Patent Application
20170303106
The present invention relates to a pre-generated geographic index to facilitate analysis of location data.
In many location based services applications, it is important to know whether a particular geographic point is inside a particular geographic region. For example, for 9-1-1 emergency services, it is important to assign a context for en event triggered from a mobile device, and to notify the authorities in the appropriate jurisdiction. Depending on which county the mobile device user is in, a different emergency response team may be assigned. This is commonly referred to as “point in poly operations.” There are many other similar use cases where dynamically generated points get compared in real time against static (or slowly changing) polygons for geometric interaction. Another common application is geofencing, in which it is determined when a mobile device user enters into a defined region, and using that information to send them targeted messages, or for tracking.
Conventionally, accurate point in poly operations are very processor intensive. Geographic boundaries are often irregular, and accurate results can require a full geometric analysis. Processing power and processing speed can be critical when large numbers of points are being processed, and when timely decisions need to be made based on the output.
An improved method is provided for determining whether a sample point is within a defined geographic area. This technique improves processing speed for responding to “point in polygon” operation requests for a large majority of locations. In this method, indexes for the geographic area of interest are generated in advance. Such index can complement a regular quad-tree or r-tree index. The geographic area, as defined by an outer boundary, is then subdivided into some regular geometric shape and indexed. Then, a simplified comparison of the sample point to the indexed regular shapes is made.
Creation of Index:
Using the rectangle implementation for this description, a first step for indexing is to define a rectangular boundary box that fully encloses the geographic area. As is conventionally known, this bounding box can be checked against a sample point. But since most geographic areas are irregular in shape, there are many areas inside the bounding box that are not inside the geographic area of interest. Thus, the initial bounding box can only be used to determine with certainty whether the sample point is outside of the geographic area.
In a next step, he boundary box is divided into a plurality of equal first level rectangular boxes at a first dividing level. Each of the first level boxes is then examined to determine whether the first level box is (a) entirely outside the geographic area; (b) entirely inside the geographic area: (c) intersecting with the defined boundary. The result of this determination will determine which boxes can be indexed, and which require further processing.
If the first level box is entirely outside the geographic area, then that box is discarded and it is not part of the indices. This step is to identify and exclude larger areas that fall into that gap between the geographic boundary and the initial bounding box.
If the first level box is entirely inside the geographic area, then that box is added to the indices and is assigned an index number that is stored with the corresponding range of coordinates for that box. This step identifies large blocks that are entirely contained within the geographic areas.
If the first level box intersects with the defined boundary then that box requires further processing, since it includes both desirable area from inside the boundary, and unwanted area that is outside. This further processing includes dividing that box into a plurality of equal second level rectangular boxes at the second dividing level.
The same steps are applied again for the second level boxes that have been created e.g. determining whether they are fully outside, fully inside, or intersecting and the same indexing is performed. In the preferred embodiment, the examination, determination and indexing steps are performed iteratively for a subsequent number of dividing levels. The iterations may continue until (1) there are no more boxes that intersect with the defined boundary, or (2) a maximum dividing level has been reached.
Searching Against an Index:
In a preferred embodiment, the search point, or sample point, to be analyzed can be checked at a high level prior to comparison to the index. That is, prior to the step of comparing the sample point to the index data, it is determined whether the sample point is inside or outside the initial boundary box. Then, if the sample point is outside the boundary box, an output indicates that the sample point is outside of the geographic area. If the sample point is inside the boundary box, then subsequent steps for comparison to the index are followed.
For analysis with the index, the sample point is compared to the coordinate ranges of the indexed boxes to determine if the sample point is included in the indices. If the sample point is within the indexed boxes then a response is provided indicating that the sample point is within the geographic area. If the sample point is not within the indexed boxes then a full geometric analysis is performed of the sample point in comparison to a full geometry of the defined boundary.
In another embodiment, multiple indices are generated for multiple layers of maps having different boundaries for a same physical location. Then, the step of comparing the sample point to the coordinate ranges of the indexed boxes is done concurrently for the multiple indices. An exemplary application of the multiple indices processing is for when the multiple layers represent different types of insurance risks. Then, the method determines whether the sample point is in located in designated risk zones (for example, flood, fire, earthquake) in each of the respective layers.
Traditionally, data structures such as R-trees and quad-trees can be used for speeding up such point in polygon searches. In the classical R-tree implementation, the primary spatial filter is performed using the bounding box of the geometry; and returns either NO (when the bounding box of the searched object does not overlap the bounding box of a record in the table) or MAYBE (when the bounding boxes overlap). In the latter case, this means that we need to actually compare the individual geometries for intersection. A quad tree index has the same characteristics, except that it uses tiles for the exterior approximation, rather than bounding boxes.
In
The current method adds an interior approximation of the polygon to speed up the searches. The method adapted for interior approximation is a novel use of quad tree tiles. The quad tree tiles are used as an inexpensive approximation of the interior of the polygon. This complements and improves the currently used exterior approximation provided by simple shapes, such as bounding boxes (in r-trees) or quad tiles (for quad tree).
For each polygon that we need to index, we store the ‘interior’ quad tree tiles that are entirely contained in the polygon in addition to the traditional R-tree or quad tree index. Any point within the interior quad tree tiles can be quickly determined to be in without constructing the topology of the polygon. The inscribed rectangles are persisted either using the usual quad tree code (a string formed by the integer encoding using quadrants 0, 1, 3) or using an integer encoding scheme and indexed using traditional B-tree or hash indices.
As seen in
To determine if a point overlaps a polygon, we see if the point overlaps any of the inscribed rectangles first. If it does, you've answered the question by purely using the indices, without decoding the polygon and constructing the topology. If not, you can fall back on prior techniques, such as ray tracing. Thus it can be seen that this technique avoids using high intensive full geometric analysis for the vast majority of the region, but does not help for the smaller region that is uncovered by the tiles, but is within the search polygon.
In the example explained with the figures, the geographic region 20 happens to be a circle. A bounding box 11 is positioned to completely enclose the region 20. Preferrably, bounding box 11 is the smallest possible rectangle that can contain the geographic region 20. The bounding box 11 can be defined by its MINX, MINY coordinates at the lower left, and the MIDX, MIDY coordinates in the middle of the box 11.
A first step for indexing is to divide the initial box 11 into equal sized smaller boxes. In this example, the box 11 is dived into four equal quadrants, numbered 0, 1, 2, and 3. Those four quadrants are now a “first level” of boxes that are the result of a “first level” division. A further step is to determine whether any of the new first level boxes can be excluded from the index, or indexed. Boxes are excluded from the index when no part of the box is within the geographic region 20. Boxes are added to the index when all of the box is contained within the geographic region 20. Neither of these cases apply in
In
In the preferred embodiment, the “0 digit indicates the upper left quadrant box for the level that the digit represpents. “1” is the upper right: “2” is the lower left; and “3” is the tower right quadrant.
In box set 61, the upper right box (index number 11) has been subdivided to the second level. Thus a new digit has been added at the end after “11, ” and that new digit is indicative of which quadrant it is in. The same process has been followed in box set 62 where box 111 has been divided into boxes 1110, 1111, 1112, and 1113.
Note that the use of Quad tree tiles implies that the operations are on a Cartesian plane. However, the core idea is the use of a hierarchical spatial tessellation scheme, whose key can be indexed using traditional scalar indexing techniques. Hence the same idea can be used to index the spherical model of the world (i.e. points and lines expressed in longitude, latitude systems) using an existing tessellation model, such as the hierarchical triangular mesh (HTM), an indexing method proposed by skyserver.org. The only requirements from the index on sphere is that it is hierarchical in nature, and has an inexpensive way to translate a point to a tile an encoded area at the lowest level of tessellation under the hierarchical scheme.
A nice property of this algorithm is that we can a point to search against multiple polygon tables at the same time. For example, if a point representing an insured property needs to be checked against fire risk, flood risk, and earthquake risk boundaries so we can assign an insurance risk score, we could check the point against a tile index that is a union of all tiles from the four individual tables. We will fall back on a table polygon check only in cases where the search does not yield a match for a particular layer.
Although the invention has been described with respect to preferred embodiments thereof, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the spirit and scope of this invention.
The following is an exemplary pseudocode algorithm for implementing the improved indexing method. This is recursive a recursive algorithm that works by dividing the area of interest into four quadrants at each step of the way, as discussed previously. The starting arguments for the recursive procedure are; the polygon to be indexed, an empty string, and the bounding box covering the whole extent of the data.
The above algorithm is invoked with the following starting values:
The following pseudocode shows an exemplary algorithm for determining point overlap
The result now in string s;
To determine if a point overlaps any of the polygons that are being searched, this algorithm is invoked to assign a quad key to the point at MAXLEVEL+1. Then you can search the polygons whose quad keys are prefixes to the point's quad key.
