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<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>To whom it may concern,<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>When we started to prototype our proof-of-concept
web map using PostGIS we needed a geocoder, but couldn't find one to fit our
needs. We did try the Tiger Geocoder and attempted to use it but the data
requirements seemed excessive and additionally we needed to operate on the
custom GIS data being developed by state and local government’s GIS
departments. So, based on our fairly intimate understanding of how people
do address searches, we set out to prototype our own geocoder. Our
geocoder functions somewhat differently from others we have worked with.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>One notable difference, we rely more on pattern
matching than upon normalization in the source data. For example, our
geocoder does not require road names in the roads table to be broken down into:
prefix direction, prefix type, street name, suffix type, and suffix
direction. We require only that a given road segment have a label. So,
for example, with other geocoders, a segment of "<st1:Street w:st="on"><st1:address
w:st="on">Main Street NorthEast</st1:address></st1:Street>" would be
attributed like so: prefix direction = "", prefix type =
"", street name = "Main", suffix type = "none",
suffix direction = "NorthEast". Our geocoder allows the segment
of road to be attributed with only: label="<st1:Street w:st="on"><st1:address
w:st="on">Main Street NorthEast</st1:address></st1:Street>". We use
pattern matching techniques to normalize that data when we create the geocoding
indexes. We then use the same pattern matching techniques to normalize
user input when someone searches for "<st1:Street w:st="on"><st1:address
w:st="on">123 Main Street NE</st1:address></st1:Street>". While
this may not be entirely revolutionary, we do get good matches. We firmly
believe that simplifying the data model to allow the computer (instead of the
GIS analyst) to do the normalization is less error-prone and can have other
side benefits as well. We perform interpolation along line segments in
regular fashion by having LeftFrom, RightFrom, LeftTo and RightTo.
Finally, we do not require zone information (Zip Left, Zip Right, Community
Left, Community Right, etc.) on the road segments themselves and instead rely
on the spatial relationship of a road segment to the zone (polygon) it
intersects or is within. <o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>When we're looking to find and sort matches by
relevance, multi-step incremental scoring algorithm (matching the street name
perfectly earns a lot of points, getting a soundex match gets you a few, bonus
points for being in the correct postal zone, etc). We use a number of
heuristics that have come out of our general experience. Those heuristics
are evident in the code.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>Essentially, we have approached geocoding as a
natural language parsing problem. Our geocoder has been constructed based
on the USPS postal service standards, it would be possible to generalize it
more to work with other locales. The key difference in the way we have
approached the problem is that the street name data is not normalized and as
such there is no requirement to break it up into components that only make
sense to a subset of localities.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>While we understand that what we have is still
essentially a prototype, we are thinking that what we have done to date could
be of some use to the people who are focussed on mapping and geocoding projects
and would be happy to provide it for the inspection of others. It is
implemented in fairly well-commented (and relatively standard) Perl, so we
think the code should be readable by most coders working in this area.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>Jason Horning<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face="Courier New"><span style='font-size:10.0pt;
font-family:"Courier New"'>BullBerry Systems, Inc.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
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