Knowledge Farming

1
Knowledge Farming

• City Knowledge and the Long Tail of Small Cities

Fabio Carrera
Santa Fe, February 20, 2007
2
Bio
Fabio Carrera

• Born in Venice, Italy
• BSEE and MSCS _at_ WPI
• PhD _at_ MIT
• Urban Information Systems and Planning
• Teaching _at_ WPI and MIT
• Director of Venice and Boston Project Centers
• Founder and Director of City Lab (WPI)
• LOUIS (Local Online Urban Information System)
• Planning Board in Spencer, MA
• Consultant to municipalities
• Forma Urbis sas and City Knowledge LLC

3
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

4
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

5
Promotes the transformation of
municipalitiesfrom hunter-gatherers of urban
data to farmers of municipal information
City Knowledge
6
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 6 tools for implementation
  and data collection

7
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 5 (or so) tools for
  implementation

• Like politics, all change is local
• Most change is filtered by municipalities
• with CK
• City departments implement information strategies
• Urban information is farmed-in at a fine grain
• Documentation becomes Information
• Intra- and Inter-departmental sharing is
  commonplace
• Regional patterns (SDI) emerge upon municipal
  foundations

8
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 5 (or so) tools for
  implementation

The Fundamental problem is to decide what the
form of a human settlement consists of
the chosen ground is the spatiotemporal
distribution of human actions and the physical
things which are the context of those actions
.

• Structures are more permanent
• Structural change can be captured
• Activities are more dynamic and fickle
• Activities can be frozen in time and space
  (snapshots)
• with CK
• Information about structures is routinely updated
• Activities are spatialized
• Activities are periodically frozen

Lynch, Good City Form, p. 48
9
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 5 (or so) tools for
  implementation

• There is a lot of reality already out there
• But the amount of information is finite
• with CK the backlog can be completely captured
• Urban change is rather slow so, with CK
• all Structural change is captured at the source
• snapshots of activities are creatively obtained
• with CK, municipal information is farmed daily

10
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 5 (or so) tools for
  implementation

• within CK
• Space plays a key role in municipal information
  farming
• Addresses are no longer primary spatial
  identifiers
• GIS means Geographic Indexing Systems
• Space indexes our datasets

11
The Premises of CK

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 5 (or so) tools for
  implementation

• Top-down is rigorous and structured but is
  received as an imposition and resisted
• Bottom-up is passionate and self-interested
  but unstructured, unscalable and unsustainable
• with CK
• Pure top-down and bottom-up approaches disappear
• Middle-out combines the positive traits of both

12
The Premises of CK

1. Ownership Operation
2. Regulation
3. Incentives/Disincentives
4. Education Information
5. Rights
6. Mitigation Compensation

• Municipalities are the locus of change
• Cities Structures Activities
• Reality Backlog Future Change
• Space Is the Glue
• Middle-out Top-down Bottom-up
• Government only has 6 tools for implementation
  (and information gathering)

• with CK
• Municipalities consciously creatively combine
  the 6 tools for
• Information Farming
• Policy/Plan Implementation

13
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

14
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

15
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• whereby municipalities
• Adopt internal mechanisms to farm THEIR OWN data
• Emphasize Information in Standard Operating
  Procedures
• Extract Informational Returns from all internal
  processes
• Change job descriptions for personnel to
  include information
• Catch up with their own backlog
• Intercept all future internal change as it happens

16
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• whereby municipalities
• Make informational Returns part of their
  regulations
• Force outside entities to provide information
  (for free)
• Change submission requirements (permits, plans)
• Modify maintenance and management contracts
• Institute yearly renewals for data updates
• Apply regulations to capture backlog as well
• Invent creative ways to acquire datasets
• Become validators instead of collectors

17
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• whereby municipalities
• Routinely entice outside entities into providing
  information
• Change submission fee structures (permits,
  plans)
• Make old ways costly (disincentives)
• Make it cheaper to do the right thing
  (incentives)
• Provide benefits for data updates
• Invent bonuses for data backlog
• Reward and enforce collaboration
• Validate incoming data

18
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• whereby municipalities
• Constantly educate citizens about the use of data
• Are always transparent about motives for data
  collection
• Explore potential for volunteer citizen input
• Incite peer-production
• Make educational institutions partners in the
  process
• Acknowledge and Reward collaboration
• Include this aspect in ALL their initiatives

19
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• Requires real creativity but is very powerful
• More useful for implementation, to
• Trade-up as-of rights in exchange for desired
  outcomes
• in the end municipalities can
• include informational returns any time rights are
  renegotiated
• increase as-of rights in exchange for data

20
6 Tools of Gov.t
applied to Data Farming

1. Ownership and Operation
2. Regulation
3. Incentives/Disincentives
4. Education and Information
5. Right swapping
6. Mitigation and Compensation

• More useful for implementation, to
• Mitigate negative consequences of initiatives
• Remove final obstacles to implementation
• with this tool municipalities could
• accumulate complaints and suggestions from
  affected parties
• provide online tools for quantifying and logging
  problems

21
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

22
Birth Certificates
the concept

• Municipalities treat their assets as newborn
  babies
• Municipalities identify parent depts
• Depts produce a birth certificate for each
  asset
• Parent dept. assigns name (and code)
• Death and Adoption certificates are treated
  similarly
• Other depts refer to assets by their given name
• A municipal spatial data infrastructure emerges

23
(No Transcript)
24
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

25
Data Farming
the future of Municipal Information Systems

• Municipal Spatial Data Infrastructures emerge
• Towns have plan-ready information
• Municipalities stop hunting-and-gathering
• Information is instead farmed
• Change is captured at the source (for free)
• Open-source web-GIS dominate
• New business models emerge
• Web services are the currency
• Profits come from changers and users
• Private sector contributes fine-grained data

26
Web-services
and City Knowledge

• Open-source web-GIS are the platform
• Light clients or AJAX apps replace standalone
  apps
• Systems are upgraded regularly on server
• Municipalities get data and applications for free
• Web services are the tools
• Depts mash-up web-services to suit needs
• Metadata is reliably available
• Web 2.0 techniques are commonplace
• Folksonomies
• Reputation Management, etc.
• Urban Information Systems exploit the Long Tail

27
Presentation Outline

• City Knowledge
• The 6 Tools
• Birth Certificates
• Data Farming
• The Long Tail of Small Cities

28
The Long Tail
163,547 towns (local gov.t) in the
world 163,239 lt 1 Million pop. 159,349 lt 100,000
pop. 130,206 lt 10,000 pop. 64,307 lt 1,000
pop.
29
The Long Tail
and City Knowledge
Size of Cities
ANY TOWN
Large Cities
Small Cities
The total population that lives in small and
medium cities greater than the population in
megacities. Small towns (tail) represent a
huge market opportunity.
30
The Long Tail
within a Municipality
Change managed by various Departments
Target main departments
ANY DEPARTMENT
Planning, Buildings, DPW
Other Departments
The Long Tail is Fractal.
The Long Tail is Fractal. Starting with the
head makes sense, but all departments ought to
eventually adopt the CK approach.
31
The Long Tail
32
The Long Tail
within a Department
Amount of Change by different agents
major agents
ANY AGENT
specific developers, contractors, staff
Other agents
Again, the head will yield instant benefits,
although the change generated by agents in the
tail may be quantitatively just as large. Target
all agents eventually
33
The Long Tail
within an administrative process
Change produced via various processes
Low-hanging fruits
subdivision approvals, construction permits,
contracts
ANY PROCESS
Other Processes
Processes in the head are major vehicles of
change. Minor processes in the tail still add up
to major change. Eventually all processes will
be addressed by CK.
34
The Long Tail
past and future
Change produced over time
BACKLOG
Future Change
The backlog may be huge but it is finite and
worth catching up with. Focusing on the long
tail of future piecemeal change will close the
loop forever.
35
In Summary

• Municipal Spatial Data Infrastructures flourish
• Departments farm their data plots
• The 6 tools make data farming perpetual/free
• Fine-grain is achieved routinely
• Backlog is completely captured
• Change is intercepted as it happens
• Technologies automate/facilitate data collection
• Web-services enable intra-/inter-dept. sharing
• Information is treated like an infrastructure

36
More about CK
carrera_at_wpi.edu http//www.wpi.edu/carrera/Public
ations/Publications.html http//www.wpi.edu/carre
ra/MIT/dissertation.html