rural urban popdens

7/24/2019 Rural Urban Popdens

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Mapping rural/urban areas from population density grids 1

Mapping rural/urban areas from populationdensity grids

F.J. Gallego,

Institute for Environment and Sustainability, JRC, Ispra (Italy)

SummaryWe discuss several possible criteria to classify the European Union (EU) intorural and urban areas. Some unexpected results are highlighted with definitionsbased only on population density per administrative unit or only on land covertype. These abnormal results come partly because the definitions depend toostrongly on the size of the communal territory.In the approach we propose as a basis for discussion, the classification units arethe communes, but a major role is played by urban agglomerations, that are

defined and determined in a way that does not depend on administrativeboundaries. The classification proposed has three major groups: urban, semi-urban and rural. Each of them has been subdivided, but different criteria for sub-classification have been prepared.

Some definitions of rural and urban areasFor the definition of a policy on rural areas development, a logical previous stepis 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 isurban or a remote area in Lapland is rural, but there are many intermediatesituations and it is difficult to give a definition that is objective, practical,applicable across the European Union (EU25), and that takes into account thedifferent aspects of rurality.One of the issues to be considered is the choice of basic units for theclassification. 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 oftenless reliable. For the approach given here, the commune is selected as basicunit, because it seems a realistic choice for policy application.

The concept of “rural area” involves a number of socioeconomic aspects, such asstructure of the employment, population age population change. Unfortunatelythese data are difficult to collect at the commune level for EU25, and we havebased the study on population density and land cover.




OECD definition.

The OECD has given a definition of rural areas based on the percentage of thepopulation of a region living in rural communes (OECD, 1994). A commune isclassified 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 awkwardeffects of this definition: In Extremadura (Spain), this definition gives only6 urban communes, out of which 4 have less than 6,000 inhabitants. Themain cities of the region (Badajoz, Cáceres and Mérida) are classified asrural because their communes include wide areas of woodland and shrub.

• It does not take into account the characteristics of the surrounding area, inparticular if it belongs to the outskirts of a big city. Figure 1 shows the caseof Aldea de Trujillo, classified as urban because the communal territoryonly 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 communesabove 20,000 inhabitants have a density < 150 and would be consideredas rural with the OECD definition. Most of them have a fairly large urbannucleus. If the commune is used as classification unit, a specific categorymight be meaningful for this type of communes containing an urbannucleus and large agricultural or natural areas.

Population Areacommune

(ha)

Density

Aldea de Trujillo 439 35 1272

Calamonte 5564 770 723

Puebla de la calzada 5480 1419 386

Valle de Santa Ana 1338 376 356

Table 1. Communes with the highest population density in Extremadura

Population Areacommune

Density

Badajoz 122225 153434 80

Cáceres 74589 175139 43

Mérida 49284 86785 57

Table 2. Main communes in Extremadura (classified as rural)




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

Areacommune(ha)

Populationcommune

density

Jerez de la Frontera 141180 183316 130

Uppsala 246459 167508 68

Albacete 124310 130023 105Linkoeping 143086 122268 85

Badajoz 153080 122225 80

Oerebro 184006 120944 66

Norrkoeping 149073 120522 81

Vaesteraas 95643 119761 125

Joenkoeping 148512 111486 75

Boraas 118039 101766 86

Rural and Urban regions (NUTS3 units)

On this basis the rural/urban classification of communes with a density thresholdof 150 inhab/km2, the OECD definition distinguishes three main categories ofregions:

• mainly rural regions: more than 50% of the region's population live in ruralcommunes;

• relatively rural regions: between 15% and 50% of the population lives inrural communes;




• mainly urban regions: less than 15% of the region's population lives inrural 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 unexpectedresults that depend on the delimitation of communal boundaries, in particular forlarge, heterogeneous NUTS3 units, for which attributing the same label of ruralityto the whole NUTS3 unit may be unfair. For example the only province in Sicilythat turns out to be “mainly urban” is Ragusa, while Stockholm is relatively rural.

Land cover in Rural/Urban communes with the OECD defini tion.




An interesting check for the delimitation of rural/urban areas is the proportion ofagricultural land. In principle we would expect that urban areas have a lowproportion of agricultural or arable land. We can estimate such proportion withthe help of the European point survey LUCAS, -Land Use/Cover Area-frameSurvey- (Bettio et al, 2002). The definition based on the population density for the

commune gives an unexpected result: the average proportion of arable land incommunes with a population density above 150 inh/km2 is 29%, versus 21% incommunes 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 ofarable land (or agricultural land) in NUTS3 regions labelled as mainly urban,relatively rural or mainly rural. Tables 6 and 7 give an estimate of suchproportions using LUCAS data.

Rural (< 150 inh/km2) Urban (> 150 inh/km

2

)LUCAS SSU LUCAS SSU

arable total%

arable arable total%

arable

AT 363 2310 16 29 217 13

BE 109 443 25 136 520 26

DE (West) 1404 4421 32 763 2906 26

DK 715 1152 62 72 183 39

ES 2973 11727 25 235 886 27

FI 705 10216 7 11 157 7

FR 4839 15285 32 429 1614 27

GR 743 3759 20 30 124 24IE 96 2090 5 5 70 7

IT 1672 6727 25 811 2462 33

LU 15 67 22 7 13 54

NL 209 398 53 398 737 54

PT 551 2404 23 70 313 22

SE 762 12902 6 37 114 32

UK (No Scotland) 1135 3910 29 265 1155 23

total 16291 77811 21 3298 11471 29

Table 4 : % of arable land in communes with population density above/below 150

inhab/km2




Rural (< 150 inh/km2) Urban (> 150 inh/km2)

LUCAS SSU LUCAS SSU

UAA total%

agricultural UAA total%

agricultural

AT 762 2310 33 64 217 29BE 242 443 55 258 520 50

DE (West) 2471 4421 56 1329 2906 46

DK 794 1152 69 83 183 45

ES 5921 11727 50 405 886 46

FI 725 10216 7 12 157 8

FR 8813 15285 58 686 1614 43

GR 1664 3759 44 64 124 52

IE 1597 2090 76 41 70 59

IT 3115 6727 46 1424 2462 58

LU 46 67 69 10 13 77

NL 229 398 58 420 737 57PT 864 2404 36 105 313 34

SE 1004 12902 8 41 114 36

UK (No Scotland) 2981 3910 76 547 1155 47

total 31228 77811 40 5489 11471 48

Table 5: % of area used for agriculture (UAA) in communes with populationdensity above/below 150 inhab/km2

NUTS3

Mainlyurban

Relativelyrural

Mainlyrural average

AT 19 28 34 33

BE 55 55 40 52

DE 38 55 55 52

DK 12 57 71 65

ES 25 49 55 50

FI n.a 19 6 7

FR 46 57 56 56

GR 36 61 43 44

IE n.a 75 76 76

IT 46 51 49 49

LU n.a 70 n.a 70

NL 56 57 69 56

PT 25 24 40 36

SE n.a 33 7 8

UK 57 75 89 65

Total 45 54 33 42

Table 6: % of the territory used for agriculture (UAA) in rural/urban NUTS3 with

the OECD definition




NUTS3

Mainlyurban

Relativelyrural

Mainlyrural average

AT 12 14 16 16

BE 31 30 9 25

DE 22 31 31 33

DK 12 49 64 59ES 10 26 27 25

FI n.a 19 6 7

FR 28 33 29 31

GR 4 23 20 20

IE n.a 10 4 5

IT 25 28 27 27

LU n.a 28 n.a 28

NL 53 53 61 53

PT 19 15 26 23

SE n.a 27 5 6

UK 24 31 1 22

Total 25 29 17 22

Table 7: % of the territory used for arable land in rural/urban NUTS3

Classifying rural/urban on the basis of artif icial area.

A method has been explored by DG Agriculture to classify communes into urbanand rural on the basis of land cover (DG Agri, 2004). The area of the communeoccupied by artificial land is estimated as well as the area of “rural” land covertypes (agriculture, forest and natural areas). The estimation is made by simplemeasurement of major land cover classes in CORINE Land Cover (CLC). CLC isa land cover map that has been produced with approximately homogeneousspecifications in all EU-25, except Sweden (CEC, 1993, EEA, 2001). Areas ofwater are excluded from this computation. A commune can be defined as urban ifthe 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, butmay produce some unexpected results, similar in some cases to the anomaliesfound with the OECD definition, for example communes with a very small territoryare likely to be classified as urban. The example of Aldea de Trujillo mentionedabove holds again, but it is not the only one: 637 communes have been identifiedwith more than 30% of artificial are in CLC and out of urban agglomerations ofmore than 5000 inhabitants (see below method to define the agglomerations).Figure 3 shows another example in Poitou-Charentes.




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 becausethe communal territories are large (table 8). As suggested above, this type oflarge communes containing a significant agricultural or natural land is considereda specific category in the draft classification suggested in this paper.

Commune CountryPopulation*(1000 inh.)

Area(km2)

% artificial inCLC

Roma IT 3089 1505 27

Valencia ES 847 135 29

Zaragoza ES 693 1065 6

Málaga ES 688 396 15

Genova IT 675 239 25Helsinki FI 551 685 15

Szczecin PL 432 301 30

Palma de Mallorca ES 413 209 22

Córdoba ES 404 1257 6

Valladolid ES 388 198 14

Murcia ES 380 888 4

Ljubljana SI 379 275 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 landcover types. CLC nomenclature contains 11 artificial classes, and some of them,such as airports, mines, dump sites and leisure facilities, are not necessarilycharacteristic of an urban environment.




Figure 4 shows some examples of communes that do not belong to any urbanagglomeration), but have a significant area for an airport, leisure zone, dump siteor mines.

Vintirov (CZ)36% dump site,15% mine12% industrial

Figure 4: Examples of communes out of urban agglomerations with a high % ofartificial land cover in CLC.

A GIS approach to define urban agglomerations.

For policy definition purposes, the natural unit might be the commune. Thecharacterisation of a commune should take into account the structure ofpopulation density inside the commune and in the neighbouring communes. Forthis purpose, it is useful to define urban agglomerations without using theadministrative boundaries of communes. This can be done if suitable informationis available on the population density. Population density represented in rastermode provides an ideal tool for this type of analysis. Two layers of informationare actually available for this task:




• Disaggregation of the 1991 census data by commune with the help ofCORINE Land Cover (Gallego and Peedell, 2001). The grid should havecovered EU15, but due to various problems of data availability (CLC orcommune 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 co-ordinates.

• Subset of the Landscan global population database (Dobson et al, 2000)in Lat-long co-ordinates. The reference date is 2002 and the resolution is0.5’, corresponding approximately to 920 meters in the north-southdirection and a varying width (750 meters at 35° latitude and 390 metersat 65°). This grid has been produced by disaggregation of regional data.

The 1 ha grid obtained from 1991 commune data is more accurate, but thecoverage incompleteness is a serious limitation. With this layer we have applieda series of filters to define urban agglomerations:

• Smoothing by averaging values in a circle of radius 500 m. Water wasexcluded 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 andeliminate 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 co-ordinates 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 valuesof the grid cells inside the polygon.

• Selecting nuclei > 5000 inh. (notice that the population estimated from thegrid layer is not very accurate, in particular for small communes).

Urban agglomerations are classified by classes of size. A total of 3886agglomerations have been identified counting for a total of 287 million inhabitantsFor the small size classes, the number of nuclei identified depends very stronglyon the characteristics of the population grid. A first visual inspection suggeststhat the population of rural centres below 20,000 inhabitants may be stronglyunderestimated. Further check is needed. Other abnormal results can be found.Figure 5 illustrates the case of Tampere (Finland) in which the algorithm




identified 2 separate agglomerations disregarding the link between them. Theshape of both polygons does not perfectly correspond to the CLC urban area.

Size (in 1000 inh) N. nuclei Total pop

5-10 1303 9302

10-20 808 11109

20-50 911 29894

50-100 453 31789

100-200 207 28148

200-500 128 39301

500-1000 36 26029

1000-2000 24 35848

2000-5000 13 45334

>5000 3 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 theRuhr-Rhine area between Dortmund and Bonn. In Both cases the area isidentified as the largest urban agglomeration in Europe, but working with thecoarse resolution layer leads to a larger single polygon where the finer resolutionhad separated several agglomerations. This is due to several reasons: differentreference date, different accuracies, different parameters chosen for smoothingand buffering, etc., but illustrates as well that a certain subjectivity remainsalways when urban agglomerations are defined, even if it is through a GISalgorithm. Some agglomerations have been estimated above 50.000 inhabitantsfrom Landscan and below for the 1991 grid.




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 urbannucleus>5,000 inhab.Semi-urban

• Communes with an urban nucleus and large rural area: Dominantcommune of an urban nucleus (Medium-small urban centres of ruralareas). A commune can be considered dominant in the nucleus if it has >50% of the population of the nucleus,




• Suburban (peripherical urban): intersects an urban nucleus >5,000 inhaband is not in any of the previous categories. This category still needsfurther analysis. Many communes that should have been classified asrural peri-urban appear in this class because of the spatial inaccuracydelineating 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 areaof influence has been defined through a “gravity indicator” (Wang andGouldmann, 1996), that takes into account the population of theagglomeration.

• 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 theagglomeration(in 1000 inh)

Pureurban

MainlyUrban

80-99%urban

agglom.

MainlyUrban

50-80%urban

agglom.

Urban withrural area

Suburban(periferical

urban)

5-10 90 109 193 991 1586

10-20 140 136 209 569 1256

20-50 362 185 407 576 1837

50-100 661 227 369 229 1340

100-200 537 172 256 83 831

200-500 908 291 352 29 860500-1000 439 187 175 5 388

1000-2000 958 212 255 375

2000-5000 858 252 246 1 310

>5000 1424 173 160 212

Total 6377 1944 2622 2483 8995

Table 10 : Number of urban communes classified as urban or semi-urban.

The geographical distribution of communes classified as “urban with a significantrural 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 sizemight be recommended.




Communes with an urban nucleusand signi ficant rural areas

Nucleus size(*1000 in hab.)

5-10

10-2020-5050-100100-200200-500500-1000>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 communalterritory inside a small agglomeration needs to be reviewed. In most cases itcorresponds to an artifact generated by the scarce spatial accuracy of theLandscan database used for the delineation of agglomerations and the excessivebuffer applicated around the nucleus due to the coarse resolution of thepopulation 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 bedebatable because the threshold of 5000 inhabitants may be too low, but this isconsistent 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.




Laureanadi Borrello

Ferloleto

Galatro

Candinoni

Serrata

3 0 3 6 Kilometers

Rural communes classifi ed as semi-urban

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 intersectwith the urban agglomerations identified with the criteria specified above. Someof these communes are close to urban nuclei and have some influence fromthem, for example part of the population may be working in the city rather than inthe commune of residence. For a specific rural commune (that has not beenclassified 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 quantifythe 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; thecriterion should not be only based on distances, but take into account as well thesize of the agglomeration. For example if a certain commune is 10 km far from anagglomeration of 10,000 inhabitants and 15 km far from an agglomeration of1,000,000, the large agglomeration will have a stronger influence on thatcommune.




Defining the most influent agglomeration to a commune.

A possible way to tackle the issue is defining an “area of influence” of an urbanagglomeration through a buffer of a width that depends on the size of thecommune; for example we could define a buffer of 5 km around communes of5,000 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 arerepresented by their barycentres. The influence of an agglomeration on acommune has been quantified through a “gravitational attraction” indicator:

( ) ( )

( )acd

a popacG

,,

2= or ( )

( )

( )acd

a popacG

,,

2= (1)

where c is the commune and a the urban agglomeration. The population of thecommune c is not considered, because comparisons are made for eachcommune separately. For a commune c, the most influent agglomeration a is the

one with the highest “gravitational attraction”:( ) ( ) ( ) aaacGacGac A ≠′∀′>⇔= ,,

Rural communes can be classified according to the intensity of this gravitationalattraction indicator or according to the size of the most influent agglomeration.Communes are classified by levels of accessibility with this gravitationalattraction index, from peri-urban to remote areas.

A provisional classi fication scheme.

With the draft scheme presented here, we would have three main classes of

communes: urban, mixed and rural. Each class can be subclassified throughseveral criteria. Figure 7 and Table 11 illustrate a classification in which ruralcommunes are sub-classified according to the gravitational indicator given in theprevious section. This scheme still needs a lot of improvements, but may beuseful as a basis for discussion.

Numbercommunes

Total Pop.(Landscan)in 1000 inh.

TotalareaKm

2

Urban Purely urban 6377 70012 19960Mainly urban 4566 123243 78235

Mixed Suburban 8995 52699 340038Urban with rural areas 2483 88782 547725

Rural Close peri-urban 4871 8176 78248Medium peri-urban 8721 13954 166910Far peri-urban 15458 22214 348840Medium remote 27461 36230 800096Remote 17839 21041 660613Severely remote 11157 13952 1067734

Unclassified 767 765




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 urbanand rural areas is probably impossible. Any method requires a choice ofthresholds, that is subjective to a certain extent. A good method should be




flexible, so that a potential user can easily tune thresholds for a better adaptationto specific needs.

We have considered the commune as the unit for classificationThe method we have used to classify the approximately 108,000 communes is

strongly based on the previous identification of urban agglomerations that do nottake into account communal boundaries.

Many issues have not been addressed, for example how to deal with majortourist areas: How to consider a coastal commune that may have 2,000inhabitants in the winter and 30,000 in August?.

We use the terms “urban nucleus” and “urban agglomeration” in a ratherunspecific and exchangeable way. It might be meaningful to classify intorelatively small nuclei, often with an approximately round shape and complexagglomerations. 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 ofarbitrary choices have been made, that need to be discussed, and someinaccuracies certainly appear, partly because of the population data grid usedand partly because of the processing.If the approach is considered to be a valid basis, significant improvements areneeded before submitting the results to validation by Member States. Someimprovements 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 thepopulation density grid used did not cover the whole EU25: Cyprus, Malta, Atlantic islands, and Caribbean territories were not included. Others havenot been classified because of recoverable problems in the GISprocessing.

• Better tuned criterion to measure remoteness. Figure 7 suggests that theimportance of major agglomerations is too strong. Formula (1) might needan adaptation.

• The class we have called “suburban communes” needs further reflection.The variety of situations included in it is too high. Many communes areclassified 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 50,000inhabitants.

At long term:



• Visual inspection of the Landscan population grid in areas well known tothe authors suggests that Landscan generally underestimates thepermanent population of this type of rural communes. Using a betterpopulation density grid would lead to more consistent results, but thisrequires conditions that may be difficult to meet in the brief term, mainly

availability of the 2001 census data by commune. CLC2000 would also bevery useful for disaggregation. A useful proxy to estimate disaggregationcoefficients would be a night time light emission map from satelliteimages.

• Distances to quantify remoteness have been measured in straight linewithout taking into account road networks, topography, etc. This should beimproved.

REFERENCES

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