Jonathan Huxley :: Consulting

Traditional search engines support the finding of documents from the Internet containing specific words or phrases. For this purpose search engines need to navigate the Internet on a regular basis, analyse the content of all found documents and generate an index from their content. This index is the core of all search engines since it facilitates efficient searching. Without this index search times would be unacceptable high.

A Spatial Search Engine on the other hand works like this. First the data to be searched is aggregated or compiled. This data is then spatially enabled. For example, your home location can be defined spatially by its latitude and longitude coordinate. Or in three dimensions by modeling that object by it's location and its elevation. Hundreds of millions of spatially-enabled objects can then be spatially indexed such that fast geometric search operations can be applied. This creates some very interesting (and potentially disruptive) solutions for many industries:

Real estate
Search for properties by geographic criteria in addition to traditional methods (sq. footage, number of bedrooms, bathrooms). Limit searches to a particular neighborhood or zip code, or only search for properties that are within a specified distance of a particular feature--a golf course or a marina, for example, or lakefront homes or ocean-facing properties. (And warn the user if the home is on a flood plain.)

Demographics
A large retail organization is launching a marketing initiative for a particular target demographic. Which neighborhoods or zip codes have the highest percentage of Spanish-speaking 25 to 35 year old females of Hispanic descent and are within five miles of a store location? Spend your marketing budget wisely.

Government
Show me a street map with the location of registered Democrats and Republicans (blue and red push pins) with roof-top location accuracy.
Climate data visualization.
Property parcel mapping.

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Key words: Linux (Red Hat / CentOS / Debian), UNIX, SOLARIS, PHP, Perl, Python, C++, Java, JavaScript, AJAX, PostgreSQL, MySQL, BerkleyDB, InnoDB, Tomcat, Apache, Lighttpd, Memcached, PostGIS, Slony, Zend Platform, GeoSer ver, Lucene, SOLR, SOAP, ReST, AMF, XML, JSON, GeoJSON, Zend Framework, CakePHP, Model-View-Controller.


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	Traditional search engines support the finding of documents from the Internet containing specific words or phrases. For this purpose search engines need to navigate the Internet on a regular basis, analyse the content of all found documents and generate an index from their content. This index is the core of all search engines since it facilitates efficient searching. Without this index search times would be unacceptable high. A Spatial Search Engine on the other hand works like this. First the data to be searched is aggregated or compiled. This data is then spatially enabled. For example, your home location can be defined spatially by its latitude and longitude coordinate. Or in three dimensions by modeling that object by it's location and its elevation. Hundreds of millions of spatially-enabled objects can then be spatially indexed such that fast geometric search operations can be applied. This creates some very interesting (and potentially disruptive) solutions for many industries: Real estate Search for properties by geographic criteria in addition to traditional methods (sq. footage, number of bedrooms, bathrooms). Limit searches to a particular neighborhood or zip code, or only search for properties that are within a specified distance of a particular feature--a golf course or a marina, for example, or lakefront homes or ocean-facing properties. (And warn the user if the home is on a flood plain.) Demographics A large retail organization is launching a marketing initiative for a particular target demographic. Which neighborhoods or zip codes have the highest percentage of Spanish-speaking 25 to 35 year old females of Hispanic descent and are within five miles of a store location? Spend your marketing budget wisely. Government Show me a street map with the location of registered Democrats and Republicans (blue and red push pins) with roof-top location accuracy. Climate data visualization. Property parcel mapping. << Back Key words: Linux (Red Hat / CentOS / Debian), UNIX, SOLARIS, PHP, Perl, Python, C++, Java, JavaScript, AJAX, PostgreSQL, MySQL, BerkleyDB, InnoDB, Tomcat, Apache, Lighttpd, Memcached, PostGIS, Slony, Zend Platform, GeoSer ver, Lucene, SOLR, SOAP, ReST, AMF, XML, JSON, GeoJSON, Zend Framework, CakePHP, Model-View-Controller.

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