Stephen Nash, Victoria Tittle, Andrew Abaasa, Richard E Sanya, Gershim Asiki, Christian Holm Hansen, Heiner Grosskurth, Saidi Kapiga, Chris Grundy
{"title":"利用卫星图像估计难以到达的种群规模的基于区域的方法的有效性:以维多利亚湖的捕鱼种群为例。","authors":"Stephen Nash, Victoria Tittle, Andrew Abaasa, Richard E Sanya, Gershim Asiki, Christian Holm Hansen, Heiner Grosskurth, Saidi Kapiga, Chris Grundy","doi":"10.1186/s12982-018-0079-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models.</p><p><strong>Results: </strong>Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant.</p><p><strong>Conclusions: </strong>In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12982-018-0079-5","citationCount":"4","resultStr":"{\"title\":\"The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria.\",\"authors\":\"Stephen Nash, Victoria Tittle, Andrew Abaasa, Richard E Sanya, Gershim Asiki, Christian Holm Hansen, Heiner Grosskurth, Saidi Kapiga, Chris Grundy\",\"doi\":\"10.1186/s12982-018-0079-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models.</p><p><strong>Results: </strong>Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant.</p><p><strong>Conclusions: </strong>In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.</p>\",\"PeriodicalId\":39896,\"journal\":{\"name\":\"Emerging Themes in Epidemiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2018-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/s12982-018-0079-5\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Emerging Themes in Epidemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s12982-018-0079-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Emerging Themes in Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12982-018-0079-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
The validity of an area-based method to estimate the size of hard-to-reach populations using satellite images: the example of fishing populations of Lake Victoria.
Background: Information on the size of populations is crucial for planning of service and resource allocation to communities in need of health interventions. In resource limited settings, reliable census data are often not available. Using publicly available Google Earth Pro and available local household survey data from fishing communities (FC) on Lake Victoria in Uganda, we compared two simple methods (using average population density) and one simple linear regression model to estimate populations of small rural FC in Uganda. We split the dataset into two sections; one to obtain parameters and one to test the validity of the models.
Results: Out of 66 FC, we were able to estimate populations for 47. There were 16 FC in the test set. The estimates for total population from all three methods were similar, with errors less than 2.2%. Estimates of individual FC populations were more widely discrepant.
Conclusions: In our rural Ugandan setting, it was possible to use a simple area based model to get reasonable estimates of total population. However, there were often large errors in estimates for individual villages.
期刊介绍:
Emerging Themes in Epidemiology is an open access, peer-reviewed, online journal that aims to promote debate and discussion on practical and theoretical aspects of epidemiology. Combining statistical approaches with an understanding of the biology of disease, epidemiologists seek to elucidate the social, environmental and host factors related to adverse health outcomes. Although research findings from epidemiologic studies abound in traditional public health journals, little publication space is devoted to discussion of the practical and theoretical concepts that underpin them. Because of its immediate impact on public health, an openly accessible forum is needed in the field of epidemiology to foster such discussion.