ºÝºÝߣshows by User: lixun910 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: lixun910 / Mon, 18 Apr 2016 22:21:38 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: lixun910 CartoDB fans: GeoDa1.8 provides extra power of spatial analysis /slideshow/cartodb-fans-geoda18-provides-extra-power-of-spatial-analysis/61065345 untitled-160418222138
@GeoDaCenter. For @CartoDB lovers: need some extra power of spatial data analysis? here’s what you can do using #GeoDa1.8 + CartoDB. ]]>
@GeoDaCenter. For @CartoDB lovers: need some extra power of spatial data analysis? here’s what you can do using #GeoDa1.8 + CartoDB. ]]> Mon, 18 Apr 2016 22:21:38 GMT /slideshow/cartodb-fans-geoda18-provides-extra-power-of-spatial-analysis/61065345 lixun910@slideshare.net(lixun910) CartoDB fans: GeoDa1.8 provides extra power of spatial analysis lixun910 @GeoDaCenter. For @CartoDB lovers: need some extra power of spatial data analysis? here’s what you can do using #GeoDa1.8 + CartoDB. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/untitled-160418222138-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> @GeoDaCenter. For @CartoDB lovers: need some extra power of spatial data analysis? here’s what you can do using #GeoDa1.8 + CartoDB.
CartoDB fans: GeoDa1.8 provides extra power of spatial analysis from Arizona State University
]]> 2950 5 https://cdn.slidesharecdn.com/ss_thumbnails/untitled-160418222138-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 CAST a software for data analysis in space and time /slideshow/cast-a-software-for-data-analysis-in-space-and-time/41271901 jsmxun-141107141851-conversion-gate01
CAST a software for data analysis in space and time]]>
CAST a software for data analysis in space and time]]> Fri, 07 Nov 2014 14:18:51 GMT /slideshow/cast-a-software-for-data-analysis-in-space-and-time/41271901 lixun910@slideshare.net(lixun910) CAST a software for data analysis in space and time lixun910 CAST a software for data analysis in space and time <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jsmxun-141107141851-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> CAST a software for data analysis in space and time
CAST a software for data analysis in space and time from Arizona State University
]]> 815 3 https://cdn.slidesharecdn.com/ss_thumbnails/jsmxun-141107141851-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Travel Plan using Geo-tagged Photos in Geocrowd2013 /slideshow/geo-crowd2013-xunli/41271016 geocrowd2013xunli-141107135104-conversion-gate02
By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.]]>
By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.]]> Fri, 07 Nov 2014 13:51:04 GMT /slideshow/geo-crowd2013-xunli/41271016 lixun910@slideshare.net(lixun910) Travel Plan using Geo-tagged Photos in Geocrowd2013 lixun910 By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/geocrowd2013xunli-141107135104-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.
Travel Plan using Geo-tagged Photos in Geocrowd2013 from Arizona State University
]]> 1010 4 https://cdn.slidesharecdn.com/ss_thumbnails/geocrowd2013xunli-141107135104-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big spatial2014 mapreduceweights /slideshow/big-spatial2014-mapreduceweights/41270776 bigspatial2014mapreduceweights-141107134314-conversion-gate02
In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data.]]>
In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data.]]> Fri, 07 Nov 2014 13:43:14 GMT /slideshow/big-spatial2014-mapreduceweights/41270776 lixun910@slideshare.net(lixun910) Big spatial2014 mapreduceweights lixun910 In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigspatial2014mapreduceweights-141107134314-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this research, we propose a MapReduce al- gorithm for creating contiguity-based spatial weights. This algorithm provides the ability to create spatial weights from very large spatial datasets efficiently by using computing re- sources that are organized in the Hadoop framework. It works in the paradigm of MapReduce: mappers are dis- tributed in computing clusters to find contiguous neighbors in parallel, then reducers collect the results and generate the weights matrix. To test the performance of this al- gorithm, we design experiment to create contiguity-based weights matrix from artificial spatial data with up to 190 million polygons using Amazon’s Hadoop framework called Elastic MapReduce. The experiment demonstrates the scal- ability of this parallel algorithm which utilizes large com- puting clusters to solve the problem of creating contiguity weights on Big data.
Big spatial2014 mapreduceweights from Arizona State University
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]]> Wed, 10 Aug 2011 04:45:50 GMT /slideshow/machine-learningmove-website/8815971 lixun910@slideshare.net(lixun910) Machine learningmove website lixun910 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/machinelearningmovewebsite-110810044553-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Machine learningmove website from Arizona State University
]]> 619 1 https://cdn.slidesharecdn.com/ss_thumbnails/machinelearningmovewebsite-110810044553-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Wxpysal website /slideshow/wxpysal-website/8815285 wxpysalwebsite-110810030510-phpapp01
geoda center at ASU has all copyrights of the content in this slides]]>
geoda center at ASU has all copyrights of the content in this slides]]> Wed, 10 Aug 2011 03:05:08 GMT /slideshow/wxpysal-website/8815285 lixun910@slideshare.net(lixun910) Wxpysal website lixun910 geoda center at ASU has all copyrights of the content in this slides <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wxpysalwebsite-110810030510-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> geoda center at ASU has all copyrights of the content in this slides
Wxpysal website from Arizona State University
]]> 665 3 https://cdn.slidesharecdn.com/ss_thumbnails/wxpysalwebsite-110810030510-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 3 d pointcloud /slideshow/3-d-pointcloud/8814972 3dpointcloudwebsite-110810020143-phpapp01
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]]> Wed, 10 Aug 2011 02:01:41 GMT /slideshow/3-d-pointcloud/8814972 lixun910@slideshare.net(lixun910) 3 d pointcloud lixun910 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3dpointcloudwebsite-110810020143-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
3 d pointcloud from Arizona State University
]]> 559 1 https://cdn.slidesharecdn.com/ss_thumbnails/3dpointcloudwebsite-110810020143-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Xelerator software /slideshow/xelerator-software/8814856 xeleratorwebsite-110810014558-phpapp02
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Xelerator software from Arizona State University
]]> 1204 1 https://cdn.slidesharecdn.com/ss_thumbnails/xeleratorwebsite-110810014558-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mining attractive places and travel patterns from photos /slideshow/mining-attractive-places-and-travel-patterns-from-photos/8814432 phototravelpatternwebsite-110809234003-phpapp02
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]]> Tue, 09 Aug 2011 23:39:59 GMT /slideshow/mining-attractive-places-and-travel-patterns-from-photos/8814432 lixun910@slideshare.net(lixun910) Mining attractive places and travel patterns from photos lixun910 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phototravelpatternwebsite-110809234003-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Mining attractive places and travel patterns from photos from Arizona State University
]]> 530 1 https://cdn.slidesharecdn.com/ss_thumbnails/phototravelpatternwebsite-110809234003-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-lixun910-48x48.jpg?cb=1522894004 Core software developer GeoDa https://geodacenter.asu.edu/projects/opengeoda PySAL https://geodacenter.asu.edu/projects/pysal http://www.public.asu.edu/~xunli/ https://cdn.slidesharecdn.com/ss_thumbnails/untitled-160418222138-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cartodb-fans-geoda18-provides-extra-power-of-spatial-analysis/61065345 CartoDB fans: GeoDa1.8... https://cdn.slidesharecdn.com/ss_thumbnails/jsmxun-141107141851-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cast-a-software-for-data-analysis-in-space-and-time/41271901 CAST a software for da... https://cdn.slidesharecdn.com/ss_thumbnails/geocrowd2013xunli-141107135104-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/geo-crowd2013-xunli/41271016 Travel Plan using Geo-...