a functional approach to classifying rurality at parish level in denmark, combining corine land...

8/9/2019 A functional approach to classifying rurality at parish level in Denmark, combining Corine Land Cover data and ad

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Paper prepared for presentation to WG4.5 Rurality on the map at theESRS conference 17-20 August 2009 in Vaasa, Finland

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A functional approach to classifying rurality at parish level in Denmark,

combining Corine Land Cover data and administrative boundaries

Niels C. Nielsen1

and Pia Heike Johansen,

Institute for Rural Research and Development, University of Southern Denmark, Esbjerg.

Abstract

Definition of what is rural and mapping rural areas are closely related tasks, continuously of great

importance, in research as well as for administrative purposes. Typically criteria based on population

density and socio-economic parameters have been applied, but following increased availability of spatial

data on land use, classifications based on geographical information have been proposed, especially in

relation to EU agricultural and regional policy. Here, a community-based model is presented, in which

territorial units are identified and classified by a simple distance-based index. Particularly important for

determining rurality status of territorial units are firstly the presence of open land between urban

centers, secondly the size of the territorial units labeled and thirdly, the number of communities to be

passed before an urban center is reached. Application of the proposed methodology is illustrated

through use of GIS to integrate land-use and statistical data from a Danish region, showing

developments in population and establishment of knowledge intensive service activities.

1. IntroductionSeen from a geographical point of view, the question of how an area is best defined as being rural is

closely related to the question ofwhere rural areas are found. Answers to such questions are of great

theoretical interest, but during recent years, also very practical applications have been found. Most of

all, this is the case in the EU system, where large funds are allocated for rural development through the

Common Agricultural Policy (CAP) and the Regional Policy programs (Jonard et al 2007), but also globally

such as shown be the efforts of FAOs Wye group to reach agreement on definitions of rurality at

regional level, based on population density (Wye Group 2007), in accordance with the OECD definition

(cited in Gallego 2008, p. 2), where a threshold value of 150 inhabitants per square km is applied at the

municipal (LAU-1) level, which for Denmark would mean that about 49 percent of the population lives in

1Presenting and Corresponding author: [email protected]


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rural areas (Svendsen and Srensen, 2007). As in many other national contexts the OECD definition is

too broad to make sense and therefore some additional criteria supplement the Danish definition of

rural (Kristensen et al. 2007).

A number of basic research projects with focus on territorial structure and cohesion (Bryden et al 2008,

ESPON 2004 and 2006, Hart 2004, Bryden and Hart 2001) as well as practical studies have searched for

rigid, logically coherent and relatively simple methods for delineation of rural areas and/or classification

of administrative and statistical areal units onto an urban-rural framework or some typology or ranking

system referring to the degree of rurality (Waldorf 2007, Kristensen et al 2007). The immediate

background for the study was a need for classification of parishes by type/degree of rurality, for a report

to the Danish Ministry of the Interior and Social Affairs on the regional dependency of rural areas

(Johansen 2008). Facing a similar task, but taking a rather different approach, Kristensen et al (2007)

devised a method for classifying Danish municipalities into one of four groups: Urban, Semi-urban, Rural

and Peripheral. Their approach was statistical, however the criteria for rurality were not deducted

from any particular understanding of the (physical/geographical) rural-urban system, rather chosen in

order to produce operational indicators, in particular a compound rurality index, to produce a politically

acceptable classification of Danish municipalities with granting of EU subsidies in mind. The criteria,

modified from the ones recommended by OECD, are derived from values at municipality level of basic

area and distance parameters (for instance describing access to motorway) and some socio-economic

and demographic parameters, describing status and trends. One advantage of using these data at

municipality level is that they are easily available from statistics Denmark, as opposed to parish-level

data, where special runs have to be ordered.

It is the challenges in defining and outlining the rural, along with the status of research described above,

that have inspired us to the work presented in this paper, guided by the following Working question:

How can land use information contribute to the identification of rural, in such a way that it takes its point

of departure in a community level and can be applied to a regional context?

2.Approach and methodology

The approach described here, for the practical calculations of rurality status has been carried out in an

experimental and explorative way, with manually defined operations in the GIS, later verified by

replication of the sequence of proposed operations. As it will appear, the problem of pointing out the


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rural areas in a country or region is closely related to the mapping or delineation of the urban system

in the same country or region. Of highest importance to the outcome is the level or scale at which the

operations are performed, a fact well known from statistics (Lilburne et al 2004, Goodchild and

Quattrochi 1997, Openshaw 1977) and ecological applications of GIS (Mackey et al 2001, Tyre et al 1997,Jennings 1996).

Two basic methodological questions occur even before our analyses can begin: namely first, how are

communities delineated and second, how is land use (possibly an activity by the community or

members thereof) mapped? We have found some rather pragmatic answers to these, and decided to

use them for the studies described here. It should be noted, that when we claim our conceptual model

to be functional, it is in the sense, that we consider the function parish boundaries in segmenting the

landscape in a meaningful way, and of open land to separate coherent urban elements from each other

and not as when amongst others Caffyn and Jones (2005) use functional regions to describe an

assemblage of urban and rural area units.

2.1 Local communities and administrative units

The physical and thus geographical manifestations of communities can be hard to delineate in practice,

especially if seen in a networks perspective (Murdoch 2000). For the Danish study (Johansen and Nielsen

2009, Johansen 2008) however, it seemed obvious to look at the parish level especially since that was

the smallest unit for which socio-economic statistics are available. At the same the very precise GIS-data

can be acquired cheaply or for free in Denmark and neighboring counties (KMS 2008, Landmteriet

2009). The use of the parish as basic building block for our model of regional landscape structure is

justified by the deep historical roots of the Danish parish structure, large parts unchanged since the 12th

century (Etting 2000), and thereby the local framework within which settlement and landscape patterns

have developed thereafter. The Danish parish level corresponds to the Local Administrative Unit (LAU) 2

as defined by Eurostat (formerly also known as NUTS-5), see Eurostat (2009). Common for the

overwhelming part of the LAU 2 units is that they have a long shared history which is reflected in for

example building, places and self-image, and sometimes even the boundaries can be recognised in the

landscape (Porsmose 2008, Frandsen 2007, Etting 2000). Such features are all central to local

development, in the sense of ability of activating local resources and amenities like nature, landscape

and cultural heritages (Johansen and Eskildsen 2008, Bryden and Hart 2004).


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2.2 Land use and Land cover mapping, data and interpretation

Land use is a human activity, affecting the physical state of the surface of the Earth, in the framework of

this study seen as the landscape (Rindfuss et al 2004). Land use is inevitably related to the Land cover,

i.e. vegetation, bare soil or artificial surfaces that can be observed on the ground or from above, and

often the combined term Land Use/Land Cover (LULC or LUC) is used in environmental and climate

studies (Verburg et al 2006, Lambin et al 2001). Mapping and inventory of Land Use, with the

opportunities it offers for is of great value for monitoring of landscape structure, state and

development, which, since the early 90es has been issues of growing importance to EUs Common

Agricultural Policy (CAP), with the shifting focus towards regional development, argi-environmental and

landscape issues, as illustrated by Jongman (1994). A central incentive for the Corine Land Cover data

project was indeed provision of timely and reliable data for agricultural and environmental policies

within the EU (EU-DG Agri et al 2000). CORINE is an acronym for Co-ordination of Information on the

Environment which is a European data collection program initiated in 1985 by the European

Commission, aimed at gathering information relating to the environment on certain priority topics for

the European Union (air, water, soil, land cover, coastal erosion, biotopes, etc.). Since 1994, the

European Environment Agency (EEA) integrated CORINE in its work program. The integrated and

continuous Corine Land Cover (CLC) project is a joint European effort to provide comparable land cover

information for statistical and environmental purposes at continental level.

The CLC has been created through manual interpretation of satellite imagery, mostly from the Landsat

Thematic Mapper and SPOT XS sensors, using what was originally termed high resolution imagery. Some

ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) has been

used to refine interpretation and assignment of the territory into the categories of the CLC

nomenclature. Still, the classification approach is physical and visual, based on observed land cover, thus

providing the most unbiased image of landscape structure available. Vard et al. (2005) and Jonard et al.

(2007) use the database intensively in their suggestions of criteria for rurality.

CLC data are available in vector (lines, polygons) and raster (pixel) format, to be used depending on the

type of analysis or illustration in case. The CLC dataset has a nomenclature of 44 land cover classes,

organised hierarchically at three levels. This makes the dataset a strong tool for investigation of man-

made structures at landscape level, with selection and combination of classes depending on the

analytical objectives. The smallest surfaces mapped (mapping units) correspond to 25 hectares. Linear


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Figure 1 Idealized landscape with community (parish) boundaries and town system (grey shades). Note that, unlike in for

instance the methodology proposed by Vard et al (2005), no criteria are here set for the proportion of different land use types.

Most parishes has a settlement core, typically a the largest (church) village, only in a few instances no traces of urban elements

are visible, compare figure 2 based on real world data.

Figure 1 summarises the approach and illustrates combinations of urban and rural communities. The

rural communities have a centre (identified from CLC data, as described below) from which there is

open land to the centres in the neighbouring communities whereas the urban centre (bolded 1-4) is

either communities which have fused ortown parishes or neighbourhoods. These would be urban

quarters or districts. The rural communities neighbouring the urban centre (5-12) can be seen as the

urban fringe (distance layer 1) whereas the rural communities next to the fringe communities can be

considered more rural (13-22) and thus constitute distance layer 2, and finally the communities 23-25

as the next degree of rural because two rural communities must be passed to come to the urban

communities rural area units of distance layer 3.

For this study, and in order to be able to apply the method outlined above, the CLC data were re-

classified into the basic categories urban system and non-urban. All land cover types at levels 2 and 3

under class 1 at level 1 are included as indicating urban. An exception is type 1.3: mine, dump and

construction sites, since even when consisting of artificial surfaces, it is not used for activities normally

considered part of an urban system. Also the class Sport and Leisure (1.4.2) facilities had to be excluded

from the urban aggregate, because it includes large coherent areas of holiday homes, where permanent


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residence is not allowed, and they therefore do not function as towns. This is in line with the ESPON

(2004) use of the concept artificial surface. Note that the concept of open land here is represented

by all of the non-urban land use categories selected here, even though they include the forest classes

(3.1.x). This is in line with our tentative findings that separation between urban entities, either bystretches of arable land, forest or wetlands are just as important as the character of landscape views of

the areas between them.

2.3 Implementing the rurality and distance indices

The classification based on the approach described here, use of smallest available territorial unit

(representing communities) and the demand for open space between the urban elements/centres of

the units) was implemented in MapInfo, a widely used GIS software. A series of operations such as split,

erase and combine were carried out along with queries regarding size, producing an index value 0 forurban and 1 for rural with visible built-up elements (villages) and 2 for rural area units without visible

concentrations of built environment, as illustrated by the flow chart in Appendix 1. For many

applications, classes with values 1 and 2 are merged to represent the rural. It should be possible to

reproduce the steps of the process in most commercial or educational GIS software. Figure 2 shows a

map of Denmark with higher-level regions and the types of rural parishes described here.


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Figure 2 Denmark, map of parishes (LAU2 level) with rurality according to the criteria defined here, and region borders.

In order to implement the distance based indexing of the total set of areal units, an identification of the

set of urban sogne (rurality 0) was carried out and used to create consecutive sets of distance layers, of

rank 1 to 4, with rank 1 taken to identify rural sogn neighbouring rurality 0 (distance layer 1), rank 2 to

identify rural sogne neighbouring rurality 1 sogne (distance layer 2) etc. Figure 3 shows a map of

Denmark with urban and rural sogne at these five degrees of rurality. The necessary operations were

performed using the Distance Calculator tool, which is included in the most recent versions of MapInfo.

Since each spatial unit has been assigned a unique ID-number, which is also used by Statistics Denmark

to identify locations, it is uncomplicated to combine for instance surface area information for the

relevant units with population, business and socio-economic data. In Denmark only a small number of

dataset can be downloaded for free on LAU 2 level (sogn) but it is possible to order specific data

deliveries. A similar situation is found in for example Sweden, for the territorial units forsamlinger (N =


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2,225) and for France, where key numbers are from the population census available online at

commune (N = 36,683) level, in both cases supplied by the national statistical agencies (Eurostat 2009).

Figure 3 Map of parishes, grouped by distance to urban system (minimum number of area units to be passed in order to reach

area with rurality 0).

2.4 Supplementary data: nature from CLC, demographic data

For analysis of particularly relations between nature and scenic values and economic and demographic

trends, we created a merged land-use class supposed to represent nature in the sense of land neither

built upon nor used for intensive agriculture. It therefore was made to include the relevant non-urban

and non-agricultural land use categories from CLC (all land cover class under type 3 and 4 and the

classes representing streams and water under type 5 and perhaps more controversially - class 2.4.3:

mixed agriculture with large parts of natural vegetation). The data was then combined with a national

spatial data of protected coastal and dune areas, assembled by the Danish Agency for Spatial and

Environmental Planning (BLST 2006). Based on a coverage created by merging the CLC-nature and the


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coastal datasets, it is possible to calculate a percentage of nature content at different unit sizes as for

example LAU 2, LAU, 1 and NUTS 3. The percentages at parish (LAU 2) level was used for segmentation

of parishes for the surveys and interviews carried out for Johansen and Eskildsen (2008).

Information on the population at LAU 2 level and changes over time was drawn from Statistics Denmark

(through the web portal www.statistikbanken.dk), where population data at parish level are freely

available, while other socio-economic information at this level is available upon request and at a price.

Data for the example about establishment of service activities in Region Midtjylland was drawn from the

Danish business database NN-Erhverv, geo-referenced and assigned to the correct region units through

use of a recent version of the official Danish address database used in public administration (since Febr.

2009 freely available for public administration and universities).

3. Results and applications

In this section, we first present the basic results of applying the proposed method to Denmark,

combined with population data and derived population density values. Then some illustrating examples

are shown, drawing on examples from a study on business activity and entrepreneurship, where the

properties related to rurality and distance are used to describe the spatial distribution of service

activities. The additional information on proportion of nature areas from CLC data described above is

used as background/contextual information. Table 1 summarizes the presence of the three basic types

of parishes according to rurality by region. There are obvious differences between the regions, with the

relative number of rural parishes being highest in the region of Nordjylland (Northern Jutland), where

also the highest percentage of parishes without distinct built-up areas are found (32%). Still, the

proportion of rural parishes in the regions outside the Copenhagen metropolitan areas (Hovedstaden)

lies in the interval 13 to 21 %. Table 2 summarizes the presence of the different types of parishes that

can be defined by distance. The parishes that end up in distance layer 4 are typically on islands and

peninsulas, but in the region Syddanmark (Southern Denmark, covering southern Jutland and Funen) a

number of parishes in the South-Westernmost part have been assigned to the most distant group,

implicitly having the most rural conditions.


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Number of LAU-2 units (sogne)

Region (NUTS-2

level)

Urban Rural with distinct

bulit-up areas

Rural with no distinct

bulit-up areas

Total

Sjlland 76 225 116 417

Hovedstaden 185 55 9 249

Syddanmark 104 305 90 499

Midtjylland 117 335 164 616

Nordjylland 51 185 104 340

Total 533 1105 483 2121

Table 1 Count of types of parishes relating to rurality. Note that region Hovedstaden approximately covers the Copenhagen

metropolitan region, North Zealand and the island Bornholm. Region delimitations shown in Figure 2.

Number of LAU-2 units (sogne)

Region

(NUTS-2

level)

Distance

layer 0

Distance

layer 1

Distance

layer 2

Distance

layer 3

Distance

layer 4+ Total

Sjlland 76 157 144 27 13 417

Hovedstaden 185 34 21 8 1 249

Syddanmark 104 145 153 54 43 499

Midtjylland 117 190 224 74 11 616

Nordjylland 51 90 126 65 8 340

Total 533 616 668 228 76 2121

Table 2 Count of types of parishes relating to distance to urban system, per region.

The distribution of the population, drawn from Statistics Denmark is shown in Table 3. The main result

here is that the proportion of people living in rural communities is found to be around 37%. This is

below 49% of the total population living in rural regions that would come out of applying the OECD

definition to the new municipal structure of Denmark, following the 2007 structural reform. This

number can be compared to the proportion of the population living in Peripheral (9%) and Rural (28%)

areas, together 37% and Semi-Urban municipalities according to the work of Kristensen et al (2007,

table 4).


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Region

Population

1. January

2008 Urban

Rural with

distinct built-up

Rural without distinct

built-up

Sjlland 817269 49.13 45.81 5.06

Hovedstaden 1633248 90.28 9.31 0.41

Syddanmark 1186692 50.40 46.21 3.40

Midtjylland 1212318 55.70 39.08 5.21

Nordjylland 575646 43.56 50.39 6.05

Danmark 5425173 62.67 33.89 3.44

Table 3 Population by region and parish types according to land-use based rurality.

The example of application of our approach to economic geography deals with locations of start-up

businesses of KISA type. KISA is the acronym for Knowledge Intensive Service Activities and is more

specifically defined as the production and integration of service activities undertaken by firm, in

manufacturing or service sectors, in combination with manufactured outputs or as stand-alone services

(OECD 2006). Examples of KISA are: engineers, consultants, auditors, legal advisers, architects,

physiotherapists, psychologists and ad agencies. At community level, settlement of KISA is key issue

because such firms may attract higher skilled labour force and younger families. At regional level,

settlement of knowledge intensive firms may attract more knowledge intensive firms and create spin

offs which would add to the regional economy (Kemppil et al., 2004). For comparison of regions across

Europe, with Denmark represented by region Midtjylland, we had to know the distribution of KISAs

between rural and urban areas. This turned out to provide a useful, though challenging test-bed for the

application of our proposed method. In Table 4 below, the segmentation by distance layers is used to

summarise data from rural communities all over the region, providing information that would be lost if

using for instance the adjusted OECD criteria at municipality level. In urban municipalities there are

actually 55 establishments of KISA in communities in distance layer 2, and in the semi-urban 170

established KISA in distance layer 2, 3 and 4 rural communities. The result suggests that also in a Danish

context, peripheral and rural municipalities include urban communities and that these like in the urban

and semi-urban municipalities tend to attract the overwhelming part of the KISA establishment. In total,

the number of KISA found to be established in rural communities is smaller when this approach is used,

relative to use of the simple or adjusted OECD criteria. However, the rural communities are more

comparable and it becomes simpler to look for explanatory factors for differences in performance at

unit size level which is pointed out as the most relevant for cultivating of local amenities.


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KISA established from 2003-2006 in Region Midtjylland

Adjusted OECD

typology

(municipal level

= LAU 1)

rurality distance index Total

KISA by

adjusted

OECD0 1 2 3 4

Peripheral

municipalities165 73 62 58 15 373

Rural

municipalities599 239 340 65 4 1,247

Semi-urban

municipalities394 192 136 31 3 756

Urban

municipalities1,802 220 55 - - 2,077

Total KISA by

distance index

2,960 724 593 154 22 4,453

Table 4Cross-tabulation of establishment if KISA in Region Midtjylland between the adjusted OECD

typology/classification and distance based index of rurality. Note that the distance index is applied to the parish withinwhich the business is located.

The findings summarized in Table 4 could also be illustrated in map format, as shown in Figure 4, where

both municipal and parish boundaries are shown, along with the structure of the urban system. This

map illustrates the substantial differences in density of KISA-activity within the relatively large

municipalities in Jutland, including the absence of newly established ones in many peripheral parishes

(relative to largest town and/or administrative center of municipality).


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Figure 4 Map of Region Midtjylland and new KISA per 10.000 inhabitant from 2003-2006, shown in rural parishes. Municipality

borders shown by bolder, black lines.

The final example shows the comparison of population development and KISA entrepreneurship, divided

into five main groups: Health care, Engineering and technology advice, Legal and economic advice, IT

consultants and advice, Creative business. From Table 5 it appears, that also for description of

population development in rural communities, the small area/community approach can provide a

simple but still rather precise picture of relations between establishments of KISA and population

development at a local level and tendencies which may vary across the larger region. Information on the

change in population at LAU 2 level was drawn from Statistics Denmark (www.statistikbanken.dk).


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Number of

enterprisesPopulation development trend in sogne in region Midtjylland,

distance layers 2, 3 and 4

KISA main

group

Label

Change 2003-

2007

1

below

-5%

2

-5 to

-1%

3

-1 to

1%

4

1 to 5%

5

above

5%

TOTAL

Health care 2 25 35 70 37 169

IT 0 15 15 29 37 96

Engineering &

Technological

Advice 1 10 12 29 32 84

Legal &

Economic

Advice 8 28 16 69 83 204

Creative

businesses 5 22 30 56 47 160

Total 16 100 108 253 236 713Table 5 KISA establishment in rural parishes, distance layer 2, 3 and 4, sorted by population trend index and KISA

main groups. 56 out of the 769 KISA enterprises found in these layers according to table 4, could not be assigned to

the main groups used here and were therefore left out.

Figure 5 illustrates the geographic location of the businesses identified and summarised in Table 5. As

background is used the nature proportions derived from CLC and coastal protection data, grouped into

four categories, as used in Johansen and Eskildsen (2008). Inspection of data extracted from the GIS

showed a tendency for KISA establishment to take place in areas with high proportions of the nature

land use classes, overruling constraints from distance and lack of service providers. However,

confirmation of nature as a pull-factor awaits the results of an ongoing survey, part of an international

comparative study.


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Figure 5 Degree of nature in rural communities and location of KISA established from 2003-2006 in Region Midtjylland.

4. Conclusions so far, Perspectives for further work

The development and application of the community-based approach, making use of the smallest

available administrative level for mapping and analyses have provided some advantages, including:

- A more fine grained picture and background data set with indications of rurality status, which is

useful for landscape and ecological applications.

- Provision of a more realistic (visually pleasing) image of where the rural areas are found within

the country and regions.- Assured compatibility with with statistical data, for further segmentation and/or providing

background parameters of (relative) rurality indices, a need made obvious by our literature

survey, summarized in the introduction..

Concerning the methodology used, it proved to be robust, and should be possible to implement in any

GIS software. The intermediate step of defining an urban system layer through merging CLC classes


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proved most useful, and the possible applications of this layer in other contexts should be explored. We

deliberately left out inclusion of transportation networks, terrain and other supplementary data, but

investigations of the relation between these features and the shape of the urban-rural system will surely

be of interest in further studies.

This paper has focused almost singly on land use, rather than on communities and local actors and social

factors. That is factors that must be included if our approach is to be used in a broader context and

tested with other GIS and statistical data. Further, we have taken the territorial units of analysis as

defined by parish boundaries as of 2008 for given, without paying attention to their historical

development or to alternative delineations. This ought also to be included in a more comprehensive

study, where attention is also paid to regions outside Denmark.

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Appendix 1:

a functional approach to classifying rurality at parish level in denmark, combining corine land...

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