reached by the individual from specified locations within a given time budget. This STP model has been widely used for various applications, such as measuring individual accessibility to urban services [3][4][5][6][7], and formulating activity-based models of travel demand [8][9][10][11].…”

A modification of the time-geographic framework to support temporal flexibility in ‘fixed’ activities

“…ly/1rKhbkU. Charleux (2014) describes an earlier version of this toolbox, which has now been upgraded to support traffic feeds and GTFS data as well as temporally flexible anchor activities. An ArcGIS™ Network Dataset was built based on street data from Esri and GTFS data from the Duluth Transit Authority.…”

“…To the point that arrival times appear as entirely stochastic to riders, who just show up, expecting to catch the bus immediately if they are lucky, to just miss one if they are unlucky, and to wait a specific time on average. Accordingly, Charleux (2014) performs time-geographic calculations with travel times on a high-frequency public transit network, incorporating average waiting times or maximum waiting times at stops. This strategy is not satisfying however to model travel times on low-frequency transit networks.…”

“…This is typically the case for shopping activities that can happen only when stores are open. This problem has been addressed by algorithms that take into account operating hours at flexible locations (Charleux 2014). Obviously, temporal constraints other than operating hours could be taken into account in the same way.…”