traveltime: an R package to calculate travel time across a landscape from user-specified locations

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Ryan" }, { "@type": "Person", "name": "Nicholas Tierney" }, { "@type": "Person", "name": "Nick Golding" }, { "@type": "Person", "name": "Daniel J. Weiss" } ], "publisher": { "@type": "Organization", "name": "Gates Open Research", "logo": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 600, "width": 47 } }, "image": { "@type": "ImageObject", "url": "https://gatesopenresearch.org/img/AMP/Gates_image.png", "height": 1200, "width": 94 }, "description": "Understanding and mapping the time to travel among locations is useful for many activities from urban planning to public health and myriad others. Here we present a software package — traveltime — written in and for the language R. traveltime enables a user to create a raster map of the travel time over an area of interest from a user-specified set of locations defined by geographic coordinates. The result is a raster of the area of interest where the value in each cell is the lowest travel time in minutes to the nearest of the supplied locations. We envisage this software having diverse applications including: estimating sampling bias, allocating defibrillators, setting health districts, or mapping access to vehicle chargers and agricultural facilities. The work-flow requires two key steps: preparing a friction surface for the area of interest, and then calculating travel time over that surface for the points of interest. traveltime is available from R-Universe and GitHub, and documented at https://idem-lab.github.io/traveltime/." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://gatesopenresearch.org/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://gatesopenresearch.org/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://gatesopenresearch.org/articles/9-50", "name": "traveltime: an R package to calculate travel time across a landscape..." } } ] } Home Browse traveltime: an R package to calculate travel time across a landscape... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Ryan GE, Tierney N, Golding N and Weiss DJ. traveltime: an R package to calculate travel time across a landscape from user-specified locations . Gates Open Res 2025, 9 :50 ( https://doi.org/10.12688/gatesopenres.16356.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article traveltime: an R package to calculate travel time across a landscape from user-specified locations Gerard E. Ryan 1,2 , Nicholas Tierney 1,3 , Nick Golding 1,4 , Daniel J. Weiss 1,3 Gerard E. Ryan 1,2 , Nicholas Tierney 1,3 , Nick Golding 1,4 , Daniel J. Weiss 1,3 PUBLISHED 24 Jul 2025 Author details Author details 1 The Kids Research Institute Australia, Nedlands, WA, 6009, Australia 2 The University of Melbourne School of Population and Global Health, Melbourne, Victoria, 3010, Australia 3 Curtin University, Bentley, WA, Australia 4 The University of Western Australia Department of Mathematics and Statistics, Perth, Western Australia, Australia Gerard E. Ryan Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Nicholas Tierney Roles: Methodology, Software, Validation, Writing – Review & Editing Nick Golding Roles: Conceptualization, Funding Acquisition, Supervision, Writing – Review & Editing Daniel J. Weiss Roles: Conceptualization, Methodology, Software, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Originally published as a preprint: https://doi.org/10.12688/verixiv.989.1 Abstract Understanding and mapping the time to travel among locations is useful for many activities from urban planning to public health and myriad others. Here we present a software package — traveltime — written in and for the language R. traveltime enables a user to create a raster map of the travel time over an area of interest from a user-specified set of locations defined by geographic coordinates. The result is a raster of the area of interest where the value in each cell is the lowest travel time in minutes to the nearest of the supplied locations. We envisage this software having diverse applications including: estimating sampling bias, allocating defibrillators, setting health districts, or mapping access to vehicle chargers and agricultural facilities. The work-flow requires two key steps: preparing a friction surface for the area of interest, and then calculating travel time over that surface for the points of interest. traveltime is available from R-Universe and GitHub, and documented at https://idem-lab.github.io/traveltime/. READ ALL READ LESS Keywords R, geographic information systems, spatial analysis Corresponding Author(s) Gerard E. Ryan ( [email protected] ) Close Corresponding author: Gerard E. Ryan Competing interests: No competing interests were disclosed. Grant information: This work was supported, in whole or in part, by the Gates Foundation [INV-021972]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Ryan GE et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Ryan GE, Tierney N, Golding N and Weiss DJ. traveltime: an R package to calculate travel time across a landscape from user-specified locations . Gates Open Res 2025, 9 :50 ( https://doi.org/10.12688/gatesopenres.16356.1 ) First published: 24 Jul 2025, 9 :50 ( https://doi.org/10.12688/gatesopenres.16356.1 ) Latest published: 24 Jul 2025, 9 :50 ( https://doi.org/10.12688/gatesopenres.16356.1 ) Introduction Understanding and mapping the time to travel among locations is useful for many activities from urban planning ( Zahavi 1974 ) to public health ( Hulland et al. 2019 ; Weiss et al. 2020 ) and myriad others ( Nelson et al. 2019 ). Global maps of travel time to cities ( Weiss et al. 2018 ; Nelson et al. 2019 ) and health care ( Hulland et al. 2019 ; Weiss et al. 2020 ) have generated much interest and use 1 , and the city data set of Nelson et al. (2019) is available to R users through the widely-used geodata package ( Hijmans et al. 2024 ). Here we extend that work to enable travel time calculations from any arbitrary set of locations and friction surface. We present a software package — traveltime — written in and for the language R ( R Core Team 2024 ). traveltime enables a user to create a raster map of the travel time over an area of interest from a user-specified set of locations defined by geographic coordinates. The result is a raster of the area of interest where the value in each cell is the lowest travel time in minutes to the nearest of the supplied locations. A gaggle of R packages provide superficially similar though fundamentally different functionality via the TravelTime.com API ( TravelTime 2024a, 2024b ; von Bergmann 2024 ; Lo Russo 2024 ). Their ‘Isochron’ polygons — areas reachable within a given time from a given location — are most comparable to what traveltime::calculate_travel_time() calculates. However, each isochron is a single polygon calculated is for a single point location and specified maximum travel time, rather than a raster of temporal gradation across a landscape, jointly for an arbitrary number of points, as in traveltime . TravelTime.com cannot provide a single result surface for time to the nearest of a group of points, and continuous time scale without extensive repeated iteration for all combinations of time and points, plus additional calculation of the minimum value for each cell from all points. Furthermore, TravelTime.com requires access keys, a paid subscription beyond a short free period, and caps queries, which add considerable friction to the use of this resource. With traveltime , we provide free and open source software to estimate travel time from any number of user-supplied locations, across a complete area of interest, and with convenient access to motorised transport or walking friction surfaces with global coverage. Methods Implementation traveltime is an R ( R Core Team 2024 ) package and requires installation of R version 4.1 or a more recent version traveltime provides a spatial interface using object classes from the terra package ( Hijmans 2024 ). Travel time is calculated as movement over a resistance ‘friction surface’ ( van Etten 2017 ). To provide easy access to the existing friction surfaces generated by Weiss et al. (2020) , traveltime internally uses the R package malariaAtlas ( Pfeffer et al. 2018 ) to download surfaces for the area of interest; though users can also supply any other friction surface raster. Operation The work-flow requires two key steps: • preparing a friction surface for the area of interest, and then • calculating travel time over that surface for the points of interest. Installation traveltime is available from R-Universe and GitHub , and documented at https://idem-lab.github.io/traveltime/ . It can be installed in R as follows: install.packages ( "traveltime" , repos = c ( "https://idem-lab.r-universe.dev" )) Example: walking from public transport in Singapore Here we provide an example to calculate and map the walking travel time from rail transport stations across Singapore. Complete code for this example is available as a vignettte in package documentation . Prepare data and friction surface We need two items of data: • our area of interest — Singapore, and • our points to calculate travel time from — Singapore’s Mass Rapid Transit (MRT) and Light Rail Transit (LRT) stations. We download singapore_shapefile , a polygon boundary of Singapore from the GADM ( GADM 2022 ) database using the geodata package ( Hijmans et al. 2024 ) as our area of interest: library (terra) library (geodata) singapore_shapefile <- gadm ( country = "Singapore" , level = 0 , path = tempdir (), resolution = 2 ) singapore_shapefile class : SpatVector geometry : polygons dimensions : 1, 2 (geometries, attributes) extent : 103.6091, 104.0858, 1.1664, 1.4714 (xmin, xmax, ymin, ymax) coord. ref. : lon/lat WGS 84 (EPSG:4326) names : GID_0 COUNTRY type : values : SGP Singapore Next we use the function traveltime::get_friction_surface to retrieve a walking friction surface for our area of interest. Alternatively, we could use surface = "motor2020" for motorised travel. We’re also only interested in walking on land so we mask out areas outside of the land boundary in singapore_shapefile . Users supplying their own friction surfaces do not need to download one in this fashion, only ensure that it is in SpatRaster format. friction_singapore terra :: mask (singapore_shapefile) |-- lowerCorner: 1.1664 103.6091 |-- upperCorner: 1.4714 104.0858Start tag expected, '<' not found friction_singapore class : SpatRaster dimensions : 37, 57, 1 (nrow, ncol, nlyr) resolution : 0.008333333, 0.008333333 (x, y) extent : 103.6083, 104.0833, 1.166667, 1.475 (xmin, xmax, ymin, ymax) coord. ref. : lon/lat WGS 84 (EPSG:4326) source(s) : memory varname : Accessibility__202001_Global_Walking_Only_Friction_Surface_1.1664,103.6091,1.4714,104.0858 name : friction_surface min value : 0.01200000 max value : 0.06192715 Our points are the traveltime::stations data, containing coordinates of all LRT and MRT station exits in Singapore ( Land Transport Authority 2019 ): library (traveltime) head (stations) x y [1,] 103.9091 1.334922 [2,] 103.9335 1.336555 [3,] 103.8493 1.297699 [4,] 103.8508 1.299195 [5,] 103.9094 1.335311 [6,] 103.9389 1.344999 We plot these data below. traveltime takes resistance values of friction ( van Etten 2017 ), so higher values of friction indicate more time travelling across a given cell. Calculate and plot the travel time With all the data collected, the function calculate_travel_time() takes the friction surface friction_singapore and the points of interest in stations , and returns a SpatRaster of walking time in minutes to each cell from the nearest station: travel_time_singapore <- calculate_travel_time ( friction_surface = friction_singapore, points = stations ) travel_time_singapore class : SpatRaster dimensions : 37, 57, 1 (nrow, ncol, nlyr) resolution : 0.008333333, 0.008333333 (x, y) extent : 103.6083, 104.0833, 1.166667, 1.475 (xmin, xmax, ymin, ymax) coord. ref. : source(s) : memory name : travel_time min value : 0 max value : Inf We present the resulting calculated travel times in Figure 2 where, as expected, the travel times are lowest near station exits (per Figure 1 ) and progressively higher further away. Note that the results in travel_time_singapore include infinite values ( Inf above). In Figure 1 , the islands to the south and north-east are shown as filled cells, but unconnected with the mainland. The raster cells for these islands appear absent in Figure 2 . Because they are not connected to any cells with a station, the calculated travel time is infinite, and so these cells do not appear in Figure 2 . Figure 1. Friction surface raster of Singapore, showing Singapore boundary in grey, and station locations as grey points. Figure 2. Map of walking travel time in Singapore, in minutes from nearest MRT or LRT station. Discussion The traveltime package is immediately suitable to be used ‘out-of-the-box’ with any set of coordinates, in any part of the globe. We envisage this software having diverse applications including: estimating sampling bias ( Dennis and Thomas 2000 ), allocating defibrillators ( Tierney et al. 2018 ), setting health districts ( Padgham et al. 2019 ), or mapping access to vehicle chargers ( Falchetta and Noussan 2021 ) and agricultural facilities ( Zhao et al. 2023 ). Nonetheless, we see opportunities to build the package utility through: • capability to better distribute a wider range friction surfaces, and • additional methods to efficiently compute results over large spatial extents. Firstly, traveltime currently facilitates access to walking and motorised friction surfaces for 2020, both at 30 arc-second resolution (approximately 0.008333 decimal degrees, or just below 1 km 2 at the equator). Although the user can presently supply their own friction surface, we expect most applications will use these existing surfaces given the extensive work needed in creating new ones ( Weiss et al. 2018, 2020 ). As landscapes are dynamic, it may be useful to incorporate updated versions of these friction surfaces if and when they are available. Furthermore, although the resolution of these data is likely to be suitable for larger landscape foci, higher resolution data may be helpful for more locally focussed analyses. For instance, although the example here was chosen for it’s simplicity and low computational demands, a ~1 km 2 cell size is a relatively large area to walk across, and thus actual waking times may vary significantly within each cell. We underline however that a user can provide their own higher resolution friction surface at present if desired. At the other end of the scale, as the area of interest increases, the size of the matrix of calculations necessary increases exponentially, making significant memory demands for analyses over large landscapes (e.g. analyses over Africa required ~ 72 GB RAM to run successfully). Developing methods to handle large landscapes either with less memory or via cloud resources would be helpful to make such analyses accessible to those without access to larger computing resources. Lastly, although this article is intended to be the key reference for the traveltime package, we suggest citations of the package are accompanied by citing the underlying methodological work as well ( Weiss et al. 2018, 2020 ). Figure permissions The authors confirm ownership of the figures used in this manuscript. Data and software availability All software described here is available from R-Universe https://idem-lab.r-universe.dev/traveltime and GitHub https://github.com/idem-lab/traveltime , and documented at https://idem-lab.github.io/traveltime/ . Code used in this paper is released via GitHub as v4 , and archived on Zenodo under DOI 10.5281/zenodo.15347016 . The package associated with this paper contains information from the dataset “LTA MRT Station Exit (GEOJSON)” accessed on the 10th of December 2024 from data.gov.sg, which is made available under the terms of the Singapore Open Data Licence version 1.0 https://data.gov.sg/open-data-licence . Acknowledgements An earlier version of this article has been archived on EarthArXiv https://doi.org/10.31223/X56M74 . References Dennis RLH, Thomas CD: Bias in Butterfly Distribution Maps: The Influence of Hot Spots and Recorder’s Home Range. J. Insect Conserv. 2000; 4 : 73–77. Publisher Full Text Falchetta G, Noussan M: Electric Vehicle Charging Network in Europe: An Accessibility and Deployment Trends Analysis. Transp. Res. Part D: Transp. Environ. 2021; 94 : 102813. Publisher Full Text GADM: GADM Maps and Data.2022. Reference Source Hijmans RJ: terra: Spatial Data Analysis.2024. Reference Source Hijmans RJ, Barbosa M, Ghosh A, et al. : geodata: Download Geographic Data.2024. Reference Source Hulland EN, Wiens KE, Shirude S, et al. : Travel Time to Health Facilities in Areas of Outbreak Potential: Maps for Guiding Local Preparedness and Response. BMC Med. 2019; 17 : 1–16. Publisher Full Text Land Transport Authority: LTA MRT Station Exit (GEOJSON) Dataset.2019. Reference Source Reference Source Lo Russo T: traveltime - a Traveltime API Wrapper for R.2024. Reference Source Nelson A, Weiss DJ, van Etten J , et al. : A Suite of Global Accessibility Indicators. Scientific Data. 2019; 6 (1): 266. PubMed Abstract | Publisher Full Text | Free Full Text Padgham M, Boeing G, Cooley D, et al. : An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services. Front. Neurol. 2019; 10 : 743. PubMed Abstract | Publisher Full Text | Free Full Text Pfeffer DA, Lucas TCD, May D, et al. : malariaAtlas: An R Interface to Global Malariometric Data Hosted by the Malaria Atlas Project. Malar. J. 2018; 17 : 1–10. R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2024. Reference Source Tierney NJ, Jost Reinhold H, Mira A, et al. : Novel Relocation Methods for Automatic External Defibrillator Improve Out-of-Hospital Cardiac Arrest Coverage Under Limited Resources. Resuscitation. 2018; 125 : 83–89. PubMed Abstract | Publisher Full Text TravelTime: TravelTime API.2024a. Reference Source TravelTime: traveltimeR: Interface to ‘Travel Time’ API.2024b. Reference Source van Etten J : R Package gdistance: Distances and Routes on Geographical Grids. J. Stat. Softw. 2017; 76 (13): 1–21. Publisher Full Text von Bergmann J : rtraveltime.2024. Reference Source Weiss DJ, Andy Nelson HS, Gibson WT, et al. : A Global Map of Travel Time to Cities to Assess Inequalities in Accessibility in 2015. Nature. 2018; 553 (7688): 333–336. PubMed Abstract | Publisher Full Text Weiss DJ, Andy Nelson CA, Vargas-Ruiz K, et al. : Global Maps of Travel Time to Healthcare Facilities. Nat. Med. 2020; 26 (12): 1835–1838. PubMed Abstract | Publisher Full Text Zahavi Y: Traveltime Budgets and Mobility in Urban Areas. United States: Federal Highway Administration; 1974. Zhao J, Elmore AJ, Lee JSH, et al. : Replanting and Yield Increase Strategies for Alleviating the Potential Decline in Palm Oil Production in Indonesia. Agric. Syst. 2023; 210 : 103714. Publisher Full Text Footnotes 1 Collectively >1600 citations per Google Scholar at the 28th of January 2025. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 24 Jul 2025 ADD YOUR COMMENT Comment Author details Author details 1 The Kids Research Institute Australia, Nedlands, WA, 6009, Australia 2 The University of Melbourne School of Population and Global Health, Melbourne, Victoria, 3010, Australia 3 Curtin University, Bentley, WA, Australia 4 The University of Western Australia Department of Mathematics and Statistics, Perth, Western Australia, Australia Gerard E. Ryan Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Nicholas Tierney Roles: Methodology, Software, Validation, Writing – Review & Editing Nick Golding Roles: Conceptualization, Funding Acquisition, Supervision, Writing – Review & Editing Daniel J. Weiss Roles: Conceptualization, Methodology, Software, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was supported, in whole or in part, by the Gates Foundation [INV-021972]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 24 Jul 2025, 9:50 https://doi.org/10.12688/gatesopenres.16356.1 Copyright © 2025 Ryan GE et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. 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