1. WoSA GAZIANTEP WORKSHOP 24 to 28 August

3. AIMS OF WORKSHOP FIRST DRAFT POPULATION ESTIMATES FINALISED FIRST DRAFT SECTOR TABLES FINALISED

4. WORKSHOP CALENDAR

5. KEY EVENTS TUESDAY 5-6pm Progress Debrief VTC Followed by Team Dinner at OCHA office WEDNESDAY 10.30-12PM SIMAWG FRIDAY 4-5PM Presentation of draft pop estimates and tables VTC

7. Sub-districts 55

8. RealitiesSD A142 B C70 B C D3 B C E3 B C F2 B C G4 B E3 B F7 C E15 C E F3 C F1 C G4 D E7 D E F1 E F7 One Reality 142 Two Plus 130 Awhole sub-district B Controlled by armed opposition groups and ANF Ccontrolled by Government of Syria Dcontrolled by ISIL EISIL affiliated groups FKurdish Forces GLebanese Armed Forces/ pro- government forces

9. 223 217 Planned Covered

10. 20716 Planned Not Covered 10 Covered Not Planned 55

11. 55 No Partners 69 One Partner Two Partners 60 Two Plus 88

12. #Questionnaire0123456789101213 #Sub district551184954538755791 1497 1334 Planned Covered

13. Organization Planned Questionnaire Covered Questionnaire % Coverage Questionnaire Planned Sub-districts Covered Sub-districts % Coverage Sub-districts ACU216199 92%727097% Binaa12 100%44 Concern12 100%44 GOAL12 100%44 IRC_TRK2412 50%8113% Kudra9648 50%322681% Masrat21 100%78114% MRFS6057 95%20 100% NPM450448 100%150154103% NRC66 100%22 PAH2124 114%78 PiN69 150%23 QRC81 100%2728104% SCI214 19%7457% Sija99 100%33 Solidarite International 36 200%12 UOSSM8756 64%292172% IRD60 100%20 100% IRC_JRD12 100%44 Reach288246 85%968184% Total 14971334 89%22321797%

14. PRESENTATION OF DATA

15. Size and Scope No of Rows: >1300 Variables: >500 columns 10 MB Records: >500,000 No of Questionnaires: 1334 No of Questions: 70

16. Questionnaire  Database Sections A- H Metadata (Location, KI & data collection method) A – Population B – Protection C – FSL D – Shelter & NFI E – Health F – WASH G – Education H – Early Recovery and Livelihood

17. Rows Sub-districts - 217 1-14 Questionnaires / sub-district  multiple rows P-code & Name

18. Columns Groups: Location, A - H First Group : Geographic locations  9 columns Common elements – Confidence Level columns A99  H99 per sector – Frequency – Number of Questionnaires / sub/district Sector specific (No of columns vary depending on Questions) – Population : 6 – Protection: 7 – FSL: 4 – Shelter & NFI: 18 – Health: 12 – WASH: 10 – Education: 5 – Early recovery and livelihood: 7

19. Aggregation Methodology

20. Types of Questions Categorical – Colours – Favourite Food – Affected Groups Ordinal – Weekdays – Months – Percentage Groups (0-25%) Interval – Height – Population figures

21. Types of Questions Categorical questions with 1 answer only – D15 Categorical questions with all that apply – F3 Categorical questions with 3 answers without ranking them - B3 Categorical questions with rank from 1 – 3, C2 Ordinal Questions where with 1 ordinal answer), G3 Ordinal Questions (with 1 ordinal answer for each ordinal category), D8 Categorical questions (ranking 1 -3 before second ordinal answer), B4 Numerical Questions (with 1 number), B6 Others

22. Types of Questions Questions with 1 answer only – D15 Questions where you tick all that apply – F3 Questions where you tick 3 answers without ranking them - B3 Questions where you rank from 1 – 3, C2 Questions where with 1 ordinal answer), G3 Ordinal Questions (with 1 ordinal answer for each ordinal category) Categorical questions (ranking 1 -3 before second ordinal answer) Numerical Questions (with 1 number) Others

24. POPULATION ESTIMATES SOURCES Dynamo, CCCM, AoO (non-WoSA), Governorate Profiles, Landscan, UN Habitat, UNHCR, WoSA data METHODOLOGY

25. In the absence of population counts: Key informant estimates – [a type of “expert judgment”] Population figures are uncertain – Encourage the KI to express this uncertainty Elicit estimates from several KI – Find a format so that estimates can be aggregated

26. Expert opinion elicitation: Lowest, most plausible and highest value

27. Procedure 1.Note lowest, likeliest and highest values from each KI 2.Compute synthetic distribution for all opinions in a given sub-district (random number technique) 3.Aggregate for a set of sub-districts (e.g., all sub- districts in a governorate 4.Compute critical statistics for the aggregate: 1.Best estimate (e.g. median of all simulated aggregates) 2.Uncertainty measure (confidence interval)

28. When there are several sources: E.g.: Field-based KI; satellite imagery; government statistics need to be combined Some will come without uncertainty measures Needs other methods of expert opinion fusion: – Weights for agreement for each sub-district estimate – Weights for consistency across all sub-districts – Combined weights used to obtain synthetic estimate Scientific consensus less strong than on probability- based simulation Always, under any method: Cooperative review and interpretation are essential.

29. GOVERNORATE PROFILES COVERAGE METHODOLOGY

30. Scope and Objective To produce quantitative estimates of sectorial PiN and inter-sectorial PiN To estimate and identify patterns of displacement as well as population estimates To identify qualitative indicators related to Protection and Livelihoods

31. Methodology Purposive Sampling GP will rely on primary data collected through UN-led focus groups and key informant interviews if needed Delphi technique to reach consensus during focus groups Questions to identify confidence level of data provided

32. Process A national taskforce representing all the sectors was established to actively manage the process and provide input/feedback. Data gathered at Damascus level was triangulated and analysed with the national taskforce Data gathered at the hub level was gathered with the subsectors and further reviewed and analysed with the national taskforce. GP data set was circuited with the sectors for final validation & endorsement

33. Outcome 14 Governorates and 99% of the sub-districts l Quantitative and Qualitative Data : Population, IDPs, Sectorial PiN Preliminary data was shared with Sectors DISCLAIMER: The data of the GPs represents the best estimate available to the UN and do not constitute accurate representation of the needs. Our estimates are subject to errors arising from limited knowledge and problems associated with incomplete or inaccurate sources of data.

34. Initial results* (1/2) GovsEstimated Population 2011 Estimated current population_Min Estimated current population _Most plausible Estimated current population_Max %Pop_ChgEstimate of IDPs Al Hasakeh 1,511,993 926,000 1,115,000 1,282,000-26% 162,700 Aleppo 4,867,977 2,442,000 2,948,000 3,582,000-39% 1,241,000 Ar-Raqqa 943,996 674,500 797,000 945,500-16% 375,100 As-Sweida 369,994 355,000 430,000 501,00016% 48,800 Damascus 1,753,953 1,500,000 1,850,000 2,000,0005% 500,000 Dar'a 1,026,992 615,000 804,000 993,000-22% 293,100 Deir-ez-Zor 1,238,992 805,000 1,001,000 1,218,000-19% 357,400 Hama 1,627,990 1,243,000 1,612,000 1,805,000-1% 415,600 Homs 1,802,990 1,233,000 1,578,000 1,829,000-12% 506,700 Idleb 1,500,986 1,288,000 1,401,000 1,680,000-7% 310,400 Lattakia 1,007,989 962,000 1,298,300 1,537,00029% 352,375 Quneitra 89,998 55,000 67,000 94,000-26% 23,000 Rural Damascus 2,835,980 2,159,000 2,939,000 3,520,5004% 1,481,000 Tartous 777,236 732,000 971,000 1,150,00025% 294,500 Grand Total 21,357,066 14,989,500 18,811,300 22,137,000-12% 6,361,675 *(NOTE: Preliminary data – subject to change)

35. Initial results* (2/2) GovsEstimated current population _Most plausible PiN of family kits (No. Of Individuals) PiN of Shelter Assistance PiN of food assistance (No. Of Individuals) PiN of hygiene kits(No. Of Individuals) PiN of WASH (No. Of Individuals) Children without access to formal/informa l education Max PiN /Sub- District Al Hasakeh 1,115,000 290,700 300,250 806,750 789,000 672,500 41,624 881,000 Aleppo 2,948,000 900,400 667,270 2,000,344 807,620 - - 2,318,044 Ar-Raqqa 797,000 421,101 294,195 567,500 641,900 895,300 426,250 954,600 As-Sweida 430,000 85,000 70,800 115,500 192,500 227,250 238,000 Damascus 1,850,000 200,000 600,000 750,000 175,000 840,000 Dar'a 804,000 160,000 134,000 600,000 90,000 213,000 637,000 Deir-ez-Zor 1,001,000 475,200 318,800 589,500 452,900 782,000 262,000 921,000 Hama 1,612,000 230,500 237,100 544,500 128,262 731,000 Homs 1,578,000 577,100 472,380 783,000 698,472 179,554 24,000 1,192,500 Idleb 1,401,000 672,950 741,728 938,000 301,075 1,370,605 211,663 1,466,375 Lattakia 1,298,300 113,250 196,500 296,050 217,275 707,800 743,250 Quneitra 67,000 52,000 28,600 62,000 72,000 87,000 Rural Damascus 2,939,000 1,991,000 1,526,600 1,946,800 1,531,800 2,006,500 - 2,662,750 Tartous 971,000 157,550 134,438 448,125 269,585 472,800 694,350 Grand Total 18,811,300 6,326,751 5,722,661 10,448,069 6,367,389 7,599,309 965,537 14,366,869 *(NOTE: Preliminary data – subject to change)

36. Next Steps GPs is standalone product Data is jointly owned by UN and partners operating from Damascus GPs is one of the data sources available to the sectors for the purpose of the HNO Contribute in the NIF work stream to estimate the current population of Syria Triangulation with WoSA

37. AGGREGATION METHOD METHODOLOGY

38. POST WORKSHOP SECTOR WORK – 1-13 SEPTEMBER

39. CONFIDENCE LEVELS, SEVERITY SCALES AND PiN

40. CONFIDENCE LEVELS SEVERITY SCALES NIF SS: Nutrition, WASH, Health, Protection, Shelter, NFI, CCCM, ERL

41. PiN ESTIMATES 1 Sector = 1+ WoSA team support

43. SECTOR DATA MAPPING SECTORS PRESENT UPDATES ON SECTOR ASSESSMENTS