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支持尼泊尔人口气候变化脆弱性高分辨率空间评估的地理空间数据集。

Geospatial datasets in support of high-resolution spatial assessment of population vulnerability to climate change in Nepal.

作者信息

Mainali Janardan, Pricope Narcisa G

机构信息

Department of Earth and Ocean Science, University of North Carolina Wilmington, 601 South College Road, Wilmington, NC 28403-5944, United States.

Research and Development Society, Nepal.

出版信息

Data Brief. 2017 Apr 29;12:459-462. doi: 10.1016/j.dib.2017.04.045. eCollection 2017 Jun.

Abstract

We present a geographic information system (GIS) dataset with a nominal spatial resolution of one-kilometer composed of grid polygons originally derived and utilized in a high-resolution climate vulnerability model for Nepal. The different data sets described and shared in this article are processed and tailored to the specific objectives of our research paper entitled "High-resolution Spatial Assessment of Population Vulnerability to Climate Change in Nepal" (Mainali and Pricope, In press) [1]. We share these data recognizing that there is a significant gap in regards to data availability, the spatial patterns of different biophysical and socioeconomic variables, and the overall population vulnerability to climatic variability and disasters in Nepal. Individual variables, as well as the entire set presented in this dataset, can be used to better understand the spatial pattern of different physical, biological, climatic, and vulnerability characteristics in Nepal. The datasets presented in this article are sourced from different national and global databases and have been statistically treated to meet the needs of the article. The data are in GIS-ready ESRI shapefile file format of one-kilometer grid polygon with various fields (columns) for each dataset.

摘要

我们展示了一个地理信息系统(GIS)数据集,其名义空间分辨率为1公里,由最初在尼泊尔高分辨率气候脆弱性模型中推导和使用的网格多边形组成。本文中描述和共享的不同数据集经过处理并针对我们题为《尼泊尔人口对气候变化的脆弱性的高分辨率空间评估》(Mainali和Pricope,即将发表)[1]的研究论文的特定目标进行了调整。我们共享这些数据,因为我们认识到在尼泊尔的数据可用性、不同生物物理和社会经济变量的空间模式以及总体人口对气候变率和灾害的脆弱性方面存在重大差距。本数据集中呈现的各个变量以及整个数据集,可用于更好地了解尼泊尔不同物理、生物、气候和脆弱性特征的空间模式。本文中呈现的数据集源自不同的国家和全球数据库,并经过统计处理以满足本文的需求。数据采用GIS就绪的ESRI shapefile文件格式,为1公里网格多边形,每个数据集都有各种字段(列)。

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