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年时该像素点到最近城市区域的预估出行时间（单位：分钟）。本数据集包含12个数据图层，基于2015年不同人口规模的城市区域定义（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年时该像素点到最近港口的预估出行时间。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，经LZW压缩。

### 单位
分钟

### 数据类型
字节型（16位无符号整数）

### 无数据值
65535

### 标志
无

### 空间分辨率
30角秒

### 空间范围
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（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，具体取决于出行时间、城市位置及人口等相关输入数据的更新可获得性。

### 研究方法
采用gdistance R扩展包（gdistance R package）中的累积成本函数（accCost）估算至最近城市或港口的出行时间。该函数需两类输入数据集：(i) 待估算出行时间的目标位置集；(ii) 表征地表通行成本或耗时的转移矩阵。

目标位置集基于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），该数据集涵盖低密度城市集群（Low Density Cluster, LDC）与高密度城市区域（High Density Cluster, HDC）。相关数据集可访问https://ghsl.jrc.ec.europa.eu/datasets.php 获取。上述城市区域以点形式表征，沿每个城市区域的周边以1km间距布设采样点。

海港数据取自第26版《世界港口索引》（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，相关页面可访问https://map.ox.ac.uk/research-project/accessibility_to_cities 查看。生成12幅出行时间地图所用的R代码已收录于报告《面向可持续乡村发展的全球可达性指标套件》（Nelson，2019），可随本数据图层一同下载。