Projected Population Proximity Indices (30km) for 2005, 2030 & 2050

Open health data Pub Date : 2013-07-11 DOI:10.5334/JOPHD.AB
N. Alexander, W. Wint
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引用次数: 5

Abstract

This data package includes nine population proximity index layers for 2005, 2030 and 2050, for rural, urban and total populations.  The layers are distributed as 1km GeoTIFFs and GeoJPGss at 1km. The aim of these layers is to describe the population which may be likely to visit a specific locality where access is determined by Euclidean distance. By using the layers alongside other geographic datasets relating to disease risk it may help identify where people may come into contact with a disease.  Human population layers are often used in models to identify risk areas where humans and viruses interact, however most pathogens are not restricted to areas of human habitation: many are found in lesser populated areas such as forests.  This dataset will help identify less populated areas that may well still receive high visitor numbers. The layers have been projected to 2030 and 2050 to enable projections of human/disease interfaces in the medium-term which are required to inform policy makers at country and continental level. Urban and rural populations have been separated into individual layers as in some cases it is useful to distinguish between the behaviour and associated risks attributed to the different population segments.  There may be a different risk of contacting diseases in rural habitats for rural workers than for than urban visitors.
2005年、2030年和2050年预计人口接近指数(30公里)
该数据包包括2005年、2030年和2050年的9个人口接近指数层,分别针对农村、城市和总人口。这些层分布为1km geotiff和1km GeoJPGss。这些层的目的是描述可能访问由欧几里得距离决定的特定地点的人口。通过将这些层与其他与疾病风险有关的地理数据集一起使用,它可能有助于确定人们可能接触疾病的地点。人口层通常用于模型中,以确定人类和病毒相互作用的风险地区,然而,大多数病原体并不局限于人类居住地区:许多病原体在森林等人口较少的地区被发现。这个数据集将有助于识别人口较少的地区,这些地区可能仍然会接待大量游客。这些层次已预测到2030年和2050年,以便能够在中期预测人/疾病的相互作用,这是国家和大陆一级的决策者所需要的。城市和农村人口已被分成不同的阶层,因为在某些情况下,区分不同人口阶层的行为和相关风险是有用的。与城市游客相比,农村工人在农村栖息地接触疾病的风险可能不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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