A SYSTEM AND METHOD OF OPTIMIZING DATABASE QUERIES IN TWO OR MORE DIMENSIONS

Abstract: A method and system for storing and retrieving spatial data objects from a spatial database is discussed. The system stores multi-dimensional objects within the database by determining their position in a multi-tiered coordinate system. Once each object has been assigned to a particular coordinate, the object is further assigned to one of many overlapping sections within the coordinate system. Each object is assigned to a particular section of the coordinate system depending on its overall size and position.

Description: A SYSTEM AND METHOD OF OPTIMIZING DATABASE QUERIES IN TWO OR MORE DIMENSIONS Background of the Invention Field of the Invention This invention relates to computer databases. Specifically, this invention relates to methods of indexing database records which contain information describing the position, size and shape of objects in two and three-dimensional space. Description of the Related Technology The purpose of a data structure is to organize large volumes of information, allowing the computer to selectively process the data structure's content. The motivation for this is simple: you always have more data than your time requirements, processor speed, main memory and disk access time allow you to process all at once. Depending on the nature of the data and application, data organizing strategies may include partitioning the content into subsets with similar properties or sequencing the data to support indexing and hashing for fast random access. Databases and database management systems extend these concepts to provide persistent storage and transaction controlled editing of the structured data. Spatial data such as that describing a two-dimensional map is no different in its need for efficient organization. Map data is particularly demanding in this regard. A comprehensive street map for a moderate sized community may consist of tens to hundreds of thousands of individual street segments. Wide area maps of LA or New York may contain millions of segments. The content of each map data object can also be some what bulky. For example, a record for an individual street segment may include the coordinates of its end points, a usage classification, the street name, street address ranges, left and right side incorporated city name and postal codes. However, spatial data at its core poses a particularly vexing organizational problem because it tries to organize objects within two-dimensional space. Spatial coordinates consist of two (or more) values which are independent, but equally important for most spatial queries. Established data structures and database methods are designed to efficiently handle a single value, and not representations of multi-dimensional space. This difficulty can be illustrated by considering the problem of creating an application which presents a small window of map data (for instance, the square mile surrounding a house) from a database of a few hundred thousand spatial objects (a map of the city surrounding the house). The motivation for doing this is really two fold: first, the typical resolution of a computer monitor is limited, allowing only a certain amount information to be expressed. Secondly, even if all the data fit within the monitor, the data processing time to calculate this much information (fetching, transforming, clipping, drawing) would be far too long for the average personal computer. To solve this problem, it is advantageous to find all of the street segments which appear in the "window" that will be generated on the monitor, and avoid as many as possible which do not. Thus, all objects which are within a particular range of x-coordinate (or longitude) values and y-coordinate (or latitude) values will be gathered. This problem is generally known as rectangular window retrieval, and is one of the more fundamental types of spatial queries. This method will be used in the following sections as a method for gauging the effectiveness of each of the following organizational methods. The most heavily researched and commonly used spatial data structures (data structures used to organize geographic and geometric data) rely on the concept of tile-based hierarchical trees. A tile in this context is a rectangular (or other regularly or irregularly shaped) partitioning of coordinate space, wherein each partition has a distinct line separating one tile from another so that no single point in the coordinate system lies within more than one tile. A hierarchical tree is one structure for dividing coordinate space by recursively decomposing the space into smaller and smaller tiles, starting at a root that represents the entire coordinate space. In this system, a "hard edge" between tiles means that every point in the space resides exactly one tile at each level of the hierarchy. No point can coexist in more than one tile. One example of a well-known hierarchical tree is the quad-tree data structure. In one example, the quad-tree could represent the surface of the Earth. At the root of the quad-tree is a node representing the entire surface of the Earth. The root, in turn, will have four children representing each quadrant of Latitude and Longitude space: east of Greenwich and north of the Equator, east of Greenwich and south of the Equator, west of Greenwich and north of the Equator and finally, west of Greenwich and south of the equator. Points on Greenwich and the Equator are arbitrarily defined to be in one quadrant or the other. Each of these children are further subdivided into more quadrants, and the children of those children, and so on, down to the degree of partitioning which is required to support the volume and density of data which is to be stored in the quad-tree. The principle problem with quad-tree structures is that they are unbalanced. Because each node in the tree has a limited data storage capacity, when that limit is exceeded, the node must be split into four children, and the data content pushed into lower recesses of the tree. As a result, the depth of a quad-tree is shallow where the data density is low, and deep where the data density is high. For example, a quad-tree used to find population centers on the surface of the Earth will be very shallow (e.g., have few nodes) in mid-ocean and polar regions, and very deep (e.g., have many nodes) in regions such as the east and south of the United States. Since quad-trees are inherently unbalanced, the rectangular window retrieval behavior of a quad-tree is difficult to predict. It is difficult for software to predict how many nodes deep it may have to go to find the necessary data. In a large spatial database, each step down the quad-tree hierarchy into another node normally requires a time-consuming disk seek. In addition, more than one branch of the tree will likely have to be followed to find all the necessary data. Second, when the content of the data structure is dynamic, efficient space management is problematic since each node has both a fixed amount of space and a fixed regional coverage. In real world data schemes, these two rarely correspond. There are several variations on the quad-tree which attempt to minimize these problems. However, inefficiencies still persist. So far, data structures containing points have only been discussed where each spatial object comprises a single set of coordinates. Lines, curves, circles, and polygons present a further complexity because they have dimensions. Therefore, these objects no longer fit neatly into tile based data structures, unless the tiling scheme is extremely contrived. There will always be some fraction of the objects which cross the hard edged tile boundaries from one coordinate region to another. Note that this fact is true regardless of the simplicity of an object's description. For example, a line segment described by its two end points, or a circle described by its center point and radius. A simple, and commonly used way around this problem is to divide objects which cross the tile boundaries into multiple objects. Thus, a line segment which has its end points in two adjacent tiles will be split into two line segments; a line segment which starts in one tile, and passes through fifty tiles on its way to its other end will be broken into fifty-two line segments: one for each tile it touches. This approach can be an effective strategy for certain applications which are read-only. However, it is a poor strategy for data structures with dynamic content. Adding new data objects is relatively simple, but deleting and modifying data are more difficult. Problems arise because the original objects are not guaranteed to be intact. If a line segment needs to be moved or removed, it must somehow be reconstituted so that the database behaves as expected. This requires additional database bookkeeping, more complicated algorithms and the accompanying degradation in design simplicity and performance. Another general problem related to organizing multidimensional objects is that many of these objects are difficult to mathematically describe once broken up. For example, there are numerous ways in which a circle can overlap four adjacent rectangular tiles. Depending on placement, the same sized circle can become two, three or four odd shaped pieces. As with a heavily fragmented line segment, the original "natural" character of the object is effectively lost. An alternate strategy is to use indirection, where objects which cross tile boundaries are multiply referenced. However, each reference requires an extra step to recover the object, and the same object may be retrieved more than once by the same query, requiring additional complexity to resolve. When the number of objects in the database becomes large, this extra level of indirection becomes too expensive to create a viable system. Another strategy used with quad-trees is to push objects which cross tile boundaries into higher and higher levels of the tree until they finally fit. The difficulty with this strategy is that when the number of map objects contained in the higher nodes increases, database operations will have to examine every object at the higher nodes before they can direct the search to the smaller nodes which are more likely to contain useful information. This results in a tremendous lag time for finding data. Query Optimization in a Conventional DBMS As discussed above, data which describes the position, size and shape of objects in space is generally called spatial data. A collection of spatial data is called a Spatial Database. Examples of different types of Spatial Databases include maps (street-maps, topographic maps, land-use maps, etc.), two-dimensional and three- dimensional architectural drawings and integrated circuit designs. Conventional Database Management Systems (DBMS) use indexing methods to optimize the retrieval of records which have specific data values in a given field. For each record in the database, the values of the field of interest are stored as keys in a tree or similar indexing data structure along with pointers back to the records which contain the corresponding values. DATABASE TABLE 1 shows an example of a simple database table which contains information about former employees of a fictional corporation. Each row in the table corresponds to a single record. Each record contains information about a single former employee. The columns in the table correspond to fields in each record which store various facts about each former employee, including their name and starting and ending dates of employment. DATABASE TABLE 1 The FormerEmployee database table. EXAMPLE QUERY 1 shows a SQL query which finds the names of all former employees who started working during 1993. If the number of records in the former employee database were large, and the query needs to be performed on a regular or timely basis, then it might be useful to create an index on the StartDate field to make this query perform more efficiently. Use of a sequential indexing data structure such as a B-tree effectively reorders the database table by the field being indexed, as is shown in DATABASE TABLE 2. The important property of such sequential indexing methods is that they allow very efficient search both for records which contain a specific value in the indexed field and for records which have a range of values in the indexed field. EXAMPLE QUERY 1 SQL to find all former employees hired during 1993. select Name from FormerEmployee where StartDate > 1/1/93 and StartDate < 12/31/93 DATABASE TABLE 2 The FormerEmployee table indexed by StartDate. For analytical purposes, the efficiencies of computer algorithms and their supporting data structures are expressed in terms of Order functions which describe the approximate behavior of the algorithm as a function of the total number of objects involved. The notational short hand which is used to express Order is O(). For data processing algorithms, the Order function is based on the number of objects being processed. For example, the best sorting algorithms are typically performed at a O( N x log(N) ) cost, where N is the number of records being sorted. For data structures used to manage objects (for instance, an index in a database), the Order function is based on the number of objects being managed. For example, the best database indexing methods typically have a O( log(N) ) search cost, where N is the number of records being stored in the database. Certain algorithms also have distinct, usually rare worst case costs which may be indicated by a different Order function. Constant functions which are independent of the total number of objects are indicated by the function O( K ). B-trees and similar Indexed Sequential Access Methods (or ISAMs) generally provide random access to any given key value in terms of a O(Iog(N)) cost, where N is the number of records in the table, and provide sequential access to subsequent records in a O(K) average cost, where K is a small constant representing the penalty of reading records through the index, (various strategies may be employed to minimize K, including index clustering and caching). The total cost of performing EXAMPLE QUERY 1 is therefore O( log(N) +( M x K )), where M is the number of records which satisfy the query. If N is large and M is small relative to N, then the cost of using the index to perform the query will be substantially smaller than the O(N) cost of scanning the entire table. DATA TABLE 1 illustrates this fact by showing the computed values of some Order functions for various values of N and M. This example, though quite simple, is representative of the widely used and generally accepted database management practice of optimizing queries using indexes. FORMULA 1 Cost of retrieving consecutive records from a database table via an index. EXAMPLE QUERY 2 shows a SQL query which finds the names of all former employees who worked during 1993. Unlike EXAMPLE QUERY 1, it is not possible to build an index using traditional methods alone which significantly improves EXAMPLE QUERY 2 for arbitrary condition boundaries, in this case, an arbitrary span of time. From a database theory point of view, the difficulty with this query is due to the interaction of the following two facts: because the two conditions are on separate field values, all records which satisfy one of the two conditions need to be inspected to see if they also satisfy the other; because each condition is an inequality, the set of records which must be inspected therefore includes all records which come either before or after one of the test values (depending on which field value is inspected first). EXAMPLE QUERY 2 SQL to find all former employees who worked during 1993. select Name from FormerEmployee where EndDate > 1/1/93 and StartDate < 12/31/93 Consider the process of satisfying EXAMPLE QUERY 2 using the index represented by DATABASE TABLE 2. The cost of performing EXAMPLE QUERY 2 using an index based on either of the two fields would be O(KχN/2) average cost and O(KχN) worst-case cost. In other words, the query will have to look at half the table on average, and may need to inspect the whole table in order to find all of the records which satisfy the first of the two conditions. Since the cost of scanning the entire table without the index is O(N), the value of using the index is effectively lost (refer to TABLE 3). Indeed, when this type of circumstance is detected, query optimizers (preprocessing functions which determine the actual sequence of steps which will be performed to satisfy a query) typically abandon the use of an index in favor of scanning the whole table. FORMULA 2 Cost of retrieving all records which overlap an interval using a conventional database index on the start or end value. O(K N/2) average, O(KxN) worst case. DATA TABLE 1 Comparison of Order function results for various values of N and M. A K value of 1.5 is used for the purpose of this example. From a more abstract point-of-view, the difficulty with this example is that there is actually more information which the conventional database representation does not take into account. StartDate and EndDate are in fact two different facets of a single data item which is the contained span of time. Put in spatial terms, the StartDate and EndDate fields define two positions on a Time-Line, with size defined by the difference between those positions. For even simple one-dimensional data, conventional database management is unable to optimize queries based on both position and size. Introduction to two-dimensional Spatial Data Spatial databases have a particularly demanding need for efficient database management due to the huge number of objects involved. A comprehensive street map for a moderate sized community may consist of tens to hundreds of thousands of individual street blocks; wide area maps of Los Angeles, CA or New York, NY may contain more than a million street blocks. Similarly, the designs for modern integrated circuits also contain millions of components. FIGURE 1 illustrates a coordinate plane with X- and Y-axes. For the purpose of the following example, the size of the plane is chosen to be 200x200 coordinate units, with the minimum and maximum coordinates values of -100 and 100 respectively for both X and Y. However, it should be noted that the principles discussed for the following example can be applied to any bounded two-dimensional coordinate system of any size, including, but not limited to planer, cylindrical surface and spherical surface coordinate systems. The latitude/longitude coordinate system for the earth's surface, with minimum and maximum latitude values of -90 degrees and +90 degrees, and minimum and maximum longitude values of -180 degrees and + 180 degrees, is an example of one such spherical coordinate system. FIGURE 2 illustrates a distribution of points on the FIGURE 1 plane. Ad discussed above, points are the simplest type of spatial data object. Their spatial description consists of coordinate position information only. An example of non- spatial description commonly associated with point objects might include the name and type of a business at that location, e.g., "Leon's BBQ", or "restaurant". FIGURE 3 illustrates a distribution of linear and polygonal spatial data objects representing a map (note that the text strings "Hwy 1 " and "Hwy 2" are not themselves spatial data objects, but rather labels placed in close proximity to their corresponding objects). The spatial descriptions of linear and polygonal data objects are more complex because they include size and shape information in addition to solely their position in the coordinate system. An example of non-spatial description commonly associated with linear map objects might include the names and address ranges of the streets which the lines represent, e.g., "100-199 Main Street". An example non-spatial description commonly associated with polygonal map objects are the name and type of the polygon object, e.g. , "Lake Michigan", "a great lake". FIGURE 4 illustrates the Minimum Bounding Rectangles (MBRs) of various of linear and polygonal spatial data objects. The Minimum Bounding Rectangle of a spatial data object is the smallest rectangle orthogonal to the coordinate axis which completely contains the object. Minimum Bounding Rectangles are typically very easy to compute by simple inspection for the minimum and maximum coordinate values appearing in the spatial description. In spatial data storage and retrieval methods, Minimum Bounding Rectangles are often used represent the approximate position and size of objects because the simple content (two pairs of coordinates) lends itself to very efficient processing. Storing two-dimensional Spatial Data in a Conventional Database Management System DATABASE TABLE 3 shows how some of the points from FIGURE 2 might be represented in a regular database table. The points in DATABASE TABLE 3 correspond to the subset of the points shown in FIGURE 2 indicated by the * markers. EXAMPLE QUERY 3 shows a SQL query which fetches all points within a rectangular window. A rectangular window query is among the simplest of the commonly used geometric query types. Inspection reveals that "Emily's Bookstore" is the only record from DATABASE TABLE 3 which will be selected by this query. FIGURE 5 shows the rectangular window corresponding to EXAMPLE QUERY 3 superimposed on the points shown in FIGURE 2. DATABASE TABLE 3 A conventional database table containing some business locations. EXAMPLE QUERY 3 SQL to find all businesses in a window. select Name, Type from BusinessLocation where X > 10 and X < 35 and Y > 15 and Y < 40 The principle problem illustrated by this example is that the traditional query optimization method of building a simple index doesn't work well enough to be useful. Consider building an index based on the X field value. Use of this index to satisfy EXAMPLE QUERY 3 will result in an over-sampling of the database table illustrated by the two thick vertical bars shown in FIGURE 6. When the query is performed, the records for all point objects which are between those two bars will need to be examined to find the much smaller subset which actually fits within the shaded window. The "Super Saver" record of DATABASE TABLE 3 is an example of a record which would be needlessly examined. While the work required to start the query is logarithmic, the expected number of point objects which are over-sampled is a linear function of the number of point objects in the database, as is shown by FORMULA 3. This means that the performance of this query will tend to degrade linearly as the number of objects in the database increases. When data volumes become large, this linear behavior will becomes much worse than the preferred O(log(N)), effectively making this style of solution ineffective. The same problem occurs with an index based on Y. The root cause of this problem is the fact that two-dimensional spatial coordinates consist of two values (X and Y) which are independent, but which are also equally important for most spatial queries. Conventional database management techniques are poorly suited to handling two-dimensional data. FORMULA 3 Average cost of performing a two-dimensional rectangular window query using conventional database indexing methods, assuming a mostly even distribution in X. Description of Related two-dimensional Spatial Data Structures The problems which conventional database management methods have with spatial data have led to the development of a variety of special purpose data storage and retrieval methods called Spatial Data Structures. The Design and Analysis of Spatial Data Structures by Hanan Samet includes a review of many of these methods. Many of the commonly used spatial data structures rely on the concept of tile based hierarchical trees. FIGURE 7 shows a rectangular recursive decomposition of space while FIGURE 8 shows how the tiles formed by that decomposition can be organized to form a "tree" (a hierarchical data structure designed for searching). Data structures of this type are called Quad-Trees. FIGURE 9 shows the points from FIGURE 2 distributed into the "leaf-nodes" of this Quad-Tree. FIGURE 10 shows the subset of the Quad-Tree which is contacted by the Rectangular Window Retrieval of EXAMPLE QUERY 3. Note the contrast between the two bottom level nodes which must be inspected in the Quad-Tree, versus the long stripe which must be inspected using conventional database indexing as shown in FIGURE 6. All of the inspected points from the two nodes in FIGURE 10 are at least in the neighborhood of the rectangle, whereas some points inside the stripe in FIGURE 6 are literally at the far edge (bottom) of the coordinate system. While the difference in number of inspected points is not great due to the simplicity of this example, the performance contrast is dramatic when the number of point objects is very large. The Quad-Tree is much better suited to storing position based data because it simultaneously indexes along both axis of the coordinate system. In the most basic implementation of Quad-Trees, each tile in the hierarchy corresponds to a "record" containing information which pertains to that tile. If the tile is at the root or at a branch level, the corresponding record will contain the coordinates of, and pointers to, the records for each child tile. If the tile is at the leaf level, the corresponding record contains the subset of the spatial data objects (point, line or polygon objects and their attributes) which are geometrically contained within the tile's perimeter. The Quad-Tree database "records" are stored in a disk file in breadth first or depth first order, with the root at the head of the file. There are also variations which keep some spatial data objects at higher levels of the hierarchy, and which don't actually create records for leaves and branches which are either mostly or completely empty. For instance, leaves 133 and 144 in FIGURE 9 are both empty. An advantage of the Quad-Tree data structure is that it exhibits O(log(N)) cost when the spatial density of data is fairly uniform, therefore resulting in a well balanced tree. The balance is driven by the construction algorithms which control the amount of branching. The amount of branching (and therefore the maximum depth) in a Quad-Tree is driven by an interaction between the local density of spatial data objects and the maximum number of such objects which can be accommodated in a leaf level record. Specifically, when the data storage in a leaf record fills up, the leaf is split into four children with its spatial data objects redistributed accordingly by geometric containment. Each time this happens, the local height of the tree increases by one. As a result of this algorithmic behavior, however, very high local data densities can cause Quad-Tree performance to degrade toward O(N) cost due to exaggerated tree deptii. There are also a wide variety of non-hierarchical uses of hard edged tiles within a coordinate system. One such method uses space filling curves to sequence the tiles. FIGURE 11 shows such a sequencing of a 4x4 tiling using the Peano- Hilbert curve. The resulting tiles are 50 units on a side. The tiles thus sequenced can be stored in records similar to the leaves in a Quad-Tree, where the data stored in each record corresponds to the subset contained within die tile's perimeter. The records can be simply indexed by a table which converts tile number to record location. The tiles can also be used as a simple computational framework for assigning tile membership. DATABASE TABLE 4 shows the business location database table enhanced with corresponding tile number field from FIGURE 11. The tile number is determined by computing the binary representations of the X and Y column and row numbers of the tile containing the point, and then applying the well known Peano-Hilbert bit- interleaving algorithm to compute the tile number in the sequence. Building an index on the tile number field allows the records to be efficiently searched with geometric queries, even though they are stored in a conventional database. For instance, it is possible to compute the fact that the rectangular window SQL query shown in EXAMPLE QUERY 3 can be satisfied by inspecting only those records which are marked with tile numbers 8 or 9. DATABASE TABLE 4 The BusinessLocations database table enhance with a Tile field. Analysis of the expected cost of this system shows the importance of tile granularity which this and all similar systems share. Extrapolating from the Order function for database queries given in FORMULA 1, the order function for this method is given by FORMULA 4. For a fixed sized window retrieval rectangle, the expected number of tiles is given by FORMULA 5, (the 1 is added within each parentheses to account for the possibility of the window retrieval crossing at least one tile boundary). For a given average size window retrieval, the value of A in FORMULA 4 is therefore an inverse geometric function of the granularity of the tiling which can be minimized by increasing the granularity of the tiling. The expected number of points per tile is given by FORMULA 6. For a given average data density, the value of B in FORMULA 4 is therefore roughly a quadratic function of the granularity of the tiling which can be minimized by decreasing the granularity of the tiling. For a given average retrieval window size and average data density, the expected value of FORMULA 4 can therefore be minimized by adjusting the granularity of the tiling to find the point where the competing trends of A and B yield the best minimum behavior of the system. FORMULA 4 Expected cost of window retrieval using tile numbers embedded in a database table. FORMULA 5 Expected number of tiles per retrieval. A round _up( W χ - / T χ + 1) X round up( W^ - / T Y + 1) where Wx = width of the rectangle, T χ = width of a tile, WY = height of the rectangle, T γ = height of a tile. FORMULA 6 Expected number points per tile. While this technique still over-samples the database, the expected number of records which will be sampled is a function of the average number of records in a tile multiplied by the average number of tiles needed to satisfy the query. By adjusting the tile size, it is possible to control the behavior of this method so that it retains the O(log(N)) characteristics of the database indexing scheme, unlike a simple index based only on X or Y coordinate. Oracle Corporation's implementation of two- dimensional "HHCODES" is an example of this type of scheme. The problem which all tile based schemes suffer is that higher dimension objects (segments, polylines, polygons) don't fit as neatly into the scheme as do points as FIGURES 12 and 13 illustrate. FIGURE 12 shows how the linear and polygonal data objects from FIGURE 3 naturally fall into the various nodes of the example Quad-Tree. Note how many objects reside at higher levels of the Quad- Tree. Specifically, any object which crosses one of the lower level tiles boundaries must be retained at the next higher level in the tree, because that tile is the smallest tile which completely covers the object. This is the only way that the Quad-Tree tile hierarchy has of accommodating the object which might cross a boundary as a single entity. FIGURE 13 shows the dramatic impact which the data that is moved up the hierarchical tree has on the example rectangular window retrieval. Since linear and polygonal data has size in addition to position, some substantial subset will always straddle the tile boundaries. As the number of objects in the database grows, the number of objects which reside in the upper nodes of the quad-tree will also grow, leading to a breakdown of the performance benefit of using the structure. This problem is shared by all hard tile-boundaried methods (Quad-Trees, K-D Trees, Grid- Cells and others). There are three principle ways used to get around the problem of managing objects that straddle tile boundaries: 1) break up any objects which cross tile boundaries into multiple fragments, thereby forcing the data objects to fit, 2) duplicate the objects once for each extra tile that the object touches, and 3) indirectly referencing each object, once for each tile that it touches. Fragmentation in particular is most often used in read-only map data applications. While each of these methods has its respective strengths, a weakness shared by all of them is the great increase in implementation complexity, particularly when the content of the spatial database must be edited dynamically. Note also that these techniques need to be applied to each of the offending objects, which, as the object population in the middle and upper level nodes of FIGURE 13 shows, is likely to be a substantial fraction of the database. The R-Tree (or Range-Tree) is a data structure which has evolved specifically to accommodate the complexities of linear and polygonal data. Like Quad-Trees, R- Trees are a hierarchical search structure consisting of a root and multiple branch levels leading to leaves which contain the actual spatial data. Unlike Quad-Trees which are built from a top-down regular partitioning of the plane, R-Trees are built bottom-up to fit the irregularities of the spatial data objects. Leaf-level records are formed by collecting together data objects which have similar size and locality. For each record, a minimum bounding rectangle is computed which defines the minimum and maximum coordinate values for the set objects in the record. Leaf records which have similar size and locality are in turn collected into twig-level records which consist of a list of the minimum bounding rectangles of and pointers to each of the child records, and an additional minimum bounding rectangle encompassing the entire collection. These twig records are in turn collected together to form the next level of branches, iterating until the tree converges to a single root record. Well balanced R-Trees exhibit O(log(N)) efficiency. The difficulty with R-Trees is that, since there definition is dependent on how the data content "fits" together to build the tree, the algorithms for building and maintaining R-Trees tend to be complicated and highly sensitive to that data content. Static applications of R-Trees, where the data content does not change, are the easiest to implement. Dynamic applications, where the data is constantly being modified, are much more difficult. This is in part because the edit operations which modify the geometric descriptions of the spatial data, by implication have the potential to change the minimum bounding rectangle of the containing record, which in turn can effect the minimum bounding rectangle of the parent twig record, and so on up to the root. Any operation therefore has the potential to cause significant reorganization of the tree structure, which must be kept well balanced to maintain O(log(N)) efficiency. In summary, a variety of special purpose data structures have evolved to meet the particular requirements of multi-dimensional spatial data storage. While these techniques effectively solve some of the problems associated with two-dimensional spatial data, they also share the same inherent weakness which one-dimensional methods have when dealing with data which represents a continuous range of values. In the one-dimensional case, the problem data object types are closed intervals of a single variable, for example, intervals of time. In the two-dimensional case, the problem data object types such as lines, circles and polygons are described by closed intervals of two variables. Description of three-dimensional and Higher Dimension Spatial Data Structures Spatial data which describe a three-dimensional surface has similar requirements for efficient organization. The added complexity is that three- dimensional spatial data consists of 3 independent variables (X, Y and Z) which have equal weight, three-dimensional geometric descriptions of lines, surfaces and volumes are also more complicated than two-dimensional lines and polygons, which make the data somewhat bulkier. However, the basic database organizational problems in three-dimensional are fundamentally the same as those in two-dimensional space, and are therefore amenable to very similar solutions. There is a three-dimensional equivalent to Quad- Tree which uses a regular cubic partitioning of three-dimensional space. Oracle Corporation has also implemented a three-dimensional version of its "HHCODE" technology for storing point objects. There is also a three-dimensional equivalent to R-Trees which uses three-dimensional minimum bounding boxes to define the coordinate extent of leaves and branches. These techniques also share the same limitations as one-dimensional and two-dimensional techniques when handling data representing continuous three-dimensional intervals. The same principles also apply to organizing higher dimension data. In particular, Oracle Corporation has extended its "HHCODE" technology to accommodate point objects of up to 11 dimensions. As described above, there are several problems associated with efficiently organizing and indexing multi-dimensional spatial data within a database. For this reason, an improved method for staring spatial data would be advantageous. This advantage is provided by the system of the present invention. Summary of the Invention As discussed above, databases of information can comprise hundreds of megabytes of data, thereby being very difficult to efficiently search. However, multidimensional data that is stored with the method and system of the present invention can be retrieved with far fewer processor cycles and disk seeks than in prior systems. In the past, one way of organizing large quantities of spatial data was to first overlay a coordinate system onto the spatial data. Each object within the spatial database would be assigned X and Y coordinates. Larger objects, such as lines, polygons and other shapes would be assigned a single location point within the coordinate system that would act like an anchor to hold the object to its position. For example, a line might have a location point that corresponds to one of its ends, and the rest of the object would contain information about the other ends' X and Y coordinates, the line's thickness, color, or other features. In this manner, each object within the spatial database would have a single location point, no matter how large the object was in the database. By separating the larger coordinate system into sub-regions, each location point could be assigned to a particular sub-region. These sub-regions are known as tiles because they resemble a series of tiles once superimposed over a coordinate system that included a set of spatial data. Each tile would, therefore, hold a particular set of spatial data. Thus, a user that knew which tiles held the desired information only needed to search those specific tiles. Once the computer user identified spatial data in a desired region of the spatial database, the system read those few tiles from memory and began the process of gathering objects from those tiles. This method thereby prevented the system from analyzing every object in the entire database for every computer user's request. While this system of assigning a title number to data objects worked well for data comprising only points, it was very slow when larger data objects were involved. Larger data objects could be lines, circles or polygons within the spatial database. Many problems related to organizing spatial data objects had to do with the difficulty of assigning these objects to only one tile, when the object traversed across many tiles. For example, a long line that crosses over three tiles can pose many problems. Since no particular tile is assigned to the long line, the line might be assigned to either the wrong tile or a series of tiles. Assigning an object, such as a line, to multiple tiles leads to a tremendous computer overhead since all of these associations must be maintained in the computer system. The one embodiment reduces these previous problems by providing a series of overlaps between every tile in a spatial database. These overlapping tiles, termed herein "shingles", represent tiles that overlap their nearest four neighbors. The area of overlap for any shingle can be pre-determined to provide the maximum efficiency. For example, a spatial database holding map data might be programmed to have a shingle size of 10 square miles with each single overlap comprising 5 square miles. Thus, every shingle would have an overlap with its nearest four neighbors that is equal to the size of the neighboring shingles. The shingle overlap allows more data objects in the spatial database to be assigned to only one shingle and not split between multiple hard edged tiles. As discussed above, dividing an object across multiple tiles is very disadvantageous because it requires the system to track every tile that is assigned to a particular object. Thus, the purpose of the tiered shingle structure is to provide a logical framework for resolving Spatial Queries into the database in a timely and efficient manner. The spatial data structure is conceptual structure that provides the organization for indexing objects within a spatial data set. The tiered shingle structure does not have to be embodied in a specific computer data structure to be useful and effective. The Tiered Shingle Structure is part of a computational tool for organizing a set of spatial data objects, such as lines, squares and polygons into subsets based on their similar position and size in space. In addition, the tiered shingle structure can provide a mechanism for identifying those subsets of the database which contain the necessary and sufficient spatial data objects required by a specific spatial query into the database. The system and method of the present invention alleviates the problems found in prior systems of small objects which cross title boundaries being moved to higher levels in the tree. In one embodiment the layers of sub-regions are generated, the tiles are calculated to have areas which overlap. Therefore, no hard edges exist between tiles or an object might reside in two tiles simultaneously. These overlapping sub-regions are termed shingles. Because a shingle might overlap with, for example, one half of its closest neighbors, objects which fit into the large shingle region will remain at the lowest possible level. Another advantage of the present invention is that it improves the efficiency of individual databases because the shingle overlap size in each layer can be pre-programmed to provide the fastest access to the spatial database. A database with numerous small objects, such as streets, can be programmed with a smaller shingle overlap size than databases that have numerous large objects, such as freeways. Tailoring the size of the shingles and overlap areas to the size of the average data object keeps more data objects at a single, lower level within the database architecture of the present invention. However, any data object that cannot fit within one shingle can be stored in the next higher level of shingling. For example, the first level of shingling might have a shingle size of 5 square miles and divide the map database into 10,000 shingles. However, the second level of shingling might have a shingle size of 10 square miles and divide the map database into 2500 shingles. This will be discussed more specifically below in reference to Figure 12. One embodiment of the invention is a method of organizing spatial data objects in a map database, including referencing data objects as location points in a region to a coordinate system; separating the region into multiple sub-regions and assigning the data objects whose location point falls within a sub-region to the sub- region so long as no part of the object extends outside the sub-region by a predetermined amount. Another embodiment of the present invention is a method of storing spatial data objects to a computer memory, comprising the steps of (1) determining the size of each data object within a coordinate system; (2) assigning each spatial data object to a location point in the coordinate system; (3) calculating the boundaries of a first tier of overlapping sub-regions of the coordinate system so that each point in the coordinate system is assigned to at least one sub-region; (4) referencing each spatial data object that is smaller than the size of said sub-regions in the first tier to a specific sub-region of the coordinate system based on the location point of each spatial data object; and (5) storing the spatial data objects along with its reference to a specific sub-region to the computer memory. Brief Description of the Drawings FIGURE 1 is a diagram illustrating a coordinate plane in two-dimensional space. FIGURE 2 is a diagram of a computer database depicting a distribution of point spatial data objects on the coordinate plane of Figure 1. FIGURE 3 is a diagram of a computer database showing a distribution of linear and polygonal spatial data objects representing a map on the coordinate plane of Figure 1. FIGURE 4 is an illustration of the minimum bounding rectangles corresponding to a line segment, a polyline and a polygon in a computer database. FIGURE 5 is an illustration of a rectangular window retrieval on the coordinate plane of Figure 1. FIGURE 6 is a depiction of the coordinate system of Figure 1 , wherein a conventional computer database indexing scheme has been applied to search for spatially distributed data within the coordinate plane. FIGURE 7 is an illustration of a regular quadrant-based decomposition of the coordinate plane of Figure 1. FIGURE 8 is a diagram of a tree that depicts how the quadrants and sub quadrants of the coordinate plane decomposition of Figure 7 can be organized to form a Quad Tree-type spatial data structure for a computer database. FIGURE 9 is an illustration of a distribution of point data objects into Quad-Tree nodes in a spatial data structure of a computer database. FIGURE 10 is a diagram of a rectangular window retrieval applied to a Quad-Tree-based data structure of a computer database that illustrates the effectiveness of this data structure for managing two-dimensional point data. FIGURE 11 is an illustration of how a computer database uses a regular, quadrant-based tiling scheme for organizing two-dimensional data by calculating the Peano-Hilbert space filling curve. FIGURE 12 is a depiction of how linear and polygonal spatial data objects fit into a two-dimensional data structure of a computer database that is organized as a Quad-Tree. FIGURE 13 is an illustration of a rectangular window retrieval applied to a computer database that is organized as a Quad-Tree and contains linear and polygonal data. This illustration demonstrates the ineffectiveness of organizing two-dimensional data into this type of data structure and managing spatial data which has an inherent size. FIGURE 14 is an illustration of the organization of a computer database having a three level tiered shingle structure applied to the coordinate plane. FIGURE 15 is an illustration of linear and polygonal map data elements distributed into a computer database that is organized using the Tiered Shingle Structure of the present invention. FIGURE 16 is an illustration of a rectangular window retrieval for a computer database applied to the Tiered Shingle Structure of the present invention and showing the effectiveness of this data structure for managing spatial data which has size. Detailed Description of the Preferred Embodiment Reference is now made to the drawings wherein like numerals refer to like parts throughout. For convenience, the following description will be organized into the following principle sections: Overview, Functional Description, Example Implementation within a Database Table, Empirical Analysis, Mathematical Analysis and Conclusion. 1. Overview The present invention is a method and system for organizing large quantities of data. Although the examples used to illustrate the embodiment of this invention are for organizing map data, the techniques can be applied to other types of data. Other applicable data types include engineering and architectural drawings, animation and virtual reality databases, and databases of raster bit-maps. Additionally, although the figures describe an embodiment of the invention that could be used to organize data representing an object of two dimensions, the present invention is applicable to organizing data for objects of three or more dimensions. Thus, as discussed above, the purpose of the tiered shingle structure is to provide a logical framework for resolving spatial queries into a computer database in a timely and efficient manner. The tiered shingle structure does not have to be embodied in a specific computer data structure to be useful and effective. The tiered shingle structure is part of a computational tool for organizing a set of spatial data objects, such as lines, squares and polygons into subsets based on their similar position and size in space. In addition, the tiered shingle structure provides a mechanism for identifying those subsets of the database which contain the necessary and sufficient spatial data objects required by a specific spatial query into the database. In addition, the tiered shingle structure can run on an Intel ® processor based computer system in one preferred embodiment. However, other computer systems, such as those sold by Apple ® , DEC ® or IBM ® are also anticipated to function within the present invention. FIGURE 14 is an illustration of a three level tiered shingle structure as it would be applied to the example coordinate plane shown in Figure 1. This Tiered Shingle Structure is similar to the regular quadrant-based decomposition of the coordinate plane shown in FIGURE 7. However, rather than each level data structure being organized with discrete, hard-edged tiles, each level consists of overlapping shingles. The overlap between adjacent shingles will be discussed in more detail below, but is indicated by the shaded bands 22 in FIGURE 14. Note that shingles 1-18 formed by regular overlapping squares or rectangles which are normal to the coordinate axis are the easiest to understand and implement, though other configurations are possible. The finest level in a Tiered Shingle Structure (shingles 1-16 in FIGURE 14) is designed to serve as the indexing medium for the vast majority of the spatial data. Thus, it is desirable for the majority of data objects to be assigned to shingles in this level of the data structure. Thus, the spatial objects which extend beyond the edge of the central portion of the shingle by more than a predetermined amount (e.g., its overlap will be assigned to the next higher tier in the hierarchy). The granularity (size of shingle and amount of overlap) of that finest level can be tuned to balance between the competing trends of maximizing the number of spatial data objects which "fit" in that level of shingling (accomplished by increasing the size of the shingles), versus maximizing the degree of partitioning (accomplished by decreasing the size of the shingles). The coarser levels of shingles (a single level in FIGURE 14 consisting of shingles 17-20) serve as an alternative indexing medium for those objects which do not fit in the finest level (i.e., any object which is spatially too large to fit within a particular tile), including its shingled overlap with its nearest neighbors. Note that the absolute size of the overlap increases as the tile size increases in each successively coarser level. Finally, there is the top-level shingle 21 (FIGURE 14) which is used to assign those few objects which are too large to fit within other tiles of the data structure. FIGURE 15 is an illustration of how each of the linear and polygonal objects depicted in the FIGURE 3 are organized within the Tiered Shingle Structure data structure of the present invention. As will be explained below, each shingle contains a subset of the objects having a similar position and size. The benefit of regular overlapping tiles provided by the data structure of the present invention can be seen by comparing the present invention data structure organization of FIGURE 15 with the data structure organization of FIGURE 12. This shingled overlap system allows the small data objects which were located on the arbitrary tile boundaries of the prior art data structures (the bulk of the population in tiles 100, 110, 120, 130 and 140 in FIGURE 12) to remain within the lowest level in the Tiered Shingle Structure. Specifically, any object which is smaller than the size of the overlap at any given level is guaranteed to fit into some shingle at or below that level. In addition, many objects which are larger than the shingle overlap may also fit within a lower level. For example, shingles 1, 6 and 9 in FIGURE 15 are mostly populated by such objects. Note the position of those same objects in FIGURE 12. DATA TABLE 2 provides a numerical comparison of the data object partitioning in FIGURE 15 versus FIGURE 12. Contrasting FIGURE 16 to FIGURE 13 shows why the improved partitioning scheme provided by the Tiered Shingle Structure translates into improved rectangular window query performance over an equivalent structure based on prior art. While the number of tiles which need to be inspected during a data query has slightly increased from five in FIGURE 13 to seven in FIGURE 16, the number of data objects which must be inspected has dropped by nearly half (sixteen versus thirty- one). This drop is directly due to the fact that many more objects can be fit into the finer partition levels with only a slight increase in the size of each partition. As discussed above, a spatial data query must inspect every object within each tile that meets the parameters of the query. Thus, for FIGURE 13, each of the data objects within the top-level tile 100 must be inspected to determine whether it meets the parameters of the spatial data query. Because so many more data objects are able to reside in the smaller tile strucmres when organized by the method of the present invention, there are many fewer data objects to inspect during a spatial data query. For this reason, computer databases that are organized by the system of the present invention can be searched more rapidly than prior art systems. Note that in practice, the equivalent structure based on prior art shown in FIGURES 12 and 13 is seldom actually implemented. This is because the number of objects which are stuck in the upper levels is too great of a burden to allow reasonable performance. Instead, hard boundaried methods resort to alternative strategies, including fragmenting individual data objects at the tile boundaries, duplicating objects once for each tile which they touch, or indirectly referencing the objects once for each tile which they touch. DATA TABLE 2 Numerical comparison of the distributions of map objects in the Tiered Shingle Structure depicted FIGURE 12 die versus Quad-Tree depicted in FIGURE 10. 2. Functional Description The preferred embodiment of the present invention provides two principle classes of functions. The first class, Shingle Assignment Functions, convert the spatial description of a spatial data object into a "Shingle-Key". A Shingle-Key is a number which uniquely represents a specific shingle in a Tiered Shingle Structure. The second class, Query Control Functions, convert the query specification of certain common geometric queries into a list of the necessary and sufficient Shingle-Keys which "contain" the data needed to satisfy the query. Appendix A contains a preferred embodiment of the invention written in the C programming language. There is one Shingle Assignment Function, KeyForBox (beginning on line 0507), which computes a Shingle-Key given a predetermined Minimum Bounding Rectangle and one Query Control Function Set, KeyRectCreate (line 0703), KeyRectRange (line 1030) and KeyRectDestroy (line 1125), which together compute and return of all Shingle-Keys which are needed to solve a Rectangular Window Query. The KeyForBox and KeyRectCreate function calls both expect their corresponding spatial description parameters to be expressed in Longitude (XI and X2) and Latitude (Yl and Y2) coordinates with decimal fractions. Those functions also both take two additional parameters: nLevelMask which controls which levels are to be included in the Tiered Shingle Structure, and nLevelLap which controls the amount of overlap between adjacent shingles. The in-line documentation included within Appendix A describes the parameter usage in greater detail. Note that in both KeyForBox and KeyRectCreate, the double precision Longitude/Latitude coordinates are immediately translated to a fixed point integer representation, where 360 degrees of Longitude are represented in 28 bits of the integer X coordinates, and 180 degrees of Latitude are represented in 27 bits of the integer Y coordinates. The resolution of this representation is precise to roughly the nearest half-foot on the ground. This translation from double precision to fixed-point allows the use of highly efficient modular binary arithmetic for computing both shingle containment and Peano-Hilbert shingle sequencing. For convenience, the remainder of this section is divided into the following three sub-sections: Shingle Assignment Functions, Query Control Functions and Implementation within a Conventional Database. 2.1 Shingle Assignment Functions The Shingle-Keys generated by a Shingle Assignment Function are used to partition the members of a set of spatial data into subsets where all members of a subset have the same Shingle-Key. This means that each member of a subset can be "fit" onto the same shingle (eg: the size of the minimum bounding box that contains the object is not larger than the tile). This further means that all members of a subset have a similar spatial size and position. Indexing and clustering the data in the storage mechanism (common database management practices intended to improve efficiency) by Shingle-Key are therefore very effective, since spatial queries usually select objects which, as a group, have similar position and size. PROCEDURE TABLE 1 shows a set of computational steps that will derive the Shingle-Key corresponding to a particular spatial data object. The steps in this table correspond to lines 0536 through 0652 of the KeyForBox function in Appendix A. The details of some of these steps are expanded upon in subsequent paragraphs. PROCEDURE TABLE 1 Sequence of computational steps required to convert a Spatial Description into the corresponding Shingle-Key within a Tiered Shingle Structure based on regular overlapping squares or rectangles. Step 1 given in PROCEDURE TABLE 1 is computing the Minimum Bounding Rectangle of the Spatial Data Object. The Minimum Bounding Rectangle of a spatial data object is the smallest rectangle which is normal to the coordinate axes and completely contains the object. The typical method of representing a Minimum Bounding Rectangle is with two points: the minimum point (lower-left comer in conventional coordinate systems) and the maximum point (upper-right comer). FIGURE 4 illustrates the minimum bounding rectangles of a few common types of spatial objects. PROCEDURE TABLE 2 describes how minimum bounding rectangles can be computed for a variety of common types of spatial data objects. In some cases, a slight over-estimate of the Minimum Bounding Rectangle may be used when the precise computation is too expensive. PROCEDURE TABLE 2 Descriptions of how Minimum Bounding Rectangles can be derived for some common types of Spatial Data Objects. In Step 3 of PROCEDURE TABLE 1 a determination is made whether the Shingle in the current level who's minimum point (lower-right comer) is both closest- to and less-than-or-equal-to the Minimum Bounding Rectangle of the spatial object. If the Tiered Shingle Structure is based on a regular rectangular or square tiling of the coordinate plane (as illustrated in FIGURE 14 and described in Appendix A) then the candidate shingle is the one corresponding to the tile which contains the minimum point of the Minimum Bounding Rectangle. In die KeyForBox function of Appendix A, lines 0590 and 0591, the coordinates of the minimum point of the Shingle are computed directly using binary modular arithmetic (the tile containment is implied). In Step 4 of PROCEDURE TABLE 1, the maximum point (upper right comer) of the candidate shingle is calculated. That point can be determined directly from the minimum point of the shingle by adding the standard shingle widtii for the current level to the x-coordinate and adding the standard shingle height for the current level to the y-coordinate. In Appendix A, this calculation is performed in lines 0598 through 0601 of the KeyForBox function. Since the Tiered Shingle Structure used in Appendix A is based on overlapping squares, the same value is added to each coordinate. In Step 5 of PROCEDURE TABLE 1, the maximum comer of the shingle is compared to the maximum comer of the Minimum Bounding Rectangle (MBR). This is accomplished through a piece-wise comparison of the maximum x-coordinate of the shingle to the maximum x-coordinate of the MBR and the maximum y- coordinate of the shingle to the maximum y-coordinate of the MBR. If each coordinate value of the shingle is greater than the corresponding value for the MBR, then the maximum comer of the shingle is said to be greater than the maximum comer of the MBR. In Appendix A, this calculation is performed on lines 0609 and 0610 of the KeyForBox function. Step 6 of PROCEDURE TABLE 1 is performed if, and only if, the repeat loop of Steps 2-5 is exhausted without finding a shingle which fits the Minimum Bounding Rectangle. The spatial object which is represented by the Minimum Bounding Rectangle therefore does not fit within any of the lower levels (eg: tiers) of the shingle structure. It therefore by definition must fit within the top-level shingle. In Appendix A, this step is performed on lines 0651 and 0652 of the KeyForBox function. Step 7 given in PROCEDURE TABLE 1 determines the Shingle-Key for the shingle which was found to "best-fit" the data object. In Appendix A, the Peano- Hubert space filling curve is used to assign Shingle-Key numbers via the KeyGenerator function call shown in lines 0623-0625 of the KeyForBox function. The KeyGenerator function is implemented in lines 0043-0485 of Appendix A. The parameters given to the KeyGenerator function include the coordinates of the minimum point of the Shingle, and the corresponding level in the Tiered Shingle Structure. Note that the uniqueness of Shingle-Key numbers across different levels is guaranteed by the statement on line 0482 of Appendix A. 2.2 Query Control Functions The second class of functions are used for controlling spatial queries into the computer database. Functions of this class convert the query specification for certain common geometric queries into a list of the necessary and sufficient shingle keys which contain die data needed to satisfy the query. The list of shingle-keys may be expressed either as an exhaustive list of each individual key, or as a list of key ranges (implying that all keys between and including d e minimum and the maximum values of the range are needed). The most common types of spatial queries are those which find all objects which overlap a region defined by a chosen perimeter. Examples include the Rectangular Window Query and the Polygon Overlap Query. PROCEDURE TABLE 3 shows the general usage of this type of Query Control Function. PROCEDURE TABLE 3 Steps in the general usage of region overlap Query Control Functions. For queries that overlap several regions (eg: tiles) of the database, the set of shingles which overlap the queried region is the union of the shingles from each hierarchical level which overlap the region. The shingles for a given level can be found by first identifying all the shingles which touch the perimeter of the region, and then filling in with any shingles missing from the middle section. One method of finding all the shingles which touch the perimeter of the query is to computationally trace the path of each component through the arrangement of shingles, taking care to eliminate redundant occurrences. A method of filling in the shingles missing from the middle section is to computationally scan bottom-to-top and left-to-right between the Shingles found on the perimeter. The software program in Appendix A implements one Query Control Function Set in lines 0655-1135. This set of functions identifies all shingles which overlap the given Longitude/Latitude rectangle. PROCEDURE TABLE 4 shows the algorithmic usage of this function set. The internal function KeyRectGenerator implemented in lines 0792-1020 of the software code in Appendix A is used to compute the set of shingles for the current level. Similar to the method outlined above, this function traces through the shingles along each edge of the rectangle. However, since the Peano-Hilbert spacefilling curve is used to sequence the shingles and the Peano-Hilbert curve by its nature is guaranteed to be continuous, it is sufficient to simply note whether the curve is headed into or out of the rectangle at each shingle on the edge and sort the resulting lists to find the minimum and maximum of each implied range, letting the curve fill in the middle. FIGURE 17 illustrates how the Peano-Hilbert space-filling curve winds its way contiguously through each tile in one level of a spatial database. PROCEDURE TABLE 4 Algorithmic usage of the KeyRectCreate, KeyRectRange, KeyRectDestroy function set. It is possible to extend the same method to perform a general polygonal retrieval instead of a rectangular retrieval. A general polygonal retrieval is similar to a rectangular window retrieval in that the purpose of the query is to fetch all database objects which are inside or which touch the boundary of an arbitrary polygon. However, do to the limitations of the System Query Language (SQL), it is not possible to express a general polygonal query in a form equivalent to EXAMPLE QUERY 3. To extend the algorithm of PROCEDURE TABLE 4 to perform a general polygonal query, care must be used to trace the path of the polygon though the perimeter shingles while simultaneously keeping track of which shingles correspond to entry and exit points, and which, if any are redundant. Note, however, that once the boundary shingles are identified, the same minimum and maximum range organization will work. In general, this method will work for finding all the shingles which overlap any closed region. 2.3 Implementation within a Conventional Database DATABASE TABLE 5 illustrates a sample database table containing data objects representing a portion of the street segments from FIGURE 3. The Shingle column contains the assigned Shingle-Keys from FIGURE 15. The Xl/Yl and X2/Y2 columns contain the coordinates of the minimum bounding rectangle for each object within the chosen shingle. EXAMPLE QUERY 4 shows how DATABASE TABLE 5 can be queried to find a portion of each data object with a minimum bounding rectangle that overlaps a the rectangular query window, assuming a functional interface similar to Appendix A existed for this tiered shingle structure. This query corresponds to Steps 3-5 in PROCEDURE TABLE 4. As such, this query would have to be repeated once for each key range in order to find all segments which overlap the rectangle. As shown in FIGURE 16, the key ranges which correspond to EXAMPLE QUERY 4 window are 8-9, 17-20 and 21-21. Note how running this query using these key ranges on DATABASE TABLE 5 will result in selecting the single overlapping segment assigned to Shingle 9. Other objects from FIGURE 3 not listed in DATABASE TABLE 5 also overlap the window. DATABASE TABLE 5 A conventional database table containing Street Segments. These objects correspond to the individual segments the highlighted highways HWYl and HWY 2 in FIGURE 3 as distributed into the Tiered Shingle Structure represented in FIGURE 15. PROCEDURE TABLE 5 Recommended Procedures for building and maintain and conventional database implementation, using functions similar to those in Appendix A. EXAMPLE QUERY 4 SQL to find all segments in a window, given a key range MinKey to MaxKey. select StreetName, XI, Yl, X2, Y2 from StreetSegments where Shingle > MinKey and Shingle < MaxKey and XI > -10 and XI < 35 and X2 > -10 and X2 < 35 and Yl > 15 and Yl < 40 and Y2 > 15 and Y2 < 40 3. Empirical Analysis The improved partitioning identified in the earlier comparison of FIGURES 12 and 15 can be validated by measuring how the present invention behaves when given a large quantity of real map data. DATA TABLE 3 shows the results of one such measurement. The data used to perform these measurements is an extract of street segments from a U.S. Census Bureau Topographically Integrated Geographic Encoding and Referencing (TIGER) database file of Los Angeles County, CA. Census TIGER files comprise the defacto industry standard street map data source. Los Angeles County is a good representative choice because of its large size (426367 segments in this extract) and diverse coverage (dense urbanized core, sprawling suburbia and sparsely populated mountain and desert regions). DATA TABLE 3 compares the natural distribution of the TIGER street segments into both a Tiered Shingle Structure having a 25% overlap and an equivalent hard boundaried tiling such as that found in the prior art. These statistics were generated by feeding each segment to the KeyForBox function from the software program given in Appendix A. To generate the Shingles with 25% Overlap statistics, a value of 2 was used for the nLevelLap parameter (shingle_overlap = tile_size * 1 / 2 ). To generate the Hard Boundaried Tiles statistics, a value of 32 was used for the nLevelLap parameter in order to force the overlap amount to zero. In DATA TABLE 3, the Lev column indicates the level of the tile/ shingle structure, 0 being the finest partitioning, 14 being the most coarse, 15 being the top- level compartment. The Size column indicates the size (both width and height) of the (15-Lev) resulting quadrant partitioning in Latitude/Longitude degrees (= 180 / 2 ). The size of the Shingles is in fact 25% larger than the value given in the Size column. Note that the software program in Appendix A implements shingles as squares in Latitudinal/Longitudinal space. The Segs column accumulates the total number of TIGER street segments which naturally fit at this level (i.e., do not cross tile/shingle boundaries - returned through the pnLevel parameter of the KeyForBox function). The Shing and Tiles columns accumulate the total number of unique Key values returned by the KeyForBox function. The Av column computes the average number of segments per unique tile/shingle. The Mx column shows the maximum number of segments which were associated with any one tile/shingle. DATA TABLE 3 A comparison of the distribution of 428367 TIGER street segments for Los Angeles County, CA. into a Hard Tile decomposition of the entire Earth's Surface, vs. the equivalent Shingle stmcture having 25% overlap at each level. The finest level of tiling (level 0) is 180 degrees / 2 in each direction, or a little less than 0.4 miles North South. The Shingles-with-25%-Overlap columns in DATA TABLE 3 shows how efficiently the tiered shingle stmcture organizes this set of data. Note the shallow distribution of segments into the lower levels of the stmcture: over 95% of the segments have settled into the lowest level of the data stmcture. Note how few additional levels are needed, and also the low average and maximum number of segments per shingle in those levels. Now contrast the Shingles- ith-25%-Overlap statistics with die corresponding Hard-Tile-Boundaries statistics in DATA TABLE 3. This serves as a rough model for how a Quad-Tree data stmcture would behave under this load of street segment data (the correspondence is not precise, however, do to the stmctural dependency on storage space per Quad-Tree node). Observe the overall trend which increases the fraction of segments cut by the new boundaries introduced at each finer level. Specifically, for levels 6 down through 1, Segs doubles with each step down in level. This doubling occurs because each finer level doubles the total length of the hard tile boundaries, therefore doubling the likelihood that a given segment will cross one (note that the trend fades above level 6 because the granularity of the partitioning begins to exceed the spatial extent of LA County.) Furthermore, note how there are over 10,000 segments located at level 4 and above. If these segments were stored in a quad-Tree in this state, they would substantially clutter up the main branches of the quad- tree, substantially impeding performance. For instance, if there was a one hundred fold increase in the amount of data being stored, there would be a corresponding one hundred fold increase in the number of tile boundary crossing segments (500K at level 4, 250K at level 5, 125K at level 6, etc.) completely overloading the upper level branches. The poor statistics of Hard-Tile-Boundaries columns show why Quad-Trees cannot be used to store this type of map data in this form. Instead, strategies such as data fragmentation, duplication or multiple indirect referencing have been used in the past to get around this type of problem. DATA TABLE 4 summarizes the number of objects which must be handled in one of these special case ways for the various tile sizes. The statistics in that table clearly show the trade-off between minimizing the number of segments per tile, versus limiting the number of segments which must be managed though a special case. DATA TABLE 4 For the same set of 428367 TIGER street segments, the accumulated number of segments which cross boundaries between tiles. 4. Mathematical Analysis The statistical trends identified in the Empirical Analysis result in very predictable logarithmic cost for spatial retrieval which can be seen through FORMULAS 7 and 8. These formulas are based on the assumption that a regular database table/index is used as the storage/retrieval mechanism for the spatial data, where the index is based on Shingle-Keys. FORMULA 7 is derived from FORMULA 4 which established the behavior of a tile-based method for storing points in a database table. The primary refinement in FORMULA 7 is that a sum must be accumulated to account for the spread of objects across multiple levels. FORMULA 11 is derived from FORMULA 5, primarily by changing the offset factor from 1 to 2 to account for the fact that the overlap will tend to increase the number of tiles touched by the query window. TABLE 13 shows the plug-in values for A L and B L for a 0.016° Longitude x 0.0145° Latitude rectangular window (a roughly 1 mile square at Los Angeles, CA's Latitude). The modest size of the values which appear in TABLE 13 compared to the huge population size is the factor which allows the O(log(N)) performance behavior. For instance, if the entire set of TIGER files for all US Counties were to be used instead of only LA County, the roughly hundred fold increase in population size should only increase by two the logarithmic component of FORMULA 7 (log 10 (400,000) = 6.6; log 10 (40,000,000) = 8.6). All other components of the formula would stay roughly the same. Furthermore, if the rectangular window retrieval size should dramatically change, causing the behavior to become unbalanced, the shingle size can be adjusted up or down to compensate. The software program given in Appendix A was tuned for handling highly detailed street map data. Note that FORMULA 7 represents a worst case which can be greatly improved in practice. Specifically, the value of A L in the portion of the formula A L x log(N) can substantially be reduced by using the Peano-Hilbert space filling curve to sequence the shingles as they are stored in the computer database, as is done in the software implementation given in Appendix A. Use of that curve guarantees that many adjacent shingles will be numbered consecutively. For instance, in any arbitrary 3x3 grouping of adjacent shingles in a field sequenced with the Peano- Hilbert curve, there can be at most 4 consecutive sequences (refer to FIGURES 8 and 17). FORMULA 7 Expected cost of window retrieval using the shingle numbers of the invention in a database table. O( Σ(A L x ( log(N) + K x B L ))) where A L = expected number of tiles needed to satisfy the query at each level, B L = expected number of objects assigned to each tile at each level. FORMULA 8 Expected number of shingles per retrieval by level. A L = round up( W χ / T X L + 2) x round_ up( W γ / T γ L + 2) where Wx = widdi of the rectangle, T XL = width of shingle for level, Wy = height of the rectangle, Tγ L = height of shingle for level. TABLE 13 Computed values for A L for an arbitrary 1 square mile rectangular window around Los Angeles County, CA. Measure values for B L mile from TABLE 7. 5. Conclusion The present invention provides an efficient method and system for organizing large quantities of data. As discussed above, databases of information can comprise hundreds of megabytes of data, thereby being very difficult to efficiently search. However, multidimensional data that is stored with the method and system of the present invention can be retrieved with far fewer processor cycles and disk seeks than in prior systems. By separating the larger coordinate system into sub-regions, each spatial object is assigned to a particular sub-region. These sub-regions are known as tiles because they resemble a series of tiles once superimposed over a set of spatial data. Each tile would, therefore, hold a particular set of spatial data. Thus, a user that knew which tiles held the desired information only needed to search those specific tiles. Once the computer user identifies spatial data located in a desired region of the spatial database, the system can read those few tiles from memory and begin the process of gathering objects from those tiles. This method thereby prevents the system from analyzing every object in the entire database for every computer user's request. The present invention provides a series of overlaps between every tile in a spatial database. These overlapping tiles, termed herein "shingles", represent tiles that overlap their nearest neighbors. The area of overlap for any shingle is predetermined to provide the maximum efficiency. The shingle overlap allows more data objects in the spatial database to be assigned to only one shingle and not split between multiple hard edged tiles, as was done in prior systems. As discussed above, dividing an object across multiple tiles is very disadvantageous because it requires the system to track every tile that is assigned to a particular object. The system and method of the present invention alleviates the problem of small objects which cross title boundaries being moved to higher levels. In the present invention, as the layers of sub-regions are generated, they are calculated to have areas of overlap. The present invention improves the efficiency of individual databases because the shingle overlap size in each layer can be programmed to provide the fastest access to the spatial database. A database with numerous small objects, such as streets, can be programmed with a smaller shingle size than databases that have numerous large objects, such as freeways. Tailoring the size of the tiles and shingles to the size of the average data object keeps more data objects at a single, lower level within the database architecture of the present invention. Thus, any data object that cannot fit within a single shingle can be stored in the next higher level of shingling. APPENDIX A 0001 0002 I* 0003 II Start of keygen . c 0004 / / 0005 // Copyright (c) 1995 Brian Smartt 0006 // Copyright (c) 1996 Telcontar 0007 // Telcontar Proprietary 0008 // All Rights Reserved 0009 // 0010 */ 0011 0012 ι*ιιιιιιιιιιιιιιιιιιιιιιιι inn i III nun i ill 11*1 0013 I* 0014 I I Typedef defining spatial key storage class 0015 // 0016 */ 0017 0018 typedef unsigned long KEY; 0019 0020 /*/////////////////////////////////////////////*/ 0021 /* 0022 // Symbols defining conversion from Latitude/ 0023 // Longitude coordinates to Spatial Key Space. 0024 // The granularity of the Spatial Key space 0025 // is 360.0 degrees / ( 2 Λ 28 ) , or approximately 0026 // 1/2 foot. 0027 */ 0028 0029 #define X_CIRCLE (OxlOOOOOOOL) 0030 #define X_MIN_COORD (0XF8000000L) 0031 #define X_MAX_COORD (0X07FFFFFFL) 0032 #define X_LONGITUDE (360.0) 0033 #define LON TO X(lon) ( (long) ( (double) (Ion) \ 0034 *X_CIRCLE/X_LONGITUDE) ] 0035 0036 #define Y_HALF_CIRCLE (0X08000000L) 0037 #define Y MI COORD (OxFCOOOOOOL) 0038 #define Y_ AX_COORD (0x03FFFFFFL) 0039 #define Y_LATITUDE (180.0) 0040 #define LAT_TO_Y (lat ) ( (long) ( (double) (lat) \ 0041 *Y_HALF_CIRCLE/Y_LATITUDE) ) 0042 0043 /*/////////////////////////////////////////////*/ 0044 /* 0045 // Seed values of Peano-Hilbert Space Filling 0046 // Curve Function. 0047 */ 0048 0049 #define X_DIVIDE (OxOOOOOOOOL) 0050 #define X_MIDDLE (0x04000000L) 0051 #define Y_MIDDLE (OxOOOOOOOOL) 0052 #define Z_EXTENT (0x02000000L) 0053 #define Z_ITERATE (15) 0054 0055 #define Z_PRIMARY_BITS (OxFFFFFOOOL) 0056 #define Z_BOTTOM_BIT (OxOOOOlOOOL) 0057 0058 /*/////////////////////////////////////////////*/ 0059 /* 0060 // Special key value for the oversize "catch- 0061 // all" shingle. 0062 // 0063 */ 0064 0065 #define K_OVERSIZE (OxFFFFFFFCL) 0066 #define K_MIN_EXCEPTION (OxFFFFFFFCL) 0067 #define K_MAX_EXCEPTION (OxFFFFFFFCL) 0068 0069 /*/////////////////////////////////////////////*/ 0070 /* 0071 // Symbols for nLevelMask parameter to KeyForBox 0072 // and KeyRectCreate functions. The parameter 0073 // is used to control which levels appear in 0074 // the structure. 0075 // 0076 // The _ALL symbol shows bits turned on for all 0077 // levels in the structure, from the _FINEST 0078 // to the _MOST_COARSE , including the _EXCEPTION 0079 // (ovesized "catch-all") level. The 0080 // _MOST_COARSE level has a base granularity 0081 // of latitude/longitude "square" tiles which 0082 // are 90.0 degrees high and wide. The _FINEST 0083 // level has a base granularity of 90.0/2 ^ 14 0084 // degrees, which is roughly 0.0055 degrees, or 0085 // a little less than 0.4 mile in the latitude 0086 // direction. The corresponding distance in the 0087 // longitude direction depends on distance from 0088 // the equator (multiply by cos (latitude) ) . 0089 // 0090 // The _0NLY_ symbols show how to specify 0091 // shingle structures which include only 0092 // a specific set of base granularities. Masks 0093 // of these types may be useful when all or 0094 // nearly all of the data has a predictable 0095 // range of sizes. Each of these masks specifies 0096 // five continuous levels, in addition to the 0097 // _EXCEPTION level. Objects which are too big for 0098 // the most coarse level of the five are assigned 0099 // to K_OVERSIZE. Object which are smaller than 0100 // the finest of the five are assigned to the 0101 // appropriate shingle in the finest level. 0102 // 0103 // The _STEP symbol shows how to specify a 0104 // shingle structure which skips every other 0105 // level. Thus, the fan out between levels is 0106 // sixteen rather than four. 0107 */ 0108 0109 #define LEVEL_MASK( level) (0xl<< (level) ) 0110 0111 #define LEVEL_MASK_ALL (OxFFFF) 0112 0113 #define LEVEL MASK FINEST (0x0001) 0114 #define LEVEL_MASK_MOST_COARSE (0x4000) 0115 #define LEVEL_MASK_EXCEPTION (0x8000) 0116 0117 #define LEVEL_MASK_ONLY_FINE (0x801F) 0118 #define LEVEL_MASK_ONLY_MEDIUM (0x83E0) 0119 #define LEVEL_MASK_ONLY_COARSE (OxFCOO) 0120 0121 #define LEVEL_MASK_STEP (0xD555) 0122 0123 /*/////////////////////////////////////////////*/ 0124 /* 0125 // Symbols for nLevelLap parameter to KeyForBox 0126 // and KeyRectCreate functions. The parameter 0127 // is used to control the amount of overlap 0128 // between adjacent shingles in the structure. 0129 // The amount of overlap is as a fraction of 0130 // the base granularity specified by: 0131 // 0132 // 1/ (2 ^ nLevelLap) 0133 */ 0134 0135 #define LEVEL_LAP_ HOLE (0) 0136 #de " fine LEVEL_LAP_HALF (1) 0137 #define LEVEL_LAP_QUARTER (2) 0138 #define LEVEL_LAP_EIGHTH (3) 0139 #define LEVEL_LAP_SIXTEENTH (4) 0140 #define LEVEL_LAP_NONE (32) 0141 0142 /*/////////////////////////////////////////////*/ 0143 /* 0144 // The following tables are used to generate a 0145 // Peano-Hilbert sequence number for a point 0146 // along the curve. Each level in the structure 0147 // has its own curve, though each finer curve is 0148 // derived from the previous coarser curve. 0149 // 0150 // The values in the tables are based on the 0151 // curve building conventions illustrated by 0152 // the following crude pictographs . Other 0153 // curve configurations (rotated, flipped) are 0154 // also possible. 0155 // 0156 // Quadrant Numbering Convention: 5 0157 // 0158 // 1 I 0 0159 // --+-- 0160 // 3 I 2 0161 // 10. 0162 // Peano-Hilbert Curve Evolution Steps: 0163 // 0164 // DOWN: 1--2 1--2 0165 // l-->--2 I I I I 0166 // I I 0 3--0 3 15 0167 // v ! | 0168 // I I 3--2 1--0 0169 // 0 3 ! I I I 0170 // 0171 // 20 0172 // 0173 // LEFT: 0174 // 3--<--2 I I I I I I 0176 // A I 25 0177 // I 1--2 3--2 0178 // 0-->--l I I I 0179 // 0 3--0--1 0180 // 0181 // 30 0182 // RIGHT: 1--0--3 0 0183 // l--<--0 I I I 0184 // I 2--3 2--1 0185 // v I 0186 // I 1--0 1--2 35 0187 // 2-->--3 I I I 0188 // 2--3--0 3 0189 // 0190 // 0191 // UP: 3--2 1--0 0192 // 3 0 1 1 1 1 0193 // 1 1 1 1 0- -1 2- -3 0194 // V 1 1 1 1 0195 // 1 1 1 1 3 0- -3 0 0196 // 2- - < - -1 1 1 1 1 1 1 1 1 0197 // 2- -1 2- -1 0198 // 0199 // Peano-Hilbert Curve Evolution Sequence: 0200 // 0201 // 14 0202 // 0203 // 0204 // I 0205 // v 0206 // 0207 // 0208 // 0209 // 0210 // 0211 // 13 >--2 0212 // I I I 0213 // V V 0214 // I I 0215 // ■ 0 0216 // I 0217 // V 0218 // I I 0219 // 3--<--2 -0 0220 // I 0221 // V 0222 // I I 0223 // 0-->--l 3-->--3 0224 // 0225 // 12 1--2 1- ■ 2 1--2 1--2 0226 // I I I I I I I 0227 // 3--0 0 3--0 0228 // 0229 // 1--0 3--2 1--0 0230 // 0231 // 0--1 2--3--0--1 2--3 0232 // I I I I 0233 // 3 0--3--2 1--0--3 0 0234 // | | | | | | 0235 // 2--1 0--1 2--3 2--1 0236 // I I 0237 // 1--2 3--2 1--0 1--2 0238 // | | | | | | 0239 // 0 3--0--1 2--3--0 3 0240 // 0241 // 11: 0242 */ 0243 0244 1*111 mi 11 iiim n in in iiiiimm iiimimm 0245 /* 0246 // Symbols defining which way the curve faces 0247 */ 0248 0249 #define DOWN (0) 0250 #define LEFT (1) 0251 #define RIGHT (2) 0252 #define UP (3) 0253 0254 /*/////////////////////////////////////////////*/ 0255 /* 0256 // Given which quadrant and the current curve 0257 // facing, what is the peano-hilbert partial 0258 // ordering: 0259 */ 0260 0261 static const int iKeys[4] [4]= 0262 { 0263 /* QU7AD 0, 1, 2, 3, */ 0264 /* DOWN */ 2, 1, 3, 0, 0265 /* LEFT */ 2, 3, 1, 0, 0266 /* RIGHT */ 0, 1, 3, 2, 0267 /* UP */ 0, 3, 1, 2 0268 }; 0269 0270 /*/////////////////////////////////////////////*/ 0271 /* 0272 // Given which quadrant and the current curve 0273 // facing, which is the next curve facing: 0274 */ 0275 0276 static const int iCurves[4] [4] = 0277 { 0278 // QUAD 0, 1, 2, 3, */ 0279 /* DOWN */ DOWN, DOWN, RIGHT, LEFT, 0280 /* LEFT */ LEFT, UP, LEFT, DOWN, 0281 /* RIGHT */ UP, RIGHT, DOWN, RIGHT, 0282 /* UP */ RIGHT, LEFT, UP, UP 0283 }; 0284 0285 /*/////////////////////////////////////////////*/ 0286 /* 0287 // Symbols defining the local shape of the curve: 0288 */ 0289 0290 #define CSAME (0) 0291 #define CDOWN (1) 0292 #define CLEFT (2) 0293 #define CRIGHT (3) 0294 #define CUP (4) 0295 0296 /*/////////////////////////////////////////////*/ 0297 /* 0298 // Given which quadrant and the current curve 0299 // facing, in which direction is the previous 0300 // point on the curve: 0301 */ 0302 0303 static const int iPrev[4] [4]= 0304 { 0305 // QUAD 0, 1, 2, 3, */ 0306 /* DOWN */ CLEFT, CDOWN, CUP, CSAME, 0307 /* LEFT */ CDOWN, CRIGHT, CLEFT, CSAME, 0308 /* RIGHT */ CSAME, CRIGHT, CLEFT, CUP, 0309 /* UP 7 CS7ΛME, CDOWN, CUP, CRIGHT 0310 }; 0311 0312 mmnmimmmmmmmimmmmm 0313 I* 0314 I I Given which quadrant and the current curve 0315 // facing, in which direction is the next 0316 // point on the curve: 0317 */ 0318 0319 static const int iNext[4] [4]= 0320 { 0321 // QUAD 0, 1, 2, 3, * 0322 /* DOWN */ CDOWN, CRIGHT, CSAME, CUP, 0323 /* LEFT */ CLEFT, CSAME, CUP, CRIGHT, 0324 /* RIGHT */ CLEFT, CDOWN, CSAME, CRIGHT, 0325 /* UP */ CDOWN, CSAME, CLEFT, CUP 0326 }; 0327 0328 mmmmmmmmmmimmmmmm 0329 I* 0330 I I Markers used to differentiate keys from 0331 // different levels of the structure: 0332 */ 0333 0334 static const KEY IMarks [Z_ITERATE] = 0335 { 0336 0, 0337 0x80000000, 0338 OxCOOOOOOO, 0339 0XE0000000, 0340 0XF0000000, 0341 OXF8000000, 0342 OxFCOOOOOO, 0343 OxFEOOOOOO, 0344 0XFF000000, 0345 0xFF800000, 0346 OxFFCOOOOO, 0347 OxFFEOOOOO, 0348 0XFFF00000, 0349 0XFFF80000, 0350 OxFFFCOOOO 0351 }; 0352 0353 mmiimimmmiimmmmmiiiiiiiimm 0354 /* 0355 // This function generates the sequence number 0356 // (key) for the given point along the Peano- 0357 // Hubert curve. The nLevel argument is used to 0358 // control the coarseness of the partitioning, 0359 // 0 == finest, 4 == most coarse. 0360 // 0361 // Note that as implemented, the curve fills 0362 // twice as much space along the X axis as along 0363 // the Y axis to mirror the equivalent imbalance 0364 // in the latitude/longitude coordinate system. 0365 // This is done by stringing together two 0366 // curves: one filling all the space where 0367 // X < 0, the other filling all space where 0368 // X >= 0. This behavior is controlled by the 0369 // X_DIVIDE symbol. 0370 // 0371 // This function uses coordinates already 0372 // converted into Spatial Key Space. 0373 // 0374 */ 0375 0376 static KEY KeyGenerator 0377 ( 0378 long IX, 0379 long 1Y, 0380 int nLevel , 0381 int *pnPrev, 0382 int *pnNext 0383 ) 0384 { 0385 long lKey = 0; 0386 long lMiddleX = X_MIDDLE; 0387 long lMiddleY = Y_MIDDLE; 0388 long lExtentZ = Z_EXTENT; 0389 int nCurve = DOWN, nQuad; 0390 int niter, nTotalIter=Z_ITERATE-nLevel ; 0391 0392 *pnPrev = CLEFT; 0393 *pnNext = CRIGHT; 0394 0395 #ifdef X_DIVIDE 0396 if( IX < X_DIVIDE ) 0397 { 0398 lMiddleX = -X_MIDDLE; 0399 } 0400 else 0401 { 0402 IKey = 1; 0403 } 0404 #endif 0405 0406 for( niter = 0; niter < nTotallter; ++nlter ) 0407 { 0408 mmmmmiimiimiiiim 0409 /* 0410 // determine quadrant relative 0411 // to (lMiddleX, lMiddleY) 0412 */ 0413 0414 if( IX < lMiddleX ) 0415 { 0416 nQuad = 1; 0417 lMiddleX -= lExtentZ; 0418 } 0419 else 0420 { 0421 nQuad = 0; 0422 lMiddleX += lExtentZ; 0423 } 0424 0425 if( 1Y < lMiddleY ) 0426 { 0427 nQuad += 2 ; 0428 lMiddleY -= lExtentZ; 0429 } 0430 else 0431 { 0432 // nQuad += 0; 0433 lMiddleY += lExtentZ; 0 0443344 } 0435 0436 mmmmmmiimiiiiiim 0437 I* 0438 II Fold in the next partial key 0 0443399 */ 0440 0441 IKey = (lKey << 2) | iKeys [nCurve] [nQuad]; 0442 0443 mmmmiiimiiimiiiiim 0 0444444 /* 0445 // Maintain prev/next point 0446 // positions 0447 */ 0448 0 0444499 if ( iPrev[ nCurve ] [ nQuad ] ) 0450 { 0451 *pnPrev = iPrev[ nCurve ] [ nQuad ] ; 0452 0453 0 0445544 if ( iNext [ nCurve ] [ nQuad ] ) 0455 { 0456 *pnNext = iNext [ nCurve ] [ nQuad ] ; 0457 } 0458 0 0445599 I III II 11 ιm 0460 I* 0461 II Evolve to next 0462 */ 0463 0464 nCurve = i Curves [ nCurve ] [ nQuad ] ; 0465 0466 mmmmmmiiiiiimiim 0467 1* 0468 II Divide by two to get next 0469 // get next quadrant size 0470 */ 0471 0472 lExtentZ >>= 1; 0473 } 0474 0475 minimum m imiiiiiiim 0476 1* 0477 II Fold in marker to 0478 // differentiate keys from 0479 // different levels: 0480 */ 0481 0482 lKey | = IMarks [nLevel] ; 0483 0484 return lKey; 0485 } 0486 0487 mmmmimmmimmiimmimiiiiiiiim 0488 /* 0489 // Generate the key for the smallest shingle 0490 // containing the given box (dXl , dYl) - (dX2 , dY2) 0491 // in the shingle structure defined by nLevelLap 0492 // and nLevelMask. Returns the shingle key as the 0493 // function return value, and returns the level in 0494 // the structure through the pointer pnLevel . 0495 // 0496 // The box is given in the latitude (Y) and 0497 // longitude (X) coordinate system. dXl and dX2 0498 // are expected to be in the range -180.0 to 180.0, 0499 // and dYl and dY2 are expected to be in the range 0500 // -90.0 to 90.0. 0501 // 0502 // The shingle structure is defined by nLevelMask 0503 // and nLevelLap parameters. See discussions of 0504 // LEVEL_MASK_??? and LEVEL_LAP_? ? ? symbols. 0505 */ 0506 0507 KEY KeyForBox 0508 ( 0509 double dXl, 0510 double dYl, 0511 double dX2 , 0512 double dY2, 0513 int nLevelMask, 0514 int nLevelLap, 0515 int *pnLevel 0516 0517 { 0518 long 1X1, 1Y1, 1X2, 1Y2 ; 0519 long lMinMBRX, lMinMBRY, lMaxMBRX, IMaxMBRY; 0520 long lMinShingleX, lMinShingleY; 0521 long lMaxShingleX, lMaxShingleY; 0522 long lPrimary, lBottom, lLap; 0523 int nLevelBit; 0524 int nLevel; 0525 int nPrev, nNext; 0526 0527 0528 I I Convert box to key space 0529 */ 0530 0531 1X1 = LON_TO_X(dXl) 0532 1Y1 = LAT_TO_Y(dYl) 0533 1X2 = L0N_T0_X(dX2) 0534 1Y2 = LAT TO Y(dY2) 0535 0536 mm mmimiiiimi m m 11 0537 // Find min and max of box 0538 */ 0539 0540 if( 1X1 < 1X2 ) 0541 { 0542 IMinMBRX = 1X1 ; 0543 IMaxMBRX = 1X2; 0544 } 0545 else 0546 { 0547 IMinMBRX = 1X2; 0548 IMaxMBRX = 1X1 ; 0549 } 0550 0551 if( 1Y1 < 1Y2 ) 0552 { 0553 IMinMBRY = 1Y1 ; 0554 IMaxMBRY = 1Y2 ; 0555 } 0556 else 0557 { 0558 IMinMBRY = ' 1Y2; 0559 IMaxMBRY = 1Y1 ; 0560 } 0561 0562 11 0563 // Starting at the finest 0564 // partitioning, iterate up until 0565 // we find a shingle which 0566 // contains the box 0567 */ 0568 0569 lPrimary = Z_PRIMARY_BITS ; 0570 lBottom = Z_BOTTOM_BIT; 0571 lLap = lBottom >> nLevelLap; 0572 nLevelBit = 1 ; 0573 0574 for ( nLevel=0; nLevel < Z_ITERATE; ++nLevel ) 0575 0576 0577 II Check the level mask to see 0578 // if this level is included in 0579 // the structure. 0580 */ 0581 0582 if ( nLevelBit & nLevelMask ) 0583 { 0 0558844 0585 II Compute the shingle base of 0586 // the lower right corner of the 0587 // box 0588 */ 0589 0590 lMinShingleX = IMinMBRX & IPrimary; 0591 IMinShingleY = IMinMBRY & IPrimary; 0592 0593 0 0559944 // Compute the upper right 0595 // corner of the shingle 0596 */ 0597 0598 IMaxShingleX = lMinShingleX + lBottom 0 0559999 + lLap; 0600 IMaxShingleY = IMinShingleY + lBottom 0601 + lLap; 0602 0603 m mill/mi miiiiiiimi mi 0 0660044 // Check if the upper left 0605 // corner of the box fits on 0606 // the shingle 0607 */ 0608 0 0660099 if(( IMaxMBRX < IMaxShingleX 0610 && ( IMaxMBRY < IMaxShingleY ) ) 0611 { 0612 / /////////////// ///////////// 0613 II Found a shingle that 0614 // contains the box! 0615 // 0616 // Compute and return the 0617 // shingle number in the 0618 // Peano-Hilbert sequence 0619 // for this level 0620 */ 0621 0622 *pnLevel = nLevel; 0 0662233 return KeyGenerator ( lMinShingleX, 0624 IMinShingleY, nLevel, 0625 &nPrev, knNext ) ; 0626 0627 0628 0629 mi m minimum m ii 11 ii 0630 II Containing shingle does not 0631 // exist at this level! 0632 // 0 0663333 // Advance shingle constructors 0634 // to next higher level 0635 */ 0636 0637 IPrimary <<= 1; 0 0663388 lBottom <<= 1; 0639 lLap <<= 1 ; 0640 nLevelBit <<= 1; 0641 } 0642 00664433 0644 II Containing shingle does not 0645 // exist within any level! 0646 // 0647 // Return oversize "catch-all" 00664488 // key value. 0649 */ 0650 0651 *pnLevel = Z_ITERATE; 0652 return K OVERSIZE; 0653 } 0654 0655 mmmmmmmimmmmmmiimiiiim 0656 /* 0657 // Rectangular retrieval key generator structure. 0658 */ 0659 0660 #define N_RANGES (100) 0661 0662 typedef struct 0663 { 0664 /* retrieval rectangle */ 0665 long IMinRectX, lMinRectY; 0666 long lMaxRectX, lMaxRectY; 0667 0668 /* shingle structure description */ 0669 int nLevelMask; 0670 int nLevelLap; 0671 0672 /* progress through structure */ 0673 int nLevel; 0674 int nLevelBit; 0675 0676 /* list of key ranges for current level */ 0677 int nRangeCount ; 0678 int nRangeIndex; 0679 0680 KEY MinKey [N_RANGES] ; 0681 KEY MaxKey [N_RANGEΞ] ; 0682 } 0683 KeyRect; 0684 0685 I i mum 0686 /* 0687 // Create a KeyRect generator structure for the 0688 // specified rectangle (dXl, dYl) - (dX2 , dY2) , using 0689 // the shingle structure defined by nLevelMask and 0690 // nLevelLap. 0691 // 0692 // The rectangle is given in the latitude (Y) and 0693 // longitude (X) coordinate system. dXl and dX2 0694 // are expected to be in the range -180.0 to 180.0, 0695 // and dYl and dY2 are expected to be in the range 0696 // -90.0 to 90.0. 0697 // 0698 // The shingle structure is defined by nLevelMask 0699 // and nLevelLap parameters. See discussions of 0700 // LEVEL_MASK ??? and LEVEL LAP ??? symbols. 0701 */ 0702 0703 KeyRect *KeyRectCreate 0704 0705 double dXl, 0706 double dYl, 0707 double dX2, 0708 double dY2, 0709 int nLevelMask, 0710 int nLevelLap 0711 0712 0713 KeyRect *kr; 0714 extern void *AllocateMemory ( int size 0715 long 1X1, 1Y1, 1X2, 1Y2 ; 0716 0717 0718 I I allocate memory to maintain 0719 // retrieval state 0720 */ 0721 0722 if (kr= (KeyRect*) AllocateMemory (sizeof (KeyRect) ) 0723 { 0724 0725 I I Convert search rectangle to 0726 // key space 0727 */ 0728 0729 1X1 = LON_TO_X(dXl) 0730 1Y1 = LAT_TO_Y(dYl) 0731 1X2 = LON_TO_X(dX2) 0732 1Y2 = LAT TO Y(dY2) 0733 0734 mmiimiiiiiii in inn ii mi 0735 I I Find min and max of rectangle 0736 */ 0737 0738 if ( 1X1 < 1X2 ) 0739 { 0740 kr->lMinRectX 1X1; 0741 kr->lMaxRectX 1X2; 0742 } 0743 else 0744 { 0745 kr->lMinRectX 1X2; 0746 kr->lMaxRectX 1X1; 0747 0748 0749 if( 1Y1 < 1Y2 ) 0750 { 0751 kr->lMinRectY 1Y1; 0752 kr->lMaxRectY 1Y2; 0753 } 0754 else 0755 { 0756 kr->lMinRectY 1Y2; 0757 kr->lMaxRectY 1Y1; 0758 } 0759 0760 0761 I I Initialize retrieval state, 0762 // bottom level, no more keys 0763 */ 0764 0765 kr->nLevelMask = nLevelMask; 0766 kr->nLevelLap = nLevelLap; 0767 0768 kr->nLevel = 0; 0769 kr->nLevelBit = 1 ; 0770 0771 kr->nRangeCount = 0 ; 0772 kr->nRangeIndex = 0; 0773 } 0774 0775 return kr; 0776 } 0777 0778 mmmmmmmmmmmmmiiiimiiim 0779 /* 0780 // Function which generates all keys for current 0781 // level in the structure. Because the size of the 0782 // rangelist has a hard limit (N_RANGES) , a fallback 0783 // strategy may needed to reduce the number of 0784 // ranges. This strategy is implemented by the 0785 // "goto TRY_AGAIN" and "nTries" mechanism, which 0786 // doubles the sampling granularity with each 0787 // extra try, forcing down (on average, dividing by 0788 // two for each new try) the number of ranges 0789 // required to search, at the cost of oversatripling. 0790 */ 0791 0792 static int KeyRectGenerator 0793 ( 0794 KeyRect *kr 0795 ) 0796 { 0797 long IPrimaryBits, IBottomBit, lLap; 0798 long lMinX, lMinY, IMaxX, lMaxY; 0799 long lSideX, ISideY; 0800 int nTries, nCount , i ; 0801 int nRangeMin, nRangeMax; 0802 int nPrev, nNext ; 0803 KEY Key; 0804 0805 00880066 // initialize level granularity 0807 // and tries counter 0808 */ 0809 0810 IPrimaryBits = Z_PRIMARY_BITS << kr->nLevel; 00881111 IBottomBit = Z_B0TT0M_BIT << kr->nLevel; 0812 lLap = IBottomBit >> kr- >nLevelLap; 0813 nTries = 0 ; 0814 0815 00881166 // Granularize the rectangle, 0817 // guarding against coordinate 0818 // over/under flow. 0819 */ 0820 00882211 lMinX = ( kr- >lMinRectX - lLap ) & IPrimaryBits; 0822 if ( IMinX < X_MIN_COORD ) 0823 { 0824 IMinX = X_MIN_COORD & IPrimaryBits; 0825 } 0826 0827 lMinY = ( kr- >lMinRectY - lLap ) & IPrimaryBits; 0828 if( IMinY < Y_MIN_COORD ) 0829 { 0830 IMinY = Y_MIN_COORD & IPrimaryBits; 0831 } 0832 0833 IMaxX = kr- >lMaxRectX & IPrimaryBits; 0834 if ( IMaxX > X_MAX_COORD ) 0835 { 0836 IMaxX = X_MAX_COORD & IPrimaryBits; 0837 } 0838 0839 IMaxY = kr->lMaxRectY & IPrimaryBits; 0840 if ( IMaxY > Y_MAX_COORD ) 0841 { 0842 IMaxY = Y_MAX_COORD & IPrimaryBits; 00884433 } 0844 0845 TRY_AGAIN: 0846 miiiiiimi iiiimi i mm 11 ii 0847 II Compute all keys along left 00884488 // and right sides of rectangle 0849 */ 0850 0851 nRangeMin = 0 ; 0852 nRangeMax = 0; 0853 0854 if ( ++nTries > 1 ) 0855 0856 0857 II If ran out of space in the 00885588 // min/max list, increase sample 0859 // granularity to reduce the 0860 // number of transitions. 0861 */ 0862 00886633 IMinX <<= 1 ; 0864 IMinY <<= 1; 0865 IMaxX = ( IMaxX << 1 ) j IBottomBit; 0866 IMaxY = ( IMaxY << 1 ) | IBottomBit; 0867 } 0868 0869 0870 II Compute all keys along left 0871 // and right sides of rectangle. 0872 // 0873 // Find the keys where the curve 0874 // moves in(nPrev) or out(nNext) 0875 // of the rectangle. 0876 */ 0877 0878 for( ISideY = IMinY; 0879 ISideY <= IMaxY; 0880 ISideY += IBottomBit ) 0881 { 0882 m mi minium mi i m i mi 0883 // Each key along the left 0884 */ 0885 0886 Key = KeyGenerator ( IMinX, ISideY, 0887 kr->nLevel, &nPrev, knNext ) ; 0888 0889 if( nPrev == CLEFT ) 0890 { 0891 if (nRangeMin >= N_RANGES ) goto TRY_AGAIN ; 0892 0893 kr->MinKey [nRangeMin++] = Key; 0894 } 0895 0896 if( nNext == CLEFT ) 0897 { 0898 if (nRangeMax >= N_RANGES ) goto TRY_AGAIN; 0899 0900 kr->MaxKey [nRangeMax++] = Key; 0901 } 0902 0903 mi i ii mm m mm i ii 111 mi 0904 // Each key along the right 0905 */ 0906 0907 Key = KeyGenerator ( IMaxX, ISideY, 0908 kr->nLevel, knPrev, knNext ) ; 0909 0910 if( nPrev == CRIGHT ) 0911 { 0912 if(nRangeMin >= N_RANGES) goto TRY_AGAIN; 0913 0914 kr->MinKey [nRangeMin++] = Key; 0915 } 0916 0917 if( nNext == CRIGHT ) 0918 { 0919 if (nRangeMax >= N_RANGES) goto TRY_AGAIN; 0920 0921 kr->MaxKey [nRangeMax++] = Key; 0922 } 0923 } 0924 0925 0926 // Compute all keys along top 0927 // and bottom sides of rectangle 0928 // 0929 // Find the keys where the curve 0930 // moves in(nPrev) or out (nNext) 0931 // of the rectangle. 0932 */ 0933 0934 for( lSideX = IMinX; 0935 lSideX <= IMaxX; 0936 lSideX += IBottomBit ) 0937 { 0938 lll l llll 0939 // Each key along the bottom 0940 */ 0941 0942 Key = KeyGenerator ( lSideX, IMinY, 0943 kr->nLevel, &nPrev, &nNext ); 0944 0945 if( nPrev == CDOWN ) 0946 { 0947 if (nRangeMin >= N RANGES)goto TRY_AGAIN; 0948 0949 kr->MinKey [nRangeMin++] = Key; 0950 0951 0952 if ( nNext == CDOWN 0953 { 0954 if (nRangeMax >= N RANGES) goto TRY_AGAIN; 0955 0956 kr->MaxKey [nRangeMax++] = Key; 0957 } 0958 0959 0960 I I Each key along the top 0961 */ 0962 0963 Key = KeyGenerator ( ISideX, IMaxY, 0964 kr->nLevel, &nPrev, knNext ) ; 0965 0966 if ( nPrev == CUP ) 0967 { 0968 if (nRangeMin >= N_RANGES) goto TRY_AGAIN; 0969 0970 kr->MinKey [nRangeMin++] = Key; 0971 0972 0973 if( nNext == CUP ) 0974 { 0975 if (nRangeMax >= N_RANGES) goto TRY_AGAIN; 0976 0977 kr->MaxKey [nRangeMax++] = Key; 0978 0979 0980 0981 m i mmiiiiiimi inn mm i 0982 // Sort Min and Max Keys. Note 0983 // that nRangeMin == nRangeMax 0984 // by implication (since curve 0985 // is continuous) . 0986 */ 0987 0988 do 0989 { 0990 nCount = 0; 0991 0992 for( i=l; i<nRangeMin; ++i ) 0993 { 0994 if( kr->MinKey [i-l] > kr->MinKey [i] ) 0995 { 0996 Key = kr->MinKey [i-l] ; 0997 kr->MinKey [i-l] = kr->MinKey [i] ; 0998 kr->MinKey [i] = Key; 0999 ++nCount; 1000 } 1001 1002 if ( kr->MaxKey [i-l] > kr->MaxKey [i] ) 1003 { 1004 Key = kr->MaxKey [i-l] ; 1005 kr->MaxKey [i-l] = kr->MaxKey [i] ; 1006 kr->MaxKey [i] = Key; 1007 ++nCount; 1008 } 1009 } 1010 } 1011 while ( nCount ) ; 1012 1013 1*11111111111111 III III III 1111117 1014 // Initialize list traversal. 1015 // return true if list not empty. 1016 */ 1017 1018 kr->nRangeIndex = 0; 1019 return ( kr- >nRangeCount = nRangeMin ) ; 1020 ) 1021 1022 mmmmmmmmimmmmiimiiiiiiim 1023 /* 1024 // Function to fetch the next key range for the 1025 // search rectangle. Returns TRUE if the key range 1026 // was returned. Returns FALSE if all keys are 1027 // exhausted. 1028 */ 1029 1030 int KeyRectRange 1031 ( 1032 KeyRect *kr, 1033 KEY *pMinKey, 1034 KEY *pMaxKey 1035 ) 1036 { 1037 mn mm mm m 11 ii i iimi i 1038 // if out of keys for previous 1039 // level (or this is the first) 1040 */ 1041 1042 if ( kr->nRangeIndex >= kr->nRangeCount ) 1043 { 1044 1*11111 I lllll I III I III I 1045 // ratchet up through each level, 1046 // skipping any which are not 1047 // marked in the mask. 1048 // 1049 // note that ratchet always stops 1050 // with nLevel pointing to the 1051 // next level. 1052 */ 1053 1054 int ratchet; 1055 1056 for( ratchet=l; 1057 ratchet; 1058 ++ (kr->nLevel) , kr->nLevelBit <<= 1 ) 1059 { 1060 if kr->nLevel < Z_ITERATE ) 1061 { 1062 if ( kr->nLevelBit & kr->nLevelMask ) 1063 { 1064 1065 I I generate keys for this level 1066 */ 1067 1068 if ( KeyRectGenerator ( kr 1069 { 1070 ratchet 0; 1071 1072 1073 1074 1075 else if( kr->nLevel == Z_ITERATE ) 1076 { 1077 if ( kr->nLevelBit & kr->nLevelMask ) 1078 { 1079 1080 // All done? set up return of 1081 // oversize "catch-all" key. 1082 */ 1083 1084 kr->MinKey [0] = K_MIN_EXCEPTION; 1085 kr->MaxKey [0] = K_MAX_EXCEPTION; 1086 kr->nRangeIndex = 0; 1087 kr->nRangeCount = 1; 1088 ratchet = 0 ; 1089 1090 } 1091 1092 else 1093 { 1094 mm m iiiiiimi limn mm 1095 // Return FALSE to indicate all 1096 // keys are exhausted. 1097 */ 1098 1099 return 0 ; 1100 } 1101 } 1102 } 1103 1104 m i l l 1105 // Copy next range from list. 1106 */ 1107 1108 *pMinKey = kr- >MinKey [kr- >nRangeIndex] ; 1109 *pMaxKey = kr->MaxKey [kr->nRangeIndex] ; 1110 ++ (kr->nRangeIndex) ; 1111 1112 1*1 ' II II ' IIIINIIII I 1113 // Return TRUE to indicate 1114 // continue searching. 1115 */ 1116 1117 return 1 ; 1118 } 1119 1120 mmmmmmmmmmmmmiimmiim 1121 /* 1122 // Destroy KeyRect generator structure. 1123 */ 1124 1125 void KeyRectDestroy ( KeyRect *kr ) 1126 { 1127 extern void FreeMemory ( void *mem ) ; 1128 1129 FreeMemory ( (void*)kr ) ; 1130 } 1131 1132 /* 1133 // End of keygen.c 1134 */ 1135 mimmmimmiiiii iiiiiim iiiiim i inn i m

Claims: WHAT IS CLAIMED IS: I. A method of organizing spatial data objects in a map database, comprising: referencing data objects as location points in a region to a coordinate system; separating said region into multiple sub-regions; and assigning said data objects whose location point falls within a sub- region to said sub-region so long as no part of said object extends outside said sub-region by a predetermined amount. 2. The method of Claim 1 wherein said data objects are spatial data objects. 3. The method of Claim 1 wherein said location point of said data object is calculated by determining the minimum bounding rectangle for said data object. 4. The method of Claim 1 wherein multiple sub-regions further comprise multiple tiers of sub-regions. 5. The method of Claim 1 wherein each of said sub-regions is assigned a unique code. 6. The method of Claim 1 wherein said predetermined amount is equal to one-half the size of said quadrant. 7. The method of Claim 1 wherein said predetermined amount is equal to one-fourth the size of said quadrant. 8. The method of Claim 1 wherein said predetermined amount is equal to the size of said quadrant. 9. The method of Claim 1 wherein said data objects are selected from the group comprising: lines, circles, squares and polygons. 10. The method of Claim 1 wherein said region is separated into multiple square sub-regions. I I . The method of Claim 1 wherein said region is separated into multiple rectangular sub-regions. 12. The method of Claim 1 wherein said region is separated into multiple round sub-regions. 13. The method of Claim 1 wherein said region is separated into multiple hexagonal sub-regions. 14. A method of storing spatial data objects to a computer memory, comprising the steps of: determining the size of each data object within a coordinate system; assigning each spatial data object to a location point in said coordinate system; calculating the boundaries of a first tier of overlapping sub-regions of said coordinate system so that each point in said coordinate system is assigned to at least one sub-region; referencing each spatial data object that is smaller than the size of said sub-regions in said first tier to a specific sub-region of said coordinate system based on the location point of each spatial data object; and storing said spatial data objects along with its reference to a specific sub-region to said computer memory. 15. The method of Claim 14 wherein said spatial data objects are part of a map database. 16. The method of Claim 14 wherein said spatial data objects are selected from the group comprising: lines, circles, squares and polygons. 17. The method of Claim 14 wherein said computer memory is a computer hard disk. 18. The method of Claim 14 wherein said referencing step comprises the Peano-Hilbert method of ordering spatial data quadrants. 19. The method of Claim 14 wherein said sub-regions are shingles and said reference is a shingle code. 20. The method of Claim 14 wherein the size of said overlap between sub-regions is equal to the size of said sub-region. 21. The method of Claim 14 wherein the size of said overlap between sub-regions is equal to one-half the size of said sub-region. 22. The method of Claim 14 wherein the size of said overlap between sub-regions is equal to one-fourth the size of said sub-region. 23. The method of Claim 14 wherein said determining step further comprises calculating a minimum bounding rectangle for said spatial data object; and 24. The method of Claim 23, wherein said assigning step comprises determining the coordinate position of the lower left corner of the minimum bounding rectangle of said spatial data object and storing said location point to said position.. 25. The method of Claim 14 wherein said spatial data objects are selected from the group comprising: engineering and architectural drawings, animation and virtual reality databases and raster bit maps. 26. The method of Claim 14 further comprising the steps of: calculating the boundaries of a second tier of overlapping sub-regions of said coordinate system so that each point in said coordinate system is assigned to at least one sub-region; and referencing each spatial data object that is larger than the size of said sub-regions in said first tier to a specific sub-region in said second tier based on the location point of each spatial data object. 27. The method of Claim 14 wherein said data objects are selected from the group comprising: lines, circles, squares and polygons. 28. The method of Claim 14 wherein said region is separated into multiple square sub-regions. 29. The method of Claim 14 wherein said region is separated into multiple rectangular sub-regions. 30. The method of Claim 14 wherein said region is separated into multiple round sub-regions. 31. The method of Claim 14 wherein said region is separated into multiple hexagonal sub-regions. 32. A database of multi-dimensional objects, comprising a set of multidimensional objects, wherein each object includes a location point within a coordinate system: a code referencing each multi-dimensional object to a subregion of said coordinate system, wherein said multi-dimensional object does not extend outside said subregion by more than a predetermined amount. 33. The database of Claim 32 wherein said multi-dimensional objects are selected from the group comprising: lines, polygons, circles and squares. 34. The database of Claim 32 wherein said location point is calculated by determimng the minimum bounding rectangle of said multi-dimensional object. 35. The database of Claim 32 wherein subregions comprise multiple tiers of subregions.

Inventors: SMARTT BRIAN E, SMARTT, BRIAN, E.
Applicants: TELCONTAR
Application year: 1998
Main classification: G06F
IPC codes: G06F17