Query searching in a quadtree efficiently locates spatial data by utilizing its hierarchical structure. The search begins at the root node, which represents the entire spatial area, and checks if the query region intersects with the node's boundaries. If so, the search recursively proceeds to the node's children, continuing this process until reaching the leaf nodes. Nodes that do not intersect with the query region are excluded from the search, reducing the number of comparisons needed. At the leaf nodes, individual data points are checked to determine if they fall within the query region. This hierarchical narrowing down of the search area allows quadtrees to perform range searches, nearest neighbor searches, and point location queries much faster and more efficiently than linear searches, making them highly effective for managing large spatial datasets.
Instructions
Hover the mouse over the canvas to get a square search region. The points inside the region will be highlighted. This is achieved using the Quadtree without searching all the points, reducing the time complexity from O(n) to O(log(n))
Note that the points are generated randomly based on Gaussian distribution for more concentration in the centre. Refresh to get a new set of points