Journal of Rural Studies 18 (2002) 385–404
Rural population density: its impact on social and demographic aspects of rural communities Peter J. Smailesa,*, Neil Argentb, Trevor L.C. Griffina a b
University of Adelaide, Adelaide, SA 5005, Australia University of New England, Armidale, NSW, Australia
Abstract Using the settled areas of South Australia as a case study, this paper seeks to demonstrate the specific importance of rural population and settlement density as an important variable in understanding the social, population and settlement geography of sparsely settled rural regions, where sparse and falling density presents both practical and conceptual problems for rural planners. After a review of the literature on population density, the case is argued for the use of net rural rather than gross density in the analysis of settlement patterns. The paper then tests a series of hypotheses on the empirical relationship between rural density as independent variable and selected demographic and socio-economic indicators as dependent variables, at two specific points in time. For the same region, points in time and set of indicators, it goes on to compare the predictive power of rural density as an independent variable with that of three other important qualities of rural settlement patterns (remoteness, settlement size and urban concentration). Rural density is found to be a significant explanatory variable, both in its own right and in comparison with the three other variables tested. In conclusion, the findings are related to policy development measures for rural Australia. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Rural population density; South Australia; Rural communities; Socio-economic indicators
1. Introduction By international standards Australia, like Canada, lies at the extreme low end of the spectrum of gross national population densities. Internally, large areas are practically uninhabited, yet peri-urban densities around the few metropolitan cities are similar to those of other Western countries. The resulting very large range of rural densities presents many problems, both conceptual and practical, for rural policy makers and planners. Over the last two decades—particularly since the rural crisis of the early 1990s—agricultural restructuring in rural Australia has with few exceptions involved a substantial net reduction in the number of rural holdings and rural employment. At the same time, the continued but spatially restricted counterurbanisation movement has brought a new exurban element into some rural areas. But, while there has been much research on the impact of falling rural population numbers and/or disposable incomes on the country town *Corresponding author. Fax: +61-8-8303-3772. E-mail address:
[email protected] (P.J. Smailes).
network and on major regional centres, there has been very little written on the independent impact of falling (or, indeed, rising) rural densities. This paper makes a start on addressing the question, and seeks to place the density variable at centre stage in rural geography, comparing it with other critical variables used to evaluate rural settlement patterns.
2. Specific aims In this paper, we hope to: (1) Provide a brief conceptual review of the density concept as it applies to rural populations in Western countries. (2) Using a case study where as many complicating variables as possible are held constant: (a) test a series of hypotheses on the empirical relationship between density as independent variable and selected demographic and socioeconomic indicators as dependent variables, at two specific points in time;
0743-0167/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 7 4 3 - 0 1 6 7 ( 0 2 ) 0 0 0 3 3 - 5
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(b) for the same region, points in time and set of indicators, compare the predictive power of rural density as an independent variable with that of three other important qualities of rural settlement patterns (remoteness, settlement size and urban concentration); (c) based on this comparison, demonstrate that rural density has an important impact on demographic and socio-economic characteristics of rural populations, that is not reducible to the effects of other qualities of rural settlement patterns. (3) Relate the findings to policy development measures for rural Australia. Within the confines of a single paper, there are limits to what can be attempted. Three caveats, therefore, are needed. First, we purposely use a relatively simple measure of rural density at a local scale, but excluding urban populations and major unpopulated rural areas from the equation. Eventually our intention is to develop a more sophisticated measure of effective density, at varied scales. Secondly, we recognise that density itself is the product of a set of other causal factors; these complex relationships are acknowledged, but not investigated here. Third, while we fully recognise the complexity of the causal relationships between density and the demographic and socioeconomic variables considered, in this paper our primary aim is to demonstrate the empirical strength of the relationships, leaving the matter of the sequence and possible reciprocality of causality to a subsequent paper.
3. Existing work on rural population density in developed countries Population density is a quintessentially spatial phenomenon, expressing the way that human beings spread out over, and occupy, the earth. As such it is a highly significant element in population geography, social geography and settlement geography. Yet a search of the literature reveals few analyses in depth; and of the work that has been done, the majority has been concerned with urban areas. An excellent review from a town planning viewpoint is provided by Saglie (1998); a general overview, including major German language contributions, is provided by B.ahr et al. (1992). For specifically rural areas, an early contribution by Robinson et al. (1961) examined the relationship between rural farm densities and rainfall, percentage of land under crop, and distance from urban centres in the Great Plains, while Aangebrug and Caspall (1970) classified Kansas counties by their pattern of density
change over time. Arguably the first work systematically to investigate the impact of density variations on an entire settlement system, however, was that of Berry (1967). Although working within the constraints of the rather rigid theoretical framework of central place theory, Berry was able to show that the size of rural service centres and their surrounding trade areas is systematically related to the broad regional population density in which they are embedded. Whatever the density, centres tend to form a discrete spatial hierarchy, but as density drops, the absolute size of places at each level falls, while trade areas increase in size to compensate partly—but only partly—for the falling density. As a result, particular types of service found at the lowest hierarchical level under high-density conditions will move up to the next higher order when density falls. Berry also introduced the concept of a ‘phase shift’ in the spatial patterning of trade centres with abrupt discontinuities in density, as between suburban areas and the peri-urban countryside, or between irrigation areas and broad-acre farming. Beavon (1977) later introduced the concept of density changes over time to central place theory, but only in an intra-metropolitan context. An extremely interesting but little known paper (Irving and Davidson, 1973) on density in an urban context (but with strong rural relevance) introduced the idea of social density, expressing the amount of personto-person interaction taking place in a given unit of area per unit of time. This was found not to be a simple function of physical density of population. In Australia, important contributions were made by Holmes (1977, 1981) who introduced the idea of critical density thresholds for particular kinds of service centre network, relating density levels to broad types of primary production land use, e.g. the marginal density zone where normal daily schooling of children using buses becomes impracticable, and gives way to distance education and ‘school of the air’, and normal ambulance coverage of patients gives way to the Flying Doctor service. Holmes introduced the suggestion that a certain critically low density (8 km2/resident person) marked the approximate limit of the Australian ecumene, separating the settled areas from the ‘sparselands’, and produced a loose classification relating population density bands to various forms of land tenure, farm size, land use, town spacing and patterns of access to services. Later the Australian Bureau of Statistics used a figure of 0.057 dwellings/km2 to define the limits of the sparsely settled areas for sampling purposes in their surveys (Hugo et al., 1997, p. 105). The terminology used by Holmes to express this important phase shift from ecumene to non-ecumene, or settled areas to sparselands, did not receive wide adoption, and was gradually replaced by a distinction (along a very similar geographic boundary) between ‘rural’ and ‘remote’ Australia, culminating in the
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adoption by the Commonwealth Government of the Rural, Remote and Metropolitan Areas (RRAMA) spatial classification system (Departments of Primary Industries and Energy and Department of Human Services and Health, 1994). The RRAMA system is interesting here in that it incorporates a density measure termed ‘personal distance’ expressing the average distance between residents within a given spatial data unit. Recently, Hugo and Wilkinson (2002) have argued that while the concepts of density, rurality and accessibility are closely related, they should be kept conceptually distinct, and a spatial classification that recognises all three of the rural/ urban, density and accessibility/remoteness dimensions is required. The latter dimension is very well captured by the Accessibility/Remoteness Index of Australia (ARIA), described by Hugo and Wilkinson (2002, pp. 16–19). This index does not seek to measure density directly. Meanwhile, Swedish geographers, examining the urbanisation of their large and in great part sparsely settled country, had devised a novel method of expressing the relationship between density and accessibility . (H.agerstrand and Oberg, 1970). A family of isopleth maps was produced showing—for any point in Sweden—how many people were contained within a series of radii of constant length, each corresponding to a typical travel distance. In Finland, early work by Hult (1962) and Saviranta (1973) showed a distance decay of population density in 5 km zones surrounding urban centres after the manner of Thunen’s . rings. RuotsalaAario and Aario (1977), however, in testing this around Kuopio, a town of 73,000 people in central Finland, found that while a strong relationship existed between distance and population density (r ¼ 0:62), this was due much more to the town’s central location in the most environmentally favourable part of its region, than to the influence of distance per se. These contributions apart the analysis of specifically rural population density has had limited attention. Fitzpatrick (1983) examined the concept of density in relation to isolation, with particular reference to education. Smailes and Mason (1995) extended the . H.agerstrand and Oberg mapping approach to examine densities of either total population, or population subgroups (e.g. school age children, total workforce, pensioners) in relation to service provision in Eyre Peninsula, South Australia. Smailes (1996) showed that total population growth/decline over a period is better predicted by rural population density at the outset of the period, than by the absolute population size at the outset. In the United States, Rank and Hirschl (1993) examined the link between population density and welfare participation, but only in the context of comparing urban, mixed and rural US counties. Lester
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(1995) finds a negative relationship at the level of whole States between density and suicide rates (r ¼ 0:5) while Fonseca and Wong (2000) find that 1980–1990 density increase by State is positively related to the 1980 density. In the British context, an important recent paper by Coombes and Raybould (2001) provides a review and critique of the formulae used for the allocation of funding to local government. They are particularly critical of the use of gross population density as an indicator in such formulae, arguing that it subsumes a number of important characteristics that are better measured separately. In our discussion (below) of population density as a concept, we return in more detail to the significant conceptual issues raised by these writers. Two other important descriptors of certain elements of national settlement patterns (isolation and clustering of mediumsized towns) developed by Portnov et al. (2000) are related to, but distinct from, population density and are not discussed further here. In summary, then, while the above review is certainly incomplete, the literature on the density of population and settlement in rural areas thus far appears to have been fragmented (spatially and by discipline) and desultory (over time), whether density is treated as a dependent or as an independent variable. As an independent variable, its influence on social, economic and demographic qualities of rural districts has often been implied, but rarely subjected to detailed investigation. A number of authors have recognised its intrinsic importance as a fundamental aspect of settlement systems, with some exploring its practical significance for planning, but to date there appears to have been no systematic or concerted investigation of how net rural densities influence the socio-economic and demographic composition of communities.
4. Why is density important, and why is falling density a problem? In developing countries, excessively high rural densities are a frequent concern in terms of overpopulation and pressure of population on the environmental carrying capacity. But when population density gets too low, it also has adverse impacts in rural areas. Farm amalgamations not only reduce rural numbers, but also inevitably increase the spacing between homesteads, the ratio of clustered (country town) to dispersed population, and the distance inputs per capita required to provide the remaining households with services, social functions and human companionship. Ladd (1992) found in a study of 247 large US counties that the per capita cost of providing public services followed a J-curve with its lowest point at about 250 persons/mile2 (ca. 98 persons/km2). As density fell below this level, per
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capita costs rose quite sharply. The gross density spectrum represented by the South Australian rural communities in this case study is almost an order of magnitude below this, yet still shows a huge range (from 58.59 to 0.13 persons/km2). Costs per capita are likely to rise quite sharply (and/or quality of service decline sharply) as density falls, if indeed the service can be maintained at all. For many services, too, as density drops not only does the local population fall below some demand threshold, but it becomes impossible to compensate for this by, say, amalgamating local government areas to achieve some arbitrary population target. In effect, it becomes unrealistic to collect enough demand within a reasonable travel distance to run the service at some minimum viable level. Aasbrenn (1998) deals well with the conceptual problem in a country where population densities are comparable to Australia, with a 1998 gross density of 0.9 persons/km2 in his East Norwegian mountain study area. As in Australia, such communities are threatened not by complete depopulation, but by thinning out of both people and the settlement pattern. Aasbrenn’s Norwegian term ‘uttynningssamfunn’ for such places has no direct and accepted English equivalent but translates as something like ‘sparsifying communities’ or ‘thinning communities’. He points out that the problems of sparsification breaks down into a distance problem and a scale problem: the extra cost in time, money and convenience of overcoming distance as the settlement pattern thins out, and the reduced opportunities for remaining services to obtain scale economies. These two factors lead to five negative features of the process of sparsification, listed by Aasbrenn (1998, p. 73) as * * * * *
intensified ageing (relative to the national trend); deterioration of social networks; changes in demand (for services); marginalised viability of service suppliers; decay of physical infrastructure.
Together these form a syndrome often seen in areas of falling density, and form part of a self-reinforcing cycle of decline, giving rise to low morale and a dispirited residual population (Smailes, 1997). The extent of several of these elements in a particular community is difficult to measure from secondary data and requires fieldwork, yet to be undertaken in the present study. However, we suggest below that a number of important features of settlement and population in a given community will tend to correlate strongly with density at a given point in time, and also with changes of density over time. First, though, we need to consider the nature and measurement of the density concept.
5. Population density as a concept 5.1. Population density as a dependent variable In the analysis below, we shall treat rural population density as an independent variable, which has a hypothesised direct or indirect causal effect on other social variables. But first, we need to recognise: (a) that any such causal relationships are at least partly reciprocal and (b) that population density itself is also a dependent variable, responding to a complex of more fundamental causative factors. That is, it can be seen as a reflection of the varied habitability and perceived opportunity that a given territory offers to humans. The strength and nature of the relationships between density and these more basic causal factors (themselves heavily intercorrelated) is not investigated here. However, in Australia we would list the most important of them as follows, in approximate order of primacy: rainfall; soil quality; remoteness (particularly from major cities); land values; farm type; population potential.
* * * * * *
In many places in Australia these variables will also bear a close relationship to the age and duration of white settlement, and hence the maturity and attractiveness (to Europeans) of the cultural landscape. The dominant environmental variables may vary from country to country. In the Great Plains of the United States rainfall dominated, with a correlation of r ¼ 0:78 between rural farm population density and average annual precipitation (Robinson et al., 1961). In Finland, land quality (as expressed by the percentages of land under forest, lakes and peat bog) was more important (Ruotsala-Aario and Aario, 1977). However, the causal sequence seems to start with natural factors and the resulting potential for primary production, but to be reinforced by a remoteness/accessibility dimension caused by the initial placement of the dominant urban centres in the most environmentally favoured locations. In Australia, the subsequent fanning out of settlement and routeways from a few dominant capitals has produced a particularly clear and powerful access/remoteness effect, so that one can conceive of a series of more or less concentric, overlapping and mutually reinforcing invisible surfaces of intensity. These surfaces express variations in land values, mean farm size, farm type, remoteness/accessibility,1 age 1
The concepts of ‘remoteness’ and ‘accessibility’ are often seen as mirror images or opposite poles on a continuum. In the case of journeys to consume services, though, ‘remoteness’ is often used as a quality of the point of origin (e.g. a rural homestead) whereas ‘accessibility’ is a quality of the target destination (e.g. a shopping centre).
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and duration of white settlement, and population potential across the nation, with high points over the capital cities, but with minor outlying peaks representing naturally favoured locations of various types. The ability of rural population density to reflect essential qualities of the social catchment areas of country towns derives from the fact that it appears to provide a single, convenient and perplexingly simple (though far from perfect) index of all or most of these factors rolled into one. 5.2. Population density as an independent variable 5.2.1. Continuous or discrete variable? As normally expressed, population density is a discrete variable expressed as an average ratio for some defined area. The above discussion implies that we can also understand density as a continuous variable forming an invisible surface over the settled areas. By knowing the location of a community on this surface, we argue that one should be able to predict quite a number of things about the population and activities of that community. To construct such a surface, however, we need to have a lattice of control points, between which isopleths can be interpolated, involving the calculation of a specific local density for some defined region surrounding each control point. The more numerous the control points, and the smaller the defined areas, the more difficult it will be to interpolate isopleths giving a smoothly defined surface, for in many cases density changes abruptly across a narrow border—as at the edge of an irrigation area in an arid landscape, or at the foot of a sharp fault scarp. 5.2.2. Local versus regional density: the scale of resolution As well as being capable of expression as either a discrete or a continuous variable, density may also be expressed at differing scales of geographic resolution. For some strategic purposes, it is necessary to disregard local peculiarities, and focus on where a community or enterprise is located on the broad national density surface. For detailed analysis of the importance of density at the individual community level, however, more specific local density measures are required. Thus, . H.agerstrand and Oberg (1970, 23, 29) vary the radius of the region around each control point to produce density maps for different purposes, while Coombes and Raybould (2001) suggest a similar moving window approach, in effect using regional density as an indicator of urbanisation, with weighting to give the zones further from the centroid diminishing impact. The latter authors’ analysis (Coombes and Raybould, 2001, p. 236) shows, for instance, that their social deprivation index correlates closely with local conditions, while home-insurance levels respond more closely with the broader setting around a locality. The present paper
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concentrates purposely on local density, within carefully defined spatial units corresponding to the functional social catchments of country towns, and using dependent variables expected to respond specifically to conditions within these catchments. 5.2.3. Measured versus perceived density Saglie (1998, Chapter 2), in an excellent discussion of the density concept as applied to the built environment, distinguishes between measured density—the quantifiable relation between physical aspects of the built environment and the number of inhabitants—and perceived density, which also incorporates the subjectively experienced impact of density within social space, including perceptions of crowding and the like. Although Saglie’s distinction between quantitative and perceived aspects of density refers principally to urban built environments, in principle the perception by rural people of the increased isolation and remoteness engendered by the loss of neighbours, and the feelings such perceptions engender are just as important in areas of sparsification as in areas of overcrowding. While measured density is a concept that can be applied at a wide range of scales, perceived density is likely to apply mostly to the local scale of daily, or frequent, interactions. Assessment of perceived density must await analysis in the field, however, and only measured densities are dealt with below. 5.2.4. Gross density versus net rural density Coombes and Raybould (2001) point out that gross population density (including urban populations) has long been used as a key variable in allocation models for funding local government in the United Kingdom. As they argue, however, gross density is a very blunt instrument with which to analyse settlement systems, since it tends to conflate and/or confuse more fundamental and specific measures, all of which impact on the cost and difficulties of service provision and human contact. These are identified as (a) the size dimension—population size of the spatial unit and of the urban settlement(s) within it; (b) the concentration/sparsity dimension—the proportion of population and dwellings in urban clusters; and (c) the remoteness/accessibility dimension—relative peripherality or centrality of the area in the relevant space economy. We would concur with Coombes and Raybould’s judgment (2001, p. 226) that (gross) ‘‘ypopulation density is seen to be a proxy of first resort which is standing in for a more specific measurement of one or other of (these) three dimensions of population distribution’’. Gross density is a particularly crude measure when applied to spatial units of variable
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size, temporally shifting boundaries, and sporadically distributed unusable land, and also where the level of spatial resolution is ill-suited to the problem at hand. Where multivariate statistical analyses are applied to a relatively simple indicator like gross density at increasing (or decreasing) scales of resolution, spatial autocorrelation and the modifiable areal unit problem (Openshaw and Taylor, 1981; Amrhein, 1995; Wrigley et al., 1996) can render findings unreliable unless complex statistical controls are employed (see Green and Flowerdew, 1996). This is particularly the case where the spatial units used form building blocks for larger units of analysis. We would therefore agree that gross density is indeed a crude concept that needs unpacking, and that qualities (a)–(c) above are essential and fundamental descriptors of settlement patterns. To these, however, we would add (d) below as a quality of coequal importance. (d) The net density of the rural (or more generally, nonurban) population of the spatial unit. We argue strongly that (d) is no mere proxy for qualities (a)–(c), but has equal or greater importance in evaluating settlement patterns from a planning viewpoint—particularly in a strongly rural region such as South Australia and when calculated for socially meaningful spatial units at a scale of resolution appropriate to the task on hand. Unlike the other three factors, it takes direct account of the size of the community’s ‘living space’, the habitability of the landscape in which the town is embedded, and the likely cost of service delivery and other forms of interaction requiring personal human contact for non-urban dwellers. Thus, the nature of the surrounding matrix in which the town is set is a vital aspect of the settlement pattern, and may be expected to have a substantial impact on the community as a whole. Moreover, the social environment in most of rural Australia (and similar countries) is radically different from that of compact, highly urbanised countries such as the United Kingdom where towns are large, closely spaced, much of the national territory is within commuting range of at least one major city, and the residual farming population of minimal importance. The median size of the main central town in the spatial units used in the present paper is just 1120 persons or 470 households, and the central town’s median percentages of the total population and total persons employed are just 52% and 46%, respectively. In the 84 studied communities, the 1996 median percentage of the workforce employed in primary production was still 26.8% for the total community, and 47.6% for the non-urban element, despite substantial labour shedding. It should perhaps be pointed out that just because gross density conflates aspects of both urban and rural
populations of a community and acts as a partial proxy for variables (a)–(c) above, it may give a superficially higher correlation with various socio-demographic qualities of a community’s population than do any of net rural density, size, concentration or remoteness acting on their own.2 This, however, does not overcome its conceptual weakness and the consequent difficulty of interpreting the correlations, and it is not used in the present paper. While gross density may have been overstressed in Britain as a measure of settlement patterns, in Australia on the other hand there has been a widespread—indeed almost universal—trend to focus on settlement size, using the population size of country towns as a single convenient indicator of the prosperity and/or likely future trends for their communities. This reductionism ignores the fact that while rural communities invariably focus on a country town, that town in many cases houses only about half of the total community population and a tiny proportion of the living space. As we shall demonstrate, while town size is indeed important to rural communities, they also have many major social, demographic and economic attributes for which town size does not provide a suitable indicator. We return later to the question of the relationship between all four of the key factors, and their relative influence on social and demographic characteristics of the studied communities. First, though, we need briefly to describe the study area.
6. The study area The paper reports on findings from the ‘settled areas’ of South Australia, excluding the Adelaide statistical division. Covering almost 150,000 km2, or roughly the size of England and Wales, South Australia’s settled areas were occupied by just 1.41 million people at the 1996 Census, of whom all but 26% (365,000) were in the metropolitan statistical division. The non-metropolitan area should thus be well suited to a study of the social significance of rural density in sparsely peopled regions where distance still presents a substantial barrier to interaction, and the settlement pattern is dominantly based on primary industry, tourism, retirement and local services. Only 20% of the State’s employment in secondary industry is outside the Adelaide statistical division, and even this is concentrated into a very small number of outlying towns. Very few other specialised employment clusters (e.g. defence, railways, mining, major tertiary institutions) exist to complicate the 2
In the present paper, gross density used as an independent variable gives an average correlation coefficient (disregarding sign) of r ¼ 0:582 for the 11 dependent variables, as against values of 0.480, 0.454, 0.449, and 0.369 (net rural density, concentration, size, and remoteness respectively) for 1996 data.
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Fig. 1. Location of places mentioned in the text.
relationships between rural density and social characteristics of the population. Fig. 1 locates the study area and shows place names mentioned in the text.
7. The measurement of population density at the local level As Fonseca and Wong (2000, p. 505) observe, population density—population per unit of area—is a very straightforward idea until one actually starts to apply it. What population, and what area, go into the equation? 7.1. The population element As people are recorded at their place of residence on census night,3 and do not live as hermits scattered 3
Although the Australian Census provides some de jure data by normal place of residence, the bulk of the data (including those used here) is de facto: people are counted wherever they were on Census night. Censuses are held on a week-night in winter to minimise the count of people away from home.
individually over the paddocks, occupied dwellings (which equate to households) are the appropriate smallest units for measurement, applying to both rural populations and rural settlement patterns. From a statistical point of view using households rather than persons as a density measure makes little or no difference, since the two are so closely correlated (r ¼ 0:999). In principle, however, households are more locationally stable over time than are the individuals within them. It is also the dwellings, and not individuals, that form the nodes and end-points on the road, postal delivery, telephone, electricity supply, school bus service networks—and indeed on almost all the physical networks of transport and communication. (Mobile phones and personal lap-top computers are significant exceptions.) For all these reasons, households rather than persons are chosen as the appropriate unit with which to construct a basic density measure (independent variable) against which other community characteristics can be compared as dependent variables. Densities are calculated as the number of inhabited rural dwellings per 100 km2; except for the dwindling number of special function towns such as Whyalla (steelworks) or Port
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Augusta (power station), this element together with tourism and services to passing traffic functions as the resource base upon which the remainder of the settlement system is largely dependent in this study region. Under ‘rural’ households are included not just the farm element, but scattered population of whatever occupation. Constrained by Census data, ‘rural’ also includes dwellings in clustered settlements with less than 200 people, as well as dispersed houses. Densities are calculated exclusive of the area occupied by the town(s) within each community. These towns are made up of one or more Census Collectors’ Districts (CCDs), which in turn are defined, before each Census, to correspond to the outer edge of the built-up area of all clustered settlements expected to exceed 200 population. While some CCDs may extend a little beyond the current built-up area in order to include enough households for a collector’s workload, there is no significant problem of urban definition. The median proportion of the total community area occupied by urban settlements is only 0.4% for our 84 communities, and its removal has minimal impact on the calculation of net rural density. 7.2. The area element Since the object of this study is to identify the impact of density upon rural society, socially meaningful spatial units of analysis are required. Australia is a country of dispersed settlement, and just about all social life is concentrated on social organisations and facilities centred in towns of various size. Country towns act as the foci of social drainage basins. Even very small centres are frequently nominated as the town of primary social importance, while the nomination of places of second and third most important social centres allows the definition of social linkage patterns at various scales (Smailes, 2000). The spatial units used in this study therefore each consist of a socially significant town, plus the surrounding households linked to the town, forming a local interaction system. The aim was to identify spontaneously evolved socially cohesive areas which should be as small as possible, but still large enough to be approximated by combinations of CCDs, splitting as few as possible. Such areas were defined using a method based on intensive fieldwork supplemented by two State-wide postal questionnaire surveys, as described in detail elsewhere (Smailes, 1999; Hugo et al., 2001). The primary spatial units used here are thus empirically validated approximations of the discrete social catchment areas of 84 country towns, with areas of overlap divided along break even lines to give a set of mutually exclusive units covering the whole of the defined settled areas of the State but excluding major empty areas (water bodies, national parks and reserves, etc.). Subjectively from field experience, empty areas of
at least 150km2 were judged large enough to cause significant gaps in the settlement pattern and communication network, often forming watersheds in the social catchments. Typically, the central town in each catchment would offer weekly shopping facilities, police, banking, school, medical and ambulance services, and in many (not all) cases, local government—though the number, variety and quality of services naturally vary widely with population size and affluence. For each of these social units, which for convenience are referred to here as ‘communities’, demographic data have been calculated from the 1981 and 1996 Censuses for the entire community, its major central town, any other small urban-like clusters of over 200 persons included in the area, and the rural balance. Census collection districts straddling boundaries are allocated pro rata using the South Australian Rural Area Property Identification Directory (RAPID) to perform the allocation where possible, and detailed topographic maps in areas not covered by RAPID. Before proceeding, we should demonstrate that the density variable is not reducible to just population numbers. It might be argued that any variance in the dependent variables could be explained by the population size alone, without the need for physical area to be considered. If, for example, the areas of the 84 communities were all quite similar, but the populations showed much greater variation, then obviously any differences in density could be sheeted home to just variations in population size. In fact, both the Pearson and Spearman correlation coefficients between area and the dependent variables to be analysed were stronger in every case than those for population, showing that the area over which the population is spread has a decisive impact on any observed relationship, whether or not the data are transformed.4
8. The pattern of local rural densities in South Australia The 1996 pattern of local rural densities revealed for the 84 communities has an extreme range from 787.5 occupied dwellings/100 km2 in Hahndorf, a suburbanising community some 40 min drive from Adelaide, to only 2.5 occupied dwellings in Elliston, on the west coast of Eyre Peninsula (Fig. 2). The median value is 29.1. If all the occupied dwellings were farms, this median density would equate to an average farm size of some
4
Additionally, after transformation, there was no significant difference between the statistical distributions of the two variables, in either skewness or kurtosis.
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Fig. 2. South Australian communities: rural population density, 1996.
344 ha. The class boundaries used in the choropleth mapping reflect the approximately log-linear distribution of the community density values. The overall basic pattern of rural density in the State is probably better revealed by the construction of a continuous surface, using the main towns of the 84 communities as control points (Fig. 3). The generally very low densities stand out clearly, especially in the peripheral broad-acre dry-farming areas, while even in the most closely settled core zone, only a very small area of South Australia has a rural density of more than one occupied dwelling in every square kilometre. The narrow strip of higher density along the eastern and north-western coasts of Yorke Peninsula is due to the presence of a string of small shack colonies and seaside holiday and retirement homes, with less than 200 permanent residents. The outlier of high density in the northeast (the ‘Riverland’) is due to the presence of a string of major irrigation areas along the course of the Murray; and in the southeast, high density is associated
with the urban influence of Mount Gambier, South Australia’s second largest country town, and the fertile basaltic soils and good, reliable rainfall of the ‘Lower Southeast’. The tongue of slightly higher density extending north-westward into the Upper Southeast includes an area of better soils, some supporting vineyards, reaching into formerly trace-element deficient country cleared under the State’s last big land settlement scheme during the 1960s (Marshall, 1972). The contour of 25 occupied dwellings/100 km2 approximately outlines the older settled areas of the State, occupied for farming by about 1880. The lowest density zone corresponds with the most recently settled areas of Eyre Peninsula and the Murray Mallee, and also includes the northern marginal lands on the flanks of the Flinders Ranges, from which overambitious settlers had to withdraw in the late 19th and early 20th. centuries. Importantly, Figs. 2 and 3 illustrate that the pattern of rural densities is not just a function of relative remoteness from the metropolitan core.
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Fig. 3. South Australia: rural population density surface, 1996.
9. Rural population density as an indicator of demographic and socio-economic structure
otherwise of settlement size, urban concentration and remoteness.
Given the correspondence of the density zones discussed above with all the fundamental aspects of the State’s space economy and both relative and relational space within it, we are now in a position to test a series of hypotheses using rural density as the primary independent variable. If we are right, in a fairly simple space economy dominated by primary industry, retirement, tourism, recreation and service industry such as that of South Australia, rural population density ought to correlate with a whole series of socio-economic variables. Where a community is located on the general density surface ought to have a major influence on its fundamental makeup, as should its local, specific density. The hypotheses outlined below relate to synoptic relationships measured at two different points of time. In testing these hypotheses, we do not suggest a simple causal relationship, for causality is likely to be complex. In the first instance, we wish to establish the degree of correlation between density and the selected variables. Later, we investigate the extent to which that correlation is independent or
9.1. Hypothesised relationships (1) The lower the rural population density, the greater will be the spatial extent of communities and the distance between neighbouring towns. This is a fundamental tenet of central place theory, and its general validity can be confirmed by a casual glance at any topographic or road map. We cannot expect the relationship to be simple or perfect, however, for various reasons—including the sporadic distribution of mineral resources, historical accidents of town siting—and the fact that coastal centres lose half their potential catchment areas to the sea. (2) The greater spatial extent of local social systems in sparsely peopled areas will compensate only partially for the low density. Thus, the lower the density, the smaller will be the total population size of communities. Hypothesis 2 has been firmly established empirically by Berry (1967) in the United States, and there is no reason to expect that Australia will differ. The reasoning only applies where towns are dominantly service centres;
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however, large mining, manufacturing or other special function enterprises are likely to stand out as outliers in the relationship. (3) Low density will be associated with a low labour force participation rate. This is expected mainly because in sparsely populated rural areas, numbers of females in the formal job market are likely to be low for a variety of reasons, including the relative shortage of both full time and part time non-farm jobs, particularly for women, and long commuting distances for the few jobs available. Among the farm population, the extent to which female partners report themselves as farmers rather than homemakers in the Census is uncertain, as is the extent of underemployment. (4) Low density will tend to be associated with a low number, but a high proportion of the labour force engaged in agriculture. A major impact on rural density is the potential productivity of the land. In densely peopled rural areas, farms are likely to be smaller and the agricultural workforce therefore larger in absolute numbers. But at the same time, a dense rural population is likely to provide opportunities for other types of enterprise to operate profitably, so that the community as a whole becomes less reliant on farming alone. (5) High rural densities will be associated with high levels of both occupational and industrial diversity of the population. Following the same line of reasoning as for Hypothesis 4, the non-farm element of the population will not only be larger, but will also have a greater diversity of livelihoods, whether measured by occupation or by industry. Densely peopled areas will bring more people and businesses into proximity, and will tend to produce a greater range of niches that can be exploited by both full-time and part-time enterprises. (6) Low rural density will be linked with a low proportion of the workforce unemployed. This is expected because in sparsely settled areas, jobs are scarce and amenities and public services few. People who become unemployed there, and want to find a new job, are often more or less forced to move out. Also, such areas (with some exceptions, such as remote places with a good surf beach) have few attractions for the intentionally unemployed, or near-unemployable, elements of the workforce. Higher-density areas with more amenities and services, on the other hand, are likely both to attract this element as in-migrants, and to have a better chance of retaining the local-origin unemployed. (Hugo and Bell (1998) provide a full discussion of ‘welfare-led’ migration in Australia.) (7) In areas of low rural density the masculinity ratio of the population (males per 100 females) will tend to be high. This hypothesis is consistent with Hypothesis 4 above for much the same reasons. (8) Low population density will be associated with a low proportion of the population born overseas. The concentration of first and second-generation migrants particu-
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larly those from non-English speaking countries in the major cities and labour markets of Australia is a wellknown demographic phenomenon. Most rural areas of low population density are subject to net out-migration and cultural homogeneity with few opportunities for people of minority cultures to become established. There are some exceptions such as remote outback mining settlements. The few concentrations of non-English speaking overseas born in the present study area tend to be in the higher-density areas as with Southern European-born people in the irrigation-based fruit blocks of the South Australian ‘Riverland’. (9) In areas of low rural density the proportion of the population who have changed address in the last 5 years will also be low. This hypothesis results from the expectation that areas of sparsification will also be areas of net out-migration attracting relatively few inmigrants. The resident population of such areas is likely to have a smaller proportion of people who have recently changed address than is that of regions of higher density which are expected to attract a higher proportion of in-migrants. (10) Low rural density will tend to be associated with a high fertility ratio (children under 5 per 100 women aged 15–44). This is expected because of the declining but still noticeable tendency for Australian rural birth rates to exceed those in metropolitan and other large urban areas. Low density is likely to correlate with high rurality, isolation, a low proportion of exurban inmigrants and the longer retention of established behaviours. (11) Low rural density will be associated with a low proportion of the population aged under 15. This hypothesis appears counter to the reasoning behind Hypothesis 10. However, the proportion of school-age children in the population reflects not only the community’s fertility rate but also the proportion of young to middle-aged adults who are their parents. The latter is expected to be more important than residual fertility differentials. 9.2. Approach to testing The above propositions were tested using rural population data derived from the 1981 and 1996 censuses and the areas of the spatial units in km2 calculated from the GIS database. The two years chosen, 1981 and 1996, represent conditions before and after the severe rural crisis of the mid to late 1980s and early 1990s, as one of the main aims of the research is to pick up any changes which may have occurred in the influence of population density over time. Use of the parametric statistic Pearson’s r with n ¼ 84 requires a reasonably normal distribution of the correlated variables. As many of our variables have a highly skewed distribution appropriate transformations (mostly log 10)
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were applied to normalise the distributions before correlation analysis using Pearson. For comparison the non-parametric Spearman’s rho statistic was also calculated based on non-transformed data sets. In almost all cases the values for rho were slightly higher than the Pearson r values. However, since we shall later need to use multiple regression methods to establish one link in our argument only the simple Pearson correlations are presented below. 9.3. Results In this type of analysis we do not expect very high correlations of the order one would look for when investigating physical processes. We know from the start that population density results from a number of other key causative variables and that it is still fairly crudely measured. Moreover, at this stage of the analysis all country towns and their immediate communities including industrial centres and regional capitals have been included. In many cases these form outliers that reduce the strength of the correlations but it was considered important initially to discover which of the relationships were robust enough to be valid for all communities before identifying groups for separate analysis. If the above hypotheses are to be supported firstly the sign of correlations must be as expected. Secondly, for n ¼ 84 a relatively low correlation coefficient can be statistically significant so we require a coefficient with the predicted sign plus a fairly stringent probability level—here set at pp0:001—before considering any hypothesis supported. As well as calculating the coefficients, scatter plots were produced for each pair of variables. Because of space limitations just one of these is included as an example (Fig. 4). The relative location of towns on the plots gives many clues to the
hypothesised relationships. Given the above caveats many of the coefficients in Table 1 are surprisingly and consistently high particularly for 1981. The first two hypotheses not surprisingly are strongly upheld by the Pearson’s r values. The positive relationship between density and total population of the community has even strengthened between 1981 and 1996, no doubt partly because of the shrinkage in the population of large industrial towns like Port Augusta and Whyalla which are highly anomalous outliers in otherwise low-density regions (Fig. 4). Hypothesis 3 suggested that the workforce participation rate would be highest in the high-density regions because it was expected that the latter would have more opportunities for women to enter the workforce. In fact in 1981 there was a significant moderately strong negative correlation between density and participation rate. In that year, before the onset of the rural crisis, most people living in the outlying areas of sparse population were evidently there because they had jobs and the percentage of older people not in the workforce had not yet been exacerbated by selective out-migration of the young. Female workforce participation may indeed have been low but this was evidently less significant than the low proportion of aged dependents relative to the numbers of people in the workforce. By 1996 a radical change had occurred. The former coupling of low density with high workforce participation had almost totally gone and in fact the 1996 r value of only 0.107 is the lowest in Table 1. The sparsely peopled areas had by then suffered accelerated ageing and growing concentrations of retirees and other nonwork seekers in mostly coastal communities at a wide variety of densities had produced a very diffuse relationship.
Fig. 4. Relationship between rural population density and total population for 84 South Australian rural communities, 1996.
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Table 1 Tests of the 11 hypotheses: simple Pearson correlation coefficients between rural density and the dependent variables, 1981 and 1996 r (1981)
r (1996)
Spatial extent of community (km ) Total population of community % Workforce participation (persons aged 15+) % Employed in agriculture Industrial diversity index % Unemployed Masculinity (males/100 females) % Born overseas % Changed address, last 5 years Fertility index (persons 0–4/100 females aged 15–45) % Population aged o15
–0.760** +0.407** –0.396** –0.547** +0.489** +0.360** –0.616** +0.632** +0.322* –0.489** –0.453**
–0.766** +0.530** –0.107 –0.622** +0.572** +0.183 –0.581** +0.675** +0.493** –0.496** –0.253
Mean value of r (disregarding sign), all 11 variables
|0.497|
|0.480|
Variable 2
**Coefficient significant at the 0.001 level. *Coefficient significant at the 0.01 level.
To test whether the above conclusions are different when the retirement age groups are excluded, for 1996 the workforce participation rate was calculated for the population aged between 15 and 64 only. The rates with and without the retirement age groups were in fact very closely correlated (r ¼ 0:883). The 1996 r value of 0.107 was raised to 0.211 when the retirement agegroups were excluded—still not a statistically significant relationship. Hypothesis 4, however, is strongly supported and in this case the relationship has also gained strength over time. The denser the rural population, the smaller is the proportion of the workforce engaged in primary production as the denser population provides opportunities for a greater variety of enterprises and business types. The study area does contain a number of special function communities with exceptionally low proportions engaged in farming; if it were not for these the relationship would be even stronger. Hypothesis 5 extends the above line of reasoning suggesting that in densely peopled areas not only would the proportion of the workforce in agriculture tend to be smaller but the non-agricultural population would also tend to be more diverse both by occupation and by industry producing a more complex and varied social mix. To test this hypothesis an index of diversity was devised using a variant of the Gini coefficient based on the Lorenz curve, which measures the degree to which the proportions of a population in a set of subgroups differ from a theoretical situation where all the subgroups are equal in size. The index of diversity produced ranges from a value of zero (if the whole population were in a single category) to 100 (if the population were evenly divided between all the categories). For the diversity index used in Table 1 index values range from a maximum of 55.6 to a minimum of 22.9 (for communities with rural densities of 279 and 10 occupied dwellings/100 km2, respectively).
The subgroups of interest here are the occupational and/or industry categories into which the workforce is divided. The social structure of a population is in principle better expressed by occupation than by industry—i.e. by analysing what jobs people actually do rather than what type of firm they work for. However, in this case the breakdown of occupational data available at Collection District level is restricted to just 10 categories and includes farmers in the general category of ‘managers and administrators’ and farm hands among general ‘labourers and related workers’, etc. The industry data on the other hand provide 13 categories which do distinguish primary industry workers adequately and which are comparable between 1981 and 1996. These were therefore used in the results in Table 1.5 Clearly the hypothesis is strongly supported. There is a very strong tendency for the more densely peopled communities to have a more diversified workforce and again this tendency has strengthened over time. Hypothesis 6 relates to the distribution of unemployment expecting that there would be a positive relation with rural density because people losing jobs would be obliged to move out of the sparsely peopled areas whereas those who lived in more densely peopled areas would be more likely either to find another job locally or to remain there for amenity reasons. This hypothesis held good in 1981 but by 1996 the general economic stress and associated general increase in unemployment had all but destroyed the relationship, which was not significant. Hypothesis 7 is strongly supported in both 1981 and 1996: the sparser the population, the greater the 5 Experimentation with the diversity index showed that, for 1996, both occupational and industry subgroupings gave very similar results. Actual values of the diversity coefficient are sensitive to the number of subgroups used. Compacting the data to 10 subgroups for each, the indices based on industry and occupation were very strongly correlated (r ¼ 0:885).
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masculinity ratio. Females tend to either remain in, or migrate into, areas of high density and to move out of sparsely peopled areas; relatively few rural communities actually have a female majority in their population, but there are some sparsely peopled areas with a ratio of over 120 males to every 100 females. This relationship had weakened by 1996 since during the rural crisis many young men were forced to leave farms which were no longer able to provide enough income for more than one generation. Hypothesis 8, expecting that rural density would be positively related to the percentage of overseas born in the population, was also strongly supported and has gained strength between 1981 and 1996. Again a few outliers, notably the steel city of Whyalla’s internationally recruited workforce and one or two resort towns, reduce what would otherwise be an even stronger relationship. The ninth hypothesis—that in-migration and local residential mobility would be positively related to rural population density—was not significant at the 0.001 level in 1981. However, by 1996 this situation had changed and the hypothesis is quite strongly supported. The reason for this radical change is almost certainly the heavy out-migration from the sparsely peopled areas in the early 1990s, with very few new in-migrants to replace those leaving, coupled with a reduction in people changing address within their own local government area during economically difficult times. Hypothesis 10, suggesting a negative relationship between density and fertility ratio, is also firmly supported. From the State’s slowly diminishing rural– urban differences in fertility one might have expected the relationship to weaken slightly between 1981 and 1996. However, by 1996 the r value had if anything strengthened slightly. Finally, Hypothesis 11 is definitely not supported and in fact resembles Hypothesis 3 in that the negative sign of the relationship is opposite to the expected positive and the strength of that negative relationship has declined sharply since 1981. Contrary to the proposition of this hypothesis, the percentage of the population aged under 15 in a community appears to have been affected more by the local fertility rate than by the in- or outmigration of young families. In the pre-crisis conditions of 1981 the gap between urban and rural birth rates was still substantial; moreover, the selective out-migration of young families from the farms had not yet been speeded up. By 1996 the general ageing of the rural population was more advanced. The relationship between rural density and percentage of children in the population was still negative but no longer significant. To summarise on the set of 11 hypotheses so far we have found that rural population density at a given point in time bears a significant negative relationship to the spatial extent of the community (km2), the percen-
tage of the workforce employed in primary production, the masculinity ratio and the fertility ratio. Prior to the recent rural crisis there was also a significant negative relationship with the workforce participation rate and the percentage aged under 15, but by 1996 these were no longer significant. Rural density bears a significantly positive relationship to total population size, the industrial diversity of the workforce, the proportion of the population born overseas and (in 1996) the proportion who have changed address over the preceding 5 years. The relationship between density and unemployment was weakly positive in 1981 and insignificant by 1996. Overall, the impact of the rural crisis of the mid1980s to the mid-1990s had weakened or destroyed the former links between density and unemployment workforce, participation and percentage of children in the population, while the earlier relationships between density and five other variables had strengthened.
10. Density’s explanatory power in comparison with that of the other independent variables So far we have demonstrated the importance of the density variable in analysing settlement patterns. However, we also need to demonstrate, rather than simply claim, that rural density affects the quality of rural settlement, landscape and population in a way that is not simply reducible to some combination of population size of the main town, urban concentration or remoteness. We also need to test whether one or more of these three might have even stronger relationships with the dependent variables than has rural density. Both of these factors are influenced by the degree to which the four independent variables themselves are intercorrelated. 10.1. Approach The intercorrelation matrix between size, concentration, remoteness and rural density is given in Table 2. For this purpose, (a) size of the main central place is measured simply by its 1996 population; (b) concentration of the population into clustered settlements is measured by the 1996 proportion of the whole community population resident in places of 200 or more population; (c) remoteness is measured by the ARIA score. This index, to be adopted by the Australian Bureau of Statistics for the 2001 Census, expresses the distance of a given place from the nearest urban centres of a varied range of population sizes (Hugo and Wilkinson, 2002). It takes the form of a continuous variable to two decimal places ranging from 0.00 (most accessible) to 12.00 (most remote);
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Table 2 Intercorrelation matrix between rural density and three other independent variables, for 84 South Australian rural communities, 1996 Size (populatn. of main town) Size Concentration Sig. (2-tailed) Remoteness Sig. (2-tailed) Rural density Sig. (2-tailed)
Concentration
Remoteness
1.000 +0.758**
1.000
0.000 –0.269
–0.154
1.000
0.013 +0.448**
0.161 +0.191
–0.689**
0.000
0.082
Rural density
1.000
0.000
Relationships significant at 0.001 level italicised and marked with **.
(d) density is the 1996 net local density of the defined community areas excluding major unpopulated areas and expressed as the average number of rural households (occupied dwellings) per 100 km2. Three of the six possible pairs of relationships are strongly correlated. Not surprisingly the degree of concentration into urban settlements is strongly linked to the size of the main town in the community. There is a somewhat weaker but still highly significant positive link between rural density and size of the main town. It might be expected that this correlation would be even stronger if all country towns were based mainly on local service functions; however, the sporadic distribution of industrial and mining activity, etc. limits this association. Finally, there is another very strong link between density and remoteness. It should be noted that the latter two measures are totally independent in their calculation: the remoteness index for any given point is based on distance-to-services measures only and pays absolutely no regard to the nature of the countryside at that point. The other pairs of correlations are not statistically significant at the 0.01 level; but do these three strong correlations render any of the four variables redundant? To test this a multiple correlation analysis was performed to analyse the combined effects of all the four independent variables upon each of the 11 dependent variables (from Table 1) in turn. The backward elimination variant of the model was employed in preference to the more common stepwise procedure.6 Collinearity tolerance calculated for the four indepen6 In the stepwise model, even if there are two independent variables with almost the same value of simple r; the slightly stronger one first enters the multiple regression model. Thereafter the model calculates which of the remaining variables gives the greatest increase in F (and multiple R); that variable then joins the multiple model. If the first two variables are intercorrelated, the variance associated with the secondranking one may be eliminated prematurely from the further stages in the model. The backward elimination procedure gives a better measure of the relative importance of the independent variables, by first forcing them all into the equation, and then eliminating the ones whose partials fail to make a significant contribution to multiple R2 :
dent variables showed that the degree of intercorrelation did not approach the low point (ca. 0.100) where the influence of any one variable would be reduced to just a linear combination of the others thus rendering it completely redundant.7 All the four independent variables are therefore initially entered into the multiple regression equation. In each subsequent iteration the partial coefficient is calculated for each independent variable along with its significance level. Partial coefficients express the correlation between density and each dependent variable after controlling for or ‘partialling out’ the separate linear impact of the other three independent variables (size concentration and remoteness). As the iterations proceed, variables whose partial (measured by Snedecor’s F ) fails to reach a probability level of po0:10 are eliminated one at a time, weakest first. The resulting regression models retain only those independent variables which make a significant contribution to multiple R2 (Tables 3 and 4). 10.2. Results: density as an independent variable in multiple versus simple correlation A comparison of the partial coefficients with the simple Pearson coefficients for rural density is presented in the first two columns of Tables 3 (1981) and 4 (1996). This clarifies the extent to which density has an autonomous influence, once the separate influence of 7 In multiple correlation analysis, the collinearity tolerance of an independent variable ranges between 0 and 1, expressing the proportion of the variance in the dependent variable that is not explained by the collective influence of the other independent variables in the equation. The lowest tolerance was 0.324 for 1996 and 0.379 in 1981, in both cases for the size of the main town, when all four variables were initially input into the models; tolerances for variables remaining in the final models after the backward elimination process were naturally higher, ranging up to over 0.900. Collinearity between ‘town size’ and ‘urban concentration’ as independent variables may cause a problem of interpretation in the case of one dependent variable (total community population): the sign of its partial with urban concentration is negative in Tables 3 and 4, as against the intuitively expected positive.
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Table 3 Comparison of the explanatory power of density, concentration, size and remoteness on 11 dependent variables: partial coefficients from multiple regression model (backward elimination), 1981, with summary comparison of Pearson’s r Variable
Rural density (simple r)
Population Area % Prim. Indust. Ind. diversity Masculinity % born o/seas Wkfce participn. Fertility index % o15 years % Unemployed % Chge. Address |Mean| simple ra Cases of pðrÞo:001 |Mean| partialb
+0.407** 0.760** 0.547** +0.489** 0.616** +0.632** 0.396** 0.489** 0.453** +0.360** +0.322*
(partial) +0.254 0.834** 0.699** +0.501** 0.621** +0.355** 0.447** 0.237 0.518** +0.324* +0.285*
0.497 10/11 0.355
Urban concentration
Size (main town)
Remoteness
(partial)
(partial)
(partial)
0.748** 0.760** 0.857** +0.628** 0.490** 0.582** 0.314* 0.425** +0.429** +0.347** 0.463 9/11 0.441
+0.958** +0.798**
+0.409** +0.292*
+0.348**
0.221 +0.241
+0.355**
0.425 8/11 0.280
R (var. 1–4)
R2 (var. 1–4)
0.974 0.934 0.902 0.734 0.727 0.716 0.694 0.596 0.592 0.538 0.460
0.949 0.872 0.814 0.539 0.528 0.513 0.482 0.355 0.350 0.290 0.212
0.409 8/11 0.129
pp0:01: pp0:001: a Mean of simple Pearson’s r for all 11 dependent variables including those not statistically significant, disregarding sign. b Mean of all 11 partials including those not statistically significant, disregarding sign.
Table 4 Comparison of the explanatory power of density, concentration, size and remoteness on 11 dependent variables: partial coefficients from multiple regression model (backward elimination), 1996, with summary comparison of Pearson’s r Variable
Rural density Simple r
Population Area % Prim indust. % Born o/seas Masculinity Ind. diversity % Chge. address Fertility index Wkfce participn. % o15 years % Unemployed |Mean| simple ra Cases of pðrÞo0:001 |Mean| partialb
+0.530** 0.766** 0.622** +0.675** 0.581** +0.572** +0.493** 0.496** 0.107 0.253* +0.183
Partial +0.277* 0.828** 0.715** +0.446** 0.310* +0.563** +0.359** 0.467** 0.185 0.324*
0.480 8/11 0.351
Urban concentration
Size (main town)
Remoteness
Partial
Partial
Partial
0.702** 0.735** 0.789** +0.340* 0.495** +0.503** +0.243 0.305* 0.485** 0.379** +0.427** 0.454 10/11 0.428
+0.953** +0.777**
+0.386** 0.245 +0.290*
+0.205 +0.318* +0.230
0.449 7/11 0.272
0.186
R (var. 1–4)
R2 (var. 1–4)
0.977 0.927 0.877 0.739 0.732 0.706 0.692 0.562 0.505 0.472 0.427
0.954 0.859 0.769 0.546 0.536 0.498 0.479 0.316 0.255 0.223 0.182
0.369 6/11 0.136
pp0:01: pp0:001: a Mean of simple Pearson’s r for all 11 dependent variables including those not statistically significant, disregarding sign. b Mean of all 11 partials including those not statistically significant, disregarding sign.
one or more of the other three variables is removed. Most frequently, the partial correlation between an independent and a dependent variable will tend to be lower than their simple Pearson correlation. The difference between the two is a function of the extent to which the simple relationship of that variable with density is affected by the joint influence of population size, concentration and remoteness. In 1981, for all 11
dependent variables simple r was significant at the 0.01 level or better, and in only two cases was the influence of the other variables sufficient to reduce the partial coefficient below this significance level (Table 3). In 1996, Pearson’s r for eight of the 11 variables was significant at the 0.001 level (nine at 0.01), and none of the equivalent partials dropped below 0.01.
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Less frequently, in a multiple correlation analysis the partial coefficient may be higher than the original simple Pearson coefficient once the confusing element of other variables is controlled for, and this is the case for a number of the hypotheses tested. In 1981, the partials exceeded simple r in six of 11 cases, and in four cases in 1996. However, as expected, the two tables show that on the average, controlling for the influence of the other three independents did indeed reduce the value of simple r (disregarding sign) for the relationships between density and the 11 dependent variables. The influence of the other variables was strongest in the cases of total community population (both years), the percentage of the population born overseas and the fertility index (1981), and the masculinity ratio (1996). However, the strength and degree of significance of the partials shows that density is not merely acting as a proxy, but retains a strong independent relationship in its own right in both years. 10.3. Results: comparison of the relative importance of density, urban size, urban concentration and remoteness We turn now to the question: although we have shown that rural density bears an important relationship to the 11 social variables investigated, does one or more of the other three critical independent variables have an even greater influence? First, to compare the four variables in the strength of their correlations using simple r; summary data appear in the two second-last rows of Tables 3 and 4. In both years, of the four independents rural density had the strongest mean value of r; disregarding sign. In 1981 it also had the greatest number of the 11 relationships significant at the 0.001 level, and in 1996 the second highest. A more important test, however, is provided by a comparison of the partial coefficients. Columns 3–6 in Tables 3 and 4 show which of the four independents were retained in the final equation in respect of each dependent variable, and the partial coefficient for each. The significance levels of the relationships are indicated by asterisks together with italicising of the appropriate numbers. The rows in Tables 3 and 4 are sorted in descending order of the strength of multiple R2 ; showing the extent to which the variance of the dependent variable is statistically explained, jointly, by the independent variables remaining in the model. Multiple R2 gives the percentage of the total variance statistically explained by the variables retained in the model, reaching 0.5 or above in six cases. The bottom row gives a comparison of the general performance of the four independent variables in the multiple regression analysis, when all four are initially included in the models, and before any are removed by the backward elimination process. Not surprisingly, over a 15-year gap spanning a major rural crisis, the relative explanatory power of the four
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independent variables changes somewhat, and the average strength of the partials has declined slightly for all except remoteness. However, in both years it is clear that rural density and in particular urban concentration—the percentage of the total community population resident in clustered settlements of at least 200 persons—are far more significant than the other two independent variables, in respect of the 11 hypotheses under consideration. Density and concentration take into account the rural matrix in which the town is set, unlike the other two measures, which relate only to the main town in the community. The former two measures do, however, require data for a meaningful surrounding rural area functionally linked to the town. This at present is a major task, due to deficiencies in the Australian Standard Geographical Classification, as used in the Census. The ready availability for many years of the town population size variable explains its widespread use as a surrogate measure of many aspects of rural communities. Population size of the main town, though, is the strongest explanatory variable only in the obvious cases of total population and total area of the whole community (both years) and in the proportion of overseas born persons (1981). As to the remoteness score, despite its close correlation with density (Table 2) after controlling for the other independent variables it remains in the multiple correlation model for only four of the 11 dependent variables (three in 1981), and never achieves more than third or fourth importance. However, the recent availability of the standard ARIA scores on the Web will also make remoteness a very useful measure for many purposes in the future, even though it does not play a very important role in the present study. The multiple correlation analysis can also answer one further pertinent question: if density measures were omitted from the multiple regression model altogether, would the other three indicators alone (as proposed by Coombes and Raybould, 2001) give equally good R2 values? A trial of the multiple correlation models with and without net rural density shows that inclusion of the density variables (a) raises the mean value of multiple R2 for the 11 dependent variables from 0.42 to 0.51 and (b) gives a more realistic picture of the role of remoteness, clarifying its close relationship with rural density (Table 2). Including rural density in the backward elimination process leaves remoteness as a significant variable in just four, instead of eight, of the 11 models for 1996.
11. Conclusions The present paper has, we hope, sufficiently demonstrated the importance of rural population density as a significant phenomenon in social, settlement and
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population geography, well worthy of investigation in its own right. We argue that, at the local level, rural density is a more sensitive and meaningful measure than the more commonly used gross density that includes both urban and rural components. Examining local rural population density, we have demonstrated that it is quite strongly associated with a large range of important demographic and socio-economic indicators in synoptic studies at the 1981 and 1996 censuses, timed to show the effects of the major rural downturn in Australia between 1985 and 1993. While three of the 11 significant 1981 relationships between density and the selected variables had lost significance by 1996, the average value of r dropped only marginally, from 0.497 to 0.480 (disregarding sign). Thus, rural density remained as a very important indicator of the chosen community qualities. A comparison of rural density with the other three independent variables was carried out, controlling in each case for the influence of the other three by the use of partial correlation coefficients. This showed that in both years the best overall predictor of the 11 dependent variables in terms of the average partial coefficients was urban concentration, closely followed by rural density; for simple r, the inverse was the case. In both years these two key variables were much less affected by controlling for the influence of the other independents, than were urban size and remoteness. The population size of the largest town in the community had the highest partial coefficient in only one of the 11 multiple regression models, and the remoteness index played only a minor role except in the case of spatial extent of the community area. In discussing these results, we have demonstrated that rural population/settlement density is a very important variable in describing, evaluating and classifying nonmetropolitan communities. It must be re-emphasised that we have used the density of the rural component of each community as an independent variable to ‘explain’ (statistically speaking) important aspects of the nature of the whole community—not just the nature of the urban or rural population components. Such explanation rests on the assumption that rural communities are typically based on a symbiosis between a central town and the rural matrix in which it is set, in respect of labour market, service provision, social activities and regular interaction. In these circumstances the whole community is likely to react to changes in the rural component, though the response time is likely to vary in different components of the economy. For our 84 communities, on the average 43% of the population lived outside the main town in 1996, and it seems selfevident to us that such an important segment of the population is likely to impact on socio-economic qualities of the whole community. This is borne out in our analysis by the fact that the two variables which do take this component into account—rural density and
urban concentration percentage—are the most successful predictors of the dependent variables both before and after the rural crisis years. It is important to note, though, that rural density is not significantly correlated with urban concentration, even at the 0.05 level of probability; these two important variables are clearly expressing different qualities of the settlement pattern. However, in analysing the relative importance of rural density against the other independent variables as a statistical ‘predictor’, we must be careful not to overstate our case. In suggesting that Australian rural and regional researchers have up to now concentrated too heavily on town population size as a catch-all shorthand measure of community fortunes, we do not in any way deny the importance of town size as a proxy for many other aspects of country life, such as the education level available, the amount of choice available in retail and service establishments, etc. The 11 hypotheses we have tested—and the dependent variables used in testing them—are specifically related to the potential effects of rural density. Had we developed another 11 hypotheses specifically intended to test the effects of variations in town population size, then density, remoteness and urban concentration might well have played much lesser roles. In Australian conditions where many communities focus on substantial towns based on extractive industry in thinly peopled areas, the link between rural density and town size may be more tenuous than in higher-rainfall countries. Our preliminary investigations suggest, though, that findings for other Australian states are likely to be very similar, at least for the neighbouring Victoria. A further limitation of the study, though obvious, needs to be acknowledged. The kind of quantitative analysis we have conducted here captures something of the most basic structure of rural communities, but says nothing of the effects of density on intangible, qualitative aspects of community life such as leadership, openness, integration, morale, perceptions of isolation, social capital and so on, which can vary substantially between neighbouring communities. Such variations may make an enormous difference in the quality of life available in statistically identical communities, but can only be captured by qualitative field work. The variations in the value of the four variables as descriptors of demographic, social and economic qualities of rural communities in the study area may well result from different mixes of the two components into which the economic support base can be divided in rural areas. On the one hand, we have what may be termed a ubiquitous element consisting of land-based primary industry (farming, pastoralism and forestry) together with local services including those to the travelling public. On the other hand, we have the more sporadically distributed employment in manufacturing, mining, and the like, selectively supplementing the
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former type. In a dominantly primary production and local-service-based rural society like that of the case study, social qualities of the population are likely to respond to variations in rural density; and also to the relative remoteness of the community within the space economy, to the extent that density is linked with remoteness. Over and above this local base, the size of the main town in the community is likely also to reflect the presence or absence of employment in other, more sporadic special functions such as manufacturing or special services such as institutions, defence facilities, etc. It may be that the power of the urban concentration variable as a predictor of social conditions results from the fact that it expresses something of both the ubiquitous and the sporadic elements in the community support base. In Australia, the attention we draw here to the importance of rural population density, and also the urban concentration index, as variables of planning significance in their own right is hopefully timely. The future development of rural and regional Australia is a matter of great political significance for both major political parties, as well as two of the most significant minor parties—the traditionally rural-based National Party, and the populist protest party ‘One Nation’. The social costs of an economic rationalisation policy that considers regional markets and service delivery only in terms of numbers and not of density may be large. In a country right at the sparse end of the rural population density spectrum found in Western nations, distance, isolation and density will continue to be important problems despite advances in electronic communication; indeed, it is still uncertain whether the arrival of the internet, email, satellite communication and mobile phones will narrow or widen the opportunity gap between densely settled and sparsely settled Australia. Our findings are consistent with the view that the trend towards polarisation of non-metropolitan Australia into clearly defined zones of growth and decline (Hugo, 1989) is closely related to the density variable. Indeed we would suggest that a good case exists for the Australian Bureau of Statistics to build a density criterion into the Australian Standard Geographical Classification, in the same way as it has recently incorporated the ARIA score as an accessibility criterion. Pending the adoption of geocoding, such a density measure would greatly benefit from a general improvement in rural collection district boundaries—or at least provision of standard concordance tables—to produce a standard set of socially defined spatial units. Yet, a great deal remains to be investigated. The density figures we have used are still relatively crude, and more work is required to refine a concept of ‘effective density’, which will take account of the regional unevenness of settlement within local communities, the presence of population concentrations in
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places of less than 200 persons, and the compensation that linearity of settlements can provide in very sparsely peopled areas. The future possibilities for this kind of analysis should also be greatly enhanced by the geocoding of census data, which is under consideration in Australia for the 2006 Census. This would greatly ease the problem of obtaining accurate data for the dispersed or non-urban element of rural communities, an extensive and time consuming task in the present investigation. Investigation of the cost implications of settlement sparsity require high priority; a further vital question is the extent to which personal mobility, distance communications and the information revolution can compensate for the increased physical distance burden imposed by sparsification. Last but far from least, the whole gamut of questions relating to the impact of perceived density can only be tackled through field work, dominantly qualitative.
Acknowledgements The authors acknowledge with thanks the fact that this project has been funded through a major grant from the Australian Research Council. They are indebted to three anonymous referees and to Prof. Graeme Hugo for their perceptive and helpful comments on earlier drafts. They also wish to express their sincere and grateful appreciation to research officer Mrs. Geraldine L. Mason (GIS) for her careful and dedicated work and to Ms. Christine Crothers for her assistance with the figures.
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