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Spatial microsimulation, Methodology

Research articles · Dec 31, 2010

Pushing it to the edge: Extending generalised regression as a spatial microsimulation method

Robert Tanton, Yogi Vidyattama

National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Australia

Volume 3(2) Autumn 2010

Cite 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

Abstract

This paper extends a spatial microsimulation model to test how the model behaves after adding different constraints, and how results using univariate constraint tables rather than multivariate constraint tables compare.

This paper also tests how well non-Capital city households from a survey can estimate areas within capital cities. Using all households available in Australian survey means that the spatial microsimulation method has more households to choose from to represent the constraints in the area being estimated. In theory, this should improve the fit of the model. However, a household from another area may not be representative of households in the area being estimated.

We found that, in the case that the estimated statistics is already closely related to the benchmarks used, adding a number of benchmarks had little effect on the number of areas where estimates couldn’t be made, and had little effect on the accuracy of our estimates in areas where estimates could be made. However, the advantage of using more benchmarks was that the weights can be used to estimate a wider variety of outcome variables.

We also found that more complex bi-variate benchmarks gave better results compared to simpler univariate benchmarks; and that using a specific sub-sample of observations from a survey gave better results in smaller capital cities in Australia (Adelaide and Perth).

This article contains 1 figure and 6 tables.

Figures

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

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

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

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

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

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

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