1. Spatial microsimulation
  2. Methodology
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Pushing it to the edge: Extending generalised regression as a spatial microsimulation method

  1. Robert Tanton  Is a corresponding author
  2. Yogi Vidyattama  Is a corresponding author
  1. University of Canberra, Australia
Research article
Volume 3(2) Autumn 2010
Cite this article as: R. Tanton, Y. Vidyattama; 2010; Pushing it to the edge: Extending generalised regression as a spatial microsimulation method; International Journal of Microsimulation; 3(2); 23-33. doi: 10.34196/ijm.00036
1 figure and 6 tables

Figures

Figure 1
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Source of households to populate SLAs in five capital cities.

Source: SpatialMSM/08c applied to SIH 2002/03 and 2003/04

Tables

Table 1
Benchmarks used in the procedures.
Number Benchmark
1 Age by sex by labour force status
2 Total number of households by dwelling type (Occupied private dwelling/Non private dwelling)
3 Tenure by weekly household rent
4 Tenure by household type
5 Dwelling structure by household family composition
6 Number of adults usually resident in household
7 Number of children usually resident in household
8 Monthly household mortgage by weekly household income
9 Persons in non-private dwelling
10 Tenure type by weekly household income

  1. Source: ABS Census of Population and Housing, 2006

Table 2
Number of SLAs dropped due to failed total absolute error.
State/Territory SLAs with failed TAE Total SLAs Percent of SLAs with failed TAE Percent of population in SLAs with failed TAE
NSW 2 200 1.0 0.4
VIC 4 210 1.9 0.0
QLD 43 479 9.0 0.8
SA 7 128 5.5 0.4
WA 17 156 10.9 0.9
TAS 1 44 2.3 0.1
NT 48 96 50.0 25.2
ACT 16 109 14.7 1.0
Australia 138 1422 9.7 0.7

  1. Source: SpatialMSM/08c

Table 3
List of univariate benchmarks.
Number Benchmark table
1 Labour force status
2 Age
3 Sex
4 All household type
5 Tenure type
6 Weekly household rent
7 Household type
8 Dwelling structure
9 household family composition
10 Number of adults usually resident in household
11 Number of kids usually resident in household
12 Monthly household mortgage
13 Weekly household income
14 Persons in non-private dwelling
Table 4
Summary of the impact of additional benchmarks.
Model SLAs with TAE < 1 SLAs with TAE >= 1 Measure of Accuracy
SPATIALMSM08c (11BM) 1284 138 0.9307
11BM + non school Qualification (NSQ) BM 1280 142 0.9268
11BM + Occupation (OCC) BM 1262 160 0.9411
11BM + NSQ + OCC BM 1257 165 0.9388

  1. Source: SpatialMSM/08c applied to SIH 2002/03 and 2003/04

Table 5
Summary of the impact of using univariate benchmarks.
Model Accepted SLAs with TAE < 1 SLAs with TAE >= 1 SEI
SPATIALMSM/08c (11BM) 1284 138 0.9307
Univariate BM 1329 93 0.8781
Univariate BM and 1284 SLAs converged in SPATIALMSM/08c 0.9100

  1. Source: SpatialMSM/08c applied to SIH 2002/03 and 2003/04

Table 6
Effect of using households from each capital city to estimate areas in the capital city using spatial microsimulation.
Source of data for estimation with SPATIALMSM/08c (11BM) Number of sample used Accepted SLAs with TAE < 1 SLAs with TAE >= 1 SEI
− Sydney for Sydney 2831 63 1 0.9676
− Australia for Sydney 23,031 63 1 0.9618
− Melbourne for Melbourne 3129 78 1 0.9263
− Australia for Melbourne 23,551 79 0 0.9511
− Brisbane for Brisbane 1778 214 1 0.9263
− Australia for Brisbane 23,668 212 3 0.9224
− Adelaide for Adelaide 1824 55 0 0.9735
− Australia for Adelaide 23,603 55 0 0.9534
− Perth for Perth 1999 35 2 0.8478
− Australia for Perth 23,552 35 2 0.7856

  1. Source: SpatialMSM/08c applied to SIH 2002/03 and 2003/04

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