Travel time to cities and ports in the year 2015

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.

本数据集及其验证内容详尽地记载于《自然科学数据描述》中，可参阅链接：https://www.nature.com/articles/s41597-019-0265-5。若需在交互式环境中使用该数据集，请访问以下链接：https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD。以下文本是对上述数据描述的摘要。

本数据集为2015年全球旅行时间可达性指标套件，全球范围内以约一公里空间分辨率为基准。指标显示了估算并经验证的陆上旅行时间，涵盖了不同规模的城市和港口。

数据集以GeoTIFF格式存储，适用于地理信息系统和统计软件，用于绘制城市和港口的可达性地图，以及不同人群群体在空间和统计层面上的可达性不平等分析。

这些地图展现了城市和港口提供的必要服务在全球范围内的物理可达性，具有独特的全球代表性。

数据集包括以下文件：
travel_time_to_cities_x.tif（其中x的值为1至12），每个像素的值代表2015年到达最近城市地区的估计旅行时间。基于2015年不同人口规模的城市地区定义了12个数据层（参见PDF报告）。
travel_time_to_ports_x（x的值为1至5），每个像素的值代表2015年到达最近港口的估计旅行时间。基于不同港口规模定义了5个数据层。

数据格式为栅格数据集，GeoTIFF，LZW压缩。
单位为分钟。
数据类型为字节（16位无符号整数）。
无数据值为65535。
标志位无。
空间分辨率为30弧秒。
空间范围为：
左上角 -180, 85
左下角 -180, -60
右上角 180, 85
右下角 180, -60
空间参考系统（SRS）EPSG:4326 - WGS84 - 地理坐标系统（经纬度）。
时间分辨率为2015年。
时间范围为：更新可能随未来年份的输入数据（旅行时间和城市位置及人口）的可用性而进行，但取决于这些更新的输入数据。
方法：使用gdistance R包中的累积成本函数（accCost）估算到达最近城市或港口的旅行时间（van Etten, 2018）。此函数需要两个输入数据集：（i）一组估算旅行时间的位置集合；（ii）代表穿越表面的成本或时间的转换矩阵。
位置集合基于2016年联合研究中心全球人类居住地层（GHSL）数据集的 populated urban areas，这些数据集代表了低密度（LDC）城市集群和高密度（HDC）城市区域（https://ghsl.jrc.ec.europa.eu/datasets.php）。这些城市区域以点表示，点间距为每平方公里1公里。
海洋港口来自第26版世界港口索引（NGA, 2017），其中包含约3,700个主要港口和码头的位置和物理特征。港口以单点表示。
转换矩阵基于2015年全球可达性地图的摩擦表面（https://map.ox.ac.uk/research-project/accessibility_to_cities）。
代码：用于生成12个旅行时间图的R代码包含在可下载的zip文件中，与这些数据层一同提供。处理区域也一并提供。
验证：通过将47,893对位置之间的旅行时间与Google API的行程时间进行比较，验证了基础摩擦表面。我们的估算行程时间通常短于Google API的行程时间。在所有图块中，我们的估算行程时间的中间值为88分钟，四分位距为48至143分钟，而Google API估算行程时间的中间值为106分钟，四分位距为61至167分钟。在所有图块中，差异向左倾斜，我们的旅行时间估算在72%的图块中短于Google API的报告值。中间差异为-13.7分钟，四分位距为-35.5至2.0分钟，对于60%的图块，绝对差异不超过30分钟，对于80%的图块，绝对差异不超过60分钟。中间百分比差异为-16.9%，四分位距为-30.6%至2.7%，在43%的图块中，绝对百分比差异不超过20%，在80%的图块中不超过40%。
此过程和结果包含在验证zip文件中。
使用说明：可达性层可以在多个地理信息系统或遥感软件中可视化和分析，如QGIS、GRASS、ENVI、ERDAS或ArcMap，以及R或MATLAB等统计和建模软件包。它们也可以用于云基础地理空间分析工具，如Google Earth Engine。
九个层代表不同人口范围的居住地旅行时间。可以通过记录层间的最小像素值将两个或多个层合并为一个层。例如，可以通过取代表5,000至10,000、10,000至20,000和20,000至50,000人口定居点旅行时间的三个层的最小值，生成到达最近5,000至50,000人定居点的旅行时间图。
可达性层还允许用户定义的层次结构，超出在层间计算最小像素值。当所有类别的并集加起来等于全球人口，任何两个类别的交集为空时，可以生成用户定义的完整层次结构。其他方面均由用户根据问题的逻辑一致性自行决定。
可达性层是给定位置到达最近目标可达性的相对度量。虽然验证表明它们确实对应于典型的行程时间，但不能将它们视为实际旅行时间。摩擦表面的错误将作为累积成本函数的一部分累积，并且远离目标的地点可能比靠近目标的地点有更大的与合理旅行时间的偏差。当感兴趣的地点极为偏远时，在提及大型城市的旅行时间时应谨慎行事，尽管它们仍然是相对可达性的合理表示。此外，模型的关键假设是所有旅行都将使用最快的交通方式，并选择最短路径。