3. 070. 555. 39.000. C. 44. D. 0. 4. 071. 555. 40.000. D. 40. D. 1. 4. S4 210. 623. 30.000. B. 32. B. 1. 5. 240. 645. 32.000. B. 42. A. 1. 3. 260. 670. 26.000. A. 20. A.
Static Spatial Microsimulation: Enriching the Census of Population
Dimitris Kavroudakis (University of Sheffield) Dimitris Ballas (University of Sheffield) Mark Birkin (University of Leeds)
Main idea ► Present a model (software) which enriches census datasets with detailed Panel data ► Why enrich the Census of Population? ░ Get detailed information about individuals in the smaller possible geographical scale ░ Useful for policy analysis ░ Understanding local impacts of national scale policies
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
2
Need for this model ► so far there has been no: ░ user-friendly (GUI, OS independent, easy to use) ░ generic (expandable, adaptable) software available to: Perform spatial microsimulation modelling Use of Simulated Annealing & Hill climbing algorithms
► Aims of this paper is to present such a prototype software
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
3
data Data sources ► Census: ░ 100% coverage ░ geographical detail ░ small area data available only in tabular format with limited variables to preserve confidentiality ░ cross-sectional ► BHPS: ░ more than 5,000 households ░ annual repetition (1991-2006) ░ Available only for large geographical areas (region or district) ░ Plethora of detailed information about individuals and households
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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Static spatial microsimulation Census (for small areas)
BHPS
Educational Level
#
income
ed
car
SG
A’
B’
C’
001
5.000
A
0
2
Male
12
50
100
002
15.000
B
0
3
Female
20
80
150
003
11.000
A
0
5
004
23.000
C
1
2
005
21.000
C
0
6
006
18.000
B
0
4
007
8.000
A
0
5
008
7.000
B
0
2
009
9.000
B
1
5
010
15.000
B
0
4
011
19.000
B
0
6
012
14.000
A
0
2
013
26.000
C
1
3
014
13.000
A
0
1
Other Probabilities include: —Social class and age —Number of cars —Age-group and sex —Employment status, age and sex
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
5
A spatially microsimulated dataset Enumeration individual District
hh
income
education
age
job
sex
social group
S1
007
100
20.000
A
28
A
0
5
013
111
19.000
A
30
B
0
2
014
111
20.000
B
31
B
1
2
S2
086
158
30.000
B
35
B
0
3
S3
099
290
27.000
B
29
B
1
1
100
210
28.000
B
28
B
0
1
67
130
25.000
A
27
0
0
3
070
555
39.000
C
44
D
0
4
071
555
40.000
D
40
D
1
4
210
623
30.000
B
32
B
1
5
240
645
32.000
B
42
A
1
3
260
670
26.000
A
20
A
1
5
263
674
17.000
A
28
A
0
2
270
690
12.000
A
35
B
0
1
300
743
30.000
B
31
C
1
1
S4
S5
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
6
Simulation methods for objects Object
Attributes
Methods
Individual
Age Income Social class Area of living Educational qualifications
Aging Change of income Change of social class Change in education
Geography (OA)
Name Population Number of individuals in each social class Number of individuals from each educational background
Change of population Change in amount of people in every social class Change in amount of people in each educational class
Institution
Acceptance procedure Graduate Capacity Capital of institution
Change in acceptance Change in graduation Change in capacity Change in capital
Policy
Target group Economic effect Educational effect Geography of policy Time
Change target group Change economic effects Change educational effects Change geographical area Change policy’s timeframe
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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A dynamic microsimulation approach 6 individuals
1 year
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
8
5 steps This is John Smith
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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age This is John Smith
This year he became 21 years old
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
10
income This is John Smith
This year he became 21 years old
…and his income had increased
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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job This is John Smith
This year he became 21 years old
…and his income had increased
…because he got a different job
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
12
education This is John Smith
This year he became 21 years old
…and his income had increased
…because he got a different job
…because he graduate from the university
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
13
geography This is John Smith
This year he became 21 years old
…and his income had increased
…because he got a different job
…because he graduate from the university
…and that’s why he moved from Sheffield to Leeds
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
14
restart
…lets see what will happen next year
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
15
Software ► Used by any scientist for fitting/optimisation purposes ► Updatable/convertible (any data, any algorithm, any problem) ► User friendly (GUI, easy to use) ► Generic: any other scientist can: ░ convert ░ expand ░ examine the code (open source) ► Internet compatible (Public participation software)…
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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Algorithms used ► Traditional: Hill Climbing (advance ONLY towards better solutions) ► Metaheuristic: Simulated Annealing (advance towards better solutions with the ability to accept a move from a candidate solution to one that appears worse, just because in the future may apper to lead to the BEST solution) ░ search techniques that are inspired by nature ░ avoid problem encountered by traditional search techniques such as hill climbing - the danger of getting stuck at a local optimum by adding a stochastic element, such as the ability to accept a move from a candidate solution to one that appears worse.
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
17
Search Space
► Search space: all possible solutions (combinations of individuals) ► Optimum: Best solution (best combination of individuals, best representation of a neighbourhood)
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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Example Trapped in local maxima, denying to accept a less good solution
Found the global maxima, by accepting less good solutions, That led to better solutions
► Hill climbing: position 6 ► Simulated Annealing: position 18
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
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Comparison Results
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
20
Complexity ► Fitting panel datasets to census constrains, is a complex process ► Complexity increases by the amount of categories of every variable ► Example: ░ Name: John Smith ░ Social class: a ░ Cars: 1 ░ House: 20 ░ Education: B
1
Cars Housing
Education
b
a
Social Class
15
A
B
2
20
C
A
25
B C
A
B
15
C
A
B
A
B
d
e
f
g
3
20
C
c
25
C
A
B
15
C
A
B
20
C
A
B
25
C
A
B
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
C
21
Complexity Categories Social Class
Complexity
7 7
Cars
3 21
Income
3 63
Education
3 189
► Every variable, introduced in simulation, adds an amount of complexity ► As variables increase, complexity reaches very high levels ► Requirements: ░ clean and robust description of algorithm ░ computational time Complexity graph, in normal absolute numbers, and in logarithmic scale
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
22
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
23
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
24
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
25
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
26
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
27
screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
28
Screenshots
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
29
Validation Microsimulation
2001
1991
2001
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
30
What-if policy analysis ► Explore “what-if” scenarios ░ “what will happen if the university fees increase?” What is the financial situation of the families that will be affected Which social categories will be affected Which areas will be affected Which universities will be affected and how? How will this affect educational migration ► Examples: 1. If a we find that ethnicity plays major role in educational attainment, we run a simulation with ethnicity factors influencing more the attainment of the individuals 2. What the population of Britain would have been if social policy strategies have been different 3. Degree of educational attainment from different social classes within the next 20 years under different university fees policies 4. Impact of EU expansion (25 countries) on educational migration to the UK 5. Impact of household economic background, to educational attainment
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
31
Questions
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
32
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
33
Extra slides
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
34
originality ► Novel aspects of this work are: ░ Use of dynamic elements on a spatial microsimulation ░ Construction of a complete software suite for planning support ░ Use of Algorithms methods for social simulation ░ Researching education with dynamic spatial microsimulation methods
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
35
Simulating Societies ► Social Simulation: Simulating social structures, events and flows ► Having in mind the complexity and randomness of human behaviour in a space, we understand the difficulty and complexity of simulating a large group of individuals and their actions/interactions in space. ► Why? : explore, proof, experiment, predict ► How? ░ set assumptions ░ set rules ░ Create abstraction of real world ░ use data ░ run simulation time interval(s) ░ generate output ░ analyse output
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
36
A static spatial microsimulation approach to generating small area microdata ► Reweighting probabilistic approaches ░ reweigh an existing national microdata set to fit a geographical area description on the basis of random sampling and optimisation techniques ► Creating synthetic probabilistic reconstruction models, with use of random sampling ░ Probabilistic synthetic reconstruction techniques (IPF-based approaches) ░ Combinatorial optimisation methods (hill-climbing, simulated annealing, genetic algorithms)
Dimitris Kavroudakis, Dimitris Ballas, Mark Birkin
37
