domoritz/leaflet-maskcanvas · GitHub
https://github.com/domoritz/leaflet-maskcanvas
A leaflet canvas layer for displaying large coverage data sets.
Features: Canvas tile layer based / High performance even for large dataset because of the QuadTree that is used internally / Custom color and circle size
https://raw.github.com/domoritz/leaflet-maskcanvas/master/screenshot.png
straup/Clustr
https://github.com/straup/Clustr#readme
Clustr takes a text file containing longitude/latitude points, tagged with a bit of text, and attempts to generate minimal polygons that “cover” those points, using an “alpha” parameter to determine the notion of “coverage”. The polygons are written to an ESRI Shapefile, suitable for use in GIS software.
k-means clustering - macwright.org
http://macwright.org/2012/09/16/k-means.html
k-means clustering is a neat way to find ‘centers of density’ in a group of points. It’s useful for applications like finding natural groups, simplifying visualizations, and summarizing data.
Une présentation de l’algo de clustering k-means par Tom MacWright de chez MapBox.
Le Sud-Liban face à l’héritage de la guerre avec Israël. Le long chantier du nettoyage des mines et bombes à fragmentation.
An insidious legacy : Removing ordnance
http://dailystar.com.lb/News/Local-News/2012/Aug-30/186113-an-insidious-legacy-removing-ordnance.ashx#axzz24vERJhB5
Conflict not only causes harm in its immediacy, but can establish a legacy that can rear its head for generations.
One of the clearest physical manifestations of this phenomenon in Lebanon is the insidious effect of land mines and cluster ordnance. Aside from the fundamental threat to human life, unexploded ordnance also detrimentally impacts everyday life.
En complément sur ce sujet, voir les cartes publiées dans l’Atlas du Liban :
Les zones minées et déminées (jusqu’en 2006)
http://ifpo.revues.org/docannexe/image/419/img-14-small480.png
Les bombes à sous-munitions au Sud-Liban après la guerre de 2006
http://ifpo.revues.org/docannexe/image/424/img-11-small480.png
#Sur-Liban
#mines
#cluster_bomb
#bombes_à_sous-munitions
jawj/OverlappingMarkerSpiderfier
https://github.com/jawj/OverlappingMarkerSpiderfier
Deals with overlapping markers (spreading de markers proches à la google earth)
La même chose pour leaflet : https://github.com/jawj/OverlappingMarkerSpiderfier-Leaflet
#map #google_maps #leaflet #cluster
KMeans - Cluster 3.0 for Windows, Mac OS X, Linux, Unix
http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/KMeans.html
“Since the initial cluster assignment is random, different runs of the k-means clustering algorithm may not give the same final clustering solution. To deal with this, the k-means clustering algorithms is repeated many times, each time starting from a different initial clustering. The sum of distances within the clusters is used to compare different clustering solutions. The clustering solution with the smallest sum of within-cluster distances is saved.
It should be noted that generally, the k-means clustering algorithm finds a clustering solution with a smaller within-cluster sum of distances than the hierarchical clustering techniques.
The parameters that control k-means clustering are the number of clusters (k) and the number of trials.”
Un peu de théorie sur l’algo de clustering k-means.
Machine #Learning in R : Clustering
http://horicky.blogspot.fr/2012/04/machine-learning-in-r-clustering.html
#Clustering is a very common technique in unsupervised machine learning to discover groups of data that are “close-by” to each other. (...) some very basic algorithms to come up with clusters, and use #R as examples.
http://4.bp.blogspot.com/-Ne599yhehn4/T4NS9Q1d3eI/AAAAAAAAAoM/Uxyl3n25OUY/s1600/p1.png
j’aimerais bien apparier ça avec #gensim tiens…
http://3.bp.blogspot.com/-8mFiw1nFfUo/T4NNE5LK6NI/AAAAAAAAAoA/i8Og4oFugIg/s320/p1.png
#programmation
Coda File System
http://www.coda.cs.cmu.edu
“Coda is an advanced networked filesystem”
#Coda #filesystem #cluster #réseau #réplication #haute_disponibilité #clevermarks #Unix #Linux