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Users want to have one search box for everything and it needs to had autocomplete functionality. This presentation will go over how to create an attribute index table for searching across multiple tables and features in different databases. Then it will cover techniques applications can use the index table to implement autocomplete functionality.
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Make it work like Google: Creating a search index
David Haines Land Use GIS Manager
Land Use
“Make it Work like Google”
#1. Auto-complete #2. One search field #3. Without at least one of the above people will use something else. #4. Good results
What Boulder County Land Use
we’re doing now…
There was this flood a few weeks ago…
Boulder County Land Use Dept. 9/17/2013
Pictometry 4/27/2011 Land Use Dept. 9/17/2013
Wasn’t Always this Way
Multiple Entry Fields
Different Types of Search
Too many databases
Hay for the Winter by Trey Ratcliff
http://www.flickr.com/photos/stuckincustoms/8672061349/
Attribution License
Hay for the Winter by Trey Ratcliff
http://www.flickr.com/photos/stuckincustoms/8672061349/
Attribution License
Here?
Here?
Here?
Here?
Here?
Here?
Here?
Here?
Here?
Address & Owner Database
Building Permit Database
Assessment Database
Place Name Database
Subdivision Database
Roll of Hay by Klearchos Kapoutsis
http://www.flickr.com/photos/klearchos/3824322183/
Attribution-NonCommercial License
Address & Owner Database
Building Permit Database
Assessment Database
Place Name Database
Subdivision Database
Roll of Hay by Klearchos Kapoutsis
http://www.flickr.com/photos/klearchos/3824322183/
Attribution-NonCommercial License
A Table to Search
What needs to be in the table?
What to search for
Where to go once you find it
Search Index Table fields Field*(ESRI) Description
SearchText The search term. The “correct” answer. A perfect hit.
Search Index Table fields Field*(ESRI) Description
SearchText The search term. The “correct” answer. A perfect hit.
Workspace The database the search term is in
Search Index Table fields Field*(ESRI) Description
SearchText The search term. The “correct” answer. A perfect hit.
Workspace The database the search term is in
FeatureClass The table in the database the search term is in
Search Index Table fields Field*(ESRI) Description
SearchText The search term. The “correct” answer. A perfect hit.
Workspace The database the search term is in
FeatureClass The table in the database the search term is in
IdField The field in the table the search term is in
Search Index Table fields Field*(ESRI) Description
SearchText The search term. The “correct” answer. A perfect hit.
Workspace The database the search term is in
FeatureClass The table in the database the search term is in
IdField The field in the table the search term is in
Id The record (or row) the the search term is in. This is what the search should go to.
Search: 1234 Main Street
Field Description
SearchText 1234 S Main Street Boulder
Workspace G:\gis.sde
FeatureClass dbo.parcel
IdField PIN
Id MKE11171971
Match
Search: 1234 Main Street
Field Description
SearchText 1234 S Main Street Boulder
Workspace \\gis\gisdata.sde
FeatureClass dbo.parcel
IdField PIN
Id MKE11171971
Zoom!
Search: 1234 Main Street
Blue skies and silos by Matthew Rutledge
http://www.flickr.com/photos/rutlo/3872475221/
Attribution-NonCommercial License
Different Data Silos?
Boulder County Land Use Search Index
Theme Example Database
Parcel Number 157416001234 Assessor - CAMA
Address 1234 S Main Street Assessor – CAMA
Owner John Doe Assessor – CAMA
Tax Account R01234567 Assessor – CAMA
Building Permit BP-13-0001 Land Use - Accela
Docket Number SPR-13-0010 Land Use – Accela
Docket Name Smith Residence Land Use – Accela
Subdivision Big Oak Meadows GIS - ArcSDE
Mining Claim Name Blue Bird Mine #2 GIS – ArcSDE
Geographic Names Longs Peak GIS - ArcSDE
Do the hard work to
make it simple
“Making something look simple is easy; making something simple to use is much harder — especially when the underlying systems are complex — but that’s what we should be doing.” https://www.gov.uk/designprinciples#fourth
Steps to update the index table
Load existing index
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Find reference data not in index
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Find reference data not in index
Add that data
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Find reference data not in index
Add that data
Search for index data not in the index (deleted)
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Find reference data not in index
Add that data
Search for index data not in the index (deleted)
Remove it
Steps to update the index table
Load existing index
Load reference data (i.e. data your searching)
Find reference data not in index
Add that data
Search for index data not in the index (deleted)
Remove it
Repeat for each dataset
Swiss Army Knife by AJ Cann
http://www.flickr.com/photos/ajc1/4663140532/
Attribution License
One Search Index Many Applications
Now that you built your table…
… optimize your applications
Optimizing wildcard searching Try First: “OnlyWord%”
Try Second: “%OnlyWord%”
Search: “tree”
Results:
“Tree View”
“Treetop”
“Green Tree”
“North Woodtree”
Optimizing wildcard searching
Word1 Word2%
Word1% %Word2%
Word1% %Word2% %Word3% …
%Word1% %Word2%
%Word1% %Word2% %Word3% …
F
a
s
t
e
r
Score Your Results
The Levenshtein distance between two words is the minimum number of single-character edits (insertion, deletion, substitution) [including spaces] required to change one word into the other.
Source: en.wikipedia.org/wiki/Levenshtein_distance
1234 Ced
1234 N Cedar Brook Lane
Statistics
Address Searches 85%
Owner Searches 5%
Parcel Number Searches 5%
Address Search Average 6.5 Characters
Address Search 1.6 Words
49% one word, 43% two words, 8% 3+ words
Pictometry 4/27/2011
Land Use Dept. 9/17/2013
