Department of Geography and Spatial Sciences, & Data Science Institute, University of Delaware, Newark, DE, 19716, USA.
European Commission, Joint Research Centre, Directorate for Space, Security, and Migration, Ispra, I-21027, Italy.
Sci Data. 2021 Oct 28;8(1):281. doi: 10.1038/s41597-021-01052-0.
Long-term, spatial urban land projections that simultaneously offer global coverage and local-scale empirical accuracy are rare. Recently a set of such projections was produced using data-science-based simulations and the Shared Socioeconomic Pathways (SSPs). These projections update at decadal time intervals from 2000 to 2100 with a spatial resolution of 1/8 degree, while many socio-environmental studies customarily run their analysis and modelling at finer spatial resolutions, e.g. 1-km. Here we develop and validate an algorithm to downscale the 1/8-degree spatial urban land projections to the 1-km resolution. The algorithm uses an iterative process to allocate the decadal amount of urban land expansion originally projected for each 1/8-degree grid to its constituent 1-km grids. The results are a set of global maps showing urban land fractions at the 1-km resolution, updated at decadal intervals from 2000 to 2100, under five different urban land expansion scenarios consistent with the SSPs. The data can support studies of potential interactions between future urbanization and environmental changes across spatial and temporal scales.
长期以来,能够同时提供全球覆盖范围和局部尺度经验准确性的空间城市土地预测很少见。最近,一组这样的预测是使用基于数据科学的模拟和共享社会经济途径(SSP)生成的。这些预测从 2000 年到 2100 年以每十年一次的时间间隔更新,空间分辨率为 1/8 度,而许多社会-环境研究通常在更精细的空间分辨率(例如 1 公里)上运行其分析和建模。在这里,我们开发并验证了一种算法,将 1/8 度空间城市土地预测向下缩放至 1 公里分辨率。该算法使用迭代过程将每个 1/8 度网格最初预测的每十年城市土地扩张量分配给其组成的 1 公里网格。结果是一组全球地图,显示了在五个与 SSP 一致的不同城市土地扩张情景下,2000 年至 2100 年每十年更新的 1 公里分辨率的城市土地份额。这些数据可以支持研究未来城市化与环境变化之间在时空尺度上的潜在相互作用。