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Knowledge Farming

Knowledge Farming. City Knowledge and the Long Tail of Small Cities. Fabio Carrera. Santa Fe, February 20, 2007. Fabio Carrera. Bio. Born in Venice, Italy BSEE and MSCS @ WPI PhD @ MIT Urban Information Systems and Planning Teaching @ WPI and MIT

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Knowledge Farming

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  1. Knowledge Farming City Knowledge and the Long Tail of Small Cities Fabio Carrera Santa Fe, February 20, 2007

  2. Fabio Carrera Bio Born in Venice, Italy BSEE and MSCS @ WPI PhD @ MIT Urban Information Systems and Planning Teaching @ 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. City Knowledge Promotesthe transformation of municipalitiesfrom hunter-gatherers of urban data to farmers of municipal information

  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. 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 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

  8. The Premises of CK • 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 • “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 […]”. 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 Lynch, Good City Form, p. 48

  9. The Premises of CK • 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 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

  10. The Premises of CK • 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 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

  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. Ownership & Operation • Regulation • Incentives/Disincentives • Education & Information • Rights • Mitigation & Compensation 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 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. applied to Data Farming 6 Tools of Gov.’t Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping Mitigation and Compensation

  15. applied to Data Farming • 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 6 Tools of Gov.’t Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping Mitigation and Compensation

  16. applied to Data Farming 6 Tools of Gov.’t • 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” Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping Mitigation and Compensation

  17. applied to Data Farming 6 Tools of Gov.’t • 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 Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping Mitigation and Compensation

  18. applied to Data Farming 6 Tools of Gov.’t • 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 Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping Mitigation and Compensation

  19. applied to Data Farming 6 Tools of Gov.’t Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping 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. applied to Data Farming 6 Tools of Gov.’t Ownership and Operation Regulation Incentives/Disincentives Education and Information Right swapping 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. the concept • Municipalities treat their assets as newborn babies • Municipalities identify “parent” dept’s • Dept’s produce a “birth certificate” for each asset • Parent dept. assigns “name” (and code) • Death and Adoption certificates are treated similarly • Other dept’s refer to assets by their given name • A municipal spatial data infrastructure emerges Birth Certificates

  23. Presentation Outline City Knowledge The 6 Tools Birth Certificates Data Farming The Long Tail of Small Cities

  24. the future of Municipal Information Systems Data Farming 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

  25. 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 • Dept’s 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 Web-services

  26. Presentation Outline City Knowledge The 6 Tools Birth Certificates Data Farming The Long Tail of Small Cities

  27. The Long Tail 163,547 towns (local gov.t) in the world 163,239 < 1 Million pop. 159,349 < 100,000 pop. 130,206 < 10,000 pop. 64,307 < 1,000 pop.

  28. and City Knowledge Size of Cities The Long Tail 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.

  29. within a Municipality Change managed by various Departments The Long Tail 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.

  30. The Long Tail

  31. within a Department Amount of Change by different “agents” The Long Tail 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

  32. within an administrative process Change produced via various processes The Long Tail 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.

  33. past and future Change produced over time The Long Tail 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.

  34. 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

  35. More about CK • carrera@wpi.edu • http://www.wpi.edu/~carrera/Publications/Publications.html • http://www.wpi.edu/~carrera/MIT/dissertation.html

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