The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

The urban accessibility (UA) classification measures the degree of urban influence New Zealand’s urban areas have on surrounding rural areas. It classifies the geographic accessibility of rural statistical area 1s (SA1s) and small urban areas according to their proximity, or degree of remoteness, to larger urban areas. This classification provides increased understanding of the heterogeneity of rural areas and small urban areas and will allow more extensive analysis and reporting. Understanding the degree of urban accessibility or remoteness is important as it has a major influence on the employment sector, accessibility to services, and population composition and change. The methodology uses drive time from an SA1 address weighted centroid to the outside boundary of the nearest major, large, and medium urban area (from Stats NZ urban rural (UR) classification) to classify rural SA1s and small urban areas to one of five categories of accessibility or remoteness. The Open Source Routing Machine service using the OpenStreetMap road network is used to calculate the drive times.

A concordance between SA1 and Urban Accessibility can be found on Aria.

Rural SA1s and small urban areas are classified to the following categories:

·High urban accessibility: 0 to15 minutes from major urban areas

·Medium urban accessibility: 15 to 25 minutes from major urban areas 0 to 25 minutes from large urban areas 0 to 15 minutes from medium urban areas

·Low urban accessibility: 25 to 60 minutes from major or large urban areas 15 to 60 minutes from medium urban areas

·Remote: 60 to 120 minutes from major, large or medium urban areas

·Very remote: more than 120 minutes from major, large or medium urban areas

For more information refer to: Urban accessibility - methodology and classification.

The full classification is shown below: 111 Major urban area

112 Large urban area

113 Medium urban area

221 High urban accessibility

222 Medium urban accessibility

223 Low urban accessibility

224 Remote

225 Very remote

331 Inland water

332 Inlet

333 Oceanic

Note: Areas of 221 High urban accessibility and 222 Medium urban accessibility may be regarded as peri-urban in nature and combined with urban areas for analytical purposes.

Monarch sampling sites throughout the Great Lakes region from June 2 to June 30, with information on sampling location (state/province, nearest city, and GPS coordinates), dates, and sample size.

This dataset includes only fire incidents as defined by the Ontario Fire Marshal (OFM) up to December 31, 2023. Refresh date displayed is not representative of the vintage of this dataset. This dataset provides more detail than the basic incidents dataset provides for only fire Incidents to which Toronto Fire Service(TFS) responds to. The format is similar to the reporting data sent by TFS to the OFM. For privacy purposes personal information is not provided and exact address have been aggregated to the nearest major or minor intersection. Some incidents have been excluded pursuant to exemptions under Section 8 of Municipal Freedom of Information and Protection of Privacy Act (MFIPPA). Incidents with incomplete data may be under investigation or is classified as a no loss outdoor fire.

Four areas (out of 22 total city areas) with the largest numbers of smear-positive tuberculosis diagnoses and nearby tertiary hospitals and TB specialty facilities in Mumbai, 2013.