Titlebook: Data Mining for Geoinformatics; Methods and Applicat Guido Cervone,Jessica Lin,Nigel Waters Book 2014 Springer Science+Business Media New Y

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書目名稱Data Mining for Geoinformatics影響因子(影響力)




書目名稱Data Mining for Geoinformatics影響因子(影響力)學(xué)科排名




書目名稱Data Mining for Geoinformatics網(wǎng)絡(luò)公開度




書目名稱Data Mining for Geoinformatics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Mining for Geoinformatics被引頻次




書目名稱Data Mining for Geoinformatics被引頻次學(xué)科排名




書目名稱Data Mining for Geoinformatics年度引用




書目名稱Data Mining for Geoinformatics年度引用學(xué)科排名




書目名稱Data Mining for Geoinformatics讀者反饋




書目名稱Data Mining for Geoinformatics讀者反饋學(xué)科排名

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tic algorithms necessary to extract knowledge from massive aThe rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementat

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Assignments and Implemented Functionsms at predicting traffic jams in the city of Frankfurt am Main with help of a learned classifier. Our results show that taking into account simple and partial information about the traffic situation can lead to a huge gain of knowledge when using data mining techniques in the face of predicting of traffic situations.

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GIS-Based Traffic Simulation Using OSM,ms at predicting traffic jams in the city of Frankfurt am Main with help of a learned classifier. Our results show that taking into account simple and partial information about the traffic situation can lead to a huge gain of knowledge when using data mining techniques in the face of predicting of traffic situations.

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https://doi.org/10.1007/978-3-662-06297-5 comprising even a single modestly sized HSI data set. The discussion in this chapter will focus on the analysis process that generally applies to all HSI data and discuss the methods, approaches, and computational issues associated with analyzing hyperspectral imagery data.

致敬

Come impariamo a muoverci nell‘a(chǎn)mbiente?rsion simulations are performed using the SCIPUFF model, using model vertical profiles and ground meteorological data. The non-stationary time-series of the Fukushima release rate is determined for a period of 5 days with a 2-h resolution.