K-D Trees and Quad Trees James Fogarty Autumn 2007 Lecture 12 Range Queries • Think of a range query. – “Give me all customers aged 45-55.” – “Give me all accounts worth $5m to $15m” • Can be done in time ________. • What if we want both: – “Give me all customers aged 45-55 with accounts worth between $5m and $15m.” 2 Geometric Data Structures • Organization of points, lines, planes, etc in support of faster processing • Applications – Map information – Graphics - computing object intersections – Data compression - nearest neighbor search – Decision Trees - machine learning 3 k-d Trees • Jon Bentley, 1975, while an undergraduate • Tree used to store spatial data. – Nearest neighbor search. – Range queries. – Fast look-up • k-d tree are guaranteed log2 n depth where n is the number of points in the set. – Traditionally, k-d trees store points in d-dimensional space which are equivalent to vectors in d-dimensional space. 4 Range Queries i i g g h h e e y d f y d f b b a c a c x x Rectangular query Circular query 5 Nearest Neighbor Search i g h e y d f query b a c x Nearest neighbor is e. 6 k-d Tree Construction • If there is just one point, form a leaf with that point. • Otherwise, divide the points in half by a line perpendicular to one of the axes. • Recursively construct k-d trees for the two sets of points. • Division strategies – divide points perpendicular to the axis with widest spread. – divide in a round-robin fashion (book does it this way) 7 k-d Tree Construction (1) i g h e y d f b a c x divide perpendicular to the widest spread. 8 k-d Tree Construction (2) x i s1 g h e y d f b a c s1 x 9 k-d Tree Construction (3) x i s1 g y h s2 e y d f s2 b a c s1 x 10 k-d Tree Construction (4) x i s1 g y h s2 e y d f x s3 s2 b a c s3 s1 x 11 k-d Tree Construction (5) x i s1 g y h s2 e y d f x s3 s2 b a c a s3 s1 x 12 k-d Tree Construction (6) x i s1 g y h s2 e y d f x s3 s2 b a c a b s3 s1 x 13 k-d Tree Construction (7) x i s1 g y h s2 s4 e y d f x y s3 s4 s2 b a c a b s3 s1 x 14 k-d Tree Construction (8) x i s1 g y h s2 s4 e y d f x y s5 s3 s4 s2 b a c a b x s5 s3 s1 x 15 k-d Tree Construction (9) x i s1 g y h s2 s4 e y d f x y s5 s3 s4 s2 b a c a b x s5 s3 s1 x d 16 k-d Tree Construction (10) x i s1 g y h s2 s4 e y d f x y s5 s3 s4 s2 b a c a b x s5 s3 s1 x d e 17 k-d Tree Construction (11) x i s1 g y h s2 s4 e y d f x y s5 s3 s4 s2 b a c a b x g s5 s3 s1 x d e 18 k-d Tree Construction (12) x i s1 g y y h s2 s6 s4 e s6 y d f x y s5 s3 s4 s2 b a c a b x g s5 s3 s1 x d e 19 k-d Tree Construction (13) x i s1 g y y h s2 s6 s4 e s6 y d f x y y s5 s3 s4 s7 s2 b s7 a c a b x g s5 s3 s1 x d e 20 k-d Tree Construction (14) x i s1 g y y h s2 s6 s4 e s6 y d f x y y s5 s3 s4 s7 s2 b s7 a c a b x g c s5 s3 s1 x d e 21 k-d Tree Construction (15) x i s1 g y y h s2 s6 s4 e s6 y d f x y y s5 s3 s4 s7 s2 b s7 a c a b x g c f s5 s3 s1 x d e 22 k-d Tree Construction (16) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f s5 s3 s1 x d e 23 k-d Tree Construction (17) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h s5 s3 s1 x d e 24 k-d Tree Construction (18) k-d tree cell x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 25 2-d Tree Decomposition 2 1 3 26 k-d Tree Splitting sorted points in each dimension 1 2 3 4 5 6 7 8 9 i x a d g b e i c h f y a c b d f e h g i g h • max spread is the max of e fx -ax and iy -ay. y d f • In the selected dimension the middle point in the list splits the b data. a c • To build the sorted lists for the x other dimensions scan the sorted list adding each point to one of two sorted lists. 27 k-d Tree Splitting sorted points in each dimension 1 2 3 4 5 6 7 8 9 i x a d g b e i c h f y a c b d f e h g i g h indicator for each set y d e f abcdef gh i 0 0 1 0 0 1 0 1 1 b a c scan sorted points in y dimension and add to correct set x y a b d e g c f h i 28 k-d Tree Construction Complexity • First sort the points in each dimension. – O(dn log n) time and dn storage. – These are stored in A[1..d,1..n] • Finding the widest spread and equally divide into two subsets can be done in O(dn) time. • We have the recurrence – T(n,d) < 2T(n/2,d) + O(dn) • Constructing the k-d tree can be done in O(dn log n) and dn storage 29 Node Structure for k-d Trees • A node has 5 fields – axis (splitting axis) – value (splitting value) – left (left subtree) – right (right subtree) – point (holds a point if left and right children are null) 30 Rectangular Range Query • Recursively search every cell that intersects the rectangle. 31 Rectangular Range Query (1) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 32 Rectangular Range Query (2) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 33 Rectangular Range Query (3) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 34 Rectangular Range Query (4) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 35 Rectangular Range Query (5) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 36 Rectangular Range Query (6) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 37 Rectangular Range Query (7) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 38 Rectangular Range Query (8) x i s1 y y g s8 h s2 s6 s4 e s6 y d f x y y y s5 s3 s4 s7 s8 s2 b s7 a c a b x g c f h i s5 s3 s1 x d e 39 Rectangular Range Query print_range(xlow, xhigh, ylow, yhigh :integer, root: node pointer) { Case { root = null: return; root.left = null: if xlow < root.point.x and root.point.x < xhigh and ylow < root.point.y and root.point.y < yhigh then print(root); else if(root.axis = “x” and xlow < root.value ) or (root.axis = “y” and ylow < root.value ) then print_range(xlow, xhigh, ylow, yhigh, root.left); if (root.axis = “x” and xlow > root.value ) or (root.axis = “y” and ylow > root.value ) then print_range(xlow, xhigh, ylow, yhigh, root.right); }} 40 k-d Tree Nearest Neighbor Search • Search recursively to find the point in the same cell as the query.