Better living with observational data

Using Suma to inform space and service planning

Jason Casden | Bret Davidson

NCSU Libraries

by Joyce Chapman, Suma Community development and data analysis specialist.

Could we collect more detailed data, more easily, with fewer errors, and manage it all more consistently?

And could we build more sophisticated and intuitive analysis tools that are totally reusable for all data by lots of people in our institution?

Could we then use data about space and service usage to make better decisions (even small ones)?

The web has had this for years

Web Analytics

Collecting rich relational data for a new service is trivial or easy

Spreadsheets

Collecting data requires error-prone and time-consuming data entry

Less data is collected, with less detail, less often.

Web Analytics

Years of data for dozens of services is in one place.

Spreadsheets

Many spreadsheets, many formats, split by year or semester, stored in a variety of locations.

Cross-service analysis is rare.

Web Analytics

Although APIs exist, analysis tools are easy to use and can be accessed by many people.

They're also the same for every dataset.

Spreadsheets

Limited analysis tools. Can a programmer help?

Fewer people use data to inform fewer decisions with less sophisticated queries.

Suma

An open source tablet-based app (well, toolkit) to aid library staff in assessment of how patrons are using library spaces.


In other words…the gathering, storing, exporting, analyzing, and visualizing of data across spaces/activities/time and around events.

Data collection

Suma data collection

Staff as sensors

Space and Service Analytics

  • Staff scheduling
  • Building hours
  • Service desk service patterns
  • Study room reservations
  • Technology and furniture use
  • Use of specialized spaces (e.g. Graduate Commons)
  • Comparing branch and main libraries, at different times of day
  • Special Collections researcher services
  • Turnaways (e.g. Technology Lending)
  • Combine with other data: circulation, gate counts, tech lending, reserves, online services

Understanding our users

  • Where do our users go?
  • What are they doing?
  • When are they doing it?
  • What could they be doing?

System Overview

System Overview

System Overview

System Overview

Data Synchronization

Core Technologies

  • Zend PHP Framework
  • MySQL, Web SQL Database, Persistence.js
  • AngularJS
  • D3.js

Analysis in Action

  • Sample Data
  • 2 of 4 reports

Time of Day

Reference v. Computing

Day of Year

Open Source

  • 45+ active academic library pilot projects
  • Hosted on GitHub
  • Pull requests are always welcome

Open Source support (free kittens)

Project team

  • Jason Casden
  • Bret Davidson
  • Joyce Chapman
  • Rob Rucker
  • Rusty Earl
  • Eric McEachern

Thank You!

http://go.ncsu.edu/Suma


Jason Casden: jason_casden@ncsu.edu

Bret Davidson: bret_davidson@ncsu.edu