1. 1 Experiences from extracting large data sets from Swedish public offices Fredrik Liljeros

2. Outline Why use data sets from public offices? Three example of available Swedish datasets Workplace and household data In-patient data Data of suspected criminals Problems with Swedish public office data

3. Sociological data Expensive to collect Time consuming (Especially time series) Low response rate Network data are associated with special problems

4. Sampling of Network Data

5. We can’t use a random sample

6. Extracting data from existing databases!

7. Sweden may be seen as an outlier when it comes to available public data 1686 All priests was ordered to keep track of all people living in their parishes (We had a state church until 2000 in Sweden) 1749 First census 1756 Foundation of the governmental office ”Tabell kommisionen” (Sweden and Finland) 1858 Foundation of Statistics Sweden SCB (www.SCB.SE)

8. All individuals officially living in Sweden have an unique identifier ”personnummer” 700209-0960

9. Example 1 The Sweden database

10. The network Individuals 8,861,392 Families4,641,829 Workplaces437,936

11. Giant component 5 942 389 Average path distance 8.5 Diameter 22

12. Send home (or vaccinate) everyone except max size of workplace

13. Send home people randomly

14. Average path distance

15. Example 2 Data about suspected criminals

16. The data All individuals that have been registered as suspected for having committed a criminal act for every year between 1997 and 2005 Total number of suspected individuals: 348 402 Types of crimes: 144 Total number of reported individual crimes:924 783 Average number of suspected crime types per individual: 2.65 Standard deviation of number of suspected crime types per individual: 3.3

17. Purpose Can social network visualization tools help us to give a better sense of how different crimes are related to each other?

18. Basic concepts Node: A specific type of crime. (For example, “Assualt, outdoors, against child 0-6year of age, unacquainted with the victim” “Trafficking for sexual purposes “ Link: Exists between two types of crimes if at least one individual have been suspected for both crimes different years

19. Example 2002 Bank “Robbery, with firearm, (Bank)” 2005 Post “Robbery, with firearm, (Post)”

20. The mess of all violent crimes

21. A minimum spanning tree

22. What is a minimum spanning tree? 1 23 4 5 2 6

23. AB Number of mutual links

24. Number of mutual links may not be a good measure

25. AB Highly correlated

26. AB Weak correlation

27. A simple measure of correlation between crimes

28. AB A simple Example

29. A minimum spanning tree based on crime correlation

30. A minimum spanning tree based on crime correlation with a lower threshold of 0.01

31. The “mess” of sexbuyers

32. A minimum spanning tree of suspected crimes of suspected sex buyers based on crime correlation

33. Conclusion To play with different graphs may give a good first picture of how different crimes are associated with each other We still need traditional statistical techniques to test hypotheses Existing software package are not very user friendly (Three different softwares was needed to produce these pictures Windows SQL server, Mathcad and Pajek)

34. Example 3 Data about inpatients in a hospital system

35. The hospital network

36. The network All hospitalizations of individuals in Stockholm 2001- 2002 295,108 individuals 570,382 institutional, healthcare occasions 702 wards located at different hospitals The mean number of patients admitted to the wards, per day, varied between one and 69 (mean 10.05 and standard deviation 9.44)

37. Degree distributions

38. Duration of hospital stays

39. Problem with Swedish public office data You usually have to pay for the data You are only allowed to use the data for the purpose you bought i for You can’t share the data for free Swedish data may not be of general interest

40. A last animation

41. Relevant publications