Address Frame Enhancement with Administrative 

Data using an Interactive Mapping Dashboard

FCSM 2022, Washington DC

Lee Fiorio

Chang Zhao

Peter Herman

Ned English
BACKGROUND: ADDRESS LISTS FOR ABS SURVEYS 2

Steady progress in ABS Frame Construction

Area probability s urveys require addres s lis ts

• Traditional Lis ting

O’Muircheartaigh et al. (2003):

• USPS Delivery Sequence Lis ts (CDS)

• Enhanced Lis ting
BACKGROUND: NORC DECENNIAL NATIONAL MASTER SAMP LE 3

CDS provides quality coverage, but listing can be costly for ABS surveys

2010 NORC National Mas ter Sample

• 1,514 s elected s egments (Cens us tracts or Cens us block groups )

• 123 s egments required enhanced lis ting due to CDS under-coverage

2020 NORC National Mas ter Sample

• 1,536 s elected s egments

• CDS coverage continually improving

• Need to lis t 34 s egments due to CDS under-coverage

Can we list low coverage segments without visiting them in the field?
4

Integrating CDS with spatial data

BACKGROUND: NEW DATA SOURCES

Source: Zamarova (2020)

Cadas tral Data

Building Footprints Data

Source: OpenStreetMaps

Street and Road Data

Source: OpenStreetMaps

Satellite Data

Source: WikiCommons
5

In-office Remote Lis ting

NEW AP P ROACH: IN-OFFICE ADDRESS LISTING

Conceptualization 

Enhanced Lis ting

CDS File

Census Blocks 

w/ HU ct

Field 

Enhanced 

Lis ting

FINAL ADDRESS LIST

+ Confirmed CDS

+ Edited CDS

- Deleted CDS

+ Field Adds

CDS File NoStat File

Census Block 

Maps

Satellite 

Imagery

Detailed Street 

and Road Map

Property 

Data/ Cadastral 

Data

In-office 

Remote 

Lis ting

FINAL ADDRESS LIST

+ Confirmed CDS

+ Edited CDS

- Deleted CDS

+ Remote Adds

Building 

Footprints
6

NORC In-office Interactive Mapping Listing Tool

NEW AP P ROACH: IN-OFFICE ADDRESS LISTING
RES EARCH QUES TIONS 7

For the 2020 Decennial Master Sample, 26 segments were listed 

with the Remote Listing Tool

34 s egments with low CDS Coverage 

• 26 listed with Remote Listing Tool 

• 8 listed in the field

What is the accuracy of the lis ts produced with the Remote Lis ting Tool?

1. What percentage of Remote Adds correspond to valid, locatable HUs?

2. After accounting for valid CDS addresses and valid Remote Adds, what 

percentage of HUs remain missing?

3. How does list accuracy vary across context?
DATA AND METHODS 8

Address lists produced with the remote listing tool 

were validated in a sample of blocks

Total Remote Listed

26 Segments 

1,460 Blocks

~ 20,000 anticipated HUs (2020 Cens us )

~7,000 CDS addres s es (35% coverage)

-----------------------------------------------------------------

+11,500 Remote Adds 

Total coverage: 93%

Sample

8 Segments 

74 Blocks

1,185 anticipated HUs (2020 Cens us )

415 CDS addres s es (35% coverage)

---------------------------------------------------------------

+951 Remote Adds 

Total coverage 115%
DATA AND METHODS 9

NORC Field Staff used standard NORC ‘Enhanced Listing’ 

procedure to validate remote lists in-person

Two FIs traveled to each s egment

• Driver

• Note-taker

Reviewed lis ts block by block

• Starting in NW corner

• Traveling in clockwis e direction

• Confirming exis ting addres s es

• Deleting non-exis tent or non-valid 

addresses

• Adding mis s ing addres s es
RES ULTS 10

Summary Findings

Initial Address List

415 CDS lines

951 Remote Adds 

--------------------------------------------

1,366 Total addresses

Field-Validated Address List

389 validated CDS lines (accuracy 94%)

889 validated Remote Adds (accuracy 93%)

+100 Field additions (7% miss rate)

--------------------------------------------------------------

1,378 Total addresses

• In aggregate, CDS and Remote Adds were validated at a s imilar rate 

• Similar number of addres s es added by field as deleted (i.e. marked not valid)

Do these rates vary by type of segment?
RES ULTS 11

Vacation Areas

2 of 8 segments (California; high income)

22% initial CDS coverage

417 Remote Adds 

97% Remote Add accuracy rate

5% Mis s Rate

Issues

• Multi-Family Hous ing (condos )

• New Cons truction

• Short-term Rentals

• Tree Cover

© Google Streetview
RES ULTS 12

Small Town Rural/Exurban

3 of 8 segments (California & Nevada; low/ middle income)

45% initial CDS coverage 

307 Remote Adds

93% Remote Add accuracy rate

6% Mis s Rate

Issues

• Multi-Family Hous ing (trailers , apartment 

buildings )

• Acces s ory Dwelling Units

• Data Quality (adminis trative data)

© Google Streetview
RES ULTS 13

Mountainous Areas

2 of 8 segments (Colorado & Cali.; low/ middle income)

44% initial CDS coverage

122 Remote Adds

90% Remote Add accuracy rate

11% Mis s Rate

Issues

• Dis pers ed 

• Data Quality (adminis trative data)

• Acces s ory Dwelling Units

• Tree Cover

© Google Streetview
RES ULTS 14

Small Town Texas

1 of 8 segments (Texas; low income)

25% initial CDS coverage

105 Remote Adds

84% Remote Add accuracy rate

14% Mis s Rate

Issues

• Data Quality (adminis trative data)

• Geocoding Error (CDS)

• Acces s ory Dwelling Units (multiple 

DUs on property)

© Google Streetview
RES ULTS : SUMMARY 15

Remote Listing Tool produces high quality address lists 

Us ing a cus tom interactive mapping tool, NORC combined CDS with adminis trative 

data to generate complete addres s lis ts in 26 low CDS coverage tracts and block 

groups 

• In-pers on field validation was conducted in a s ample of blocks in 8 of 26 s egments

• 35% initial CDs coverage; added 951 addresses remotely

• 93% of all remote adds reviewed were located and deemed to be valid HUs

• 7% of the final field-validated list were comprised of addresses missing from both CDS and 

administrative data

• Some variation by context, with remote address lists performing best in vacation areas and 

small towns
DIS CUS SION 16

Benefits of Remote Listing

Cons iderably more cos t-effective and les s timecons uming than field lis ting

Will perform better than field lis ting when:

• Hous e numbers are pos ted on mail-boxes rather than 

HUs or are otherwis e not vis ible

• Acces s to HUs is limited (e.g. gated mountain road)

It is eas ier to refres h remote lis ts (i.e. field lis ts are 

‘frozen’ for the duration of the frame)
DIS CUS SION 17

Limits of Remote Listing

Quality is highly dependent on accuracy of adminis trative 

data

• Does not work well at all in places where adminis trative 

data contain limited or inaccurate addres s information

Multi-family HUs and acces s ory dwelling units remain an 

is s ue

• Getting an accurate count of units in a multi-unit building 

is difficult

• Hard to accurately reference acces s ory dwelling units 

even if we can s ee them in s atellite imagery
CONCLUSION 18

Next Steps

- Multi-unit HUs – how well did remote lis ts perform in 

s ituations with multi-family hous ing or acces s ory dwelling 

units ?

- Improve remote lis ting tool and protocol – reduce 

ambiguous comments left by lis ters

- Identify covariates as s ociated with accuracy of remote 

lis ts – us ing s egment level data from ACS on 

demographics and s ocio-economic characteris tics to 

model lis t accuracy (if s ample s ize not too s mall)
Lee Fiorio

fiorio-lee@norc.org