Kazumasa HANAOKAAssociate prof., Department of Geography Ritsumeikan University

Deputy director, Institute of Disaster Mitigation for Urban Cultural

Heritages, Ritsumeikan University

People. Policy. Place Seminars

Charles Darwin University

Spatial microsimulation techniques for

constructing a spatially disaggregated

population micro dataset

TokyoKyoto

Aim and Outputs

• The aim of this research is to create spatially

disaggregated microdata (synthetic microdata) of

population in a spatial microsimulation approach.

• Such synthetic microdata allows us to understand

population characteristics at the small area level for the

whole country (Japan).

• Our general purpose microdata can be used for spatial

analysis on provision of public service, retailing, disaster

risk management, etc.

Backgrounds

• In social science, disaggregated approaches using

microdata (data of decision-making units) have gained

increasing interests since 1950s.

• Microsimulation model was proposed by an economist

(Guy Orcutt) in the late 1950s. It considers heterogeneity of

individuals by using microdata.

• The model has been applied in wide disciplines but each

discipline applied it in slightly different ways. The meaning

of “microsimulation” are thus varied by discipline nowadays.

Microsimulation means:

• In economics and demographics

• Population projection and simulation based on an individual dataset

• Applications: Population projection, tax transfer, pension

• In transportation studies

• Traffic simulation (behavior of individual vehicles)

• Applications: optimization of traffic light, congestion

• In geography/geographic information science

• Small-area population estimation method and policy applications

• Application: health mapping, demand analysis in retailing

Microsimulation in Australia

• Example: Developments by the National Centre for Social

and Economic Modelling (NATSEM)

• Non-spatial and spatial microsimulation: STINMOD+, APPSIM,

SpatialMSM

Microsimulation in Geography

• Microsimulation was introduced in geography in the 1980s.

Geographers tried to add geographical element in the

simulation.

• However, spatially disaggregated microdata sets were not

readily available (even at municipality level) for

researchers in many countries.

• Thus, methods to create such microdata were studied in

1980-90s in the UK and they are often called as “spatial

microsimulation”.

Spatial microsimulation• = A method to create spatially disaggregated microdata by combining

various data sources such as census tables and survey samples

• Two major approaches

• (1) Iterative proportional fitting: A method to estimate table cell

counts based on marginal totals of benchmark tables

• (2) Reweighting by combinatorial optimization algorithm: A

method to estimate a new combination of survey samples which

agrees to marginal totals of benchmark tables at the small area level

• Others: regression type etc.Public

survey micro

data

Synthetic

microdata with

area code

Census

tables by

area

reweighting mapping

Area A Smoker Non-smoker Marginal total

Male ? ? 58

Female ? ? 42

Marginal total 30 70 100

Illustrative explanation

of combinatorial optimization

algorithm (Simulated annealing

method)

Seed microdata

(survey samples)

ResamplingGoodness-of-fit

score

Area B

Area CArea D

Area A

Benchmark

tables

tables

tabulation

Feedback to resampling

Estimated microdata

Census

tables

Datasets• Census samples for seed microdata

• 1% anonymized samples of Japanese population

census 2000 (approx. 1 million individuals)

• Resampling by household unit (no change in

household members)

• Census tables for benchmark (marginal totals)

• Small-area statistics of population census 2010

(Approx. 200,000 neighborhood areas)

Individual level

(1) sex * age

(2) sex * nationality

(3) sex * marital status

(4) sex * type of industry

(5) sex * occupation

(6) sex * work/school place

Household level

(7) building type

(8) housing tenure

(9) floor size

(10) household type

(11) household size

Total categories: 161

Small-area

statistics

Outline of our estimation method

Example: Area i in Tokyo

Select census

samples of

Tokyo only Small-area

statistics

Benchmark

tables for Area i

Area i Area k

Area j Area 1, Hokkaido

Area n, Okinawa

Area i, Tokyo

・・・

・・・

Stack all micro

data sets

Synthetic microdata of the whole Japan

= 120 million individuals

Goodness-of-fit measures

• Absolute Error(AE)

• = |census count – estimated count| for cell i

• Total Absolute Error (TAE)

• = sum of absolute error for table j

• Squared Error(SE)

• = (census count – estimated count)2 for cell i

• Total Squared Error (TSE)

• = sum of squared error for table j

For sample replacement only

Distribution of absolute error

• The 77% of all cells in estimated tables agreed with those

of benchmark tables. The 96% are within ±1.

60.0

70.0

80.0

90.0

100.0

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

0 1 2 3 4 5 6 7 8 9 10111213141516171819

20+

Freuquency

Cum ulative %

The num ber of tab le cells ％

77.3％

96.8％

N= 32,584,307 tab le cells

Absolute Error = |Oi - Ei|

Household size1

person2 3 ・・・ 7+

Benchmark table 6 10 12 1Estimated table 5 10 12 0Absolute error 1 0 0 1

Cell counts by attribute

Goodness-of-fit measure• Average scores of TAE among all neighborhood areas

0 2 4 6 8 10 12 14

1

2

3

4

5

6

7

8

9

10

11

Average score of Total Absolute Error

Individual level

household level

Bad fitGood fit

Mapping results

• Average age of household head at

the neighborhood level

Age of

household

head

<=45

70+

Tabulations by any census variables

are virtually possible

Tokyo

CBD

Application

• In March 2011, the Great East Japan Earthquake gave

divesting impacts on millions of people by it huge tsunami

and radioactive contamination.

• In recent, natural disasters such as earthquake, typhoon,

heavy snow, tornado, landslide, and volcano eruption occur

almost every year in Japan.

Disaster impact assessment

• Overlay analysis of population distribution and natural

hazard distribution to present vulnerable areas.

× = vulnerability

Example1

Tohoku

Example1

Mie

Example2

Tokyo

0

0

0

0

0

7

7

7

8

0

5

9

4

6

68

5

5

7

9

4

8

5

6

4

8

7

9

6

7

8

6

89

8

9

10

1

8

4

7

8

7

9

3

4

3

6

5

8

7

8

2

4

5

8

9

6

6

9

6

8

1

0

0

3 1

3

15

14

20

42

69

85

96

60

39

61

22

13

21

27

27

21

98

28

13

28

48

46

15

41

68

69

32

26

25

22

98

15

30

36

19

21

10

16

13

10

13

12

24

21

23

90

86

29

29

18

13

11

27

13

22

41

20

11 15

11

24

29

26

36

41

15

18

2917

75

31

70

13

10

63

25

58

54

20

63

3017

73

22

58

15

20

15

26

4044

31

25

23

41

39

28

51

46

73

44

56

26

18

10

46

16

14

34

2115

1725

10

10

3125

1213

2810

2218 37

35

11

22

24

19

14102029

15

13

13

23

1611

41

2026

222127

161110

13

1510

26

4425

26

1615

1922

1327

11

34

26

27

19

25

14

20

68

21

27

23

1210

27

58

4338

20

22

93

24

32

24

12

30

10

23

59

53

34

45

15

2114

28

2525

151711

11

13

54

79

234116

22

62

63

21

44

54

7781

36

83

58

1427

7576

31

50

38

20

64

12

19

13

15

27

64

21

50

28

12

33

16

33

58

72

19

53

20

21

17

14

17

25

87

15 35

56 13

72

50 42

98

41 2236

35

34

5058

2254 99

21

58

902631

4878

7963

50

9059

3575

532869

82

10

90

346728

93

2582

58

76

40

736659

4587

6290

67

70

26

1811

55

54

33

97

51

21

43

26

2764

7298

91

1088

89

525672

271

108

125

197

179

217

243

155

117

236

355

116

191

178

260

173

172

110

171

128

109

292

701

340

476

351

786

558

336

297101

210

421

842111

239

392

156

158 152

133

105

132

154

268

104

188

508 157

129

181

125

355

117

123

南伊勢町

大紀町

志摩市

度会町

伊勢市

多気町

大台町

玉城町松阪市

明和町

鳥羽市

一関市

気仙沼市

大船渡市

陸前高田市

藤沢町

奥州市

住田町

登米市

平泉町

0

0

0

0

0

9

7

35

9

9

4

69

9

6

4

7

1

2

783

7

6 0 9

8

7

0

0

0

0

0

0

0

0

0

0

0

0

9

9

8

8

9

9

5

9

7

9

8

6

4

8

8

88

6

8

6

99

9

6

7

4

8 9

49

9

5

7

7

8

6

8

6

7

1

6

9

8

7455

55

6

6

3

7

6

7

7

9

0

6

20

42

15

19

33

38 37

302740

26

2021

2519

22

28 11

18

20

17

34

20

59171217

64

15

21 28

22 22

17

1115

1216

13

18

16

17

2722

1610

1245

19

2016 14

2328

13

11

27

19

20

29

23

11

19

16

21

3134

6514

44 53

53

28

64

2626

75

4643

3748

38 86 20

2749

2122

24

25

29

29

2735

14

12

11

11

13

54

1113

22

17

10

34

56

35

10

57

3560

5927

5458 72 16

3024 42

7141

15

15

4632 60

3637

4724

4425

24

2426

1717

21

32

23

61

47

54

20

31

17

2942

40

16

24

4033

2037

7238

33

26

24

4731

56

28

1714

1932

484647 29

142123

26

19

341515

2625

2156

545647

4244

57 4814

2251

48

85

67

525162

10

12

18

25

2220

2019

12

19

13

15

1511

10

12

13

1012

22

20

13

1819 18

2221

21 27

23

26

16

30

26

20

22

3243

2128

32

243212

1337

376026

39

24

14

21

2533

14

15

15

10 35 45

21

23

2423

20

13

1739

23

1121

14

33

13

31

25

17

1529

256231

57

42

36

38

29

52

70

342439

5753

44

25

19

19

20

1445

25

31

2629 3026

21

20

19

18

15

23

23

28

15

20

31

31 10

26

18

17

18

13

27

19

17

15

23

24

22

12

37

14

18

2024

19

31

36

36

31

59

40

17

16

18

31

25

23

1214

38

25

21

19

12

33

17

13

17

19

10

11

15

20

31

14

30

56

2442

42

17

33

23

30

49

24

38

23

20

26

34

2923

28

15

33

34

45

24

50

65

65

82

94

4695

9843

23

21

27

22

11

14

14

2816 20

18

12

12

12

21

16

14

11

11

17

31

2716

29

27

28

22

19

28

25

26

28

26

19

19

19

18

24 1520

46

16

3024

3219

18

1115

13

26

12

1444

11

11

1013

1929

332728

1526

5518

2828

1828

24

12

12

19

35

20

17

18

23

11

21

13

13

10

1515

23

20

1116

16

10

10

15

40

11

16

24

16

14

10

10

22

19

7219

16

282014

22

17

41

534224

29

10

20 28

17

13

18

3319

23

3523

12 1315

48

2135

1850

1710

46

3432

45

46

1114

28

25

18

23

26

4221

62

14

60

28

41

20

17411514

13

14

16 2871

3838

45

29

32

11

11 28

21

23

30

663246

6045

6946

20

90

92

14

2147

62

59

33

372750

1036

53 5924

4818

40

266

131

273

148

125

174

116

103

295

131

145

N

0 10km

N

Example 1 Tusnami

世帯割合(%)

0 - 10

11 - 20

21 - 30

31 - 40

41 - 50

51 - 100

浸水深(m)

0.0 - 2.0

2.1 - 5.0

5.1 - 10.0

10.1 - 15.0

15.1 -

Both size of circle and value represent

the number of households living in a

neighborhood areaNon-three generation households living in owned house

and the household head aged 65 years old and over

Proportions(%) Tsunami depth(m)

Tohoku region Mie region