r-tree | 易学教程

ELKI DBSCAN R* tree index

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问题 In MiniGUi, I can see db.index . How do I set it to tree.spatial.rstarvariants.rstar.RStartTreeFactory via Java code? I have implemented: params.addParameter(AbstractDatabase.Parameterizer.INDEX_ID,tree.spatial.rstarvariants.rstar.RStarTreeFactory); For the second parameter of addParameter() function tree.spatial...RStarTreeFactory class not found // Setup parameters: ListParameterization params = new ListParameterization(); params.addParameter( FileBasedDatabaseConnection.Parameterizer.INPUT

Android SQLite R-Tree - How to install module?

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问题 http://www.sqlite.org/rtree.html says that the r*tree is "included as part of the amalgamation but is disabled by default" and to enable it "simply compile with the SQLITE_ENABLE_RTREE C-preprocessor macro defined" Well I want to use R-trees in my android app, but clearly SQLite is all pre-installed etc. Is there a way to enable it on a user's phone/device? Alternatively, is it possible to use the NDK and the freely available source code for SQLite? 回答1: You can absolutely compile your own

Remove 2D points from rtree in Python

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问题 I'm trying to store 2D points in an rtree (ver. 0.8.2) and then delete them using Python. I understand that rtree works with rectangles (or boxes in 3D), but I guess points are a subset of rectangles. I'm getting strange behavior while deleting items from the rtree. The script below shows the behavior: from rtree import index as rtindex def pt2rect(pt): return pt[0], pt[1], pt[0], pt[1] pts = [(0.0, 0.0), (1.0, 1.0), (0.0, 1.0)] rt = rtindex.Index() # Add the points [rt.add(0, pt2rect(pt))

SQLite query using rtree and normal index slow

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问题 I have geospatial data, names with coordinates, in a SQLite table and created an rtree for the location and a normal index on the name-column. Rtree is used according to this docs: http://www.sqlite.org/rtree.html When I query records in a specific area, the rtree is used and it works fast: SELECT demo_data.* FROM demo_data, demo_index WHERE demo_data.id=demo_index.id AND minX>=-81.0 AND maxX<=-79.6 AND minY>=35.0 AND maxY>=36.2; When I query just for names, it also goes fast, because the

(Re)loading the R Tree with spatialindex library

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问题 I am bulkloading an R Tree with spatialindex (http://libspatialindex.github.com/) library: string baseName = "streets"; size_t capacity = 10 * 1024 * 1024; bool bWriteThrough = false; indexIdentifier = 0; IStorageManager *disk = StorageManager::createNewDiskStorageManager(baseName, 512); fileInMem = StorageManager ::createNewRandomEvictionsBuffer(*disk, capacity, bWriteThrough); // bulkLoads my tree bulkLoadRTree(); cout << "tree info:" << endl; cout << *tree << endl; delete disk; The

Neo4J huge performance degradation after records added to spatial layer

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问题 So I have around 70 million spatial records that i want to add to the spatial layer (I've tested with a small set and everything is smoothly, queries returning the same results as postgis and the layer operation seems fine) But when i try to add all the spatial records to the database, the performance degrades rapidly to the point that it gets really slow at around 5 million (around 2h running time) records and hangs at ~7.7 million (8 hours lapsed). Since the spatial index is an Rtree that

efficient way to handle 2d line segments

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问题 I am having huge set of 2D line segments. So, I know; Line number, Begin (X,Y,Z) and End (x,Y,Z) of each line segment. I want to get proximity line segments for a given line segment. Likewise for all. To find the proximity I can apply this If I say my data it is as; So, at the end I want to get proximity lines as a vector for each line segment . I heard this type of vector of vector can be taken with r-tree data structures. I was searching it but still could not find the relevant one for me.

R-tree总结

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R-tree R-tree是用来做空间数据存储的树状数据结构。R-tree是B-tree向多维空间发展的另一种形式，并且R树也是平衡树。 R树的核心思想是聚合距离相近的节点并在树结构的上一层将其表示为这些节点的最小外接矩形，这个最小外接矩形就成为上一层的一个节点。因为所有节点都在它们的最小外接矩形中，所以跟某个矩形不相交的查询就一定跟这个矩形中的所有节点都不相交。叶子节点上的每个矩形都代表一个对象，节点都是对象的聚合，并且越往上层聚合的对象就越多。 R树的主要难点在于构建一棵既能保持平衡（所有叶子节点在同一层），又能让树上的矩形既不包括太多空白区域也不过多相交（这样在搜索的时候可以处理尽量少的子树）的高效的树。 1 R-tree数据结构通常，我们不选择去索引几何物体本身，而是采用最小限定箱 MBB（minimum bounding box) 作为不规则几何图形的 key 来构建空间索引。在二维空间中，我们称之为最小边界矩形MBR(minimum bounding retangle)。叶子节点（leaf node）则存储每个对象需要的数据，一般是一个外接矩形和指向数据的标识符(Index，Obj_ID)。如果是点数据，叶子节点只需要存储这些点本身。如果是多边形数据，一般的做法是在叶子节点中存储多边形的最小外接矩形和指向这个多边形的数据的唯一标识符。而非叶子节点

R-tree implementation in matlab

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问题 please, any one tell me how we can implement the R-tree structure in matlab to speed the image retrieval system , I would like to inform you that my database space a feature vector of Color Histogram (Multidimensional ) and also I I have a distance vector for similarity measure... thanks 回答1: I don't use Matlab. So I do not have any idea how much cost is associated in Matlab with index structures. It doesn't appear to be designed for such things. R-Trees seem to make quite a difference.

(Python) Rtree intersection and fiona questions

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问题 I'm trying to create a function for an intersection where the input file is of some urban area, and a query box is used to create an output file that has the intersection containing just the buildings found in that query box. import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.basemap import Basemap import fiona import fiona.crs import rtree input_file = 'se_england_clean.shp' out_file = 'se_england_out' file_index = 'Rtree_index_east.idx' query_box = [-0.0957870483,51