Mapping rural/urban areas from population
density grids
F.J. Gallego,
Institute for Environment and Sustainability, JRC, Ispra (Italy)
Summary
We discuss several possible criteria to classify the European Union (EU) into
rural and urban areas. Some unexpected results are highlighted with definitions
based only on population density per administrative unit or only on land cover
type. These abnormal results come partly because the definitions depend too
strongly on the size of the communal territory.
In the approach we propose as a basis for discussion, the classification units are
the communes, but a major role is played by urban agglomerations, that are
defined and determined in a way that does not depend on administrative
boundaries. The classification proposed has three major groups: urban, semiurban and rural. Each of them has been subdivided, but different criteria for subclassification have been prepared.
Some definitions of rural and urban areas
For the definition of a policy on rural areas development, a logical previous step
is defining which areas are rural and which areas are urban. The issue is not
trivial, as we can think at first sight. There is no doubt that the centre of Paris is
urban or a remote area in Lapland is rural, but there are many intermediate
situations and it is difficult to give a definition that is objective, practical,
applicable across the European Union (EU25), and that takes into account the
different aspects of rurality.
One of the issues to be considered is the choice of basic units for the
classification. Homogeneous areas can be defined by clustering small basic units
(Geddes and Flowerdew, 2000); one option is starting with cells of a regular grid
(Librecht et al., 2004). However for small units, few data are available, and often
less reliable. For the approach given here, the commune is selected as basic
unit, because it seems a realistic choice for policy application.
The concept of “rural area” involves a number of socioeconomic aspects, such as
structure of the employment, population age population change. Unfortunately
these data are difficult to collect at the commune level for EU25, and we have
based the study on population density and land cover.
Mapping rural/urban areas from population density grids
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OECD definition.
The OECD has given a definition of rural areas based on the percentage of the
population of a region living in rural communes (OECD, 1994). A commune is
classified as rural if the population density is below 150 inhabitants per km2. This
definition has the merit of being easily applicable, but has some limitations:
• The commune classification into urban/rural depends too much on the
area of the commune. Let us put an example to illustrate some awkward
effects of this definition: In Extremadura (Spain), this definition gives only
6 urban communes, out of which 4 have less than 6,000 inhabitants. The
main cities of the region (Badajoz, Cáceres and Mérida) are classified as
rural because their communes include wide areas of woodland and shrub.
• It does not take into account the characteristics of the surrounding area, in
particular if it belongs to the outskirts of a big city. Figure 1 shows the case
of Aldea de Trujillo, classified as urban because the communal territory
only includes the small urban nucleus (439 inhabitants in 0.3 km2), without
taking into account that it is surrounded by natural and agricultural areas.
• Some relatively large towns are labelled as rural because the communal
territory contains large “empty” areas (table 1). More than 250 communes
above 20,000 inhabitants have a density < 150 and would be considered
as rural with the OECD definition. Most of them have a fairly large urban
nucleus. If the commune is used as classification unit, a specific category
might be meaningful for this type of communes containing an urban
nucleus and large agricultural or natural areas.
Population
Aldea de Trujillo
Calamonte
Puebla de la calzada
Valle de Santa Ana
439
5564
5480
1338
Area
commune
(ha)
35
770
1419
376
Density
1272
723
386
356
Table 1. Communes with the highest population density in Extremadura
Population
Badajoz
Cáceres
Mérida
122225
74589
49284
Area
commune
153434
175139
86785
Density
80
43
57
Table 2. Main communes in Extremadura (classified as rural)
Mapping rural/urban areas from population density grids
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Figure 1: Example of small village (Aldea de Trujillo) classified as urban because
the communal territory is small.
Table 3: largest towns with density < 150 inhab/km2
Jerez de la Frontera
Uppsala
Albacete
Linkoeping
Badajoz
Oerebro
Norrkoeping
Vaesteraas
Joenkoeping
Boraas
Area
commune
(ha)
141180
246459
124310
143086
153080
184006
149073
95643
148512
118039
Population
commune
183316
167508
130023
122268
122225
120944
120522
119761
111486
101766
density
130
68
105
85
80
66
81
125
75
86
Rural and Urban regions (NUTS3 units)
On this basis the rural/urban classification of communes with a density threshold
of 150 inhab/km2, the OECD definition distinguishes three main categories of
regions:
• mainly rural regions: more than 50% of the region's population live in rural
communes;
• relatively rural regions: between 15% and 50% of the population lives in
rural communes;
Mapping rural/urban areas from population density grids
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•
mainly urban regions: less than 15% of the region's population lives in
rural communes.
Figure 2: Rural and urban NUTS3 units according to OECD definition. (Scotland,
East Germany and islands missing).
The criterion is reasonable but has some limitations and produce unexpected
results that depend on the delimitation of communal boundaries, in particular for
large, heterogeneous NUTS3 units, for which attributing the same label of rurality
to the whole NUTS3 unit may be unfair. For example the only province in Sicily
that turns out to be “mainly urban” is Ragusa, while Stockholm is relatively rural.
Land cover in Rural/Urban communes with the OECD definition.
Mapping rural/urban areas from population density grids
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An interesting check for the delimitation of rural/urban areas is the proportion of
agricultural land. In principle we would expect that urban areas have a low
proportion of agricultural or arable land. We can estimate such proportion with
the help of the European point survey LUCAS, -Land Use/Cover Area-frame
Survey- (Bettio et al, 2002). The definition based on the population density for the
commune gives an unexpected result: the average proportion of arable land in
communes with a population density above 150 inh/km2 is 29%, versus 21% in
communes with less than 150 inh/km2 (table 4). For Utilised Agricultural Area
(UAA), the proportion is 48% in “urban” communes versus 40% in “rural”
communes.
The same question can be put for NUTS3 units: which is the percentage of
arable land (or agricultural land) in NUTS3 regions labelled as mainly urban,
relatively rural or mainly rural. Tables 6 and 7 give an estimate of such
proportions using LUCAS data.
Rural (< 150 inh/km2)
LUCAS SSU
%
arable
total
arable
AT
BE
DE (West)
DK
ES
FI
FR
GR
IE
IT
LU
NL
PT
SE
UK (No Scotland)
total
363
109
1404
715
2973
705
4839
743
96
1672
15
209
551
762
1135
16291
2310
443
4421
1152
11727
10216
15285
3759
2090
6727
67
398
2404
12902
3910
77811
16
25
32
62
25
7
32
20
5
25
22
53
23
6
29
21
Urban (> 150 inh/km2)
LUCAS SSU
%
arable
total
arable
29
136
763
72
235
11
429
30
5
811
7
398
70
37
265
3298
217
520
2906
183
886
157
1614
124
70
2462
13
737
313
114
1155
11471
13
26
26
39
27
7
27
24
7
33
54
54
22
32
23
29
Table 4 : % of arable land in communes with population density above/below 150
inhab/km2
Mapping rural/urban areas from population density grids
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AT
BE
DE (West)
DK
ES
FI
FR
GR
IE
IT
LU
NL
PT
SE
UK (No Scotland)
total
Rural (< 150 inh/km2)
LUCAS SSU
%
UAA
total
agricultural
Urban (> 150 inh/km2)
LUCAS SSU
%
UAA
total
agricultural
762
242
2471
794
5921
725
8813
1664
1597
3115
46
229
864
1004
2981
31228
64
258
1329
83
405
12
686
64
41
1424
10
420
105
41
547
5489
2310
443
4421
1152
11727
10216
15285
3759
2090
6727
67
398
2404
12902
3910
77811
33
55
56
69
50
7
58
44
76
46
69
58
36
8
76
40
217
520
2906
183
886
157
1614
124
70
2462
13
737
313
114
1155
11471
29
50
46
45
46
8
43
52
59
58
77
57
34
36
47
48
Table 5: % of area used for agriculture (UAA) in communes with population
density above/below 150 inhab/km2
AT
BE
DE
DK
ES
FI
FR
GR
IE
IT
LU
NL
PT
SE
UK
Total
Mainly
urban
19
55
38
12
25
n.a
46
36
n.a
46
n.a
56
25
n.a
57
45
NUTS3
Relatively
rural
28
55
55
57
49
19
57
61
75
51
70
57
24
33
75
54
Mainly
rural
34
40
55
71
55
6
56
43
76
49
n.a
69
40
7
89
33
average
33
52
52
65
50
7
56
44
76
49
70
56
36
8
65
42
Table 6: % of the territory used for agriculture (UAA) in rural/urban NUTS3 with
the OECD definition
Mapping rural/urban areas from population density grids
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AT
BE
DE
DK
ES
FI
FR
GR
IE
IT
LU
NL
PT
SE
UK
Total
Mainly
urban
12
31
22
12
10
n.a
28
4
n.a
25
n.a
53
19
n.a
24
25
NUTS3
Relatively
rural
14
30
31
49
26
19
33
23
10
28
28
53
15
27
31
29
Mainly
rural
16
9
31
64
27
6
29
20
4
27
n.a
61
26
5
1
17
average
16
25
33
59
25
7
31
20
5
27
28
53
23
6
22
22
Table 7: % of the territory used for arable land in rural/urban NUTS3
Classifying rural/urban on the basis of artificial area.
A method has been explored by DG Agriculture to classify communes into urban
and rural on the basis of land cover (DG Agri, 2004). The area of the commune
occupied by artificial land is estimated as well as the area of “rural” land cover
types (agriculture, forest and natural areas). The estimation is made by simple
measurement of major land cover classes in CORINE Land Cover (CLC). CLC is
a land cover map that has been produced with approximately homogeneous
specifications in all EU-25, except Sweden (CEC, 1993, EEA, 2001). Areas of
water are excluded from this computation. A commune can be defined as urban if
the artificial areas occupy more than a certain threshold (for example 10%, 30%).
This type of definition is easy to apply if CORINE Land Cover is available, but
may produce some unexpected results, similar in some cases to the anomalies
found with the OECD definition, for example communes with a very small territory
are likely to be classified as urban. The example of Aldea de Trujillo mentioned
above holds again, but it is not the only one: 637 communes have been identified
with more than 30% of artificial are in CLC and out of urban agglomerations of
more than 5000 inhabitants (see below method to define the agglomerations).
Figure 3 shows another example in Poitou-Charentes.
Mapping rural/urban areas from population density grids
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Argenton-Château (Deux-Sèvres)
~1000 inhab, 49% artificial (CLC)
Figure 3: An example of small commune in Poitou-Charentes (FR) with a high %
of artificial land in CLC.
With this definition, a number of major cities might be classified as rural because
the communal territories are large (table 8). As suggested above, this type of
large communes containing a significant agricultural or natural land is considered
a specific category in the draft classification suggested in this paper.
Commune
Roma
Valencia
Zaragoza
Málaga
Genova
Helsinki
Szczecin
Palma de Mallorca
Córdoba
Valladolid
Murcia
Ljubljana
Country
IT
ES
ES
ES
IT
FI
PL
ES
ES
ES
ES
SI
Population*
(1000 inh.)
3089
847
693
688
675
551
432
413
404
388
380
379
Area
(km2)
1505
135
1065
396
239
685
301
209
1257
198
888
275
% artificial in
CLC
27
29
6
15
25
15
30
22
6
14
4
24
Table 8: some cities with less than 30% artificial land in CLC.
* Census data were not available for this work. Population data have been derived from the
Landscan raster layer. These figures are not very inaccurate and should be seen as an indication
of the order of magnitude.
This classification can be nuanced by separating several groups of artificial land
cover types. CLC nomenclature contains 11 artificial classes, and some of them,
such as airports, mines, dump sites and leisure facilities, are not necessarily
characteristic of an urban environment.
Mapping rural/urban areas from population density grids
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Figure 4 shows some examples of communes that do not belong to any urban
agglomeration), but have a significant area for an airport, leisure zone, dump site
or mines.
Vintirov (CZ)
36% dump site,
15% mine
12% industrial
Figure 4: Examples of communes out of urban agglomerations with a high % of
artificial land cover in CLC.
A GIS approach to define urban agglomerations.
For policy definition purposes, the natural unit might be the commune. The
characterisation of a commune should take into account the structure of
population density inside the commune and in the neighbouring communes. For
this purpose, it is useful to define urban agglomerations without using the
administrative boundaries of communes. This can be done if suitable information
is available on the population density. Population density represented in raster
mode provides an ideal tool for this type of analysis. Two layers of information
are actually available for this task:
Mapping rural/urban areas from population density grids
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•
•
Disaggregation of the 1991 census data by commune with the help of
CORINE Land Cover (Gallego and Peedell, 2001). The grid should have
covered EU15, but due to various problems of data availability (CLC or
commune boundaries), several major areas are missing: Sweden, Finland,
Scotland and the eastern Länder of Germany. The resolution of this
population density grid is 1 ha in equal-area Lambert Azimuthal coordinates.
Subset of the Landscan global population database (Dobson et al, 2000)
in Lat-long co-ordinates. The reference date is 2002 and the resolution is
0.5’, corresponding approximately to 920 meters in the north-south
direction and a varying width (750 meters at 35° latitude and 390 meters
at 65°). This grid has been produced by disaggregation of regional data.
The 1 ha grid obtained from 1991 commune data is more accurate, but the
coverage incompleteness is a serious limitation. With this layer we have applied
a series of filters to define urban agglomerations:
• Smoothing by averaging values in a circle of radius 500 m. Water was
excluded from this operation by masking with CLC.
• Applying a threshold of 500 inh/km2.
• Majority filter with a 11x11 moving window to smooth the shape and
eliminate small centres (with mask).
• Buffering with a 5x5 moving window (no mask).
• Converting to polygon shape.
• Computing the total population of each polygon. Selecting polygons >
5000 inhab.
The operation was slightly different with the 2002 Landscan layer in lat-long coordinates to adapt for the coarser resolution. Steps were:
• Converting the population layer (estimated number of inhabitants per cell)
into a density layer dividing by the area of each cell (varying with latitude).
• threshold of 500 inh/km2
• majority filter 3x3 cells (with coast line as mask)
• Buffer by “maximum” in a 3x3 moving window (no mask)
• Converting to polygons
• Estimating the population of each urban polygon by summing the values
of the grid cells inside the polygon.
• Selecting nuclei > 5000 inh. (notice that the population estimated from the
grid layer is not very accurate, in particular for small communes).
Urban agglomerations are classified by classes of size. A total of 3886
agglomerations have been identified counting for a total of 287 million inhabitants
For the small size classes, the number of nuclei identified depends very strongly
on the characteristics of the population grid. A first visual inspection suggests
that the population of rural centres below 20,000 inhabitants may be strongly
underestimated. Further check is needed. Other abnormal results can be found.
Figure 5 illustrates the case of Tampere (Finland) in which the algorithm
Mapping rural/urban areas from population density grids
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identified 2 separate agglomerations disregarding the link between them. The
shape of both polygons does not perfectly correspond to the CLC urban area.
Size (in 1000 inh)
5-10
10-20
20-50
50-100
100-200
200-500
500-1000
1000-2000
2000-5000
>5000
N. nuclei
1303
808
911
453
207
128
36
24
13
3
Total pop
9302
11109
29894
31789
28148
39301
26029
35848
45334
30142
Table 9 : number of nuclei identified > 5000 inhab. per size class.
Figure 5: a slightly abnormal result in Finland.
Figure 6 shows the different results obtained from both population layers in the
Ruhr-Rhine area between Dortmund and Bonn. In Both cases the area is
identified as the largest urban agglomeration in Europe, but working with the
coarse resolution layer leads to a larger single polygon where the finer resolution
had separated several agglomerations. This is due to several reasons: different
reference date, different accuracies, different parameters chosen for smoothing
and buffering, etc., but illustrates as well that a certain subjectivity remains
always when urban agglomerations are defined, even if it is through a GIS
algorithm. Some agglomerations have been estimated above 50.000 inhabitants
from Landscan and below for the 1991 grid.
Mapping rural/urban areas from population density grids
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Figure 6: different urban agglomerations (>50.000 inh.) identified from the 1991
population grid (1 ha resolution) and the 2002 Landscan grid.
Draft proposal for a typology of rural/urban communes
We have classified nearly 108,000 communes into the three major categories,
each of which has been subdivided and can be further split with different criteria.
Each class can be subdivided by size of nucleus, by land cover profile
(predominantly arable, forest…), by topographic roughness (mountain, hill, plain),
by soil quality, etc. The major classes proposed are:
Urban
• Fully urban communes: > 99% in an urban nucleus>5,000 inhab.
• Mainly urban communes with moderate rural area: 50-99% in an urban
nucleus>5,000 inhab.
Semi-urban
• Communes with an urban nucleus and large rural area: Dominant
commune of an urban nucleus (Medium-small urban centres of rural
areas). A commune can be considered dominant in the nucleus if it has >
50% of the population of the nucleus,
Mapping rural/urban areas from population density grids
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•
Suburban (peripherical urban): intersects an urban nucleus >5,000 inhab
and is not in any of the previous categories. This category still needs
further analysis. Many communes that should have been classified as
rural peri-urban appear in this class because of the spatial inaccuracy
delineating urban agglomerations with Landscan database.
Rural
• Peri-urban rural areas: does not intersect with any urban nucleus>5,000
inhab., but is in the area of influence of an urban agglomeration. The area
of influence has been defined through a “gravity indicator” (Wang and
Gouldmann, 1996), that takes into account the population of the
agglomeration.
• Remote rural: distant from urban agglomerations.
Urban and semi-urban communes.
We found 10999 communes with more than 50% of the territory in an urban
agglomeration. 6377 were >99% inside the urban agglomeration.
Size of the
agglomeration
(in 1000 inh)
5-10
10-20
20-50
50-100
100-200
200-500
500-1000
1000-2000
2000-5000
>5000
Total
Pure
urban
90
140
362
661
537
908
439
958
858
1424
6377
Mainly
Urban
80-99%
urban
agglom.
109
136
185
227
172
291
187
212
252
173
1944
Mainly
Urban
50-80%
urban
agglom.
193
209
407
369
256
352
175
255
246
160
2622
Urban with
rural area
Suburban
(periferical
urban)
991
569
576
229
83
29
5
1586
1256
1837
1340
831
860
388
375
310
212
8995
1
2483
Table 10 : Number of urban communes classified as urban or semi-urban.
The geographical distribution of communes classified as “urban with a significant
rural area” is quite uneven (Figure 7). In several countries they cover a large
proportion of the territory. A modification in the threshold of the nucleus size
might be recommended.
Mapping rural/urban areas from population density grids
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Communes with an urban nucleus
and significant rural areas
Nucleus size
(*1000 inhab.)
5-10
10-20
20-50
50-100
100-200
200-500
500-1000
>1000
Figure 7: Communes with an urban nucleus and a significant rural area.
The classification as “semi-urban” of communes that have part of the communal
territory inside a small agglomeration needs to be reviewed. In most cases it
corresponds to an artifact generated by the scarce spatial accuracy of the
Landscan database used for the delineation of agglomerations and the excessive
buffer applicated around the nucleus due to the coarse resolution of the
population grid. Figure 7 shows an example of these effects: Laureana di Borrello
(Calabria) is classified as a “commune with a small urban nucleus and a large
rural area” in the category “between 5000 and 10000 inhabitants”. This may be
debatable because the threshold of 5000 inhabitants may be too low, but this is
consistent with the definition. 4 communes around are classified as “suburban”
because of the coarse delimitation of the urban nucleus, with a too wide buffer,
that appears to touch the 4 communes.
Mapping rural/urban areas from population density grids
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Rural communes classified as semi-urban
Serrata
Candinoni
Laureana
di Borrello
Ferloleto
Galatro
3
0
3
6 Kilometers
Figure 7: Example of rural communes classified as suburban due to the coarse
identification of urban nuclei.
Peri-urban rural communes.
In this draft classification, we consider rural all communes that do not intersect
with the urban agglomerations identified with the criteria specified above. Some
of these communes are close to urban nuclei and have some influence from
them, for example part of the population may be working in the city rather than in
the commune of residence. For a specific rural commune (that has not been
classified as urban), we may want to know if it is near an urban agglomeration,
and if this agglomeration is large or small.
In order to classify communes into peri-urban and remote, we have to quantify
the influence of an urban agglomeration. It is also useful to give a criterion to
decide which is the most influent agglomeration to a given commune; the
criterion should not be only based on distances, but take into account as well the
size of the agglomeration. For example if a certain commune is 10 km far from an
agglomeration of 10,000 inhabitants and 15 km far from an agglomeration of
1,000,000, the large agglomeration will have a stronger influence on that
commune.
Mapping rural/urban areas from population density grids
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Defining the most influent agglomeration to a commune.
A possible way to tackle the issue is defining an “area of influence” of an urban
agglomeration through a buffer of a width that depends on the size of the
commune; for example we could define a buffer of 5 km around communes of
5,000 to 10,000 inhabitants, and of 50 km around communes of more than
5,000,000 inhabitants. We have chosen an alternative approach based on a
“gravitational index”
To simplify the problem, both communes and urban agglomerations are
represented by their barycentres. The influence of an agglomeration on a
commune has been quantified through a “gravitational attraction” indicator:
pop (a )
pop (a )
or G (c, a ) = 2
(1)
G (c, a ) = 2
d (c, a )
d (c, a )
where c is the commune and a the urban agglomeration. The population of the
commune c is not considered, because comparisons are made for each
commune separately. For a commune c, the most influent agglomeration a is the
one with the highest “gravitational attraction”:
A(c ) = a ⇔ G (c, a ) > G (c, a′) ∀ a′ ≠ a
Rural communes can be classified according to the intensity of this gravitational
attraction indicator or according to the size of the most influent agglomeration.
Communes are classified by levels of accessibility with this gravitational
attraction index, from peri-urban to remote areas.
A provisional classification scheme.
With the draft scheme presented here, we would have three main classes of
communes: urban, mixed and rural. Each class can be subclassified through
several criteria. Figure 7 and Table 11 illustrate a classification in which rural
communes are sub-classified according to the gravitational indicator given in the
previous section. This scheme still needs a lot of improvements, but may be
useful as a basis for discussion.
Urban
Mixed
Rural
Purely urban
Mainly urban
Suburban
Urban with rural areas
Close peri-urban
Medium peri-urban
Far peri-urban
Medium remote
Remote
Severely remote
Unclassified
Mapping rural/urban areas from population density grids
Number
communes
6377
4566
8995
2483
4871
8721
15458
27461
17839
11157
767
Total Pop.
(Landscan)
in 1000 inh.
70012
123243
52699
88782
8176
13954
22214
36230
21041
13952
765
Total
area
Km2
19960
78235
340038
547725
78248
166910
348840
800096
660613
1067734
16
Table 11: A possible scheme of classification.
Figure 8: A possible classification of communes
Discussion
Giving an absolutely objective criterion to classify a geographical area into urban
and rural areas is probably impossible. Any method requires a choice of
thresholds, that is subjective to a certain extent. A good method should be
Mapping rural/urban areas from population density grids
17
flexible, so that a potential user can easily tune thresholds for a better adaptation
to specific needs.
We have considered the commune as the unit for classification
The method we have used to classify the approximately 108,000 communes is
strongly based on the previous identification of urban agglomerations that do not
take into account communal boundaries.
Many issues have not been addressed, for example how to deal with major
tourist areas: How to consider a coastal commune that may have 2,000
inhabitants in the winter and 30,000 in August?.
We use the terms “urban nucleus” and “urban agglomeration” in a rather
unspecific and exchangeable way. It might be meaningful to classify into
relatively small nuclei, often with an approximately round shape and complex
agglomerations. A shape indicator, such as perimeter/sqrt(area), might be useful
for this purpose.
The results presented here are conceived as a basis for discussion. A number of
arbitrary choices have been made, that need to be discussed, and some
inaccuracies certainly appear, partly because of the population data grid used
and partly because of the processing.
If the approach is considered to be a valid basis, significant improvements are
needed before submitting the results to validation by Member States. Some
improvements can be made at short term, and some need a long-term work.
At short term:
• Some communes have not been classified. Some of them because the
population density grid used did not cover the whole EU25: Cyprus, Malta,
Atlantic islands, and Caribbean territories were not included. Others have
not been classified because of recoverable problems in the GIS
processing.
• Better tuned criterion to measure remoteness. Figure 7 suggests that the
importance of major agglomerations is too strong. Formula (1) might need
an adaptation.
• The class we have called “suburban communes” needs further reflection.
The variety of situations included in it is too high. Many communes are
classified in this group just because they are not too far from a medium
size town.
• Changing the size threshold for urban agglomerations; for example
Vanhove (1999) suggests 30,000 inhabitants, and the Buckwell report
(European Commission, 1997) considers a threshold of 50,000
inhabitants.
At long term:
Mapping rural/urban areas from population density grids
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•
•
Visual inspection of the Landscan population grid in areas well known to
the authors suggests that Landscan generally underestimates the
permanent population of this type of rural communes. Using a better
population density grid would lead to more consistent results, but this
requires conditions that may be difficult to meet in the brief term, mainly
availability of the 2001 census data by commune. CLC2000 would also be
very useful for disaggregation. A useful proxy to estimate disaggregation
coefficients would be a night time light emission map from satellite
images.
Distances to quantify remoteness have been measured in straight line
without taking into account road networks, topography, etc. This should be
improved.
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