2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)最新文献_第3页

Extraction 3D road boundaries from mobile laser scanning point clouds 从移动激光扫描点云中提取三维道路边界

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298045

L. Fang, Bisheng Yang, Chongcheng Chen, Huasheng Fu

引用次数: 10

Distributed clustering algorithm for spatial data mining 空间数据挖掘的分布式聚类算法

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298026

Malika Bendechache, Mohand Tahar Kechadi

{"title":"Distributed clustering algorithm for spatial data mining","authors":"Malika Bendechache, Mohand Tahar Kechadi","doi":"10.1109/ICSDM.2015.7298026","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298026","url":null,"abstract":"Distributed data mining techniques and mainly distributed clustering are widely used in the last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally. Current distributed clustering approaches are normally generating global models by aggregating local results that are obtained on each site. While this approach mines the datasets on their locations the aggregation phase is complex, which may produce incorrect and ambiguous global clusters and therefore incorrect knowledge. In this paper we propose a new clustering approach for very large spatial datasets that are heterogeneous and distributed. The approach is based on K-means Algorithm but it generates the number of global clusters dynamically. Moreover, this approach uses an elaborated aggregation phase. The aggregation phase is designed in such a way that the overall process is efficient in time and memory allocation. Preliminary results show that the proposed approach produces high quality results and scales up well. We also compared it to two popular clustering algorithms and show that this approach is much more efficient.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131180957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

Optimal allocation of service area of urban elusion and dispersion based on GIS 基于GIS的城市避散服务区域优化配置

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298033

B. Shan, Xue-fei Zhu

{"title":"Optimal allocation of service area of urban elusion and dispersion based on GIS","authors":"B. Shan, Xue-fei Zhu","doi":"10.1109/ICSDM.2015.7298033","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298033","url":null,"abstract":"In order to obtain the study area of the residential area, the method of visual interpretation combined with the actual survey were adopted to get the elusion and dispersion zone and road network spatial data and attribute data by using remote sensing image of the part area in Jinan. First of all, by using ARCGIS10.0, space buffer analysis was done based on the risk place, and then overlay analysis was done with the buffer analysis results and elusion and dispersion zone. Thus we can get the effective elusion and dispersion zone by eliminating the danger point affected. Secondly, based on road network and effective elusion and dispersion zone entrances, the elusion and dispersion service area was divided. In order to realize recently evacuated, the methods of Euclidean allocation were used to divide the elusion and dispersion service area. Finally, based on overlay analysis of the division of the elusion and dispersion service area and residential area, we study relations of elusion and dispersion zone and residential area, and knowing if the elusion and dispersion service area can accommodate the people of the residential area who come there. If not, by using the buffer analysis and linear programming, the new service area of elusion and dispersion was redrawn, thus we can ensure that all residents of the residential area can go to the nearest elusion and dispersion zones.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134177416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An analysis of influence on nonnavigable shallow areas using different methods to interpolate depths 不同插值方法对非通航浅区的影响分析

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298030

Tao Wang, Lihua Zhang, Rencan Peng, Shuaidong Jia, Hongbo Cao, Jian Dong

{"title":"An analysis of influence on nonnavigable shallow areas using different methods to interpolate depths","authors":"Tao Wang, Lihua Zhang, Rencan Peng, Shuaidong Jia, Hongbo Cao, Jian Dong","doi":"10.1109/ICSDM.2015.7298030","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298030","url":null,"abstract":"Currently, various water depth interpolation methods used to construct nonnavigable shallow areas are simply and indiscriminatingly used without taking their respective adaptabilities into consideration. Firstly, three typical methods used to estimate a water depth at an arbitrary position on a chart are summarized as follows: (1) estimating the depth with the shallowest depth of the three vertexes of a triangle; (2) estimating the depth with linearly interpolated by the depths of the three vertexes of a triangle; (3) estimating the depth with linearly interpolated by the depths of the three vertexes of a triangle as well as the node depths of isobaths. Correspondingly, three methods, namely is shallowest interpolation method, linear interpolation method, and additional interpolation method, are proposed for constructing nonnavigable shallow areas based on three aforementioned different ideas. Finally, the adaptability of the three different interpolation methods used to construct nonnavigable shallow areas is analyzed. The experimental results demonstrate that (1) the shallowest interpolation method can ensure the navigation safety better than the other two methods, and linear interpolation method is worse than the shallowest interpolation method, and the additional interpolation method is the worst. Besides, the shallowest interpolation method loses most utilizing rate of the navigable resource, and linear interpolation method is less than the shallowest interpolation method, and additional interpolation method is the least. (2) In the areas of the narrow straits or waterways on small-scale chart or medium-scale chart, both shallowest interpolation method and linear interpolation method may make some mistakes that some areas in the narrow straits or waterways are nonnavigable, whereas additional interpolation method can express nonnavigable shallow areas correctly.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scale selecting of building information statistical grids with spatial autocorrelation 基于空间自相关的建筑信息统计网格尺度选择

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298029

Jiangping Chen, L. Ding, W. Shi

引用次数: 1

Application of trajectory clustering and regionalization to ocean eddies in the South China Sea 轨迹聚类与区划在南海海洋涡旋中的应用

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298023

Yang Mo, Di Wu, Yunyan Du

引用次数: 4

Person trip data browser, analyzer and space-time visualizer 人员行程数据浏览器、分析器和时空可视化器

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298017

K. Lwin, Y. Murayama

引用次数: 5

Scale-location specific analysis of temporal vegetation coverage in China 中国植被覆盖度的尺度区位特征分析

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298037

Zhuangzhuang Wang, Bingwen Qiu, Zhanling Fan

{"title":"Scale-location specific analysis of temporal vegetation coverage in China","authors":"Zhuangzhuang Wang, Bingwen Qiu, Zhanling Fan","doi":"10.1109/ICSDM.2015.7298037","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298037","url":null,"abstract":"China has been the study area for this paper. In this paper, we took series of MODIS Enhanced Vegetation Index (EVI) products from 2001 to 2012 to create the temporal vegetation coverage (TVC) as indication factor of vegetation cover. Then we selected a latitudinal transect along the latitude 30°N and used the continuous wavelet variance to test the characteristic scale of vegetation distribution with the wavelet coherency to detect the multi-scale relationship between vegetation cover with the effect factors through the continuous wavelet transform. It was shown that: (1) The study area is divided into multiple nested pattern levels which are larger river effect pattern, physiognomy effect pattern and the macro structure of Chinese Three Gradient Terrains. (2) In the scales of 0-300km, vegetation cover were greatly influenced by factitious factors; In the 300-600km scales, a major scale characteristic, vegetation cover has been influenced by elevation, temperature, land use/cover and factitious factors, and vegetation heterogeneity is greatly strong on this scale; In the scales of 600-1,200km, vegetation cover is intensity influenced by climate factors. (3) Elevation plays an important role to vegetation cover. At low elevation area, vegetation cover increases with elevation growth, while it appears the reverse relation in high elevation area. In the west area, the correlation between vegetation cover and precipitation is negative, while vegetation cover and temperature presents a strong positive correlation. In the whole eastern area, this relationship is not as strong as in western area, and in the mountains or hilly area, temperature and vegetation cover has a positive correlation. (4) Artificial factors show some inhibitory effects of vegetation cover that the vegetation coverage is relatively poor in the area with intensive human activities.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"476 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131989047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Making knowledge discovery services scalable on clouds for big data mining 使知识发现服务在云上可扩展，用于大数据挖掘

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298015

D. Talia

{"title":"Making knowledge discovery services scalable on clouds for big data mining","authors":"D. Talia","doi":"10.1109/ICSDM.2015.7298015","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298015","url":null,"abstract":"The amount of digital data is increasing beyond any previous estimation and data stores and sources are more and more pervasive and distributed. Professionals and scientists need advanced data analysis tools and services coupled with scalable architectures to support the extraction of useful information from big data repositories. Cloud computing systems offer an effective support for addressing both the computational and data storage needs of big data mining and parallel knowledge discovery applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large and efficient storage facilities together with high performance processors to get results in acceptable times. In this paper we introduce the topic and the main research issues. We discuss how to make knowledge discovery services scalable and present the Data Mining Cloud Framework designed for developing and executing distributed data analytics applications as workflows of services. In this environment we use data sets, analysis tools, data mining algorithms and knowledge models that are implemented as single services that can be combined through a visual programming interface in distributed workflows to be executed on Clouds. The main features of the programming interface are described and performance evaluation of knowledge discovery applications are reported.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129210346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Global and collective outliers detection on hotspot data as forest fires indicator in Riau Province, Indonesia 印度尼西亚廖内省森林火灾指标热点数据的全球和集体异常值检测

2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) Pub Date : 2015-07-08 DOI: 10.1109/ICSDM.2015.7298027

I. S. Sitanggang, Dhiya Aulia Muhamad Baehaki

{"title":"Global and collective outliers detection on hotspot data as forest fires indicator in Riau Province, Indonesia","authors":"I. S. Sitanggang, Dhiya Aulia Muhamad Baehaki","doi":"10.1109/ICSDM.2015.7298027","DOIUrl":"https://doi.org/10.1109/ICSDM.2015.7298027","url":null,"abstract":"Forest fire in Indonesia is considered as an annual event that causes serious problems in health and environment especially in Sumatera and Kalimantan Islands. Studies on analyzing hotspot data as forest fire indicators are required for predicting hotspot occurrences. The objective of this work is to detect global and collective outliers on hotspot data in Riau Province in Sumatera Island for the period 2001-2012. The data used in this work are 4383 daily hotspots and 144 monthly hotspots. The method applied to discover outliers is the k-means clustering algorithm. The best clustering results are obtained on the number of clusters of 10 and the sum of squared error value is 18526.14. Based on the clustering results, we obtain 59 collective outliers and 30 global outliers on the hotspot dataset. The outliers on the hotspot data mostly occur in February, March, June, July, and August. The average frequency of outliers is 482.22 and the highest frequency of outliers is occurred in 2005. As many 1118 hotspots were found in the northern part of the Riau province on 21 June 2005. In August 2005 outliers spread on the whole area of Riau Province. For the period 2001-2012 there are no outliers occurred in April, November and December. This information is essential for an early warning system in forest fires prevention.","PeriodicalId":404001,"journal":{"name":"2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121719651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9