This paper introduces a 2-d space-filling approach for visualizing tree structures, known as tree-maps. Traditional tree representations are often hierarchical, with root nodes at the top and children below, but this method uses rectangles whose areas are proportional to node sizes. This allows for a more effective visualization of large tree structures, particularly for representing file systems where the size of files and directories is important. Tree-maps are designed to help users quickly identify large files in a directory structure, which is crucial for managing disk space. The algorithm recursively partitions a rectangular area, with each partition representing a subtree. The algorithm alternates between vertical and horizontal partitions, depending on the level of the tree. Each rectangle is colored to represent different attributes, such as file type or size, aiding in quick visual interpretation. The algorithm requires that each node contains information about its size and children. The root node's size is used to determine the size of each subtree. If the system does not maintain this data, it must be computed beforehand. Additionally, the root node may have a dummy child to represent the unused disk space, allowing the display to show the percentage of disk space utilization. The tree-map algorithm is efficient, running in linear time with the number of nodes. It can be adapted for different traversal methods, including depth-first and breadth-first. The algorithm is also flexible, allowing for different color coding schemes and user interactions such as file name display and pop-up menus. The paper discusses various applications of tree-maps, including organization charts, library catalogs, stock portfolios, and computer programs. The visual representation helps users understand the relative sizes and importance of different elements within the tree structure. The paper concludes that tree-maps provide a practical and effective way to visualize complex tree structures, with potential for further refinement and application in various domains.This paper introduces a 2-d space-filling approach for visualizing tree structures, known as tree-maps. Traditional tree representations are often hierarchical, with root nodes at the top and children below, but this method uses rectangles whose areas are proportional to node sizes. This allows for a more effective visualization of large tree structures, particularly for representing file systems where the size of files and directories is important. Tree-maps are designed to help users quickly identify large files in a directory structure, which is crucial for managing disk space. The algorithm recursively partitions a rectangular area, with each partition representing a subtree. The algorithm alternates between vertical and horizontal partitions, depending on the level of the tree. Each rectangle is colored to represent different attributes, such as file type or size, aiding in quick visual interpretation. The algorithm requires that each node contains information about its size and children. The root node's size is used to determine the size of each subtree. If the system does not maintain this data, it must be computed beforehand. Additionally, the root node may have a dummy child to represent the unused disk space, allowing the display to show the percentage of disk space utilization. The tree-map algorithm is efficient, running in linear time with the number of nodes. It can be adapted for different traversal methods, including depth-first and breadth-first. The algorithm is also flexible, allowing for different color coding schemes and user interactions such as file name display and pop-up menus. The paper discusses various applications of tree-maps, including organization charts, library catalogs, stock portfolios, and computer programs. The visual representation helps users understand the relative sizes and importance of different elements within the tree structure. The paper concludes that tree-maps provide a practical and effective way to visualize complex tree structures, with potential for further refinement and application in various domains.