Abstract
Rural population density has a very significant independent influence over important socio-economic and demographic characteristics of developed world rural communities. Additionally, it is a fundamental variable in public policy and planning, both expressing and influencing the relative cost-efficiency of servicing populations. Yet density is itself produced by more fundamental qualities (e.g. environmental resources, nature and time of colonisation) which may themselves change over time. Treating rural population density as a dependent variable produced by a wide variety of factors, we build and test two causal models that attempt to explain the observed pattern of rural densities across south-eastern Australia (n = 414 communities). We distinguish between a “productivist” model—applicable for most of white Australia’s history—and a consumptionist model that takes account of recent counter-urbanisation trends. These models are applied to the entire study area and, in recognition of the study area’s internal heterogeneity, to five clusters of communities. In the drier inland and remoter zones, the productivist model exhibits the greatest explanatory power, while in the more accessible and well-watered “multifunctional” zones, an expanded model that incorporates a measure of “amenity” produces the best results. The research finds that simple environmental factors, coupled with relative location within the national space economy, act as dominant controls over rural population density in early 21st century Australia.
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Notes
Clarke (1968, 28) observes that, “The concept of population density, relating numbers of people to the space occupied by them, is one of the most intriguing and most hazardous correlations employed by geographers”.
A sizeable literature emerged during the 1970s and 1980s to elaborate upon the “developed but dependent” nature of such “settler societies” (e.g. Australia, Canada, New Zealand, Paraguay). The “dominion capitalism” thesis argued that, amongst other aspects, these countries’ economic structures (i.e. dependence upon primary commodity exports to the imperial core to fund the purchase capital and manufactured goods from the same country/ies) and settlement and transport networks (high levels of metropolitan primacy but extensive hinterland development) were directly attributable to their subservient position within an imperial hierarchy. See Ehrensaft and Armstrong (1978); Armstrong (1978), Alexander (1983), Armstrong and Bradbury (1983) and Schwartz (1989).
The 1996 ARIA scores are used in this paper to correlate with both the 1996 and 1981 density figures. That is, the ARIA figures have been treated as a static, rather than a dynamic, indicator. A comparison was made of the population levels at both the 1981 and 1996 censuses of the various service centres that form the datum points for the calculation of ARIA scores to investigate the extent to which the network of service centres would have changed between these two years and, hence affected the accessibility “scores”. While a considerable number of localities along the New South Wales’ far north and mid-north coasts grew to become Level C or D service centres by 1996, overall levels of accessibility in these regions did not alter significantly over this period. This was due to the relative high density of settlements of all sizes along the littoral fringes of the eastern seaboard. Even though the overall number of service centres (as defined by the ARIA methodology) in these regions was less in 1981 than it was in 1996, the density and spacing of major and minor service centres was such that accessibility to the full range of services in 1981 was very close to that observed in 1996.
Spatial autocorrelation is a basic feature of even relatively simple biophysical processes in space (e.g. stream sediment flow) and databases collated to measure these processes and, therefore, a commonsense feature of the interdependence between biophysical, economic and social phenomena and the spatial environment. However, it can invalidate inferential statistical testing, where significance in relationships is sought. According to Robinson (1998, 272), spatial autocorrelation introduces bias into inferential statistical testing in the following ways: first, it undermines the basic condition of independence between observations; second, estimates of standard error in hypothesis testing are corrupted; and third, regression coefficients are unreliable—overestimated in the case of negative spatial autocorrelation and underestimated for positive autocorrelation. Positive spatial autocorrelation is generally evident in spatial clusters of similar values for a variable, whereas negative spatial autocorrelation is indicated by spatial units or points of a value surrounded by units or points of highly dissimilar values (Anselin & Bera, 1998, 241).
Cluster analysis is an umbrella term for a battery of techniques used to develop classificatory schemes and concepts in data analysis (Aldenderfer & Blushfield, 1984, p. 9). Although not supported by a wealth of statistical knowledge and literature (Aldenderfer & Blushfield, 1984; Fotheringham, Brunsdon, & Charlton, 2000, pp. 188–190), the approach is widely used to establish structure within large data sets. Wide ranges of both clustering algorithms and similarity/distance measures are available, all of which can produce varying results from the same data set. Following the advice of Fotheringham, Brunsdon and Charlton (2000), therefore, a range of algorithms was trialled for the input variables, using the SPSS statistical package. Methods tried were the Ward’s, median and centroid linkage methods, using squared Euclidean distance as the distance measure. All input variables were transformed where necessary to give a normal distribution, and scaled to give values ranging from −1 to +1.
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Appendix A
Appendix A
Indicator | Comment |
|---|---|
1. Per cent clay content | A useful descriptor of soil texture. Generally, the “best” soils contain a texture within the mid-ranges of sand, silt and clay. Soils with texture of approx. over a third clay content susceptible to water logging and the “locking-up” of nutrients for plant growth |
2. Electrical conductivity | Measured in 1:5 soil/water suspension. This is a traditional measure of soil salinity, and assumes considerable significance given the relatively recent discovery of the widespread incidence of dryland and irrigated land salinity. The distinction between saline and non-saline soils drawn at ECe of 4 ds m−1 (White, 1997, p. 289). |
3. Per cent organic carbon (C) | Organic carbon is not taken up by plants directly but is a critical indicator of soil fertility and productivity. It is a vital influence on soil structure and texture and the relative availability of essential plant nutrients. A high percentage therefore indicates fertile, well-managed soils |
4. Extractable potassium (K) | Potassium is one of the three essential plant nutrients, with nitrogen and phosphorus. It is vital to plant metabolism and growth. Exchangeable K is a form held by soil clays and organic matter and, hence, relies upon cation exchange capacity (CEC) within the soils in order for it to become available to plants (Glendinning, 2000, pp. 45–51) |
5. Average exchange acidity | This is a measure of cation exchange capacity (CEC) in non-calcareous soils (White,1997 , p. 134). The higher CEC is, the greater ability a soil has of exchanging cations. This is important for plant growth. CEC is closely related to soil management, with high organic matter soils, exhibiting generally high CEC values (Glendinning, 2000, p. 5) |
Soil pH | Standard measure of soil’s relative acidity or alkalinity. Although some plants can grow in a relatively wide band of soil pH, most crops grow best in a pH range of 6–7 (slightly acid to neutral) (Glendinning, 2000, pp. 15–16) |
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Argent, N.M., Smailes, P.J. & Griffin, T. Tracing the Density Impulse in Rural Settlement Systems: A Quantitative Analysis of the Factors Underlying Rural Population Density Across South-Eastern Australia, 1981–2001. Popul Environ 27, 151–190 (2005). https://doi.org/10.1007/s11111-006-0018-1
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DOI: https://doi.org/10.1007/s11111-006-0018-1