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admin | Category: Free Phone Number Lookup No Charge | 27.02.2016
One approach is to use a bounding box to create a range-based query based on upper and lower bounds for latitude and longitude fields.
We needed to come up with an index that allows us to scan a smaller set of documents than using the range-based approach above. The spatial library provided with Lucene comes with code to plot any number of tiers for a given latitude and longitude. An important property of this algorithm is that shorter strings will contain the locations of longer strings.
We can then implement a custom query that decodes the geohash to return a score based on the distance.
Another notable system is the Military Grid Reference System, which is the geocoordinate system used by NATO militaries for locating points on the earth. Often, a user won’t know the exact location of the person they are searching for, but they will know approximately where the person lives or works.
Instead of looking for people in Seattle and the adjacent towns, we look for people within some number of miles of the center of Seattle. The problem with this approach is that Lucene will have to potentially scan some very large document sets.


Additionally, the library can calculate best fit and a list of tier box IDs given latitude, longitude and radius.
However, without creating many granular tiers, we can’t use them for scoring matches based on their distance from the search location. Again, at WhitePages we do this using a custom QParserPluging based on the IBM spatial samples. For example, a geohash has the unique property that you can decrease the precision of the geohash by simply looking at a subset of the geohash string. Here is how we work behind the scenes to connect the dots so that our users can find the most accurate and up-to-date contact information for the people they are intending to locate.
However, when working with really big data sets in Solr like we do, radius searching can present some problems.
For example, a latitudinal range for a San Diego query might also include Dallas-Fort Worth.
We can then query for a number of specific Cartesian tier boxes based on the level of precision we required. Based on a tier box ID, we can use the Lucene index to list the documents in the index that have been tagged with that box ID.


As Lucene performs this query, it will create one document set for the latitudinal range and one for the longitudinal range. At a precision of 8, the geohash “c23p6xug” represents a projection that includes this location within an approximately 19m x 19m region.
It must then intersect these geographic sets along with the matching document sets for other query terms. To improve query latency and maximize our cache effectiveness, we want to minimize the size of these document sets as much as possible. This algorithm would allow us to eliminate the need for Cartesian tiers and we could perform filtering and scoring in one step. Note however, that using Cartesian tiers as a filter allows us to effectively cache tier filters for commonly searched areas.
For instance, the various tiers around New York will generally always be cached for us in production.



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