{"title":"合成地理空间数据和假地理:人工智能衍生数据在数据密集型社会中的影响案例研究","authors":"Antonello Romano","doi":"10.1016/j.diggeo.2024.100108","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a case study that aims to analyze and compare original and synthetic geospatial data at the intra-urban scale. The goal is to explore the potential implications of the spread of synthetic data in scenarios where geospatial data are essential for decoding socio-spatial changes and where Geo-visualization is pivotal for spatial decision support. The methodology is based on a) the production of a synthetic dataset and b) the evaluation of the spatial similarity with the original one. Specifically, we employ a synthetic data provider, namely Mostly.AI, alongside geospatial data related to Airbnb listings in Florence, Italy. Results show which criticalities are linked to AI-derived data compared to the original ones, highlighting crucial spatial similarities and dissimilarities. Finally, the work critically discusses the broader societal implications of the widespread online synthetic data platforms, exploring the impacts of such a technological (re)evolution in a data-intensive society.</div></div>","PeriodicalId":100377,"journal":{"name":"Digital Geography and Society","volume":"8 ","pages":"Article 100108"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synthetic geospatial data and fake geography: A case study on the implications of AI-derived data in a data-intensive society\",\"authors\":\"Antonello Romano\",\"doi\":\"10.1016/j.diggeo.2024.100108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a case study that aims to analyze and compare original and synthetic geospatial data at the intra-urban scale. The goal is to explore the potential implications of the spread of synthetic data in scenarios where geospatial data are essential for decoding socio-spatial changes and where Geo-visualization is pivotal for spatial decision support. The methodology is based on a) the production of a synthetic dataset and b) the evaluation of the spatial similarity with the original one. Specifically, we employ a synthetic data provider, namely Mostly.AI, alongside geospatial data related to Airbnb listings in Florence, Italy. Results show which criticalities are linked to AI-derived data compared to the original ones, highlighting crucial spatial similarities and dissimilarities. Finally, the work critically discusses the broader societal implications of the widespread online synthetic data platforms, exploring the impacts of such a technological (re)evolution in a data-intensive society.</div></div>\",\"PeriodicalId\":100377,\"journal\":{\"name\":\"Digital Geography and Society\",\"volume\":\"8 \",\"pages\":\"Article 100108\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Geography and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666378324000308\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Geography and Society","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666378324000308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/4 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Synthetic geospatial data and fake geography: A case study on the implications of AI-derived data in a data-intensive society
This paper presents a case study that aims to analyze and compare original and synthetic geospatial data at the intra-urban scale. The goal is to explore the potential implications of the spread of synthetic data in scenarios where geospatial data are essential for decoding socio-spatial changes and where Geo-visualization is pivotal for spatial decision support. The methodology is based on a) the production of a synthetic dataset and b) the evaluation of the spatial similarity with the original one. Specifically, we employ a synthetic data provider, namely Mostly.AI, alongside geospatial data related to Airbnb listings in Florence, Italy. Results show which criticalities are linked to AI-derived data compared to the original ones, highlighting crucial spatial similarities and dissimilarities. Finally, the work critically discusses the broader societal implications of the widespread online synthetic data platforms, exploring the impacts of such a technological (re)evolution in a data-intensive society.