利用遥感技术确定人口数量

K. Karume, C. Schmidt, K. Kundert, M. Bagula, B. F. Safina, R. Schomacker, D. Ganza, O. Azanga, C. Nfundiko, N. Karume, G. Mushagalusa
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引用次数: 9

摘要

理想情况下,一个国家定期(每隔五年或十年)进行人口普查,然后在人口普查期间进行人口调查,称为“控制调查”。后者,以及公民身份登记册(人口流动信息),有助于确定一个代表性样本,称为“人口比例模型”。它是随机的、分层的和加权的,它的优点是为关于目标人群的任何概括提供一个良好的统计数据库,误差风险相对较小。我们的研究区域布卡武市不符合传统的数据收集方案,主要有两个原因:-该省进行了20多年的真正人口普查,“公民身份”记录保存不佳,而且经常不完整-如果有的话!-特别是在这一冲突后时期。在布卡武市进行了一项研究,以确定居住在该市的人数。使用了2012年7月拍摄的两张分辨率为50厘米的GeoEye卫星图像。利用ArcGIS创建了一张200 × 200米的网,将卫星图像划分为规则的单元。总共创建了2772个单元格来覆盖两幅卫星图像,但只有2353个单元格被考虑用于分类。根据房屋密度,将卫星影像划分为高密度区、中密度区和低密度区。选取了三个样本,针对每种不同的密度类型,创建了一个点图,覆盖所选样本区域的每个房屋,并接收到一个点。利用三种不同的密度模式,确定了95个高密度区,307个中密度区和800个低密度区,每个区分别有30'400,46'050和4万套住房。在高、中、低密度区,用房屋数乘以平均每户8人、7人、6人来计算城市人口。布卡武市总共获得了805550名居民,这与负责南基伍省疫苗接种运动的卫生检查省估计的人数(83万)几乎相同。在没有人口普查数据的地区,只要需要快速估计人口数量,就可以使用这种方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use of Remote Sensing for Population Number Determination
Ideally, in a country the population censuses are held regularly (five or ten-year intervals), population surveys, called “control surveys” are then conducted during the intercensal period. The latter, as well as the registers of civil status (information on the movements of the population), help determining a representative sample, called “scale model of the population.” Random, stratified and weighted, it has the advantage of providing a good statistical database for any generalizations about the target population with relatively little risk of error. Our study area, Bukavu city, doesn't comply with the classical scheme of data collection for two main reasons: - there are more than twenty years that real demographic censuses have been carried out in the province, the records of the `civil status is poorly maintained and often incomplete—if any!—Especially during this post-conflict period. A study was conducted in Bukavu City to determine the number of people living in this city. Two GeoEye satellite images of 50 cm resolution captured in July 2012 were used. A net of 200 × 200 meters was created with ArcGIS to divide the satellite images into regular cells. In total 2772 cells were created to cover the two satellite images but only 2353 cells were considered for classification. Three classes were identified in the satellite images according to houses density: High density, medium density and low density zones. Three samples were selected and for each different density type, a point map was created covering each house of the selected sample zones received a point. Using the three different density patterns, 95 highly populated zones were identified, 307 medium density zones and 800 low density zones having each respectively a total of 30'400, 46'050, and 40'000 houses. The population of the city was obtained by taking the number of houses times an average of 8, 7 and 6 habitants per house respectively in high, medium and low density zones. A total of 805550 habitants was obtained for Bukavu city which is almost the same number of people estimated (830'000) by the Inspection Provinciale de la Sante which is the health office in charge of vaccination campaign in South-Kivu Province. This method can be used whenever there is a need to quickly estimate the number of the population in a region where there is no census data.
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