{"title":"SpatialzOSM: A Python package for supporting the explicit spatialization in the population synthesis process","authors":"Bladimir Toaza, Domokos Esztergár-Kiss","doi":"10.1016/j.simpa.2024.100724","DOIUrl":null,"url":null,"abstract":"<div><div>SpatialzOSM, a package to spatialize aggregated locations into coordinates, thereby supporting population synthesis processes. This paper addresses the need for high-resolution data while ensuring data privacy. SpatialzOSM features include the generation of coordinates using three random distribution techniques: across zones, along road networks, and within buildings for residential locations. For non-residential locations, the package extracts points of interest from open sources. By leveraging open-source data, SpatialzOSM minimizes the risks of reidentification associated with census and survey datasets, ensuring privacy protection. This package is valuable for researchers and modelers engaged in synthetic population generation for models requiring explicit geographic location data.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"23 ","pages":"Article 100724"},"PeriodicalIF":1.3000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software Impacts","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266596382400112X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
SpatialzOSM, a package to spatialize aggregated locations into coordinates, thereby supporting population synthesis processes. This paper addresses the need for high-resolution data while ensuring data privacy. SpatialzOSM features include the generation of coordinates using three random distribution techniques: across zones, along road networks, and within buildings for residential locations. For non-residential locations, the package extracts points of interest from open sources. By leveraging open-source data, SpatialzOSM minimizes the risks of reidentification associated with census and survey datasets, ensuring privacy protection. This package is valuable for researchers and modelers engaged in synthetic population generation for models requiring explicit geographic location data.