1. Chuck Humphrey, Leah Vanderjagt and Anna Bombak University of Alberta The Winter Institute on Statistical Literacy for Librarians Demystifying statistics for the practitioner

2. Outline Introductions Statistics and data: what are we talking about? Definitions and standards Metadata and tools Official statistics Non-official statistics Small area statistics

3. Summary When looking for statistics, think about a likely data source from which they would be produced and the likely agency that would produce such statistics. Use the official and non-official classification to help identify producers of statistics. The path toward official statistics takes you to governmental sources. The path toward non-official statistics takes you to commercial and non-governmental organizations.

4. Non-official statistical sources Marketing and consumer research Academic research Private research institutes Professional associations & unions Trade organizations Special interest groups Commercial statistics vendors Non-governmental organizations

5. An example: political corruption A patron wanted to know the level of political corruption for the countries of the world.  Who would produce such a statistic?  What possible data source would exist? From the perspective of international investors, this is a useful statistic. The Political Risk Yearbook A pathfinder at Princeton on Economic and Political Riskpathfinder

6. Consumer patterns Let’s break into our groups and work on exercise 5.

7. Aggregate statistics There are a number of organizations that compile statistics from official and non-official statistical produces and assemble them into large aggregate databases. This includes the UN, International Monetary Fund, OECD and the World Bank. The link to this video provides a demonstration of the use of such aggregate statistics.video

8. Chart of numeric information DataStatistics Numeric Information Non-officialOfficial Print Databases E-tables E-pubsAggregateMicrodata

9. Small Area Statistics (Tiny geography, and supply/demand for related tiny statistics)

10. Geography

11. Geography and Statistics: the ‘Where” There’s always a ‘where’ The importance of the where varies depending on what you’re examining Like statistics, ‘where’ displays often spur new ‘why’ questions ‘Where’ sometimes answers ‘why’, and ‘how’, and ‘who’…and these other questions sometimes spur ‘where’ questions

12. Why learn about small area stats? Small area statistics:  are essential for certain types of analyses  can be challenging to find, understand and to work with;  can answer local and very specific questions  can be expensive to produce and obtain (i.e. present access challenges)

13. Small Area Statistics Objectives today - to build understanding of: The relationship between geography and statistics Spatial display of statistical information The terminology and hierarchical structure of Census geography, to understand commonly accessible smaller units Other small geographical units important to statistical display which are important/frequently requested How to use key Statistics Canada tools to find or generate spatial display of statistics

14. Statistics and Geographic Display We’re going to talk about both small area statistics and spatial display; they go well together; (sometimes, they are joined at the hip)

15. What are ‘small area statistics’ about? High demand for information at the ‘lowest geographic level available’ Statistics at sub-provincial, or sub-municipality level, are critical to analyses of:  health (e.g. spread of disease),  housing, crime, social issues (e.g. emerging patterns of concern or interest),  emergency preparedness (analysis of this doesn’t work at a whole-municipality level),  market analysis, (why do they want my postal code anyway?)  and much, much more!

16. Geographic display What can a map display do that a listed table cannot?  Summarize the big picture – with a picture  Rapidly show PATTERNS of disparity that might have some unexpected explanation  Allow display of statistics without knowledge of coding structure for viewers

17. Privacy and confidentiality Keeping the unit of analysis anonymous is a challenge with small area information (if one has good local knowledge, you can identify a person) There are rules in place about what population counts are required in order for small area statistics to be released (e.g. income)

18. Expense and access Authoring agencies, because of budget limitations, sometimes have to strike balance between availability of variable detail and finer levels of geography

19. Expense and Access More variables? > $$$ Smaller geography? > $$$ !! You just can’t please everyone. Especially librarians. Kudos, DLI! Thanks, GeoConsortium! Hello, happy user community!

20. Brief words on: why Geographic Information Systems? Small area statistics are not easily read in tables Graphic display becomes much more important at smaller levels GIS increasingly used as a tool for small-area analysis and summary Online mapping tools don’t always go into ‘deep’ enough details Map displays can convey unintended messages through design/analytical choices made in a GIS

21. Electoral Results: ‘Gut’ knowledge

22. John Snow’s Cholera Map

23. No need to label the areas: the image says enough

24. Revisiting why - Geographic display The big picture Rapid pattern identification Display without coding display or knowledge

25. Geographic displays always involve choices Simple shade of color choices imply different meanings Ranges of statistics (‘breaks’ in the data) can be manipulated to imply different things Statistics can be left out of maps easily; what is missing? Source data may be bad

26. Sierra Club: Deforestation

27. Where would you like to live when you graduate?

28. Geographic displays always balance accuracy with meaning

29. Geographic displays of statistics are subject to metadata review Evaluation of an online map display is as required as an evaluation of statistics via metadata review; metadata criteria also apply to maps (sources should be cited, survey specified; see yesterday’s slide)

30. The Census Geography Hierarchy Organizing a national system of statistical reporting depends on a full-coverage nested geographic hierarchy; i.e. geography/GIS for StatsCan is about more than making maps The hierarchy helps to ensure 100% coverage of the population during Census collection by organizing the country’s geography The hierarchy also defines ‘level’ of the release of statistics Small area statistics exist at the ‘bottom’ (yet $$) end of the hierarchy

31. The Statistics Canada Hierarchy

32. Great StatsCan Geography Tools for understanding hierarchy Nice quick tutorial: http://geodepot.statcan.ca/Diss/Referenc e/Tutorial/HC_tut1_e.cfm http://geodepot.statcan.ca/Diss/Referenc e/Tutorial/HC_tut1_e.cfm Fantastic glossary: http://geodepot.statcan.ca/Diss/Referenc e/COGG/Index_e.cfm http://geodepot.statcan.ca/Diss/Referenc e/COGG/Index_e.cfm

33. A brief hierarchy overview All levels of the hierarchy have definitions and corresponding codes  Eg. Canada – 00; Alberta (Province) – 48 The levels and codes have defined relationships  Below provinces, we have Census Divisions: eg 4801  Below provinces, Census Metropolitan Areas and Census Subdivisions  Below those, Census tracts and Dissemination Areas (SMALL AREA STATISTICS)

34. Hierarchy continued Hierarchy is defined administratively (ie political decision) and statistically (ie StatsCan’s reporting requirements) Not everything in the hierarchy relates to every other unit (see chart); i.e. not a straight, linear hierarchy Eg. Forward Sortation Areas Odd units: ‘Designated Places’

35. Hierarchy applied to statistics Not all statistics are available for all levels of the hierarchy; parts of the hierarchy may not exist in some places Statistical analysis is more appropriately applied to some units than to others: eg. CMA vs CSD

36. Hierarchy and Small Area Statistics What are the important small area statistics in the hierarchy? Most commonly: Census Tracts and Dissemination Areas

37. Other small-area data units Ironically, what people want geographically is not how the data is compiled by Statistics Canada! Data typically compiled into statistics to meet the needs of the authoring organization Who ELSE cares about these areas/what demands are in place for this information? Solutions are available!

38. Neighbourhoods Frequent need for statistics at this level of geography Census tracts vs. neighbourhoods Municipalities: purchasing profiles and sharing agreements

39. Postal Codes Frequently requested for market analysis/business applications Represented graphically by dots in a product called the Postal Code Conversion File (PCCF) Postal codes are regions! The PCCF allows matching of postal codes to the best corresponding dissemination area

40. Roads and their attributes

41. “Unavailable” Statistics Canada geographic areas Some data resellers ‘impute’ or calculate estimates of ‘missing data values’ for small area statistics

42. Exercise: Explore the hierarchy and statistics for your favorite geographic area using StatsCan Tools Start here:  http://geodepot.statcan.ca/Diss/Maps/Maps_e.cfm http://geodepot.statcan.ca/Diss/Maps/Maps_e.cfm Explore the three sources available and evaluate them for usability, metadata, and for what information they have to offer you: What were you able to discover about your chosen area from each source? To what level of geography were you able to reach using each source?

43. Leah’s final word: Enjoy the statistics journey…you will learn so much about the world!