Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia
Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia
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DOI：
10.1111/j.1365-3156.2008.02185.x
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发表时间：
2009-01-01
影响因子：
3.3
通讯作者：
Moss, William J.
中科院分区：
文献类型：
--
作者：
Lowther, Sara A.;Curriero, Frank C.;Moss, William J.
为了描述我们的经验，使用卫星图像为基础的采样进行儿童的健康调查，在城市地区的卢萨卡，赞比亚，作为一种方法来采样时，人口是不好的特点，现有的人口普查数据或maps.Using公开可用的Quickbird（TM）图像的几个乡镇，我们创建了数字记录的结构内的住宅城市研究区使用ArcGIS 9.2。划定边界是为了根据自然和人为障碍（如道路）建立地理分区。由生物医学研究学生和当地社区卫生工作者组成的调查小组遵循标准协议，在选定的结构内登记儿童，或在选定的结构不符合资格或拒绝登记的情况下搬到邻近的结构。利用K-差异函数对研究区域内16105个结构进行空间聚类分析。在750个随机选择的建筑物中，有六个（1%）没有被调查小组发现。共有1247个结构进行了资格评估，其中691个符合条件的家庭参加了评估。大多数登记的住户是最初选定的建筑物（51%）或第一个选定的邻居（42%）。拒绝登记的家庭往往比登记的家庭聚集在一起，在非洲城市环境中，从卫星图像中取样是可行的。卫星图像可能有助于对人口普查数据或地图不准确的人群进行公共卫生监测，并有助于进行空间分析，如确定拒绝接受的家庭之间的聚集情况。
To describe our experience using satellite image-based sampling to conduct a health survey of children in an urban area of Lusaka, Zambia, as an approach to sampling when the population is poorly characterized by existing census data or maps.Using a publicly available Quickbird (TM) image of several townships, we created digital records of structures within the residential urban study area using ArcGIS 9.2. Boundaries were drawn to create geographic subdivisions based on natural and man-made barriers (e.g. roads). Survey teams of biomedical research students and local community health workers followed a standard protocol to enrol children within the selected structure, or to move to the neighbouring structure if the selected structure was ineligible or refused enrolment. Spatial clustering was assessed using the K-difference function.Digital records of 16 105 structures within the study area were created. Of the 750 randomly selected structures, six (1%) were not found by the survey teams. A total of 1247 structures were assessed for eligibility, of which 691 eligible households were enroled. The majority of enroled households were the initially selected structures (51%) or the first selected neighbour (42%). Households that refused enrolment tended to cluster more than those which enroled.Sampling from a satellite image was feasible in this urban African setting. Satellite images may be useful for public health surveillance in populations with inaccurate census data or maps and allow for spatial analyses such as identification of clustering among refusing households.