- freely available
ISPRS Int. J. Geo-Inf. 2017, 6(2), 55; https://doi.org/10.3390/ijgi6020055
2. Conceptual Background
3. Data and Methods
- Global Urban Footprint (GUF): With the GUF  (as well as with other current initiatives such as the Global Human Settlement Layer (GHSL) ) mapping of global settlements and their patterns entered a new era with an unprecendented spatial resolution of 12 m. Using TerraSAR-X/TanDEM-X data, the classification algorithm detects high reflectance values (scattering centers mainly caused by vertical man-made structures such as buildings) in areas of comparatively high texture measures. The mapping result must be understood as an abstract delineation of settlement areas .The most important advantage over previous large area settlement classifications (e.g., global classifications based on MODIS , MERIS (e.g., ) or night-time lights  or continental data sets such as CORINE land cover or soil-sealing ) is the improved capability of preserving the small-scale complexity of settlement patterns beyond the urban core in rural environments. As shown by Klotz et al.  the high spatial resolution of TerraSAR-X/TanDEM-X data allows detecting scattered small settlements; these low density settlement regions are significantly underrepresented in previous data sets due to larger minimum mapping units. In consequence, this improved capability of the GUF data espcially improves the accuracy of the settlement density calculation in peri-urban or low dense rural environments, even when aggregated onto the 1 km grid (see below). This is a decisive improvement, when analyzing the contiguity of the built environment between nodes along with its area-wide coverage in consistent manner.
- Derivation of settlement density: Density refers to the accumulated settlement area within a respective reference area. The size and location of the reference areas is crucial in the context of density and subject to the modifiable areal unit problem (MAUP) (e.g., ). As there is no ‘one-size-fits-all’ geographical unit and for reason of consistency, we calculate settlement density with respect to the spatial unit of 1 × 1 km using the INSPIRE (Infrastructure for the Spatial Information in the European Community) grid, which is a standardized raster across Europe. The resulting gridded settlement density serves as one, consistent input geodata set to identify nodes across Europe, to evaluate connectivity between nodes and to define territorial regions related to the nodes.
- Auxillary data: Along the various components of the developed methodology, we make use of auxillary data sets for reasonable threshold development or plausibilization of results. For the identification of urban nodes (cf. Figure 1d and (d) below), we use the Larger Urban Morphologogical Zones (LUMZ)  to determine the minimum settlement density necessary to qualify for a node; the LUMZ describe the tissue and function of a zone integrating thematic urban core classes; it is based on core classes of the Corine Land Cover data set such as ‘continuous urban fabric’ or ‘industrial and commercial units’, among others . Compared to the approach in our study using solely settlement density dervied from the GUF data, the LUMZ result from the combination of multiple information on urban functions and built-up. For the derivation of minimum sizes of nodes (cf. Figure 1d and (d) below), we utilize the accepted standard of Level-2 Local Administrative Units (LAU-2). These entities entail municipalities or the like as a common statistical unit across the 28 EU member states . For threshold development evaluating if nodes are connected or not (cf. Figure 1g and (g) below), we use the urban-rural typology at NUTS-3 regional level featuring the classes predominantly urban, intermediate and predominantly rural . For plausibilization (cf. Figure 1j and (j) below), we draw on the population grid at the 1 km INSPIRE grid . As the population grid does not include non EU member states, the plausibilization is not done for the entire spatial extent of Europe. Beyond that we use Urban Audit Cities (UAC) for plausibilization of our results by comparing them to our identified nodes (cf. Figure 1j and (j) below). The UAC are classified based on certain criteria such as ‘population must exceed 50.000 inhabitants’, among others .
- Identification of urban nodes: What defines an urban node? In other studies relevant urban nodes are often defined by thresholds applied to data on population, concentration of acitivies, or economic turnovers, among other parameters of the respective city (e.g., [14,36,61,62]). Taking advantage of the GUF classification, we approach this issue uncoupled from, e.g., population data as these data often have consistentcy issues at continental scale (e.g., the population grid from Eurostat  (cf. (c) above) is not available in consistent manner outside EU member states). With a solely physical approach using one conistent data set, we aim at identifying relevant urban nodes by the pattern of the settlement. We assume a node of relevance features a comparatively large area of high settlement density.With respect to those two variables—high settlement density and comparatively large area of high settlement density—we aim at finding meaningful thresholds for identifying urban nodes. Of course, any threshold defining an urban node of relevance is at risk to be subjective. However, for a reasonable approach we use the introduced auxilliary data set, the LUMZ, to develop a density threshold. We calculate the mean settlement density for all LUMZ derived from the GUF data across Europe and use the resulting mean density value (61.3%). We apply this threshold to define pixels indicating a node by settlement density. However, as a very large number of individual pixels as well as small adjacent groups of pixels fulfill this requirement across Europe, we use the second criterion to identify only nodes with a significant size of area of continuous high settlement density. What defines a significant size is, of course, also malleable; for our straight-forward approach, we rely for the development of a reasonable threshold on a previous study, in which the Ruhr-Randstaad mega-region was analyzed with respect to the built environment . We take from all nodes in their study (identified by population) the smallest administrative size (as provided in the LAU-2 regions) as threshold defining the mimimum size for a node of relevance (30 km2). For every area detected across Europe fulfilling both criteria—high settlement density and comparatively large area of high settlement density—we locate the urban node as geometric centroid within the respective area.
- Triangulation: After having located all urban nodes by the conditions introduced above, we aim to assess spatial connectivity between these cardinal points. We use a triangulation technique for creating a mesh of contiguous, non-overlapping triangles from this dataset of nodes. With it, we identify which conjugation lines between nodes need to be analyzed regarding settlement contiguity.
- Calculation of conjugation lines by a least-cost path method: We aim at evaluating the connectivity of settlement patterns between two identified urban nodes based on the density of the settlement pattern in between. To find the highest possible density in combination with the shortest possible distance between nodes, we apply a least-cost path method (as introduced by ). With the settlement density as cost surface layer, the algorithm calculates the undirected least accumulated travel cost (or shortest weighted distance) from the starting node to the destination node. For the special case that the density of the cost surface layer is 0%, we double the costs for this specific grid. We do so as we intend to find contiguity of the settlements between nodes. In Figure 1c the example between Middlesborough and Hull shows the effect of this measure. With it we achieve least-cost pathes favouring settlement areas over possibly shorter distances with non-settlement areas. The algorithm adds all costs per pixel of the cost surface layer raster for every individual path possible until a particular path reaches the destination node. The path with the least costs accumulated between two hubs is then finally selected.
- Classification of the conjugation lines: We assume spatial connectivity between two nodes is given if settlement patterns are continuous without significant interruptions or decrease to rural environments (low settlement density). To account for these assumptions, we classify the magnitude of spatial connectivity (MoC) along the conjugation lines using two parameters: average settlement density and percentage of pixels featuring a settlement density higher than 10%. The average settlement density ensures that the demanded high share of settlement between nodes is measured; the percentage of low density areas ensures that continunity by a low share of spatial disconnections along the path is measured.We evaluate higher connectivity with a rising average settlement density. Simultaneously a higher MoC relates to a higher percentage of pixels with settlement densities higher than 10%. To take both variables into account, we combine them by multiplication. Thus, the magnitude of spatial connectivity (MoC) between two nodes is calculated as follows:The resulting MoC index has a range between 0 and 100. Nevertheless, the MoC index does not define whether conjugation lines are spatially connected or disconnected. Being conscious of the fact that there may no obvious, natural or objective threshold exist that definies whether two nodes are spatially connected or not, we again use an auxilliary data set, the urban-rural typology, for a reasonable threshold development. We assume that predominantly urban zones may function as a good indicator whether nodes are connected or not. We calculate all MoC values for all conjugation lines with a starting and a destination node located in one or in coalescent predominantly urban zones. From all MoC values derived we use the minimum MoC as threshold to define whether two nodes are classified spatially connected or not.Subsequently, as we find a high variability of MoC values classified as connected across Europe, we group them into three classes. To do so, we apply the Jenks-Caspall natural breaks classification method . The data clustering method allows identifying the best arrangement of values into different classes (we define three classes for all conjugation lines classified as connected—coalesced, high spatial connectivity and low spatial connectivity). We additionally classify all conjugation lines assigned ‘not connected’ into two classes, ‘very low’ and ‘no’ connectivity using the natural break algorithm. This lets us assume which conjugation lines may hold potential to become ‘connected’.Overall, the result is a classification of all conjugation lines into two major classes: disconnected (with specifications into very low and no connectivity) and connected (with specifications into low connectivity, high connectivity and coalesced). The connected conjugation lines may span unterritorial regions by these bounded nodes.
- Delimitation of city regions via region-growing: Let us assume two pairs of nodes are spatially eqally connected via a classified conjugation line; one along a narrow strip of high settlement density along a development axis (e.g., a highway) and, in contrast, one additionally surrounded by high settlement densities in the hinterland. Both conjugation lines of both pairs of nodes indicate a connected network by settlement patterns; however, the region to be considered part of the network may vary significantly due to their different shapes of high dense settlement patterns.Since no unambiguous classification criteria for a distinction between the node and the surrounding hinterland of lower settlement density exists, we delimit city regions via a region-growing approach. To do so, we consider all identified nodes featuring at least one conjugation line classified as connected as seed pixels. We allow a region-growing around the seed pixels if neighoring pixels feature a mean settlement density not lower than 2.5% settlement density, as this density value is above the European mean (2,05%) and these areas cover only 11.14% of the whole of Europe. The region-growing is conducted until a loop is not changing the resulting pattern. If regions originating from different nodes merge by the region-growing we consider them as one region.
- Categorization of regions: From the region-growing approach we derive spatially coalesced patches defining territorial entities related to the identified nodes. As we find a high variability of regions across Europe (in extent, in number of nodes, in connectivity, etc.), we categorize the resulting regions by the following variables: the number of nodes within the region; a higher number of nodes indicates a higher relevance of the region. The average path length of connected cities within the region; a smaller average path lenghts indicates a more clustered arrangement of nodes. In consequence, we assume the degree of connectivity within the region is higher. A class index, quantifying the percentage share of low connectivity, high connectivity and coalsced types of all conjugation lines within one region. We assume the higher the shares of stronger connectivity, the higher the degree of settlement continuity is within the region. The spatial dimension of the extent of the region; with larger extents indicating higher relevance of a region.For the categorization we take all four variables into account and combine them by multiplication. From the resulting index values—a higher value indicating a region with more nodes, more clustered arrangement, higher connectivity and larger extent—we classify five groups (Category A–E) using the natural breaks algorithm. Furthermore, we classify a Category F for all remaining nodes not associated to a region.
- Plausibilization of classification results: It is obvious that there is not ‘one truth’ for an urban node of relevance, a specific region, or the spatial delimitation of conceptual approaches such as a mega-region. Beyond this, reference data are either inexistent or rely on other data, concepts and methods. An assessment of correctness in its original sense is thus not meaningful. What is considered a node or a region is, although mathematically reasonable, subjective to the selected indicator ‘settlement pattern’ and the related threshold selections and, it is also relative at the same time. We disregard a sensitivity analysis here, as the systematic evaluation of the manifold influences onto the results from the scale of measurement (here 1 × 1km), the thresholds for defining nodes of relevance (by density and size), the thresholds evaluating connectivity or defining the region-growing is beyond the scope of one paper. Instead we refer to a recent paper by Taubenböck et al.  performing sensitivity analysis for settlement density variables, and argue that the main point in our study is the consistency of the application. However, to provide an assessment of the results, we check the plausibility of our physical approach to identify nodes ((1) and (2) in the following) and regions (3) in relation to other related data sets as well as to constructed regional spaces in other studies (4).(1) We relate all identified nodes to the urban-rural typology at NUTS-3 regional level. As our physical analysis relies on high settlement density for the identification of nodes, we await identified nodes to be located in the predominantly urban class. (2) We relate identified nodes to Urban Audit Cities (UAC). We assess plausibility of our physical analysis by checking the percentage of identified nodes located within a certain distance to an UAC. We assume our approach is feasible if the data sets do not differ significantly. (3) We check the plausibility of the derived regions by checking the following assumption. As the detected regions are areas of physical concentration (high settlement densities), we expect the population in the mapped areas to have a higher share than the settlement share. (4) We qualitatively compare constructed territorial regions from other studies (based on varying data, concepts and methods) with our results.
4.1. Mapping of Nodes, Connectivities of Nodes and Regions
4.1.1. The Nodes and Spatial Connectivity between Nodes (Constructing Non-Territorial Regions)
4.1.2. Spatial Mapping of Territorial Regions
4.2. Plausibilization and Classification of the Results
Conflicts of Interest
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|Category||Classified Nodes (Cities per Region)||Spatial Expansion||Physical Indicators||Index||Absolute Population|
|No. (Cities)||Areal Expansion (km2)||Mean Length Conjugation Lines (km)||Connectivity Index|
|A||Lens, Lille, Kortrijk, Roeselare, Bruegge, Gent, Brussels, Charleroi, Antwerpen, Luettich, Aachen, Moenchengladbach, Duesseldorf, Solingen, Wuppertal, Leverkusen, Koeln, Bonn, Duisburg, Essen, Dortmund, Krefeld, Muenster, Bielefeld, Osnabrück, Hannover, Braunschweig, Eindhoven, Breda, Dordrecht, Rotterdam, Utrecht, Den Haag, Leiden, Amsterdam, Zaanstad, Apeldoorn||Transnational (France, Belgium, The Netherlands, Luxembuorg, Germany)||37||65,253||69||1.32||28.04||51,048,827|
|B||Swindon, Bristol, Cardiff, Gloucester, Birmingham, Coventry, Leicster, Nottingham, Derby, Stoke, Warrington, Liverpool, Birkenhead, Manchester, Sheffield, Leeds, Blackpool||National (United Kindom)||17||22,080||76||1.24||3.720||23,897,718|
|Turin, Busto Arsizio, Mailand, Bergamo, Brescia, Verona, Padua, Venice, Bologna||National (Italy)||9||31,388||100||1.22||2.105||20,809,095|
|London, Southend on the sea, Rochester, Ipswhich, Norwhich, Brighton, Farnborough, Reading, Southampton, Portsmouth, Peterberough, Luton, Milton Keys||National (United Kindom)||13||16,473||83||1.31||1.891||21,020,358|
|C||Frankfurt, Wiesbaden, Mannheim, Karlsruhe, Stuttgart, Straßburg||Transnational (Germany, France)||6||13,656||88||1.19||0.673||13,303,103|
|Augsburg, Munich, Vienna, Linz (the city Linz is not connected via the conjugation line, but part of a region)||Transnational (Germany, Austria)||3(4)||17,794||64||1.0||0.509||9,399,535|
|Neapel, Caserta, Scafati||National (Italy)||3||4462||37||1.5||0.333||5,283,131|
|D||Basel-Zurich||Transnational (Switzerland, France, Germany)||2||9457||88||1.25||0.165||7,178,119|
|Cracow-Bielitz Biala||Transnational (Poland, Czech Republic)||2||9959||79||1.00||0.161||7,503,593|
|Geneva-Lausanne||Transnational (Switzerland, France)||2||2392||60||1.25||0.061||1,853,763|
|Dublin-Belfast||Transnational (Ireland, North Ireland)||2||7555||167||1.25||0.057||3,237,883|
|Nancy-Metz||Transnational (France, Luxembuourg, Belgium)||2||2383||59||1.00||0.050||1,229,523|
|Lyon-Saint Etienne-Valence||National (France)||3||5479||87||1.17||0.132||3,422,285|
|Ruoen-Le Havre||National (France)||2||3502||83||1.00||0.050||1,492,658|
|Category||Classified Nodes||Country (No. of Nodes)||Category||Classified Nodes||Country (No. of Nodes)|
|E||Paris, Montpellier, Caen, Tours, Clermont-Ferrand, Orleans, Dijon, Troyes, Reims||France (9)||F||Lviv, Schytomyr, Winnyzja, Kiev, Tschernihiw, Poltawa, Carkiw, Dnipropetrowsk, Sapoischschja||Ukraine (9)|
|Dresden, Nuernberg, Berlin, Bremen, Oldenburg, Magdeburg, Kassel||Germany (7)||Brest, Nantes, Bordeaux, Toulouse, Rennes, Le Mans||France (6)|
|Northhampton, Bournemouth, Hull, Aberdeen, Dundee||UK (5)||Wladimir, Pensa, Tambow, Woronesch, Krasnodar||Russia (5)|
|Moscow, Rostow, Tula, Taganrog||Russia (4)||Temeswar, Cluj-Napoca, Bucharest, Konstanza||Romania (4)|
|Warzaw, Lodz, Teschenstochau, Radom||Poland (4)||Wroclaw, Posen, Bydgoszcz, Lublin||Poland (4)|
|Donezk, Luhansk||Ukraine (2)||Bari, Catania, Palermo, Cagliari||Italy (4)|
|Valencia||Spain (1)||Saragossa, Madrid, Seville||Spain (3)|
|Kopenhagen||Denmark (1)||Minsk, Wizebsk, Homel||Belarus (3)|
|Budapest||Hungary (1)||Stockholm, Gothenburg||Sweden (2)|
|Ljubljana||Slovenia (1)||Prague, Brno||Czech Republic (2)|
|Grenoble||Switzerland (1)||Sofia, Plowdiw||Bulgaria (2)|
|Rome||Italy (1)||Athens, Thessaloniki||Greece (2)|
|Malmoe||Sweden (1)||Riga||Latvia (1)|
|Bratislava||Slovakia (1)||Belgrad||Serbia (1)|
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