Travel time to cities and ports in the year 2015

travel_time_to_cities_x.tif (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).<br><br>travel_time_to_ports_x (x ranges from 1 to 5)<br>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.<br><br>FormatRaster Dataset, GeoTIFF, LZW compressed<br><br>UnitMinutes<br><br>Data typeByte (16 bit Unsigned Integer)<br><br>No data value65535<br><br>FlagsNone<br><br>Spatial resolution30 arc seconds<br><br>Spatial extentUpper left -180, 85<br>Lower left -180, -60Upper right 180, 85Lower right 180, -60<br>Spatial Reference System (SRS)EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)<br>Temporal resolution2015<br>Temporal extentUpdates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.<br>MethodologyTravel 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.<br>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<br>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). The R code used to generate the 12 travel time maps is included in the report “A suite of global accessibility indicators for sustainable rural development” (Nelson, 2019) that can be downloaded with these data layers.<br>travel_time_to_cities_x.tif（x取值范围为1至12）：每个像素的数值代表2015年时到最近城市区域的预估出行耗时（单位：分钟）。本数据集包含12个数据图层，基于2015年按人口规模划分的不同城市区域集定义（详见PDF报告）。 travel_time_to_ports_x（x取值范围为1至5）：每个像素的数值代表2015年时到最近港口的预估出行耗时。本数据集包含5个数据图层，基于不同港口规模划分。 数据格式：栅格数据集，采用GeoTIFF格式，LZW压缩。 单位：分钟。 数据类型：字节（16位无符号整数）。 无数据值：65535。 标记：无。 空间分辨率：30角秒。 空间范围：左上角坐标(-180, 85)、左下角坐标(-180, -60)、右上角坐标(180, 85)、右下角坐标(180, -60)。 空间参考系统（SRS）：EPSG:4326 - WGS84 - 地理坐标系（纬度/经度）。 时间分辨率：2015年。 时间范围：未来或可推出更新版本，但需依赖更新后的出行耗时、城市位置及人口等输入数据的可获得性。 研究方法：前往最近城市或港口的出行耗时，通过gdistance R包（van Etten，2018）中的累积成本函数（accCost）进行估算。该函数需两类输入数据集：(i) 用于估算出行耗时的目标位置集合；(ii) 表征地表通行成本或耗时的转移矩阵。目标位置集合基于2016版欧盟联合研究中心全球人类住区图层（Global Human Settlement Layers，GHSL）数据集（Pesaresi与Freire，2016）中的人口聚居城市区域，涵盖低密度城镇集群（Low Density Cluster, LDC）与高密度城区（High Density Cluster, HDC），相关数据可通过https://ghsl.jrc.ec.europa.eu/datasets.php获取。上述城市区域以点位形式表征，沿每个城市区域的周边以1km间隔布设点位。 海洋港口数据提取自第26版《世界港口索引》（World Port Index，NGA，2017），该索引包含约3700个主要港口及码头的位置与物理特征。港口以单点形式表征。 转移矩阵基于2015年全球可达性地图（Weiss等，2018）中的摩擦表面（https://map.ox.ac.uk/research-project/accessibility_to_cities）。用于生成12张出行耗时地图的R代码可随本数据图层一并下载，详见报告《面向可持续乡村发展的全球可达性指标集》（Nelson，2019）。