{"title":"Bayesian location estimation of mobile devices using a signal strength model","authors":"M. Tennekes, Y. Gootzen","doi":"10.5311/josis.2022.25.166","DOIUrl":null,"url":null,"abstract":"Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Spatial Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5311/josis.2022.25.166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 2
Abstract
Mobile network operator (MNO) data are a rich data source for various topics in official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. We propose an alternative location estimation method following a Bayesian approach and using a physical model for the received signal strength. Our Bayesian framework allows for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. For the likelihood module we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate of the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.