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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
Here?
Here?
Here?

Here?

Here?
Here?
Here?

Here?
Here?
Hay for the Winter by Trey Ratcliff
http://www.flickr.com/photos/stuckincustoms/8672061349/
Attribution 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
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
Search: 1234 Main Street

Match
Field
SearchText
Workspace
FeatureClass
IdField
Id

Description
1234 S Main Street Boulder
G:gis.sde
dbo.parcel
PIN
MKE11171971
Search: 1234 Main Street

Field
SearchText
Workspace
FeatureClass
IdField
Id

Description
1234 S Main Street Boulder
gisgisdata.sde
Zoom!
dbo.parcel
PIN
MKE11171971
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
One Search Index
Many Applications

Swiss Army Knife by AJ Cann
http://www.flickr.com/photos/ajc1/4663140532/
Attribution License
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%
F
a
s
t
e
r

Word1% %Word2% %Word3% …
%Word1% %Word2%
%Word1% %Word2% %Word3% …
Score Your Results
The Levenshtein distance between two
words is the minimum number of singlecharacter edits (insertion, deletion,
substitution) [including spaces] required
to change one word into the other.

1234 Ced
1234 N Cedar Brook Lane

Source: en.wikipedia.org/wiki/Levenshtein_distance
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
Land Use
Pictometry 4/27/2011
Land Use Dept. 9/17/2013

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2013 Tips and Tricks Track, Make it work like Google: Creating Search Indexes by David Haines

  • 1. Make it work like Google: Creating a search index David Haines Land Use GIS Manager Land Use
  • 2. “Make it Work like Google”
  • 3. #1. Auto-complete #2. One search field #3. Without at least one of the above people will use something else. #4. Good results
  • 4. What Boulder County Land Use we’re doing now…
  • 5. There was this flood a few weeks ago…
  • 6. Boulder County Land Use Dept. 9/17/2013
  • 7.
  • 9.
  • 10.
  • 13. Too many databases Hay for the Winter by Trey Ratcliff http://www.flickr.com/photos/stuckincustoms/8672061349/ Attribution License
  • 14. Here? Here? Here? Here? Here? Here? Here? Here? Here? Hay for the Winter by Trey Ratcliff http://www.flickr.com/photos/stuckincustoms/8672061349/ Attribution License
  • 15. Address & Owner Database Building Permit Database Assessment Database Place Name Database Subdivision Database
  • 16. Roll of Hay by Klearchos Kapoutsis http://www.flickr.com/photos/klearchos/3824322183/ Attribution-NonCommercial License
  • 17. 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
  • 18. A Table to Search
  • 19. What needs to be in the table? What to search for Where to go once you find it
  • 20. Search Index Table fields Field*(ESRI) Description SearchText The search term. The “correct” answer. A perfect hit.
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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.
  • 26. Search: 1234 Main Street Match Field SearchText Workspace FeatureClass IdField Id Description 1234 S Main Street Boulder G:gis.sde dbo.parcel PIN MKE11171971
  • 27. Search: 1234 Main Street Field SearchText Workspace FeatureClass IdField Id Description 1234 S Main Street Boulder gisgisdata.sde Zoom! dbo.parcel PIN MKE11171971
  • 28. Blue skies and silos by Matthew Rutledge http://www.flickr.com/photos/rutlo/3872475221/ Attribution-NonCommercial License Different Data Silos?
  • 29. 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
  • 30. 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
  • 31. Steps to update the index table Load existing index
  • 32. Steps to update the index table Load existing index Load reference data (i.e. data your searching)
  • 33. Steps to update the index table Load existing index Load reference data (i.e. data your searching) Find reference data not in index
  • 34. 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
  • 35. 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)
  • 36. 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
  • 37. 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
  • 38. One Search Index Many Applications Swiss Army Knife by AJ Cann http://www.flickr.com/photos/ajc1/4663140532/ Attribution License
  • 39. Now that you built your table… … optimize your applications
  • 40. Optimizing wildcard searching Try First: “OnlyWord%” Try Second: “%OnlyWord%” Search: “tree” Results: “Tree View” “Treetop” “Green Tree” “North Woodtree”
  • 41. Optimizing wildcard searching Word1 Word2% Word1% %Word2% F a s t e r Word1% %Word2% %Word3% … %Word1% %Word2% %Word1% %Word2% %Word3% …
  • 42. Score Your Results The Levenshtein distance between two words is the minimum number of singlecharacter edits (insertion, deletion, substitution) [including spaces] required to change one word into the other. 1234 Ced 1234 N Cedar Brook Lane Source: en.wikipedia.org/wiki/Levenshtein_distance
  • 43. 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
  • 45.
  • 46.
  • 48. Land Use Dept. 9/17/2013