Aiding Address-Based Matching Through Building Name Standardization
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1 Aiding Address-Based Matching Through Building Name Standardization Census and Statistics: Innovations in U.S. Census Bureau Geographic Systems ESRI User Conference Wednesday, July 12, 2017 Kevin Holmes Geographer Address Standards, Criteria, and Quality Branch (ASCQB): Geography Division U.S. Census Bureau This presentation does not contain Census Title 13 information
2 Census Addressing Master Address File (MAF) Address repository for Censuses and Surveys MAF/TIGER database (relational) Developed for 2000 Census, maintained throughout decades Group Quarters (GQ) Addresses Specific living/service arrangements Prisons, nursing homes, college dormitories, etc. Different enumeration methodology Additional attribution: Facility or Building Name 999 University Blvd., > Redlands Residence Hall 2 This presentation does not contain Census Title 13 information
3 Matching Abstract Administrative Datasets Authoritative resources for continual MAF improvement (e.g. tribal, state, local governments, USPS Delivery Sequence File, etc.) MAF record matching primarily address-driven Can additional information be used to aid matching? GQ Name Standardization and Matching (SAM) Aiding address matching for datasets with: Missing/Incomplete address elements Non-city style addresses (P.O. or R.R. boxes) Data variations not conducive to typical address-based matching Additional attribution available 3 This presentation does not contain Census Title 13 information
4 Among Other Challenges MAF and administrative GQ names may also differ SPRINGFIELD CO CORR CTR SHELBYVILLE UNIV. VILLAGE RES HALL GENERAL HOSP. EXT. CARE SMITHERS GRP HME 4 This presentation does not contain Census Title 13 information
5 Name Standardization to Facilitate Matching SPRINGFIELD CO CORR CTR -> SPRINGFIELD COUNTY CORRECTIONAL CENTER SHELBYVILLE UNIV. VILLAGE RES HALL -> SHELBYVILLE UNIVERSITY VILLAGE RESIDENCE HALL GENERAL HOSP. EXT. CARE -> GENERAL HOSPITAL EXTENDED CARE SMITHERS GRP HME, INC. -> SMITHERS GROUP HOME, INCORPORATED 5 This presentation does not contain Census Title 13 information
6 Standardization Standardization affects match score: 6 This presentation does not contain Census Title 13 information
7 Standardization Word replacement dictionaries Multiple JSON file(s) Replace bad version of word with good version BAD APTS. HOSP. UNIV GOOD APARTMENTS HOSPITAL UNIVERSITY Current standardization replacement dictionary includes ~1,000 substitutions 7 This presentation does not contain Census Title 13 information
8 Substitution: Not So Fast CO : COUNTY? COLORADO? ST : STATE? STREET? SAINT? During standardization: Iterate through each replacement word option within the GQ name If both MAF & local GQ name cite same multi-replacement string, do not iterate/replace Compare source & MAF GQ name strings String with best match score *may* be best match 8 This presentation does not contain Census Title 13 information
9 Multi-replacement substitution Easier said than done... JEFFERSON CTY CO EXT. CARE FAC : 'JEFFERSON COUNTY COUNTY EXTENSION CARE FACILITY', 'JEFFERSON CITY COUNTY EXTENSION CARE FACILITY', 'JEFFERSON COUNTY COLORADO EXTENSION CARE FACILITY', 'JEFFERSON CITY COLORADO EXTENSION CARE FACILITY', 'JEFFERSON COUNTY COUNTY EXTENDED CARE FACILITY', 'JEFFERSON CITY COUNTY EXTENDED CARE FACILITY', 'JEFFERSON COUNTY COLORADO EXTENDED CARE FACILITY', 'JEFFERSON CITY COLORADO EXTENDED CARE FACILITY' 9 This presentation does not contain Census Title 13 information
10 Matching Exact & Equivocated After standardization, GQ Names may still not match exactly Equivocated matching to close enough strings Jaro-Winkler (JW) string similarity scoring Used in street name equivocation 0.0 >>>>>>>>>>>>>>>>>>>>>> 1.0 No Match Equivocated Match Exact Match 10 This presentation does not contain Census Title 13 information
11 Matching - Examples MAF GQ NAME SOURCE GQ NAME JW SCORE CENSUS NURSING CENTER CENSUS NURSING CENTER 1 MAF HEALTH CARE CENTER MAF HEALTHCARE CENTER TIGER REHABILITATION CENTER WEST GERIATRIC UNIT TIGER REHABILITATION NURSING CENTER This presentation does not contain Census Title 13 information
12 Blocking Source datasets may span the nation 1 vs. national record matching not efficient Multiple matches may exist across nation Compartmentalizing source and MAF datasets increase efficiency/accuracy Too large, processing run time becomes a factor Too small, match candidate universes too small 12 This presentation does not contain Census Title 13 information
13 STATE? Blocking COUNTY? ZIP-5? CENSUS BLOCK? Direct attribution File fields/values Spatial interpolation Derived from source X/Y (if available) 13 This presentation does not contain Census Title 13 information
14 Creates blocking list of values passes blocking values as SQL selection parameter This presentation does not contain Census Title 13 information
15 Implementation GQ SAM = Blocking + Standardization + Matching
16 Initialization ArcMap Toolbox *RESULTS REPORTED TO ORACLE TABLES FOR POST-MATCH PRIORITIZATION* 16 This presentation does not contain Census Title 13 information
17 Matching Examples: Good Exact match, no standardization SOURCE: BREMEN NURSING HOME MAF: BREMEN NURSING HOME Match score: 1.0 Exact match, with Standardization SOURCE: WEST PARK REHAB AND NURSING CENTER WEST PARK REHABILITATION AND NURSING CENTER MAF: WEST PARK REHABILITATION AND NURSING CENTER Match score: This presentation does not contain Census Title 13 information
18 Matching Examples: Probably? Equivocated (.9 < score > 1.0) SOURCE: LIFE CARE CENTER MAF: LIFE CARE CENTER OF ATLANTIS Match score: SOURCE: KEVIN HOLMES RESIDENCE HALL MAF: K. HOMES RESIDENCE HALL Match score: This presentation does not contain Census Title 13 information
19 Matching Examples: Probably Not Equivocated (.9 < score > 1.0) SOURCE: MAJESTIC OAKS WEST WING MAF: MAJESTIC OAKS NORTH Match score: SOURCE: GOLDEN GIRLS CENTER SPRINGFIELD MAF: GOLDEN GIRLS CENTER - SHELBYVILLE Match score: Future iterations may restrict matching of names containing directionals etc. 19 This presentation does not contain Census Title 13 information
20 Matching Examples: No Matches Unqualified match score: less than.9 Non-match does not = missing! Missing/incomplete/incorrect blocking information Incomplete word substitution directory Names simply too different SOURCE: ROLLING HILLS NURSING CENTER AT WAYFIELD MAF: ROLLING HILL REHABILITATION AND NURSING Match score: If scoring break is reduced, greater chance of bad matches 20 This presentation does not contain Census Title 13 information
21 Challenges/Issues Name standardization replacement dictionary always improving Existing MAF GQ name elements can be evaluated (spell checker) New GQ names could be standardized before MAF ingestion External source datasets a constant unknown No best matching score break.90 used for our equivocation cut-off Some good matches found <.90, but lends to more questionable/bad matches IF THE DATASETS ARE TOO DIFFERENT, THE PROCESS DOES NOT WORK 21 This presentation does not contain Census Title 13 information
22 Multiple MAF Matches Multiple MAF record matches are common Duplication (new/retired records) GQ vs. Facility name One-to-Many: HOLMES HALL A matches to HOLMES HALL 1, HOLMES HALL 2, HOLMES HALL B, etc. A match does not necessarily mean it s the best match Additional MAF attribution filtering may be used to prioritize better/best matches 22 This presentation does not contain Census Title 13 information
23 Evaluation Strategies For Matches (single or multiple): How many matches? Exact or equivocated? Are matches to good MAF records (recently enumerated, other source verified, etc.) How many? If multiple, which one is best? If 0, manual MAF searching may be required For Non-Matches: Manual MAF searching may be required Why couldn t a match be made? Blocking elements different Replacement dictionary lacking? Name too different? Potential MAF adds? 23 This presentation does not contain Census Title 13 information
24 Post-Match Filtering Post-match filtering & selection to choose best match, factoring in: Number of matches Exact or equivocated Nature of matched MAF record 24 This presentation does not contain Census Title 13 information
25 Interactive Capabilities: ArcMap 25 This presentation does not contain Census Title 13 information
26 Questions? Kevin Holmes
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