Dynamic Data Displays: Experiences from USDA’s Economic Research Service

1. Dynamic Data Displays: Experiences from USDA’s Economic Research Service Vince Breneman GIS Coordinator USDA-Economic Research Service March 5, 2007

2. Economic Research Service Overview • 400 employees (300 social scientists) • ERS conducts a research program to inform public and private decision-making on economic and policy issues involving: • agriculture • trade • food markets • food safety, nutrition, and assistance • natural resources • rural America

3. Data visualization objectives • Clients • Policy/legislative gatekeepers • Press/Media • Commodity industry/Agribusiness • Research community • Public • Goal is to provide our clients with robust data products that will allow them to find, explore, extract data and build custom presentation products with some level of consistency

4. Challenges • Lots of products often in the wrong format to create dynamic displays • 100 data products that in include 1000’s of datasets • Most products are file based with meta data in the file • A variety of users wanting different things from the data • Some want a number • a relationship • a table or database • graphics • Finding data • How do we expose data and information that are contained in our automated data delivery systems • How do users find cross tabulations and geographic subsets etc.

5. Challenges • Departmental guidelines • limited screen real-estate: 767 pixel width,140 pixel header, ¼ left nav box, ¼ right nav box • 508 compliance – must provide reasonable access to the visually impaired • Popups not advised because of popup blockers • Departmental guidelines have and will change so building applications with a level of flexibility becomes part of our requirements

6. Commercial packages we use for developing Web data visualization applications • Corda’s PopChart – graphing tool • Generates flash files as well as jpgs, eps, pdf … • Provides some standard graph types but we have done a bit of customization • To reduce development time and provide some consistency we have been building graphing web services • Provides a d-link for visually impaired • ESRI’s ArcIMS 9.1 • for mapping of detailed geographies generally county level of lower • Utilizes our SQL Server and spatial data repository • Able to incorporate a host of spatial reference layers

7. ERS data sets page Lots of products Departmental guidelines Finding data

8. Total domestic population ERS food consumption application • Provides food availability estimates of food supplies moving from production through marketing channels for domestic consumption. • U.S. national level annual time series 1909 - 2005 • Includes hundreds of commodities from beef and pork to coffee and tea to potato chips

9. Food Consumption data selection page • - Application instructions • - User defined graph-able subset • Output options • Challenge: • Wanted to show over 100 • variables creating a LIL • (Long Intimidating List)

10. Solution: Break list into categories Challenge: Application description and data query component takes up lots of screen real-estate

11. Solution: Jump return page to data selection and results Challenge: Want to illustrate the group total and commodity shares - this can create scaling issues

12. Solution: Allow user to add or delete items from selected group

13. PopChart features: - Graph dynamically scales Y-axis for a given set of data - PopCharts generates flash files with rollovers to get actual values -PopCharts provides d-link for visually impaired

15. Challenge: Wanted graph to standalone and be clear about what it was representing Solution: Include extensive footnotes

16. Challenge: How do we handle to many variables?

17. Solution: Panel plots?

18. Profiles of America mapping applicationspatial data • Purpose: • Help users visualize over 100 important US county level demographic and socio-economic indicators and a number of ERS rural indicators/geographies • Summarize demographic data by rural geographies and by State • Allow the application to be extended with new indicators of regional geographies • Outputs include maps, graphs and tables • This is one of our first highly interactive data visualization products

19. Regional geographies/typologies Rural-Urban Continuum Code, 2003 County Typology Codes, 2004 Metro-Nonmetro status, 2003 Metro-Micro-Noncore status, 2003 Urban Influence Code, 2003

20. Example Demographic and Socio-economic data Population and Migration Age and Sex Race and Ethnicity Educational Attainment Households and Families Journey to Work Employment and Unemployment Income and Poverty Unemployment rate, 2003

21. How do unemployment rates compare between metro and nonmetro regions?

23. State selection – US default

24. Topic groupings

25. Rural indicators

26. Challenge: How to map 3000+ counties quickly…under 2 seconds? Solution: Currently we use ESRI ArcIMS - thin client

27. Regions view Standard map navigation

28. Issue: Do you just show the state or surrounding states? Challenge: Where to display identity tool results?

29. Solution: Used pop-ups - Now posting table results below the map

30. Challenge: How to link map regions and chart regions Solution: Used color linkages

31. Same population change variable aggregated to a different geography

32. How to aggregate data? • Make sure all variables were using the same county base geography • Develop a data sturcture to handle regional aggregations • Identified variables as totals, ratio or categorical • Ratio data (rates, percentages) need to know the numerator and the denominator by region • Totals sum variable by region

33. Agricultural Resource Management Survey (ARMS) application • ARMS is USDA's primary source of information on the financial condition, production practices, resource use, and the economic well-being of America's farm households • It’s a large and complicated dataset with over 650 variables before you start summarizing the data with various categorical variables

34. It’s a large complicated dataset with over 650 variables before you start slicing and dicing All these variable are embedded in an application and it’s quit difficult to wrap a search engine around it Challenge: How do you search this variable list and provide useful result to the user? Solution: ???

38. Commercial farms: Very large family farms, non-family farms Intermediate farms: farming occupation/high sales farms, large family farms Rural residence farms: limited-resource, retirement, residential/lifestyle, farming occupation/low sales 2005 view of farm assets per farm

39. 2005 view of farm assets Percent Challenge: How do we show farm assets by farm typology over time? How do we provide statistical significance to the estimates?

40. Solution: Panel plots Provide confidence bounds Issue: We had to calculate confidence bounds in the application….Would prefer the graphing software to handle it. Challenge: How do we show farm assets by region?

41. Solution: Charts on a map

42. Farm assets over time by region – with confidence bound

43. Challenge: There are several ways to show the same data each with different users in mind Solution: Display data in multiple formats

44. Challenge: How to provide an easy way for comparison of multiple graphs? Solution: Here grabbed jpgs and lined up in PowerPoint Farm household income from farming and from off farm sources

45. Useful approaches for developing Web data visualization applications • Identifying a few graphing products to concentrate development…Corda’s PopChart and ESRI ArcIMS • Developing data exploration apps for in house use – provides utility to research staff and a test bed for external applications • In house usability testing – really helps you identify the common information people are looking for from a dataset/application

46. Concluding thoughts • Building dynamic data displays helps ERS and its clients in a number of ways • Makes data products more accessible and usable • Provides ways of turning data into information • Provides better ways to find information • Provides incentives to organize and manage our data in better ways supporting both our research and data dissemination missions