{"title":"基于神经网络的地理趋势可视化","authors":"Hajime Hotta, M. Hagiwara","doi":"10.1109/WIIAT.2008.141","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a neural-network-based visualization system of geographic tendency. In general, there are some needs of understanding statistical data of geographic tendency, such as geographic marketing data, real-estate prices, and so on. The main purpose of the proposal is to visualize the tendency of these data online with interactive mapping systems, such as Google Maps. The proposed system generates translucent images of a heatmap, which shows the geographic tendency like thermograph. It consists of two steps: (1) construction of a tendency model with a neural network, (2) determine the color scale for the output heatmap. As for (1), a general regression neural network (GRNN) is employed to compose a tendency model by function approximation. As for (2), the output color scale is optimized and the heatmap is finally generated using the composed tendency model.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Neural-Network-Based Geographic Tendency Visualization\",\"authors\":\"Hajime Hotta, M. Hagiwara\",\"doi\":\"10.1109/WIIAT.2008.141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a neural-network-based visualization system of geographic tendency. In general, there are some needs of understanding statistical data of geographic tendency, such as geographic marketing data, real-estate prices, and so on. The main purpose of the proposal is to visualize the tendency of these data online with interactive mapping systems, such as Google Maps. The proposed system generates translucent images of a heatmap, which shows the geographic tendency like thermograph. It consists of two steps: (1) construction of a tendency model with a neural network, (2) determine the color scale for the output heatmap. As for (1), a general regression neural network (GRNN) is employed to compose a tendency model by function approximation. As for (2), the output color scale is optimized and the heatmap is finally generated using the composed tendency model.\",\"PeriodicalId\":393772,\"journal\":{\"name\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WIIAT.2008.141\",\"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/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIIAT.2008.141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Neural-Network-Based Geographic Tendency Visualization
In this paper, we propose a neural-network-based visualization system of geographic tendency. In general, there are some needs of understanding statistical data of geographic tendency, such as geographic marketing data, real-estate prices, and so on. The main purpose of the proposal is to visualize the tendency of these data online with interactive mapping systems, such as Google Maps. The proposed system generates translucent images of a heatmap, which shows the geographic tendency like thermograph. It consists of two steps: (1) construction of a tendency model with a neural network, (2) determine the color scale for the output heatmap. As for (1), a general regression neural network (GRNN) is employed to compose a tendency model by function approximation. As for (2), the output color scale is optimized and the heatmap is finally generated using the composed tendency model.