More than travel time: New accessibility index capturing the connectivity of transit services

How do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection

“…In recent years, advances in geospatial technologies and GIScience methods have allowed researchers to analyze and visualize geospatial data in great detail (Gutmann et al, 2008;Kwan, 2012;Richardson et al, 2013). For instance, maps have been widely utilized to visualize the complex patterns of diverse social phenomena, such as infectious diseases and human daily mobility (e.g., Kim & Kwan, 2019Kim & Lee, 2019;Kwan, 2004;Reich & Haran, 2018). At the same time, however, mapping people's confidential geospatial data (e.g., people's homes, workplaces, or GPS trajectories) may allow an individual's identity (e.g., name and street address) to be identified from a map (A.J.…”

The Disparity in Transit Travel Time between Koreans and Japanese in 1930s Colonial Seoul

“…Using the GTFS dataset as a basis, we leveraged an open-source transport analysis tool -r5r R-package (Pereira et al 2021)-to estimate total transit travel times as well as out-of-vehicle walking/waiting times, which are important factors for measuring transit-based accessibility (Kim and Lee 2019;Lee and Miller 2020). We also used the r5r toolkit to measure variances in travel times from each neighborhood unit to City Hall within a two-hour time window (6-8 AM).…”

Travel time errors caused by geomasking might be different between transportation modes and types of urban area

“…For each individual's original home location and the six associated geomasked locations (derived with two geomasking methods and three parameter values), we calculate car‐ and transit‐based travel times to the nine selected hospitals using the Google Maps application programming interface (API). The Google Maps API utilizes real‐world transportation networks, including detailed local road networks, historical traffic congestion conditions, and actual public transit routes/schedules, to estimate the most realistic shortest travel time path for any pair of origin and destination (Barth, 2009; Kim & Kwan, 2019, 2021b; Kim & Lee, 2019). One of the advantages of using the Google Maps API is that users can obtain estimated travel times by simply providing trip information (e.g., the geographic coordinates of origins/destinations, departure time, and transportation mode) without the need to prepare massive transportation network datasets (Barth, 2009; Kim & Kwan, 2019, 2021b; Kim & Lee, 2019).…”

“…Further, to evaluate the spatial accessibility of certain facilities (e.g., hospitals or healthy food outlets), it is essential to preserve travel times as much as possible after geomasking. Preserving the original travel times is critical because travel times provide fundamental and vital information when assessing how easily people can access facilities by traveling (Apparicio et al., 2017; Delamater, Messina, Shortridge, & Grady, 2012; Delmelle et al., 2013; Kim & Lee, 2019; Luo & Wang, 2003; Penchansky & Thomas, 1981). However, because of the errors introduced by geomasking methods, travel times to facilities obtained from using geomasked locations can be significantly different from those obtained from using the original locations.…”

“…Answering these questions would have important methodological and policy implications for the accurate evaluation of the spatial accessibility of hospitals or healthy food outlets (e.g., Hawthorne & Kwan, 2013; Lee & Miller, 2018; Paez, Mercado, Farber, Morency, & Roorda, 2010; Shannon, 2016; Wang & Kwan, 2018). Particularly, results related to errors in transit‐based travel times would be important because socially disadvantaged people (e.g., low‐income people and minorities) are likely to use public transit for their trips as they often do not own private vehicles (e.g., Federal Highway Administration, 2019; Kim & Lee, 2019). Moreover, in light of the recent emphasis on the “people‐based” approach in accessibility studies (Kwan, 2009), our results would provide important methodological implications for these studies.…”