Georeferenced data as a tool for monitoring the concentration of
population in Finland in 1970–1998
JARMO RUSANEN, TOIVO MUILU, ALFRED COLPAERT AND ARVO NAUKKARINEN
Rusanen, Jarmo, Toivo Muilu, Alfred Colpaert & Arvo Naukkarinen (2003).
Georeferenced data as a tool for monitoring the concentration of population
in Finland in 1970–1998. Fennia 181: 2, pp. 129–144. Helsinki. ISSN 0015-
0010.
Changes in the distribution of population in Finland over the period 1970–
1998 are examined in terms of a co-ordinate system of 1 x 1 km grid cells.
The results indicate that this system provides suitable areal units for a variety
of statistical and GIS methods aimed at describing and explaining the spatial
distribution of population. The system is capable of yielding more detailed
information than heretofore on topics such as the concentration of population
in the urban centres of Finland – a process that has been going on since the
beginning of the last century, but has slowed down noticeably in the recent
years.
Jarmo Rusanen, Toivo Muilu, Alfred Colpaert & Arvo Naukkarinen, Depart-
ment of Geography, PO BOX 3000, FIN 90014 University of Oulu, Finland. E-
mail: jarmo.rusanen@oulu.fi. MS received 07 January 2003.
Introduction
Demographic research has a long tradition with-
in geography and represents a discipline that is
eminently suited to the study of the spatial distri-
bution of population and its changes. The results
of these studies are required for planning purpos-
es in many sectors of the society, e.g. determina-
tion of the capacity and location requirements for
various public services at the national, regional
or local level. Although it is primarily the author-
ities that have been interested in the direction of
demographic trends, it is by no means a matter
of indifference for individual citizens or families
whether they live in an area of expanding or di-
minishing population. The general image of an
area is to a considerable extent shaped by trends
in population, as well as with structural changes
of the area. There is thus an obvious need for sta-
tistical data that are both up to date and geograph-
ically more detailed than earlier, and for precise
interpretations of such data. This poses new chal-
lenges not only for the producers and users of the
statistics but also for those engaged in develop-
ing and applying analytical and interpretative
methodologies.
The geographical examination of population
distributions and their temporal changes has typ-
ically relied on notions such as spatial concen-
tration and deconcentration (Lipshitz 1996;
Borgegård et al. 1995), or urbanization and coun-
terurbanization (Håkansson 2000), while migra-
tion research has adopted perspectives such as
that of the ‘turnaround’ phenomenon (Long &
Nucci 1997; Lewis 2000). The actual terms in use
vary not only according to the theoretical basis
from which the research sets out but also in rela-
tion to the volume of migration concerned and
its temporal duration.
Our picture of the spatial concentration and
agglomeration of population in Finland has large-
ly been shaped by the publications of Hustich
(1977) and Alestalo (1983). More recently, how-
ever, the present authors have discussed changes
in the settlement structure and the concentration
of population with the aid of georeferenced data
and GIS methods (see Naukkarinen et al. 1993;
Rusanen et al. 1997).
As pointed out by e.g. Håkansson (2000), re-
search into the distribution of population can be
greatly affected by the employed geographical
unit, so that models that apply at one spatial lev-
130 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
el do not necessarily hold good at another. One
particularly difficult problem concerns boundary
changes, which indicates that census statistics for
different years do not necessarily apply to the
same areal units and therefore, cannot be com-
pared easily (Howenstine 1993; Openshaw 1995;
Rees 1998).
The requirements placed on population statis-
tics and the usefulness of the data available can
vary greatly from one group of users to another.
Public authorities, for instance, are accustomed
to using statistical means and definitions ex-
pressed in terms of administrative areas and de-
ductions made from these when they require in-
formation on which to base their decisions. Re-
searchers, in contrast, prefer to go back to the
sources of the original raw data in order to test
their hypotheses and construct their models. For
this reason we will attempt to analyse and exam-
ine demographic data using various methods in
terms of both administrative and non-administra-
tive areal units, and to evaluate the subsequent
results.
Aims, data and methods
We set out here to describe 1) the changes in pop-
ulation that have taken place within the spatial
and settlement structure of Finland by means of
1 x 1 km grid cell data, 2) the methods available
for the analysis of population data, and 3) the fac-
tors highlighted by the grid cell method that can-
not be identified when using administrative areal
units. Finally, the usefulness of the resulting in-
formation for the purposes of demographic re-
search will be evaluated.
Georeferenced data on Finland are available at
many spatial levels. In practice, any individual
citizen can be assigned a set of coordinates de-
scribing the geographical location of the house
in which he or she lives. The resulting data can
be aggregated to represent higher-level units, e.g.
postal districts, parts of municipalities, whole
municipalities, groups of municipalities or the
NUTS hierarchy as used by the EU. It must be re-
membered, however, that the above-mentioned
problem of comparability over time emerges in
the case of these areal units whenever adminis-
trative areas are combined or boundaries are al-
tered. On the other hand, the fact that the initial
information is available at the individual level is
regarded as a significant advantage of GIS meth-
ods (Martin & Higgs 1997; see also Openshaw &
Turton 1996).
Thus, the principal units employed here are the
1 x 1 km grid cells defined in the data produced
by Statistics Finland for the years 1970, 1980 and
1990–1998 (see Rusanen et al. 1997). These grid
cells are of fixed location and are independent of
any changes in administrative boundaries. Statis-
tical information on human activities in Finland
has been available annually since 1987. Current
methods enable Statistics Finland to revise their
georeferenced population data very quickly, so
that figures for the population on 31st December
are available by the end of the following Febru-
ary, together with statistics for the individual mu-
nicipalities. This annual information is based on
registers, so that no separate population census
is necessary as in the USA, for example (see
Crews 2000).
The second basic areal unit employed here is
the municipality, or independent local govern-
ment district. These districts have numerous rights
and obligations that define them as administra-
tive entities, including the right to levy taxes (Min-
istry of the Interior 2000). In accordance with the
above principle, the populations of the munici-
palities are calculated here from the grid cell fig-
ures, and for this reason the figures differ slightly
from those quoted in the official statistics, as for
instance in 1998, 1.42% of the national popula-
tion could not be assigned coordinates on the ba-
sis of their place of residence. However, this dis-
crepancy can be assumed not to affect the results.
It should be noted that we are not concerned
here with studying the potential factors responsi-
ble for demographic trends in particular areas,
e.g. birth-rates, mortality or migration – topics
which have traditionally been to the fore in pop-
ulation geography (Ogden 1998; 1999). Instead,
we focus on describing solely the concentration
of population. This is defined here in two ways:
as an increase in population density per square
kilometre when using grid cell data, and as an in-
creasingly larger proportion of the population liv-
ing in a progressively smaller number of munici-
palities.
Data on population changes in the 1 x 1 km
grid cells and municipalities are analysed here by
a number of methods, and assessments are made
as to whether these methods yield similar or di-
vergent results. The methods tested include the
Gini Coefficient, which is more commonly used
for analysing the spatial distribution of income
FENNIA 181: 2 (2003) 131Georeferenced data as a tool for monitoring the…
rates (see Chakravorty 1994) rather than popula-
tion (see Bradford & Kent 1986; Lovell-Smith
1993). In addition, the concentration of popula-
tion is described in terms of mean values and the
Moran and Geary Indices used in version 8.0 of
the Arc/Info software. Changes in population are
also described by reference to deciles and by clas-
sification of the ‘spatial demographic structure’.
Long-term patterns are visualized by means of a
rank size model and the kriging interpolation
method contained in version 8.0 of Arc/Info. The
Hoover Index that is frequently used in studies of
population concentration (see Borgegård et al.
1995; Long & Nucci 1997) is not employed here,
as the emphasis is on analysis at the grid cell lev-
el rather than at the municipality level.
Decile analysis involves ranking the data units
in size order from largest to smallest with respect
to the studied variable, and dividing them into ten
equal groups in terms of population described by
the variable, regardless of whether the areal unit
is a grid cell or a municipality.
Population concentration as reflected
in the Moran and Geary indices and
the Gini coefficient
Population density in Finland in 1998 relative to
land area was very low by European standards,
only 16.9 inhabitants per square kilometre. If,
however, we use the grid cell material to calcu-
late the mean density for inhabited cells only, we
see that the density has increased steadily over
the period examined here, and is now approach-
ing 50 inhab./km2 (Table 1).
The Moran and Geary methods allow a spatial
autocorrelation index to be determined for the
grid cell material as follows: when the Moran In-
dex is positive and the Geary Index varies in the
range 0–1 the distribution of population can be
described as ‘similar’, ‘regionalized’, ‘smooth’ or
‘clustered’. If the Moran Index is negative and the
Geary Index greater than 1, the corresponding
terms are ‘dissimilar’, ‘contrasting’ and ‘checker-
board’ (for details, see Arc/Info User Manual
2000). In the present case, the gradual increase
in the Moran Index and the simultaneous de-
crease in the Geary Index lend further support to
the impression gained from the previous table of
a process of consolidation of the inhabited area
rather than a scattering or dispersal of population
(Table 2).
One typical technique employed to describe
evenness or concentration in the distribution of
population is the Lorenz diagram (Alestalo 1983),
or its derivative, the Gini Coefficient (Fainstein
1996). Regardless of whether we use grid cells or
municipalities as the areal units, the obtained Gini
Coefficients indicate a concentration of popula-
tion (Table 3), although the higher value of the
grid cell material as compared to the municipali-
ty level also indicates the essentially local nature
of this concentration. The fact that the majority
of the population of Finland is located within a
relatively small number of grid cells is indicative
of the low proportion of built-up areas within the
country’s total settled area.
Measured in both of the above ways, the rate
of change was greatest in the 1970’s, an observa-
tion that confirms the general impression regard-
ing the concentration of population in Finland. It
is consistent with the results of the examination
by deciles published by Alestalo (1983). On the
other hand, analysis of the trend over the last two
decades in terms of the Gini Coefficient provides
deviant results for the two sets of areal units. The
rate of population concentration apparently has
slowed down when assessed in terms of the grid
Table 1. Number of inhabited grid cells and mean population density in Finland in 1970–1998 (Data: Statistics Finland).
1970 1980 1990 1992 1994 1996 1998
Number of inhabited grid cells 110477 104540 103242 103020 103036 103045 102873
Inhab./km2 41.3 44.1 47.8 48.4 48.9 49.1 49.4
Table 2. Moran (I) and Geary (C) Indices in 1970–1998
(Data: Statistics Finland).
Year
1970 1980 1990 1992 1994 1996 1998
I 0.543 0.531 0.568 0.570 0.581 0.590 0.597
C 0.427 0.435 0.400 0.394 0.384 0.377 0.370
132 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
cell data, being only 1.1% in the 1990’s, where-
as the figures for the municipalities indicate ac-
celeration with a terminal rate of 3.5%.
The result obtained from the grid cell data may
indicate a decreasing trend in population concen-
tration in the densest areas, the pattern being at-
tributable to the exclusion of uninhabited grid
cells and the concentration of population in a
constantly decreasing number of cells and a more
restricted geographical area. This trend, which is
largely internal to individual municipalities, fails
to be reflected in the analyses based on munici-
palities as the areal units.
Decile analysis
Decile analysis employs divisions of the total ma-
terial into tenth parts. It can be regarded as a flex-
ible, non-given means of classification, and since
the boundaries of the deciles tend to vary from
one year to the next, the method is well suited to
studies of concentration or dispersal, e.g. in pop-
ulation or incomes. According to Alestalo (1983),
the population of Finland was fairly evenly dis-
tributed over the deciles at the end of the 19th
century, the agrarian society of the time showing
little agglomeration of the population into urban
centres. From that time onwards, however, the
population gradually became concentrated in a
smaller number of municipalities (Table 4), so that
by the 1970’s, half of the country’s population
lived in 52 municipalities, 10.1% of their total
number. The trend continued so that in 1998, the
corresponding figure was 33 municipalities, i.e.
7.3% of the total. Part of this effect may be attrib-
uted to the amalgamation of municipalities with-
in the system of local government, but the princi-
pal factor has without doubt been the actual con-
centration of population within a progressively
smaller number of towns and cities (Rusanen et
al. 2000).
Is this concentration visible in the grid cell
data? For answering this, a decile analysis com-
parable to that in Table 4 is presented in Table 5.
The deciles indeed indicate a continuation of the
concentration process, so that half of the coun-
try’s population (the population of deciles 1–5)
occupied a total of 1541 km2 in 1970, 1167 km2
in 1980, 1294 km2 in 1990 and 1284 km2 in
1998. The 1970’s were a period of heavy popula-
tion concentration, whereas from the end of that
decade onwards Finland was affected by a ‘turn-
around’ phenomenon, as noted by several authors
(Kauppinen 2000). This process was experienced
in many western countries, entailing above all a
Table 3. Concentration of population as shown by the Gini Coefficients for the years studied and percentage change
over the period 1970–1998 (Data: Statistics Finland).
Year
Areal unit 1970 1980 1990 1998
Grid cell 0.78558 0.83450 0.85365 0.86324
Municipality 0.59228 0.62145 0.62962 0.65186
Change in Gini Coefficient (%)
1970–1980 1980–1990 1990–1998
Grid cell 6.2 2.3 1.1
Municipality 4.9 1.3 3.5
Table 4. Concentration of population by deciles of munici-
palities in 1970, 1980, 1990 and 1998 (Data: Statistics Fin-
land).
Number of municipalities
Year
Decile 1970 1980 1990 1998
1 1 1 2 1
2 4 4 3 3
3 6 5 5 4
4 15 10 10 9
5 26 20 18 16
6 38 29 27 23
7 50 42 40 38
8 66 58 57 56
9 97 88 87 88
10 212 204 206 214
Total 515 461 455 452
FENNIA 181: 2 (2003) 133Georeferenced data as a tool for monitoring the…
decline in the popularity of urban areas as living
environments for families with children.
The inhabited area of Finland in 1998 amount-
ed to ca. 30.4% of the total surface area of 338
145 km2, with a consistent decline of this propor-
tion from one decade to the next since 1970. The
most pronounced decline, almost 6000 km2, took
place during the 1970’s, although it has also been
claimed that the material for 1970 contained
some errors in the coordinates determined for in-
dividual dwellings and that the actual net de-
crease was not necessarily as great as this. It
should also be noted that the trend was a rela-
tively steady over the period 1990–1998 relative
to the 1980’s, although data on the final years of
the decade are still lacking.
Changes may also be observed in the popula-
tion densities for the deciles (Table 6). The high-
est densities of all were recorded in deciles 1 and
2 in 1970, when Finland was experiencing a pro-
nounced migration from the countryside into the
towns and also abroad, primarily to Sweden (see
Kauppinen 2000). At that time the mean popula-
tion density in the top decile was around 9700
inhab./km2, whereas the corresponding figure in
1998 was about 6200. The maximum population
density was reached in Helsinki, the capital. In
the 1970 data the density peaked at 29 234 in-
hab./km2, whereas the figure for 1998 showed a
reduction of more than a third, 19 172 inhab./
km2.
A number of factors can be identified that con-
tributed to the reduction of population density in
Finland over the study period. The most signifi-
cant factor was the transfer in the urban centres
from dwellings to other uses, mainly offices and
commercial premises. Other factors were chang-
es in the structure of households, including reduc-
tions in the average family size and the number
of families with children and an increase in the
number of single-person households.
It should be pointed out, however, that the
mean population density of the two most densely
inhabited deciles began to increase again in the
1990’s, partly on account of the efforts made to
fill in the settlement pattern in built-up areas in
accordance with the principles of sustainable de-
velopment.
Andersson (1988) recognizes four stages in the
development of towns in Finland: urbanization,
suburbanization, disurbanization and reurbaniza-
tion. Urbanization stage refers to the growth of a
central urban nucleus, while the suburbanization
stage involves a slowing down and eventually ces-
sation of this trend. At the disurbanization stage,
Table 5. Numbers of inhabited grid cells by deciles in 1970, 1980, 1990 and 1998 (Data: Statistics Finland).
1970 1980 1990 1998 Inhab. km2
Decile N N N N in 1998
1 densest 47 66 84 82 4171–19172
2 settlement 116 133 159 158 2633–4170
3 207 200 234 234 1795–2632
4 384 297 336 334 1257–1794
5 787 471 481 476 881–1256
6 1 856 825 751 721 536–880
7 4 992 1 928 1 420 1 299 267–535
8 11 290 6 816 4 635 3 813 70–266
9 sparsest 22 331 19 590 16 579 14 980 20–36
10 settlement 68 462 74 209 78 561 80 770 1–19
Number of grid cells 110 472 104 535 103 240 102 873
Table 6. Population density by deciles in 1970, 1980, 1990
and 1998 (Data: Statistics Finland).
Inhab. km2
Decile 1970 1980 1990 1998
1 9703 6980 5876 6202
2 3932 3464 3104 3219
3 2203 2303 2109 2173
4 1188 1551 1469 1523
5 579 978 1026 1068
6 246 558 657 705
7 91 239 348 392
8 40 68 106 133
9 20 24 30 34
10 7 6 6 6
134 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
the trend is reversed, until the reurbanization
stage marks new growth in the urban nucleus. It
is interesting to consider how these stages might
be reflected in an empirical decile analysis.
The urbanization stage seems to have taken
place in Finland before 1970, as the population
of the most densely inhabited central areas
(deciles 1 and 2) began to decline after that time.
Hence, the 1970’s and 1980’s may be assigned
to the suburbanization and disurbanization stag-
es, as these figures in particular declined mark-
edly at first and then levelled out somewhat in
the 1980’s, when the changes were less pro-
nounced in other respects, too. The 1990’s repre-
sent the reurbanization stage, in which the popu-
lation of the most densely inhabited areas again
started to increase.
It is important to bear in mind when evaluat-
ing these findings that the stages are distinguished
on the strength of only a single variable. A more
precise consideration would call for an internal
analysis of the structure of the ten largest cities,
for example, and also other information relevant
to the growth of urban areas, e.g. their develop-
ment and planning policies.
Population changes by deciles in
1990–1998
Dual trends in population density and in the
number of inhabited grid cells are observable dur-
ing the 1990’s, the cut-off point being reached in
1993. The minimum area occupied by half of the
population of Finland, i.e. deciles 1–5, increased
numerically over that time (Table 7a), implying a
slight decline in population density in the urban
nuclei and suburbs (Table 7b). From 1993 on-
wards, the trend towards denser communities can
be observed, although not quite amounting to the
figures recorded in 1970 and 1980.
The most sparsely populated rural areas (decile
10) increased in number throughout the 1990’s,
whereas their mean population density remained
more or less stable. The increase may be attribut-
ed almost entirely to reductions in the population
of some grid cells previously contained in decile
9, representing the rural areas proper, causing
their transfer to decile 10.
The reversal in the trend in 1993 is probably
linked to the fact that this was the worst year of
the economic recession in Finland, i.e. the peri-
Table 7. Changes in the number of grid cells (A) and population density (B) in the 1990’s (Data: Statistics Finland).
A. Number of grid cells Trend
Decile 1990 1991 1992 1993 1994 1995 1996 1997 1998 1990– 1994– 1990–
1993 1998 1998
1 84 85 86 86 85 84 83 82 82 + –
2 159 162 164 164 163 161 160 160 158 + – –
3 234 238 241 241 240 239 236 235 234 + – 0
4 336 339 342 344 343 339 338 336 334 + – –
5 481 484 487 488 485 482 480 477 476 + – –
6 751 751 750 751 744 736 728 724 721 0 – –
7 1420 1406 1400 1390 1364 1345 1329 1316 1299 – – –
8 4635 4500 4409 4332 4214 4096 4012 3915 3813 – – –
9 16579 16305 16105 15930 15746 15548 15380 15204 14980 – – –
10 78561 78815 79038 79314 79652 80007 80299 80583 80770 + + +
B. Population density inhab./km2
1 5876 5840 5802 5831 5923 6015 6100 6191 6202 – + +
2 3104 3064 3043 3058 3089 3138 3165 3173 3219 – + +
3 2109 2086 2071 2081 2098 2114 2145 2160 2173 – + +
4 1469 1464 1459 1458 1468 1491 1498 1511 1522 – + +
5 1026 1026 1025 1028 1038 1048 1055 1064 1068 + + +
6 657 661 665 668 677 687 695 701 705 + + +
7 348 353 356 361 369 376 381 386 391 + + +
8 106 110 113 116 119 123 126 130 133 + + +
9 30 30 31 31 32 32 33 33 34 + + +
10 6,3 6,3 6,3 6,3 6,3 6,3 6,3 6,3 6,3 0 0 0
FENNIA 181: 2 (2003) 135Georeferenced data as a tool for monitoring the…
od in which the unemployment peaked. Similar-
ly, housing production declined steadily, to reach
its lowest ebb in 1994. This was followed by a
period of economic recovery (SVT 1999), with a
new stimulation of building activity, especially in
the metropolitan area of Helsinki and other large
municipalities. This trend was accompanied by a
rise in population densities, as the housing capac-
ity of the built-up areas increased while the land
areas concerned remained more or less constant.
The increased housing density was hence reflect-
ed in the population density.
The above observation demonstrates the usabil-
ity of the grid cell data as an aid in monitoring
changes in spatial structure, and potentially
achieving detailed explanation. This method al-
lows, for instance, observing small changes in
population within a municipality very easily.
The spatial demographic structure in
1970–1998
The repeatability of the decile method allows it
to be applied to any country or any areal unit,
and any researcher can arrive at the same results.
It should be remembered, however, that such ac-
curate geofererenced data are available in select-
ed countries only. We will turn our attention now
to structural features of the spatial distribution of
population and the changes detected in the dis-
tribution in the cross-sectional data for 1970,
1980, 1990 and 1998, with particular reference
to developments during the 1990’s. The classifi-
cation employed may be referred to as the ‘spa-
tial demographic structure’, as it represents an at-
tempt to describe the relation between popula-
tion density and settlement structure. Hence, it
does not take into account the functional ele-
ments normally implied in the term regional struc-
ture, e.g. dwellings, jobs and the aspects of infra-
structure that support these. The concept has been
used earlier for classification purposes by
Räisänen et al. (1996) and Rusanen et al. (1997).
We will first consider the situation over the whole
of Finland and subsequently concentrate the anal-
ysis on one specific region, Kainuu.
The interpretation provided here is based on the
assumption that the character of a grid cell can
be deduced from the size of its population. The
classification concerned is not necessarily appli-
cable to all areas or to all countries, and it has
been constructed knowing well that the distinc-
tion between rural and urban is by no means un-
ambiguous (see Malinen et al. 1994; Berry et al.
2000). It is impossible to build a model that would
apply equally well to all countries and under all
conditions. The following classification of spatial
demographic structure is used:
Inhab./km2 Element of spatial demographic
structure
1–5 Scattered settlement
6–20 Rural areas proper
21–100 Rural areas with built-up features
101–1000 Build-up areas and suburbs with
mostly private housing
More than High-rise centres and suburbs of
1000 major cities
Examination of the situation in the years men-
tioned above indicates that the most densely pop-
ulated areas, with over 1000 inhab./km2, grew
most rapidly in the 1970’s, the rate of growth di-
minishing in the 1980’s and reaching its slowest
in the 1990’s (Fig. 1). The areas of suburban pri-
vate housing departed from this pattern some-
what, however, as these underwent their greatest
population growth in the 1980’s. By contrast, the
transitional category between rural and urban
conditions, that with densities of 21–100 inhab./
km2, declined in population throughout the peri-
od studied here, although most markedly in the
1970’s and least so in the 1990’s. The rural areas
proper decreased substantially in extent, while the
areas of scattered settlement increased somewhat
in both total population and extent, largely as a
result of contractions in the population of grid
squares previously included in the category of ru-
ral areas proper.
The trend in population density over the whole
country in the 1990’s was polarized. The popula-
tion of the densely inhabited areas increased
steadily throughout the decade, amounting to a
total rise of 4.6%, or 110 000 persons, between
1990 and 1998 (Fig. 2). The suburban private
housing population grew in a similar manner,
with the exception of a small decline in 1997. The
population of the urban-rural transition zone with
densities of 21–100 inhab./km2, declined from
1993 onwards, however, and simultaneously the
area contracted slightly. The category of rural ar-
eas proper declined in both population and total
area throughout the studied period, whereas scat-
tered settlement category increased slightly in
both total population and area.
136 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
Fig. 1. Population changes in
different parts of the spatial
demographic structure over
the whole of Finland in
1970–1998 (Data: Statistics
Finland).
Fig. 2. Population changes in
different parts of the spatial
demographic structure over
the whole of Finland in
1991–1998 (Data: Statistics
Finland).
The Kainuu region in Northern Finland, select-
ed here for more detailed examination, is char-
acterized by economic and demographic reces-
sion. It belongs to the Objective 1 EU support ar-
eas on account of its low income levels and
sparse settlement. Its population, which has been
on the decline since the 1960, was 93 218 per-
sons in 1998, and the region has consistently
been one with pivotal unemployment in the
whole country.
The demographic trend in Kainuu during the
1990’s departed markedly from that obtained for
the whole country (Fig. 3). The most densely in-
habited areas experienced a population decline
from 1992 onwards, and the same trend affected
the private housing areas from 1996 onwards.
Correspondingly, the transition zone and the ru-
ral areas proper lost population throughout the
decade, as in the whole country, and the popula-
tion increase in the areas of scattered settlement
FENNIA 181: 2 (2003) 137Georeferenced data as a tool for monitoring the…
appears to have come to an end in 1998. This was
probably the first occasion during the century
when the population of Kainuu decreased in eve-
ry single category of its spatial structure. It is prob-
able that demographic trends in the sparse popu-
lation density classes in Fig. 9 were the same as
in Kainuu. Elsewhere they have resembled those
of the whole country.
Finland became a member of the EU in 1995.
Kainuu was an Objective 6 area in 1995–1999
and is currently an Objective 1 area for the peri-
od 2000–2006. Based on the above results one
can argue that the development measures imple-
mented in the region under the EU programmes
have failed to improve the negative demographic
trend in all elements of the spatial system.
Detailed analyses of population trends are pos-
sible only with the aid of georeferenced data that
allow precise location of the population units. A
‘sliding scale’ evaluation of population density
based on local level data helps avoiding the fal-
lacies that arise when one examines the whole
country or large aggregate areas (Martin 1991).
The unravelled negative demographic trend in
Kainuu serves as an example of the use of grid
cell data for monitoring regional development.
The rank size model
Rank size models represent a well-established
method of geographical research that has been
applied to the study of hierarchical structures in
various areal units and changes taking place in
these structures. The units employed for this pur-
pose are usually towns or other administrative or
functional entities (see, e.g. Bradford & Kent 1986;
Das & Dutt 1993). The method involves arrang-
ing the data units in size order and presenting the
results in diagrammatic form. We intend to apply
the rank size approach to the 1 x 1 km2 grid cells,
and use the density classes for describing the
types of settlement and any changes in popula-
tion. For comparison purposes, the approach was
done at two levels: the whole country and the
Kainuu region.
The 452 municipalities of Finland varied in
population from a mere 100 up to 500 000 in-
habitants in 1998 (Fig. 4). With the municipality
level as the areal unit of interest, the rank size
model indicates that about one hundred largest
municipalities showed an increase in population
over the period 1970–1998, while the others ex-
perienced population decline.
At the grid cell level, the rank size model fails
to provide detailed information of spatial demo-
graphic structure as efficiently the classification
(Figs. 5 and 6). It does, however, serve well in
highlighting the decline in the total number of in-
habited grid cells over the whole country, i.e. the
contraction in the area of human settlement. Fur-
thermore, it emphasizes certain major turning
points such as the beginning of the decline in
population in the most densely inhabited areas
Fig. 3. Population changes in
different parts of the spatial
demographic structure in
Kainuu in 1991–1998 (Data:
Statistics Finland).
138 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
Fig. 4. Changes in popula-
tion density over the whole
country in 1970–1998 ac-
cording to the rank size
model, areal unit = munici-
pality (Data: Statistics Fin-
land).
Fig. 5. Changes in popula-
tion density over the whole
country in 1970–1998 ac-
cording to the rank size
model, areal unit = grid cell
(Data: Statistics Finland).
and the subsequent resumption of growth. Other
well-presented features are the thinning of the
population of the rural districts and the contin-
ued growth in agglomerations with a population
of 80–3000 persons throughout the studied peri-
od. The points at which the curves intersect mark
population thresholds of various kinds, with con-
trasting trends on either side.
The rank size model for the Kainuu region
(Fig. 6) yields rather similar results to that for the
whole country, the greatest difference being in the
curve for 1998, which shows a decrease in pop-
FENNIA 181: 2 (2003) 139Georeferenced data as a tool for monitoring the…
ulation in the most densely inhabited grid squares
as opposed to an increase at the national level.
According to polls, the most preferred form of
living for Finns is a detached house on lakeside
in the middle of a city. The rank size model for
the whole country indeed seems to indicate that
apartment blocks in areas with more than 3000
inhab./km2 are not considered attractive as plac-
es to live, since the model showed highest popu-
lation growth in areas with population densities
of 80–3000 inhab./km2. It is the grid cells that fall
into this category that may be regarded as the
most popular and attractive living environments
in recent decades, a situation which has been pro-
moted further by contemporary urban planning
measures. This impression is confirmed by the re-
sult of the Residents’ Barometer survey carried out
by the Ministry of the Environment in 1998. The
survey showed that 57% of Finnish population
preferred to live in a private house, 22% in an
apartment and 20% in a semi-detached or ter-
raced house. In reality, only 30% of the popula-
tion in that year were living in a private house,
50% in an apartment and 20% in a semi-detached
or terraced house (Ministry of the Environment
2000). In the light of the above figures, however,
the observed increase in population in the most
densely inhabited areas during the 1990’s would
appear to be inconsistent with the realities of the
Finns’ living habits and with the preferences that
they have expressed.
The rank size approach also shows a decrease
in population in the density class of less than 80
inhab./km2. This must be partly attributable to the
decline in the number of active farms, a trend,
which has greatly accelerated since Finland
joined the EU in 1995. This becomes evident from
the fact that no fewer than approximately one
quarter of the farms closed down between 1995
and 2002. The rural areas can no longer provide
good opportunities for making a living. As a con-
sequence their population is declining. On the
other hand, Silvasti (2002) has examined the
changing meanings of the countryside for rural
and urban inhabitants, noting that for farmers the
countryside is traditionally a space in which pro-
duction takes place, while for urban dwellers it is
becoming more and more a locus of consump-
tion, a source of recreation and beautiful land-
scapes. In Holland, Haartsen et al. (2003) have
developed an empirical method for measuring the
interpretations placed on rural areas by persons
of different ages (Muilu & Rusanen 2003).
Fig. 6. Changes in popula-
tion density in Kainuu in
1970–1998 according to the
rank size model (Data: Statis-
tics Finland).
140 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
Spatial distribution of population in
1970–1998
The most concrete, often the best and sometimes
the only way of depicting spatial information is
by means of a map, and a typical and popular
way of depicting population data is on a chorop-
leth map (see Bachi 1999). On the other hand, it
is as well to bear in mind the comment of Lang-
ford and Unwin (1994) that “Where the purpose
of a population map is to convey an accurate im-
pression of density distribution the conventional
choropleth map representation is a poor choice”.
The map of the distribution of inhabited areas
presented in Fig. 7 is derived from a coloured map
of Finland and Sweden first published by Rusanen
et al. (1997), based on the kriging interpolation
method (Figs. 8 and 9) and a choropleth map. The
information depicted by shading has been con-
verted to a dot-based vector form before interpo-
lation. For the sake of comparison, the popula-
tion density data are presented on a convention-
al choropleth map in Fig. 10, employing munici-
palities as the areal units.
The pair of maps contained in Figs. 8 and 9
indicate detailed locations for the areas of pop-
Fig. 7. Distribution of inhabited grid cells (at least one per-
son per square kilometre) in Finland in 1998 (Data: Statis-
tics Finland).
Fig. 8. Population density (inhab./km2) in Finland in 1970
(Data: Statistics Finland).
FENNIA 181: 2 (2003) 141Georeferenced data as a tool for monitoring the…
ulation decline and serve particularly well to
depict the pronounced expansion of the areas of
scattered settlement. The extreme phenomenon
that affects the settlement structure, namely the
abandonment of the countryside, took place in
Finland primarily in areas where habitation at
present is sparsest. Future abandonment of
dwellings and farms is likely to affect these same
areas.
On account of the scale at which the two maps
are reproduced, the concentration of population
in the built-up areas does not stand out very clear-
ly. They do, however, highlight relatively well the
population growth that has taken place in built-
up areas and their environs, the areas in which
population concentrated in 1998 and the loca-
tions of municipal population centres.
The information contained in this pair of maps
highlights the situation regarding permanent set-
tlement. It does not, however, tell the whole truth
about the potential use being made of the areas
concerned for leisure purposes. Some of the are-
as of population decline, even ones that have lost
their population entirely, have been transformed
into a new kind of resource periphery, which peo-
ple who have moved to the cities and urban are-
Fig. 9. Population density (inhab./km2) in Finland in 1998
(Data: Statistics Finland).
Fig. 10. Population density by municipalities in 1998 (Data:
Statistics Finland)
142 FENNIA 181: 2 (2003)Jarmo Rusanen, Toivo Muilu, Alfred Colpaert and Arvo Naukkarinen
as of the south have begun to exploit for summer
cottages and second homes.
Although the zonal maps in Figs. 8 and 9 are
easy to interpret, the unfortunate aspect of them
is that they give an impression of a wholly inhab-
ited country. In contrast, the grid cell analysis in-
dicates that in reality only 30.4% of its surface
area had any permanent settlement in 1998
(Fig. 7) and that the focus of this settlement was
explicitly in Southern Finland. The information
contained in the zonal maps is nevertheless very
detailed in comparison to the choropleth maps.
Evaluation of the methods used
Altogether 10 methods were tested in the course
of the work at hand. Six methods involved numer-
ical interpretation, four methods visual interpre-
tation only. The rank size method yields diagrams,
while the maps are descriptive in character. In
terms of the hierarchy of potential areal units from
the whole country to regions and further to mu-
nicipalities, the grid cell is the most widely ap-
plicable data unit. It permits aggregation to all
spatial levels and can be used with all the meth-
ods investigated. For reasons of scale, it is obvi-
ous that a classification used for the whole coun-
try will not necessarily be viable at the local lev-
el. The same holds true for classifications used in
cartographic presentations, as these, too, have to
be altered according to the scale on which one is
operating.
The statistical mean, Gini Coefficient and Mo-
ran and Geary Indices provided numerical proof
of the continuing process of population concen-
tration, hence confirming the conclusions. In the
case of the Gini Coefficient, the employed areal
unit influenced the results quite substantially. The
data for the municipalities showed continuing
concentration at a more pronounced level than
did the grid cell data. Decile classification proved
to be an objective method capable of demonstrat-
ing changes in population density on a sliding
scale from the sparsest to the densest forms of set-
tlement. The spatial demographic structure proved
the most adept at indicating what part of the spa-
tial system is under examination – especially to
those who are unfamiliar with the material. The
last two classifications complement each other in
the sense that the former is objective and the lat-
ter subjective. The rank size model and the use of
maps both allowed visualization of changes in
population equally well over the whole country
and at the local level.
The methods can be regarded as complemen-
tary in population concentration studies. It would
be difficult and unnecessary to try to select the
best method. Each method has its own strengths,
and each one brings out some new information
on changes in population density. A few recently
published papers have pointed to a decline in the
use of maps in geographical articles (Wheeler
1998; Martin 2000), which is somewhat surpris-
ing, since GIS makes it relatively easy to present
material in a map form. This trend may be regard-
ed as an unfortunate one, since the present work
and feedback received from the users of spatially
analysed data indicate that maps, as a visual pres-
entation technique, are the best means of describ-
ing spatial variations in place-bound phenome-
na. It is true, however, that one cannot visualize
all the population changes taking place in a spa-
tial structure by means of just a few maps.
The other methods used here should be treat-
ed as complementary to visualization and as ca-
pable of lending support to each other. In the end
it is essential that the available data be as accu-
rate as possible in its location properties, so that
GIS or potential other methods can be applied
freely in accordance with the needs of different
user groups. The optimum situation would natu-
rally be the use of coordinate data for individual
persons without any spatial aggregation. This,
however, is depicted difficult or impossible by the
legislation protecting personal privacy.
Conclusions
Demographic trends are crucial variables for use
in regional policy, regional planning and moni-
toring of regional development. Hence, the aim
here was to investigate the distribution of popu-
lation by a variety of methods. The results indi-
cated that in Finland the process of population
concentration in the early part of the 20th centu-
ry, as identified by Alestalo (1983), continued up
to the very end of the millennium. This finding is
consistent with that obtained for Sweden, a coun-
try with very similar conditions for settlement
(Borgegård et al. 1995). One significant result of
the analysis of the grid cell material, however, was
that the concentration trend is now slowing down.
This finding became evident also in terms of both
the Gini Coefficient and the classification by spa-
FENNIA 181: 2 (2003) 143Georeferenced data as a tool for monitoring the…
tial demographic structure when the data units
were grid cells, whereas more or less the oppo-
site result was obtained when municipalities were
used as areal units. In any case, the results do not
correspond to the modest resurgence of popula-
tion growth in non-metropolitan areas observed
in the United States during the 20th century, a
trend that can be regarded in the long term as rep-
resenting a third decentralization phase (Long &
Nucci 1997).
According to the equilibrium theories of region-
al economics, social structure will react to a dis-
turbance by seeking a new state of equilibrium.
In the light of its rates of change during the 20th
century, the spatial demographic structure seems
to be approaching such a state. At least the rate
of population concentration has begun to slow
down. The negative demographic trend obtained
for the Kainuu region during the 1990’s neverthe-
less demonstrates that various parts of the coun-
try are progressing according to quite distinct
timetables, not to mention the situation locally,
i.e. at the level of the municipality or some small-
er areal unit. The results from Kainuu are compa-
rable to most parts of Finland, especially North-
ern and Eastern and Central Finland, when the
total land area is considered.
In Finland the availability of annual population
statistics for 1 x 1 km2 grid cells makes it possible
to identify and monitor even quite small changes
in different parts of the spatial structure, and
thereby to quickly detect any violation of local
or regional danger limits that are of importance
for decision-makers and planners. It is also possi-
ble to use grid cell data for predicting changes in
population, whereupon the use of variables rep-
resenting the age structure of the population or
aspects of human activity are expected to add
greater depth to such analyses. Finnish georefer-
enced data can be subjected equally well to scru-
tiny over medium or short time intervals, as in-
formation is available from 1970 onwards and
since 1987 on an annual basis.
Grid cell data can provide information on lo-
cal conditions and can be used to analyse differ-
ences within municipalities. The ability to aggre-
gate data to any grid size or areal system adds
greatly to the applicability of the method. The per-
manence of the location of the grid cells is also
an important advantage, as administrative bound-
aries tend to alter with time.
Georeferenced data are flexible in terms of ar-
eal unit, and are well suited to the analysis and
visualization of features that are internal to given
areas or regions. They bring information to the
fore that could easily remain concealed were ad-
ministrative units such as municipalities used.
Similarly, georeferenced data allow analyses of
spatial structures that cannot be distinguished in
material based on municipalities, e.g. the urban-
rural continuum.
Membership of the European Union involves a
transfer of responsibility for regional development
in Finland to the local level, where most of the
data required for regional policy purposes had
previously been compiled nationally. Georefer-
enced data can be of considerable value in these
situations, as even quite detailed analysis, moni-
toring and prediction of demographic trends can
be undertaken at any level whatsoever in the spa-
tial hierarchy.
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