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.

As of 2019, most rural inhabitants in Africa resided close to small and mid-sized towns. The nearest city to almost 70 percent of the rural population had between 10,000 and 50,000 inhabitants. Smaller shares of rural households, on the other hand, lived closer to larger urban areas. As of the same year, roughly half of the rural residents lived within 14 kilometers from a city.

This dataset presents the school zones for single sex schools in Victoria for the year of 2020. All public primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools …Show full descriptionThis dataset presents the school zones for single sex schools in Victoria for the year of 2020. All public primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have enrolment zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have zones and have special enrolment criteria. Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas). Zones were produces in Datum 1994 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight line distance and calculations using road classes 0 to 7 in the VicMap road network layer were used to define the measure of shortest practical route. The zones of schools defined as metropolitan have taken preference over the zones of regional schools where they interface. A small number of zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some schools zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose. School enrolment zones are reviewed annually and updated as government school provision changes. The school zone dataset is comprised of distinct map layers for primary schools and for each year of secondary school, acknowledging the different year levels offered by schools. For more information please visit the Victorian Government Data Portal or the Find My School website. Please note: The Victorian school zone layers can be used in conjunction with the Victorian school location layers. Copyright attribution: Government of Victoria - Department of Education and Training, (2019): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

This dataset presents the school zones for Secondary schools in Victoria with the Year 12 curriculum for the year of 2020. All public primary and secondary schools, including Prep/Foundation to Year …Show full descriptionThis dataset presents the school zones for Secondary schools in Victoria with the Year 12 curriculum for the year of 2020. All public primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have enrolment zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have zones and have special enrolment criteria. Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas). Zones were produces in Datum 1994 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight line distance and calculations using road classes 0 to 7 in the VicMap road network layer were used to define the measure of shortest practical route. The zones of schools defined as metropolitan have taken preference over the zones of regional schools where they interface. A small number of zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some schools zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose. School enrolment zones are reviewed annually and updated as government school provision changes. The school zone dataset is comprised of distinct map layers for primary schools and for each year of secondary school, acknowledging the different year levels offered by schools. For more information please visit the Victorian Government Data Portal or the Find My School website. Please note: The Victorian school zone layers can be used in conjunction with the Victorian school location layers. Copyright attribution: Government of Victoria - Department of Education and Training, (2019): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

This table presents experimental counts of businesses that open, close, or continue their operations each month for various levels of geographic and industry detail across Canada going back to January 2015. The data are available as series that are adjusted for seasonality. The level of geographic detail includes national, provincial and territorial, as well as census metropolitan areas (CMA). The data are also broken down by two-digit North American Industry Classification System (NAICS) with some common aggregations, including one for the total business sector for national, provincial and territorial levels of geography.

All government primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have school zones. This does not include schools with specific enrolment criteria including …Show full descriptionAll government primary and secondary schools, including Prep/Foundation to Year 9 and multi-campus schools have school zones. This does not include schools with specific enrolment criteria including English Language Schools and Select Entry Schools. Specialist schools also do not have school zones and have special enrolment criteria. Designated neighbourhood schools are generally the public school within closest proximity to the student’s permanent residential address, unless the Minister for Education or Regional Director has restricted the zone of the school. Closest proximity is calculated as the nearest school by straight line distance in metropolitan areas (including Geelong, Ballarat and Bendigo), or the nearest school by shortest practical route (in regional areas). School zones were produced in GDA94 VicGrid projection (EPSG: 3111) using locations that represent the front of the school or driveway access. Voronoi polygons define the measure of straight-line distance and calculations using road classes 0 to 7 in the VicMap Roads dataset were used to define the measure of shortest practical route. School zones defined as metropolitan have taken preference over school zones of regional schools where they interface. A small number of school zones have been restricted by the Minister for Education to support schools in managing their enrolments. Some school zones have been aligned with structural and geographic barriers recognising the significant accessibility issues they impose. School zones are reviewed annually and updated as government school provision changes. The school zones ZIP file consists of distinct spatial datasets for primary school zones and school zones for each year of secondary school, acknowledging the different year levels offered by schools. The spatial datasets can be used in conjunction with the school locations CSV file also available on data.vic.gov.au. School zones have been published on the findmyschool.vic.gov.au website. The Victorian government school zones are intended to be relied on by parents and families for the purposes of making enrolment decisions for their children. They should not be relied on for making property purchase decisions, or by any party other than parents and families seeking to enrol their child in a government school.