1001 Smart Cities

Permanent project revision

Street Bump Road Defect App

Revision 2026-08-20-claim-baseline · effective 2026-07-26

Superseded. A later revision exists: 2026-08-21-source-dates →

unreviewed

Created the first structured Evidence Passport with 8 claims and 11 source dossiers.

sha256:69883d2fcfb388747b71dc24ddf9b0fbb025408fed03af5e7307a625d16b05b4
All revisions → Current entity page →

Change set

  • add /evidencePassport — Added claim-level evidence mapping and source dossiers.
  • add /revisionHistory — Established a permanent, hash-addressed baseline revision.
8
of 8 claims mapped
11
source dossiers
5
publication dates recorded
0
archives linked
0
exact locators
0
content captures hashed

Claims, one by one

The text below is copied verbatim from the published record. Each claim names the source dossiers that carry it; caveats stay attached to the claim they qualify.

Claim 1 clm-a7d9ed76e73ce1fa quantitative
Boston's New Urban Mechanics office started Street Bump in 2011 and put the public version out in 2012
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
The operator page records the three phases — a 2011 alpha, the public algorithm competition, and the 2012 accelerometer-and-GPS release — and states 'The involvement of New Urban Mechanics in this project was from 2011 to 2014.' Local public radio covered the 2012 public release, giving a second source for the launch year.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)
  2. WBUR — Hit A Pothole? Tell Boston With New Smartphone App (24 April 2012)
Claim 2 clm-ba4c49c4726d851e descriptive
an app that read a driving phone's accelerometer and GPS and filed road defects without anyone tapping anything
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
CNN describes the mechanism and the aggregation rule: the phone sends accelerometer data to a server that combines many phones, and 'If at least three people hit a bump in the same spot, the system recognizes it as a pothole.' GovTech describes the pipeline — data to the contractor's servers, likely problems submitted to the city through Open311 and classified as potholes to fix or known obstacles such as speed bumps.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. CNN — Street Bump app detects potholes, tells city officials (16 February 2012)
  2. GovTech — Boston Testing App for Auto-Detecting Potholes
Claim 3 clm-735e8a3b5c4200c9 descriptive
Its biggest finding was not potholes but sunken manhole covers
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
The operator page states 'Residents most frequently reported problems about potholes, but the biggest cause of bumps is sunk manhole covers.' The Globe carried the same finding independently at the time.
Caveat
The Globe paywall blocks automated fetching; the outlet, date and subject were confirmed by search, and the substance is carried sentence-level by the city page.

Sources carrying this claim

  1. City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)
  2. Boston Globe — App shows jarring role of cast-metal covers in Boston (16 December 2012)
Claim 4 clm-fb7e02ba229c13a6 quantitative
The city ended the project in 2014
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
The operator page states 'The involvement of New Urban Mechanics in this project was from 2011 to 2014. The project is not currently active.'
Caveat
No statement of why the project ended appears anywhere in the sources reached.

Sources carrying this claim

  1. City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)
Claim 5 clm-17e20d1f4eb8ece6 quantitative
The city reports that sunken manhole covers caused about four times more bumps than potholes, that it fixed 1,250 of the worst covers by working with the utilities that own them, and that testing held false positives under 10%
Published field
impact · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
All three figures come from the operator's own page: the roughly fourfold rate, the 1,250 covers fixed in cooperation with the utilities that own them, and 'false positives under 10%'.
Caveat
Operator self-report throughout; no independent evaluation of the app's output was found.

Sources carrying this claim

  1. City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)
Claim 6 clm-2b163f319872cd68 descriptive
Boston never published how many reports the app generated, how many were confirmed, or whether repairs got faster
Published field
impact · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
The operator page carries the findings above and no report volume, no confirmation rate and no repair-time series; no other source reached in this review supplies them.
Caveat
A negative finding: absence in the sources reached is not proof that no such figures exist.

Sources carrying this claim

  1. City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)
Claim 7 clm-37b1856b6d20c8cd quantitative
Kate Crawford's 2013 Harvard Business Review essay made Street Bump the standard example of a sensing system's 'signal problem': a phone-and-car sensor samples people who own phones and cars
Published field
lesson · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
Crawford writes that 'StreetBump has a signal problem' because lower-income and older residents are less likely to own smartphones. The textbook reproduces the passage verbatim and shows how the case entered the algorithmic-fairness canon, describing data that reflect 'the patterns of smartphone ownership, which are higher in wealthier parts of the city compared to lower-income areas and areas with large elderly populations.'
Caveat
This is a business-magazine essay, not a peer-reviewed paper. HBR is paywalled to automated fetching; the passage is read through the textbook that reproduces it.

Sources carrying this claim

  1. Kate Crawford — The Hidden Biases in Big Data, Harvard Business Review (1 April 2013)
  2. Barocas, Hardt and Narayanan — Fairness and Machine Learning (Cambridge University Press), Introduction
Claim 8 clm-66150ef40dcf9372 descriptive
That was an argument from smartphone ownership, not a measurement of Street Bump's own reports — Boston never published a neighbourhood breakdown, so the most-cited critique in civic sensing was never tested against the data it describes
Published field
lesson · verbatim
Mapping
mapped · no interpretation or interpolation added by this passport
What the mapping says
The textbook presents the case as an observation about the method, with no measurement of the project's data. The FTC report is the closest documented Boston response found: it uses Street Bump as its worked example of a 'data desert', stating that once the team recognised that lower-income residents were less likely to carry smartphones, it recognised its data was not representative of road conditions across Boston. Neither source, and no other reached in this review, contains a neighbourhood breakdown of Street Bump's reports.
Caveat
The FTC report PDF resisted text extraction, so it is verified at search level only and is not quoted as the city's own words. The claim that no breakdown was published is a negative finding across the sources reached.

Sources carrying this claim

  1. Barocas, Hardt and Narayanan — Fairness and Machine Learning (Cambridge University Press), Introduction
  2. US Federal Trade Commission — Big Data: A Tool for Inclusion or Exclusion? (January 2016)

Source dossiers and revision state

This layer records what is known about each source, what is still missing, and the exact snapshot this page is rendering. A missing date, archive or hash stays visible as missing.

unreviewed Revision 2026-08-20-claim-baseline effective 2026-07-26 · created 2026-08-21

Awaiting operator and domain-expert review; publication of this revision implies neither.

sha256:69883d2fcfb388747b71dc24ddf9b0fbb025408fed03af5e7307a625d16b05b4
Revision history → Permanent version link → Machine-readable record → Evidence Constitution →
8
of 8 claims mapped
11
source dossiers
5
publication dates recorded
0
archives linked
0
exact locators
0
content captures hashed

Governance facts carried by the record

Operator
Mayor's Office of New Urban Mechanics, City of Boston; app built by Connected Bits with IDEO, building on research by Fabio Carrera and the Red Fish Group
Actor types
city-government, private
Funding model
public
Funding description
City of Boston; the algorithm competition's $25,000 prize pool was donated by Liberty Mutual through InnoCentive
Cost
Not recorded
Ownership
Not recorded
Decision rights
Not recorded
Exit conditions
Not recorded

11 source dossiers

City of Boston — Street Bump (Mayor's Office of New Urban Mechanics) boston.gov · 5 linked claims

Open original source ↗

Stable source ID
src-8a8315485119ded3
Publisher
Name not classified · boston.gov
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

The operator's own page and the authority for the load-bearing facts. Status, sentence-level: 'The involvement of New Urban Mechanics in this project was from 2011 to 2014. The project is not currently active.' Also the three phases (2011 alpha, the public algorithm competition, the 2012 accelerometer-and-GPS release), the finding 'Residents most frequently reported problems about potholes, but the biggest cause of bumps is sunk manhole covers' at roughly four times the rate, the 1,250 covers fixed with the utilities, 'false positives under 10%', and the partner list: Fabio Carrera, Red Fish Group, IDEO, Connected Bits, InnoCentive and Boston University researchers. No statement of why the project ended appears anywhere.

Descriptive only — not page/paragraph exact
Claims linked
CNN — Street Bump app detects potholes, tells city officials (16 February 2012) cnn.com · 1 linked claim

Open original source ↗

Stable source ID
src-8470dc136a3b9792
Publisher
Name not classified · cnn.com
Type / language
web-page · language not classified
Published
2012-02-16
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Independent account of the mechanism and the aggregation rule: the phone sends accelerometer data to a server that combines many phones, and 'If at least three people hit a bump in the same spot, the system recognizes it as a pothole.' Names Nigel Jacob of New Urban Mechanics as project lead and states the city's hope of replacing survey trucks with a real-time map.

Descriptive only — not page/paragraph exact
Claims linked
WBUR — Hit A Pothole? Tell Boston With New Smartphone App (24 April 2012) wbur.org · 1 linked claim

Open original source ↗

Stable source ID
src-efba295fd7e79df6
Publisher
Name not classified · wbur.org
Type / language
web-page · language not classified
Published
2012-04-24
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Local public-radio coverage of the 2012 public release, used as the second source for the launch year.

Descriptive only — not page/paragraph exact
Claims linked
MIT Technology Review — Road Repair via Crowdsourcing (13 May 2011) technologyreview.com · background

Open original source ↗

Stable source ID
src-d9389d8839e144f0
Publisher
Name not classified · technologyreview.com
Type / language
web-page · language not classified
Published
2011-05-13
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Contemporary account of the algorithm competition: after the first build could not tell potholes from other bumps and produced too many false positives, the city turned to InnoCentive, with $25,000 in prize money donated by Liberty Mutual. The contest drew more than 700 solvers and 19 submitted solutions.

Descriptive only — not page/paragraph exact
Claims linked
Background source; no baseline claim points here
Aboufadel, Marculis et al. — Wavelet-Kruskal Solution to the InnoCentive Boston Pothole Challenge (Grand Valley State University) scholarworks.gvsu.edu · background

Open original source ↗

Stable source ID
src-5247cec9c3bf8a8c
Publisher
Name not classified · scholarworks.gvsu.edu
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

A winning entry, from the winners' own institutional record: three winning teams were announced in December, two of them awarded $9,000 each. The method was wavelet analysis and Kruskal's algorithm — signal processing, not machine learning, which is why this entry carries ai: false.

Descriptive only — not page/paragraph exact
Claims linked
Background source; no baseline claim points here
Boston Globe — App shows jarring role of cast-metal covers in Boston (16 December 2012) bostonglobe.com · 1 linked claim

Open original source ↗

Stable source ID
src-60cc2fcbe258dab6
Publisher
Name not classified · bostonglobe.com
Type / language
web-page · language not classified
Published
2012-12-16
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Contemporary independent press on the manhole-cover finding. The Globe paywall blocks automated fetch; the outlet, date and subject were confirmed by search, and the substance is carried sentence-level by the city page above.

Descriptive only — not page/paragraph exact
Claims linked
Kate Crawford — The Hidden Biases in Big Data, Harvard Business Review (1 April 2013) hbr.org · 1 linked claim

Open original source ↗

Stable source ID
src-57f3dd7e678eeb9c
Publisher
Name not classified · hbr.org
Type / language
web-page · language not classified
Published
2013-04-01
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

The origin of the critique, and the reason for the lesson's careful wording. This is a business-magazine essay, NOT a peer-reviewed paper: Crawford writes that 'StreetBump has a signal problem' because lower-income and older residents are less likely to own smartphones. The argument is drawn from smartphone-ownership demographics; neither this piece nor any source found analyses Street Bump's own reports by neighbourhood. HBR is paywalled to automated fetch; the passage is reproduced by the textbook below.

Descriptive only — not page/paragraph exact
Claims linked
Barocas, Hardt and Narayanan — Fairness and Machine Learning (Cambridge University Press), Introduction fairmlbook.org · 2 linked claims

Open original source ↗

Stable source ID
src-e11fe8f03da13ba1
Publisher
Name not classified · fairmlbook.org
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Carries Crawford's Street Bump passage verbatim and shows how the case entered the algorithmic-fairness canon: 'Consider Street Bump, a project by the city of Boston to crowdsource data on potholes' … the data reflect 'the patterns of smartphone ownership, which are higher in wealthier parts of the city compared to lower-income areas and areas with large elderly populations.' The textbook presents it as an observation about the method, with no measurement of the project's data.

Descriptive only — not page/paragraph exact
Claims linked
US Federal Trade Commission — Big Data: A Tool for Inclusion or Exclusion? (January 2016) ftc.gov · 1 linked claim

Open original source ↗

Stable source ID
src-71bfb24de52b4587
Publisher
Name not classified · ftc.gov
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

The closest thing found to a documented Boston response: the FTC uses Street Bump as its worked example of a 'data desert', stating that once the team recognised that lower-income residents were less likely to carry smartphones, it recognised its data was not representative of road conditions across Boston. The report PDF resisted text extraction, so this is verified at search level only and is not quoted as the city's own words anywhere in this entry.

Descriptive only — not page/paragraph exact
Claims linked
GovTech — Boston Testing App for Auto-Detecting Potholes govtech.com · 1 linked claim

Open original source ↗

Stable source ID
src-20be4dee91740ccb
Publisher
Name not classified · govtech.com
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Confirms that the pre-release version was run on city inspectors' vehicles before public release, and describes the pipeline: data to Connected Bits' servers, likely problems submitted to the city through Open311 and classified as potholes to fix or known obstacles such as speed bumps.

Descriptive only — not page/paragraph exact
Claims linked
City of Boston Analytics Team — Automated Pothole Detection (Mercedes-Benz USA pilot, 2023) boston.gov · background

Open original source ↗

Stable source ID
src-b949a0b6729e13bf
Publisher
Name not classified · boston.gov
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-07-26
Dossier created
2026-08-21
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

What came after, from the same offices. The Analytics Team and New Urban Mechanics tested anonymised Mercedes-Benz vehicle sensor data against 311 reports and found it 'particularly helpful for identifying potential potholes on larger streets with higher speed limits, where 311 reports are relatively rare' — passive sensing used explicitly to cover what the complaint channel misses. It also repeated Street Bump's classification problem: 'other types of irregularities in the road were getting picked up, such as depressions near utility covers.' The page does not mention Street Bump, so no lineage is claimed as fact.

Descriptive only — not page/paragraph exact
Claims linked
Background source; no baseline claim points here
Known limits of this passport
  • Publisher names and source languages have not yet been classified; domains are recorded without guessing.
  • Existing source notes are descriptive locators, not page- or paragraph-exact citations.
  • A missing archive, publication date or content hash is shown as null rather than inferred.
  • The project-level evidence grade ranks the strongest source in the entry; it is not a truth score for every claim.