1. Turning Data Into Information with Geo- Ontologies Justin Lewis - @jmapping - TerraFrame

2. A bit about us

5. Some Basic Requirements Remote data collection (no internet) Data syncing across systems Data manipulation & analysis Dynamic data mapping & charting Report generation Complex domain models Mapping data with NO geometries Expansive configuration...

6. Not Easy

7. Some Common Challenges 1.Disparate data sources 1.Limited organizational resources (no GIS staff) 1.A lack of quality GIS data 1.Need for robust data + vis manipulation tools

8. DATA

9. Turn messy & incomplete data into useful data

10. How do we meet these needs?

11. FOSS + 4G Community And many others

12. A Different Approach A method for modeling data as ontologies that can help turn messy and/or incomplete data into useful data.

14. Ontology Crash Course

15. Ontologies | User Data

16. What are ontologies? A style of programming that allows you to define human-like inferences about data objects.

17. To Elaborate Ontology is a has a Geo-Ontology is a located in

18. An Ontology Model for Geo Universal A collection of geographic locations representing a common political hierarchy. Ex: Countries GeoEntity A single geographic entity within a Universal collection. Ex: South Korea

21. GeoEntity

22. Universal

24. Purpose of Universals / GeoEntities Provide a central geographic context for the system

25. Strengths Of This Approach 1.Well defined spatial and non-spatial relationships 1.No dependency on geometries (less GIS)

26. Ontologies in the OPEN RunwaySDK An ontology based data engine. GeoDashboard A visualization layer that sits on top of RunwaySDK.

27. What about user data?

28. User Data Is Different User data can maintain relationships to GeoEntities and Universals giving user data spatial context.

29. JSON { "sales":[ {"product":"widget 1", "amount":"2", "loc":"denver"}, {"product":"widget 2", "amount":"5", "loc":"seattle"} ] }

30. GeoJSON { "type": “feature”, “geometry”:{ “type”:”point”, “coordinates”:[124.6, 10.1] }, “properties”:{ "location":"denver" }

31. Common GIS Formats

32. Spreadsheet

33. The Reality of User ALL Data Incomplete Messy Non-Existent (geometry)

35. Why is this valuable? Generic data integration, manipulation and visualization

36. What do I mean by “generic”?

37. My data, Your data, Everyone’s data

38. No Problem No Geom?

39. How does this work in a web application?

42. What about geometries?

43. Geometries Are Used To Visualize GeoEntities Visualize lowest level data points Algorithmically enhance data Validate spatial relationships However, geometries are optional

44. RunwaySDK in The Wild Deployed to 7 countries and growing

45. Demo

46. Thank You! @jmapping

47. GitHub Links github.com/terraframe/Runway-SDK github.com/terraframe/geodashboard