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SCECSAL (Nairobi)

FIRST DRAFT.
This is the plan for a half-day workshop at the SCECSAL conference in  Nairobi June 4-8, 2012.

A. Context

  1. Why bother?
  2. Building Strong Library Associations (BSLA). See IFLA > BSLA
  3. Statistics for Advocacy (SFA). See Statistics for Advocacy at the project blog.
  4. Statistics in Africa. See Library statistics in Africa by Elisha Chiware
  5. IFLA Statistics Manifesto. See home page.
  6. Evidence-based librarianship and information practice. See EBLIP.
  7. The web as a basis for EBLIP

B. Content

  1. How to design a library census. See Dominican census.
  2. How to collect data on library services by statistical sampling. See Sampling.
  3. How to collect data on library zones and user behavior by systematic observation. See TTT method

C. Zones

  • We divide the library into different functional zones. See Zones.
  • We make a floor plan (map) that shows where the zones are located. See Floor plan from Gjøvik University College.
  • We trace and test an observation route through all the zones on the map.

D. Activities

  • We define sixteen observation activities. See Activities.

E. How many visitors?

  • We estimate the average number of library users on the premises (occupancy, population)
The actual number of people in the building does of course vary. Using TTT we can estimate how the number of users vary
If the library has a manual or electronic counter at the entrance, we can also measuree how the number of visits vary
  • Day by day
  • Week by week
  • Year by year. See Time Series > Nedre Eiker
This information is useful both for planning and for advocacy.

F. What do our visitors do in the library?

  • When we analyze our observations, we group the observation activities into social categories. See Activities > Analysis
  • We calculate diagrams (information graphics) based the social categories.  See Activities > Analysis
  • To facilitate rapid scanning we combine the diagrams into dashboards. 

G. Zones and traffic

Using TTT we can estimate
  • The average occupancy (number of users) in each zone
  • The variation in occupancy through the day
If we know the number of library PCs, and the number of seats, in each zone, we can also estimate the rates of occupancy
  • no. of persons using library PCs/total no. of library PCs
  • no. of persons sitting/total no. of seats
at various times during the day. As the rates approach 100%, it become harder and harder to find a free seat or an available computer.

F. Zones and activities

Subpages (1): Paper (notes)
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