{"title":"一种发现空间共位模式的方法","authors":"Fadi Deeb, L. Niepel","doi":"10.1109/AICCSA.2008.4493527","DOIUrl":null,"url":null,"abstract":"Spatial co-location patterns represent the subsets of events (services/features) whose instances are frequently located together in a geographic space. The co-location patterns discovery presents challenges since the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships. In this paper, we provide a study based on some previous approaches, the concepts that were used, and some of their limitations. We propose a methodology which overcomes the shortcomings of some other approaches. This methodology is based on a spatial access method (KD-tree) with its basic operations and the apriori generation algorithm. The results of conducted experimentation show the correctness and completeness of our approach. The results also illustrate the effect of input data on the performance.","PeriodicalId":234556,"journal":{"name":"2008 IEEE/ACS International Conference on Computer Systems and Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A methodology for discovering spatial co-location patterns\",\"authors\":\"Fadi Deeb, L. Niepel\",\"doi\":\"10.1109/AICCSA.2008.4493527\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spatial co-location patterns represent the subsets of events (services/features) whose instances are frequently located together in a geographic space. The co-location patterns discovery presents challenges since the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships. In this paper, we provide a study based on some previous approaches, the concepts that were used, and some of their limitations. We propose a methodology which overcomes the shortcomings of some other approaches. This methodology is based on a spatial access method (KD-tree) with its basic operations and the apriori generation algorithm. The results of conducted experimentation show the correctness and completeness of our approach. The results also illustrate the effect of input data on the performance.\",\"PeriodicalId\":234556,\"journal\":{\"name\":\"2008 IEEE/ACS International Conference on Computer Systems and Applications\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/ACS International Conference on Computer Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICCSA.2008.4493527\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/ACS International Conference on Computer Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICCSA.2008.4493527","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A methodology for discovering spatial co-location patterns
Spatial co-location patterns represent the subsets of events (services/features) whose instances are frequently located together in a geographic space. The co-location patterns discovery presents challenges since the instances of spatial events are embedded in a continuous space and share a variety of spatial relationships. In this paper, we provide a study based on some previous approaches, the concepts that were used, and some of their limitations. We propose a methodology which overcomes the shortcomings of some other approaches. This methodology is based on a spatial access method (KD-tree) with its basic operations and the apriori generation algorithm. The results of conducted experimentation show the correctness and completeness of our approach. The results also illustrate the effect of input data on the performance.