The document discusses a conference on business analytics and data science in Thailand that included several presentations and topics: 1) Using data science to predict the results of Thailand's upcoming election and factors like social, economic, geographic, and demographic variables that could influence election outcomes. 2) Building spatial regression models to predict election results based on past voting data and other metrics. 3) One presentation discussed building a spatial negative binomial regression model to predict Thailand's 2011 election results based on 2005 and 2007 election data as well as socioeconomic and demographic variables.

1. The First NIDA Business Analytics and Data Sciences Contest/Conference
วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
https://businessanalyticsnida.wordpress.com
https://www.facebook.com/BusinessAnalyticsNIDA/
-ผลเลือกตั้งจะออกมาเป็นเช่นไร หากรัฐธรรมนูญผ่าน
-ทานายผลการเลือกตั้งด้วย Data Sciences ได้หรือไม่
-ปัจจัยด้านสังคมเศรษฐกิจ ภูมิศาสตร์ ประชากรศาสตร์
พฤติกรรมศาสตร์ ปัจจัยใดที่ทานายผลการเลือกตั้งได้ดี
-พื้นที่เขตเลือกตั้งแบบใดมีแนวโน้มที่จะมีบัตรเสีย
Vote No และ No Vote เพื่อไทย หรือประชาธิปัตย์
-แบบจาลองแบบไหนที่ใช้ทานายผลเลือกตั้ง สร้างได้อย่างไร
Spatial regression model predicting Thailand’s election
อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
นางสาวรัชนีพร จันทร์สา คณะสถิติประยุกต์ NIDA
นวมินทราธิราช 4001 วันที่ 2 กันยายน 2559 13.30-14.00 น.

2. Spatial regression model predicting
Thailand’s election result.
Arnond Sakworawich, Ph.D.
Ratchaneeporn Jansa
Graduate School of Applied Statistics
National Institute of Development Administration, Bangkok, Thailand

3. Abstract
The purpose of the current research are to 1) investigate the spatial relationships of voting
behaviors among each electorates, 2) investigate geographical, behavioral, socio-economic, and
demographic components related to election results, and 3) build up the spatial negative binomial
regression models predicting Thailand election results. Election results in 2005 and 2007 retrieved
from Election Commission of Thailand (ECT) were used to predict % vote for no vote, vote No, voided
ballot, as well as % vote for Democrat Party, Pheu Thai Party, Chartthaipattana Party, and Bhumjaithai
Party as behavioral components for 2011 election results. Socio-economic and demographic variables
were from socio-economic status survey in 2010 from National statistical office. Geographic variables
were from department of land development and department of royal irrigation. Moran’s I statistics and
the spatial negative binomial regression model were used to investigate the spatial autocorrelation of
election results among electorates and the relationship between geographical, behavioral, socio-
economic, and demographic components and election results. This current research will shed light on
how to develop Thailand’s politics and it can also be applied for election and campaign management.
The spatial negative binomial regression model can be used to predict an incoming election results by
substitute 2011 election results with the near future election poll.
Keyword: Election, Spatial Model, geography, social, economics, demography, behavior

4. 84 ปี ประชาธิปไตยไทย
ภาพ : บีบีซีไทย

15. Attachai Ueranantasun, (2012). Analyzing National Elections of Thailand in 2005, 2007, and 2011 –
Graphical Approach. International Journal of Business and Social Science Vol. 3 No. 19

16. Objective
1)To investigate the spatial relationships of voting behaviors
among each election district.
2)To investigate geographical, behavioral, socio-economic, and
demographic components related to election results.
3)To build up the spatial regression models predicting Thailand
election results.

17. Demographic
- % Male, %Female
- Average Age
- Age Standard Deviation
- %Religion
- Population Density
Socio-economic components
-Average monthly Income per capita
-Poverty Rate
-Gini coefficient of monthly income
-Average monthly expenditures per capita
-Gini coefficient of monthly expenditures
-%Occupation Category
-%Type of business
-Work Status Category
-Education Level
Geographic
- Land Use
% of Urban and Built-up land
% of Agricultural land
% of Forest land
% of Water Body
% of Miscellaneous land
- % of Irrigation Area
- Region
Spatial Autocorrelation
- Moran’s I
% of Voting (2011)
- % of PeauThai
- % of Democrat
- % of Chat Thai Pat
- % of Poom Jai Thai
- % of Vote No
- % of No Vote
- % of Voided ballots
% of Voting (2005,2007)
% TRT 2005
% Democrat 2005
% ChatThai 2005
% MaHaChon 2005
% Other Party 2005
% PPP 2007
% Democratic 2007
% Chatthai 2007
% Pueapandin 2007
% Ruamjaithai Chatpattana
2007
% Matchimathipahai 2007
- % of Vote No
- % of No Vote
- % of Voided ballots

18. Source of data
• Office of the Election Commission of Thailand
• National Statistical Office Thailand
• Land Development Department
• Royal Irrigation Department

19. Party
2548
TotalElectorate Party list
ThairukThai 308 67 375
Democrats 71 25 96
ChatThai 18 8 26
Mahachon 3 0 3
Total 400 100 500
Electorate Party list
National Election of Thailand in 2548

20. Party
2550
Total
Electorat
e Party list
PPP 199 34 233
Democrats 132 33 165
ChatThai 33 4 37
PueaPanDin 17 7 24
RuamJaiThaiChatPattana 8 1 9
MatchimmaThipaThai 7 0 7
PraChaRat 4 1 5
Total 400 80 480
National Election of Thailand in 2550
Electorate Party list

21. Party
2554
TotalElectorate Party list
PueaThai 204 61 265
Democrats 115 44 159
PhumJaiThai 29 5 34
ChatThaiPattana 15 4 19
ChatPattana
PueaPanDin 5 2 7
PalungChon 6 1 7
RukPraThesThai 0 4 4
MaTuPhum 1 1 2
RukSanti 0 1 1
Mahachon 0 1 1
PrachathipathaiMai 0 1 1
Total 375 125 500
National Election of Thailand in 2554
Electorate Party list

22. "Everything is related to everything else, but near things are more
related than distant things.”
Tobler W., (1970) "A computer movie simulating urban growth
in the Detroit region". Economic Geography, 46(2): 234-240.
Geographer Waldo R. Tobler’s stated in the first law of geography:

23. Spatial Autocorrelation
Geographer Waldo R. Tobler’s stated in the first law of geography:
"Everything is related to everything else, but near
things are more related than distant things.”
Source:
http://resources.arcgis.com/en/help/main/10.1
/index.html#//005p00000006000000

24. PueaThai Vote Share in 2554 (Electorate)
Moran's I for PueaThai Vote Share in 2011 (Moran’ s I =0.7287)
Local Spatial Autocorrelation (LISA) for PueaThai Vote
Share in 2011

25. Democrat Vote Share in 2554 (Electorate)
Moran's I for Democrats Vote Share in 2011 (Moran’ s I =0.7864)
Local Spatial Autocorrelation (LISA) for Democrats Vote
Share in 2011

26. ChatThaiPhatThana Vote Share in 2554 (Electorate)
Moran's I for ChatThaiPhattana Vote Share in 2011 (Moran’ s I =0.2822)
Local Spatial Autocorrelation (LISA) for ChatThaiPhattana
Vote Share in 2011

27. PhumJaiThai Vote Share in 2554 (Electorate)
Moran's I for PhumJaiThai Vote Share in 2011 (Moran’ s I =0.3482)
Local Spatial Autocorrelation (LISA) for PhumJaiThai
Vote Share in 2011

28. PueaThai Vote Share in 2554 (Party List)
Moran's I for PueaThai Vote Share in 2011
(Moran’ s I =0.8786)
Local Spatial Autocorrelation (LISA) for PueaThai Vote
Share in 2011

29. Democrat Vote Share in 2554 (Party List)
Moran's I for Democrats Vote Share in 2011
(Moran’ s I =0.8803)
Local Spatial Autocorrelation (LISA) for Democrats Vote
Share in 2011

30. ChatThaiPhatThana Vote Share in 2554(Party List)
Moran's I for ChatThaiPhattana Vote Share in
2011 (Moran’ s I =0.24872)
Local Spatial Autocorrelation (LISA) for ChatThaiPhattana
Vote Share in 2011

31. PhumJaiThai Vote Share in 2554 (Party List)
Moran's I for PhumJaiThai Vote Share in 2011
(Moran’ s I =0.5391)
Local Spatial Autocorrelation (LISA) for PhumJaiThai
Vote Share in 2011

32. Demography M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo54
%Novote
54
Density/Square km 832.29 1995.40 -.05 .22 -.20 -.12 -.07 -.59 .55 .24
PrctFemale 50.75 1.22 -.09 .21 -.17 .01 -.14 -.51 .46 -.17
% Buddhist 94.30 16.50 .36 -.22 .06 -.03 -.15 -.03 -.03 .17
% Islam 5.10 16.46 -.37 .22 -.05 .03 .16 .03 .01 -.18
% Population age less than 15
years
20.80 5.14 -.04 -.11 .16 .03 .13 .38 -.43 .28
Average age 36.75 3.24 .18 -.16 .01 .12 -.10 .17 -.07 -.19
Age Standard Deviation 21.24 1.53 .08 -.26 .23 .15 .03 .56 -.50 .11

33. Education Variables M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Payty54
%Voided
Ballot54
%VoteNo54
%Novote
54
% Never attend school 8.64 4.55 -.12 .03 .01 .06 .02 .39 -.12 -.06
% Pre primary school 4.89 1.92 -.06 -.03 .12 .01 .05 .24 -.28 .12
% Primary school 48.31 9.74 .28 -.35 .18 .07 .06 .58 -.64 .09
% Post secondary school 3.22 2.01 -.23 .32 -.14 -.04 -.12 -.51 .48 -.19
% Bachelor Degree 8.64 5.76 -.10 .21 -.12 -.08 -.10 -.58 .52 .02
% Master Degree 1.08 1.33 .01 .04 -.07 -.05 .00 -.43 .36 .12

34. Income and Inequality M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo54
%Novote
54
Average Income 5970.01 2960.63 -.20 .42 -.25 -.14 -.13 -.70 .53 .09
Poverty rate 8.90 12.12 .00 -.09 .17 -.05 .03 .30 -.15 .00
Gini coefficient of total Income 56.21 75.03 -.12 .05 -.04 -.06 .18 -.05 .27 -.10
Average total expenditures 4688.62 1751.55 -.16 .43 -.30 -.18 -.15 -.80 .64 .01
Gini coefficient of total
expenditures
28.16 3.49 -.05 .05 .14 -.08 -.13 .08 .00 -.06

35. Employment Status M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Payty54
%Voided
Ballot54
%VoteNo54
%Novote54
% economically inactive 20.95 5.20 -.01 .09 -.15 .05 -.10 -.35 .42 -.12
% Employer 2.34 2.18 -.19 .22 -.02 .01 -.15 -.13 .17 -.13
% Own account worker 20.07 6.82 .26 -.30 .11 .08 .03 .58 -.56 .06
% Contributing family worker 10.72 5.52 .27 -.37 .19 .06 .10 .49 -.55 .21
% State enterprise employee 0.48 0.68 -.06 .16 -.12 -.03 -.09 -.32 .33 .02
% Private company employee 18.47 10.54 -.26 .40 -.21 -.17 -.04 -.62 .62 -.22
% Housewife 5.35 2.62 -.35 .40 -.21 .03 -.08 -.44 .50 -.12
% Students 5.33 2.13 .02 .06 -.03 -.05 -.09 -.28 .23 .03
% Children elderly person 6.83 2.79 .25 -.26 -.02 .12 .04 .15 -.09 -.07
% Unemployed 0.55 0.70 .04 .05 -.10 -.03 -.07 -.19 .24 -.09

36. % work in agriculture sector 19.60 13.87 .21 -.28 .15 .05 .07 .66 -.68 .06
% work in Fishery sector 0.53 1.54 -.31 .36 -.08 -.06 -.06 -.10 .11 -.10
% work in manufacturing sector 7.32 7.13 .01 .12 -.11 -.09 -.08 -.37 .34 -.26
% work in wholesale and retail sector 11.10 4.67 -.24 .21 -.06 -.01 .01 -.39 .47 -.07
% work in hotel and restaurant sector 4.11 3.20 -.21 .22 -.11 -.03 .01 -.42 .55 .05
% work in transportation sector 1.69 1.73 -.17 .28 -.18 -.11 -.01 -.62 .56 .05
% work in fiinancial Intermediary
sector
0.56 0.80 -.06 .19 -.18 -.05 -.07 -.49 .45 .10
% work in realty sector 1.13 1.71 -.02 .24 -.22 -.10 -.13 -.60 .43 .13
% work in community service sector 1.99 1.46 -.10 .11 -.02 -.06 .01 -.30 .34 -.04
% work in private households 0.42 0.69 .03 .08 -.10 -.11 -.01 -.35 .33 .19
Employment Sector M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo
54
%Novote
54

37. % Legislators and senior officers
occupation
0.49 0.63 .19 -.24 .12 -.03 .10 .13 -.20 .16
% Corporate managers occupation 0.49 0.78 .03 .07 -.07 -.03 -.13 -.39 .28 .11
% Physical, mathematical, engineering
occupation
0.22 0.49 -.02 .15 -.15 -.07 -.06 -.46 .37 .05
% Other professionals occupation 0.53 0.82 -.01 .09 -.06 -.05 -.05 -.41 .29 .04
% Physical & engineering technicians
occupation
0.77 1.18 -.05 .13 -.11 -.03 -.07 -.38 .39 -.18
% Other technicians occupation 1.24 1.40 -.06 .25 -.20 -.14 -.08 -.58 .50 .09
% Office clerks occupation 1.99 1.68 -.12 .24 -.15 -.07 -.09 -.56 .51 -.10
% Customer services occupation 0.43 0.68 -.09 .15 -.14 -.12 .07 -.36 .39 .04
% Personal protective services
occupation
2.25 1.59 -.14 .14 -.07 .00 -.02 -.30 .40 .09
% Models and sales representative
occupation
9.14 4.11 -.33 .21 -.01 .04 .03 -.29 .48 -.07
Occupations M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo
54
%Novote
54

38. % Agricultural with market skill
occupation
12.86 11.12 -.10 .03 -.01 .11 .05 .49 -.43 -.10
% Sufficient agricultural occupation 4.33 7.99 .42 -.43 .23 -.13 .08 .23 -.47 .34
% Agricultural labourers occupation 2.21 3.27 .09 -.12 .05 .08 -.06 .45 -.21 -.21
% Metal and machinery occupation 1.81 1.37 -.11 .17 -.10 -.06 -.02 -.37 .34 -.10
% Stationary machine occupation 0.23 0.57 -.05 .03 .05 -.06 -.02 -.10 .16 -.21
% Machine assemblers occupation 1.81 3.05 .04 .02 -.08 -.07 .04 -.26 .22 -.16
% Drivers occupation 2.26 1.64 -.17 .26 -.13 -.15 .01 -.57 .50 .02
% Sales services occupation 3.57 2.27 -.03 .14 -.10 -.09 -.07 -.45 .40 .04
Occupations M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo
54
%Novote
54

39. % Agricultural land use 55.86 24.79 .04 -.27 .29 .15 .07 .37 -.42 .08
% Forest area 19.88 22.30 .02 .03 -.07 .01 -.03 .46 -.22 -.32
% Miscellaneous land use 4.52 3.98 -.08 .11 -.10 -.09 .14 -.25 .10 .09
% Urban area 16.81 24.64 -.04 .22 -.22 -.13 -.06 -.73 .60 .18
% Water area 1.98 3.03 -.02 -.03 .13 -.03 .01 .12 -.27 .13
% Irrigation area 21.36 33.37 -.03 .11 -.11 .11 -.17 -.40 .32 -.13
North region 0.10 0.29 .25 -.11 -.06 -.04 -.13 .25 -.10 -.37
West region 0.05 0.22 -.14 .17 .01 -.02 -.11 .07 .14 -.16
Northeast region 0.34 0.47 .47 -.61 .33 -.08 .25 .22 -.51 .42
East region 0.07 0.25 -.11 .09 -.08 -.07 .15 -.06 .20 -.09
Central region 0.22 0.41 -.03 -.04 -.01 .29 -.17 .05 .13 -.09
South region 0.14 0.35 -.66 .63 -.16 -.03 .01 -.11 .09 -.17
Geography M SD
%PT54
%Dem54
%PJT54
%CTP54
%OtherParty54
%VoidedBallot54
%VoteNo54
%Novote54

40. % ThairukThai 48 58.86 18.87 .84 -.78 .26 -.14 .07 .16 -.33 .27
% Democrat 48 22.24 20.79 -.76 .90 -.29 -.13 -.13 -.38 .40 -.21
% Chat Thai 48 6.22 8.78 -.02 -.24 .01 .55 -.03 .15 -.02 -.01
% MaHaChon 48 4.21 4.74 .10 -.34 .15 .10 .25 .32 -.23 .00
% Other Party 48 4.37 2.64 .04 -.12 .12 .00 .04 .58 -.43 -.13
% Voided ballot 48 3.00 1.03 -.25 -.02 .05 .21 .16 .61 -.18 -.26
% Vote No 48 2.29 1.31 -.07 .13 -.10 -.17 .05 -.44 .53 -.17
% No Vote 48 27.37 5.82 .36 -.42 .16 -.03 .18 .17 -.42 .82
Past Behavior (Election 48) M SD
%PT54
%Dem54
%PJT54
%CTP54
%OtherParty54
%VoidedBallot54
%VoteNo54
%Novote54

41. % Palungprachachon 50 29.18 14.23 .75 -.53 .07 -.16 -.06 .02 -.23 .22
% Democratic 50 24.56 21.59 -.68 .91 -.36 -.20 -.16 -.46 .50 -.19
% Chatthai 50 7.21 11.57 -.02 -.24 -.05 .59 .02 .13 -.06 -.03
% Pueapandin 50 7.77 9.26 .20 -.37 .19 -.09 .34 .20 -.39 .13
% Ruamjaithai Chatpattana 50 3.73 6.45 .06 -.24 .09 -.03 .33 .21 -.12 .13
% Matchimathipahai 50 4.58 6.02 .04 -.23 .35 .06 -.07 .28 -.11 .00
% Voided 50 2.54 1.05 -.21 -.02 .06 .33 -.07 .52 -.08 -.21
% Vote No 50 4.56 1.85 -.08 .21 -.16 -.17 -.05 -.48 .64 -.23
% No Vote 50 25.38 4.99 .25 -.26 .07 -.10 .17 -.18 -.04 .72
Past Behavior (Election 50) M SD
%PT54
%Dem54
%PJT54
%CTP54
%OtherParty54
%VoidedBallot54
%VoteNo54
%Novote54

42. % PT 54 40.44 19.63 1.00 -.65 -.01 -.14 -.16 .14 -.36 .28
% Dem54 28.75 22.52 -.65 1.00 -.46 -.22 -.25 -.42 .45 -.19
% PJT 54 10.01 14.26 -.01 -.46 1.00 -.15 -.09 .24 -.26 .00
% CTP 54 4.34 10.88 -.14 -.22 -.15 1.00 -.09 .28 -.12 -.08
% Other Payty 54 6.67 11.23 -.16 -.25 -.09 -.09 1.00 .07 -.05 .05
% Voided Ballot 54 4.90 1.64 .14 -.42 .24 .28 .07 1.00 -.56 -.10
% Vote No 54 2.71 1.57 -.36 .45 -.26 -.12 -.05 -.56 1.00 -.12
% No Vote 54 24.96 4.37 .28 -.19 .00 -.08 .05 -.10 -.12 1.00
Variables M SD
%PT54
%Dem54
%PJT54
%CTP54
%Other
Party54
%Voided
Ballot54
%VoteNo54
%Novote
54

43. Y = β0 + λ WY + Xβ + ε
Y = β0 + Xβ + ρWε + ξ
ξ is “white noise”
Spatial Lag Regression Model Spatial Error Regression Model
residuals in neighboring locations (Wε)
OLS SPATIAL LAG SPATIAL ERROR
Baller, R., L. Anselin, S. Messner, G. Deane and D. Hawkins. 2001. Structural covariates of US
County homicide rates: incorporating spatial effects,. Criminology , 39, 561-590

44. Variable Coefficient SE z-value p-value
Constant -5.88 2.87 -2.05 .040
North region 6.66 2.66 2.50 .012
% ThairukThai 48 0.66 0.05 14.04 .000
% Palungprachachon 50 0.31 0.05 5.87 .000
% Housewife -0.45 0.21 -2.14 .033
Lambda 0.47 0.07 7.19 .000
R-squared 0.80
-2LL 2707.72
AIC 2717.73
BIC 2737.36
2554

45. Variable Coefficient SE z-value p-value
Constant 8.30 1.20 6.91 .000
Northeast region -4.82 1.39 -3.48 .001
% Democrat 48 0.43 0.06 7.73 .000
% Democrats 50 0.51 0.05 9.49 .000
Lambda 0.23 0.08 3.00 .003
R-squared 0.87
-2LL 2640.34
AIC 2648.35
BIC 2664.06
2554

46. Variable Coefficient SE z-value p-value
Constant -6.47 1.43 -4.51 .000
% ChatThai 48 0.32 0.07 4.67 .000
% Chat Thai 50 0.37 0.05 7.18 .000
% Voided Ballot 1.26 0.28 4.51 .000
Lambda 0.09 0.08 1.01 .311
R-squared 0.43
-2LL 2641.7
AIC 2649.69
BIC 2665.40
2554

47. Variable Coefficient SE z-value p-value
Constant 8.74 2.15 4.07 .000
Northeast region 6.14 2.38 2.58 .010
% Democratic 50 -0.12 0.05 -2.51 .012
% Matchimathipahai 50 0.48 0.12 4.12 .000
Lambda 0.39 0.07 5.53 .000
R-squared 0.30
-2LL 2933.68
AIC 2941.69
BIC 2957.39
2554

48. Variable Coefficient SE z-value p-value
Constant -4.15 2.37 -1.75 .080
Density/Sqkm 0.00 0.00 2.71 .007
% Female 0.13 0.05 2.85 .004
% Primary school -0.02 0.01 -2.78 .006
% Contributing family worker 0.03 0.01 2.48 .013
% work in agriculture sector -0.03 0.01 -4.56 .000
% Urban area 0.04 0.01 7.04 .000
% Vote No 48 0.17 0.04 4.04 .000
% Vote No 50 0.09 0.04 2.32 .020
Lambda 0.87 0.03 30.11 .000
R-squared 0.78
-2LL 914.68
AIC 932.67
BIC 968.02
2554

49. Variable Coefficient SE z-value p-value
Constant 2.44 0.83 2.93 .003
% Contributing family worker -0.04 0.01 -3.54 .000
% work in agriculture sector 0.04 0.00 7.78 .000
Age Standard Deviation 0.09 0.03 2.82 .005
Average total expenditures 0.00 0.00 -7.31 .000
%Voided Ballot 48 0.44 0.05 8.22 .000
%Voided Ballot 50 0.16 0.04 3.62 .000
Lambda 0.65 0.05 12.69 .000
R-squared 0.85
-2LL 753.82
AIC 767.82
BIC 795.31
2554

50. Variable Coefficient SE z-value p-value
Constant 5.43 0.86 6.28 .000
Northeast region -1.10 0.42 -2.63 .009
North region -1.27 0.60 -2.12 .034
% No Vote 48 0.51 0.03 16.20 .000
% No Vote 50 0.24 0.03 7.39 .000
Lambda 0.46 0.07 6.95 .000
R-squared 0.80
-2LL 1586.5
AIC 1596.51
BIC 1616.14
2554

51. -Spatial autocorrelation cannot be ignored when we want to study election in Thailand.
- Spatial autocorrelation for PhueThai party is way higher than Democrat party.
- Spatial autocorrelation for party list is way higher than Electorate.
- Regionalism strongly influences election results.
-Socio-economic, demographic, geographic, and past behavioral factors are related to voting
behaviors.
- Past voting behaviors is the best predictor of future voting result.
- It is harder to predict voting result for small and medium sized political party.
- Urban, city, middle income, educated, working profession, and females tend to Vote NO.
- Low income, work in agricultural sector, without contributing family business tend to have VOIDED
ballots.
- Northerners and Northeasterners tends to participate in election more than other regions.
- Two large party is majority of Party list Voting.
Conclusion and Discussion

52. -Spatial regression models predicting party list voting result
- Develop model to predict future voting result when public election polls is available.
- Political development and socio-economic development
Future Research

53. The 2010 Household Socio-Economic Survey Whole Kingdom.
National Statistical Office (NSO)
• Area Survey: Whole Kingdom (both municipal and non-municipal areas)
• Duration: January to December, 2010
• Sample: 52,000 Households