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A regional plan for the SEQ region introduced a balanced growth strategy to promote compact growth and polycentric development (OUM, 2006). To achieve such objectives,

employment growth in the existing regional activity centres and the locations of the economic activity (OUM, 2006).

The spatially disaggregated population forecasts are used for analysing the pattern of the retail employment as a result of the population growth. I consider the global Moran’s I statistics for the spatial autocorrelation can be less effective in measuring the patterns of the employment growth, because the employment distribution is more centralised than the population. Instead of using global Moran’s I statistics, I focus on measuring the local spatial clustering and spatial concentration of the retail jobs to uncover their underlying patterns.

Figure 5.9 shows the spatial distributions of the spatial autocorrelation (local clustering) of the retail jobs and their significant maps of univariate local Moran’s I. The spatial autocorrelation of the retail employment for SEQ does not spread out but is highly concentrated in the major activity centres. Some coastal SLAs in the Sunshine Coast and the Gold Coast are characterised by a positive HH clustering and this effect will remain stable throughout the forecast years. The retail jobs do not show significant spatial associations beyond the existing employment centres referred to in the local Moran’s I, except for a new HH clustering that occurs at the inner western areas.

Figure 5.9 reflects a new HH growth effect that will take place in the inner west suburb (North Beaudesert) in the year 2011, with the growth effect moving into Ipswich City by the year 2016. In the following forecast years, such a locally clustered increase in the retail employment will go steady and expand to the adjacent SLA in the south. This growing HH clustering indicates that a potential urban expansion is likely to occur in the inner west and southern suburbs. Based on a distinguishable pattern from those, the development is more evenly distributed across the region; a new employment centre is emerging at the inner western suburbs. The new areas of the HH employment clustering are mainly located in the inner western suburbs and can be the result of economic restructuring, because the land uses for industry or agriculture purposes will be replaced by the growing service sectors. Moreover, new migrants tend to move into less expensive areas. This can be influential on a faster retail jobs growth in the western suburbs.

Figure 5.9: Distribution of spatial clustering of retail trade jobs for each forecast year from 2006 to 2026

Another question that can be explained by the spatially disaggregated employment forecasts is the changing spatial structure and the location of the new employment centres.

I resolve this issue by mapping the spatial concentrations of the employment over time.

Spatial concentration distinguishes the urban areas in which most employment are located in the relatively few places at the relatively high densities from those in which the development is more evenly distributed across the region (Galster et al., 2001). To detect the potential employment centres where the retail jobs are highly concentrated, I define the high density value (in the SLAs) as more than twice the standard deviation of the density values for the disaggregated dataset. Thus, the predicted high degree spatial

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region (using Kernel interpolation)— see Figure 5.10. The purpose is to better visualise the location of the emerging retail employment centres.

Figure 5.10: A 3-D visualisation of density of retail trade employment greater than two times of standard deviation from 2006 to 2026

Figure 5.10 illustrates that by measuring the spatial concentration, the retail employment is highly concentrated in the Brisbane City in the base year (2006) and characterised a

2006 2011

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mono-centric pattern. From 2011, a new employment centre is emerging through the western corridor. The increase in job concentration stretching to the west tends to be higher than the existing employment centre through the forecast years. Some commercial suburbs (Surfer’s Paradise and Southport) in the Gold Coast were the key employment areas. Their pattern was reported as more sprawl-like and evenly distributed owing to the larger size of these suburbs. After 2021, there appears to be an additional densely-developed and concentrated employment centre. Overall, Figure 5.10 indicates a changing growth pattern of the retail employment by time series, that is, a clear tendency for a transition from the mono-centric pattern to a more balanced polycentric development. This is in line with the South East Queensland Regional Plan that the urban growth is expected to shift towards a balanced and self-contained development.

In summary, these findings confirm that the extent of the suburbanisation of the retail employment remains limited in SEQ and the retail employment at the city centres remains as more dense and centralised patterns. The significant spatial clustering of he employment in the western suburbs will have a crucial impact on the urban spatial structure. Thus, the SEQ employment growth reflects a different spatial scenario from the population growth that tends to be more spread out.

5.8 Conclusions

In this chapter, we presented a new method for spatially-disaggregating the regional employment forecast into smaller spatial areas. Rather than being a simple device to spatially subdivide the forecasts, our method consolidates the existing empirical and theoretical knowledge of how the spatial structure of employment is formed. One major issue that is resolved in this chapter is the spatial effects and their impact upon the disaggregation result. We argue that a high degree of spatial variability is caused by spatial heterogeneity, spatial dependence. To remedy this situation, we applied a GWR method to account for such spatial effects using a case study of the retail employment. The method uses the locally-regressed relationships to estimate employment numbers in the smaller geography whilst being constrained by the regional forecast. It utilized the characteristics

The outputs of the method are spatially disaggregated regional employment forecasts across the local metropolitan areas and now at a scale that is of greater use to the urban planner. The disaggregation outputs indicate that, driven by the increased population suburbanisation, the growth pattern for the service employment will expand its centralised development that forms the region towards a more polycentric structure. New retail and service centres are emerging in the southern and western parts of the region (for instance, the Ipswich area) to accommodate the growing demand from the population in the surrounding suburbs.

A number of limitations in the current approach that have been previously noted are concerned with the dynamic changes of the employment processes over time. Further work is now required to consolidate the findings of this work and resolve the areas of current limitation. This will involve the acquisition of additional forecasts for the determinant variables (for example, the locations of the planned shopping centres) to unconditionally drive the employment disaggregation. In addition, the growth capacity in the local metropolitan areas can be imposed to control the employment allocation over time. However, the inclusion of these future research foci will depend upon the availability of the data.

Despite these limitations the spatial disaggregation process presented here marks the first stage in the development of a robust, transparent methodology that can be applied to other industry sectors and study areas.