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Mapping the porosity of international border topedestrian traffic: a comparative data classificationapproach to a study of the border region in Austria,Italy, and SloveniaNao Hisakawa a , Piotr Jankowski a c & Gernot Paulus ba Department of Geography, San Diego State Universityb Department of Geoinformation and Environmental Technologies, Carinthia University ofApplied Sciencesc Institute of Geoecology and Geoinformation, Adam Mickiewicz University

To cite this article: Nao Hisakawa , Piotr Jankowski & Gernot Paulus (2013): Mapping the porosity of international border topedestrian traffic: a comparative data classification approach to a study of the border region in Austria, Italy, and Slovenia,Cartography and Geographic Information Science, 40:1, 18-27

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Mapping the porosity of international border to pedestrian traffic: a comparative dataclassification approach to a study of the border region in Austria, Italy, and Slovenia

Nao Hisakawaa*, Piotr Jankowskia,c and Gernot Paulusb

aDepartment of Geography, San Diego State University; bDepartment of Geoinformation and Environmental Technologies, CarinthiaUniversity of Applied Sciences; cInstitute of Geoecology and Geoinformation, Adam Mickiewicz University

(Received 23 February 2012; accepted 31 October 2012)

National borders play an important role in everyday life. Interest in border studies has increased with recent changes ingeographical locations of the border or the fluctuation of the permeability of the border between some countries, such as inthe European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter bordercontrol, having appropriate information of the border is crucial for effective policymaking.The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the

southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputationalmethods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, wereapplied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, anobject can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of thenatural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-meansclustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters arerequired to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neuralnetwork and was chosen for its ability to automatically determine the number of classes and handle categorical data. TheSelf-Organizing Map is unique because it can transform high dimensional data into low dimensional display whilepreserving the topology and spatial distribution of the input parameters. The results of the two classification methodssuggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However,more research is needed to determine the fit of these algorithms for particular spatial data classification tasks.The results obtained from this research provide an insight into the permeability of the border region of Carinthia,

Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism developmentand road transportation management in that region. Furthermore, the approach presented in this article can be applied toother national borders to identify zones permeable to pedestrian traffic.

Keywords: GIS; border studies; spatial clustering; visual analytics; spatial decision support

Introduction

Borders play a large role in people’s daily lives as theyshape cultural and ethnic identities of an individual. Theyhave served a dual purpose of slowing down flows ofundesirable entrants and enticing visits of others (e.g.,tourists and shoppers). The duality of the border functionis visible perhaps nowhere better than in the EuropeanUnion (EU). Although the physical borders of the coun-tries do not change with nations joining the EU, thedynamics of the inner and outer EU borders are greatlyaffected. The EU implemented the Single Market Policy,which supports the free movement of people, goods, ser-vices, and capital by bringing down the barriers to giveEuropean citizens easier access to EU’s 27 countries(European Commission 2010a). This gives European citi-zens the freedom to study, work, and retire in another EUnation as well as travel without visas (EuropeanCommission 2010b). In an effort to encourage EU citizens

to move around within the EU, the European Commissionhas begun a cross-border cooperation program, alsoknown as territorial cohesion. The objectives for this pro-gram include encouraging entrepreneurship in the devel-opment of tourism, culture, and cross-border trade;improving joint management of natural resources; devel-oping joint use of infrastructure; and increasing employ-ment opportunities (European Union Regional Policy2007). Although regional diversity is valued, the intercon-nectedness of EU countries is sought after in order for theEU to economically prosper. In order to promote this goal,there is a need to analyze border areas from multipleperspectives including social, political, cultural, and phy-sical aspects.

Mountainous regions are one of the many physio-graphic land types within the 27 countries of the EU.These areas cover approximately 39% of EU’s land terri-tory and are generally seen as geographical barriers

*Corresponding author. Email: nghisakawa@gmail.com

Cartography and Geographic Information Science, 2013Vol. 40, No. 1, 18–27, http://dx.doi.org/10.1080/15230406.2013.762141

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because they are difficult to traverse (EuropeanCommission 2001). Although activities mostly center inthe valleys and on other usable land, increased traffic ofgoods has caused mountainous areas to become transpor-tation bottlenecks and has led to concerns over safety risksand the environment (European Commission 2001).Further, most mountainous regions have experienced pro-blems becoming economically viable, as 95% of theseareas have been receiving funds for development or socio-economic conversion (European Commission 2001). Thisis a strong indication that these regions have great diffi-culty in becoming economically self-sustaining. Borderanalyses can contribute to finding solutions for theseissues as well as increase the flow of people between thecountries of EU where physical barriers exist.

Austria’s most southern state of Carinthia includesmountainous regions and shares its border with two otherEU countries, Slovenia and Italy, making it an interestingcandidate for a border analysis study. Carinthia’s southernborder with Slovenia and Italy is important for cross-bordercooperation and the mountainous regions near the southernborder are of special interest to the EU’s program of regio-nal development promoting mountain regions (EuropeanCommission 2001). In addition, there have been localefforts within Carinthia and the neighboring province ofUdine in Italy to reestablish an ancient route that was usedby people since Roman times to transport goods, called ViaJulia Augusta (http://www.turismoruralefvg.it/page.php?l=2&path=5_30). The Via Julia Augusta transnational col-laboration project aims to develop cultural and touristicroutes connecting the Italian region of Friuli VeneziaGiulia with Carinthia utilizing this rich historical heritage.

This article reports the results of a study in which thesouthern border of Carinthia was analyzed according totopographic and infrastructural characteristics andassigned porosity values. The term porosity in this articlerefers to the level of ease for pedestrians to cross anygiven border zone. The easier it is for pedestrians tocross, the higher the porosity value. Border porosity isoften associated with homeland security, where high por-osity is generally undesirable when managing illegal bor-der crossings. However, in the context of tourism, highborder porosity is desirable as these are the areas that havehigh potential for new development.

Topographic characteristics such as land cover influ-ence the rate of pedestrian travel and the amount of timethat is needed to travel a given distance (Kaiser and Stow2007). Vegetation type can slow down a pedestrian byacting as a barrier of the route of travel, or can aid thepedestrian by increased traction due to soil stabilization.Our daily experience shows that walking over a grassyarea takes less energy and time than walking over a sur-face with thick vegetation cover. The slope and aspect ofthe terrain determines the magnitude and direction oftopographic relief. It is intuitive that the severity of the

slope of an area will greatly affect the ease with which itcan be traversed; low slope is much easier to traverse thanhigh slope. Infrastructural characteristics such as roads andtrails also influence porosity levels. Existence of roads andtrails reduce the level of walking difficulty by creatingpathways through vegetation and hills that may otherwisebe very challenging to traverse. Previous research hasshown that terrain and vegetation are the main physicaldeterminants of ease/difficulty of traversing land by foot(Axelson and Chesney 1999).

The motivation for this study has been to develop amethod for classifying (segmenting) the border area,which can be utilized for development and promotionof tourist and transportation infrastructure in the borderregion separating Carinthia from Slovenia and Italy andin similar mountainous border regions. Border areasidentified as highly porous can potentially be developedfor different types of tourism including pedestrian traffic,because such conditions indicate easy access through thatregion. Border areas identified as difficult to penetrate(low porosity) but with nearby tourist attractions can alsobe considered for potential development for tourism, butrequire additional effort to do so. Since the topographiccharacteristics of the border are interrelated with otheraspects of the border such as social and political, theresults of this research can influence policies aimed atthe development of the region shared by the three coun-tries. Information obtained from this study may be valu-able to organizations such as the Association ofEuropean Border Regions, whose aims include identify-ing problems, representing common interests, coordinat-ing inter-region cooperation and encouraging informationexchange (Association of European Border Regions2010). More specifically, information obtained from thisstudy can assist with decision making processes in tour-ism and transportation planning. The increased ease oftraveling between the EU nations is beneficial to theeconomy of the countries involved. The EuropeanCommission has identified tourism as a significant con-tributor to employment and to social benefits for localcommunities as well as providing a framework forunique cultures and environments (EuropeanCommission 2003). The border segmentation methodpresented in this article can also be used with otherinput parameters such as those related to population,climate, or financial costs, and can be applied at otherscales, such as counties and cities.

The study methods including the analysis workflow,study area, and data used are presented in the followingsection. The results of the study are presented in the“Results” section and discussed in the “Discussion”section. The article closes with outlining future steps inthe development and improvement of the method for map-ping and analysis of border porosity in the context oftourism planning.

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Study methods

The method developed for this study was driven by: 1) thegeographical extent of the border region analyzed, and 2)the study’s objective of segmenting the border into thezones of homogenous porosity and subsequently identify-ing areas with high pedestrian porosity for potential futuretourism development in the border region of Carinthia(Austria), Gorenjska, Savinusko, Koroska (Slovenia), andFriuli-Venezia Giulia (Italy).

Study area and data

As previously mentioned, the area in focus for this studywas the southern border of Carinthia that neighborsSlovenia and Italy. The geographical extent of the studyarea was approximately 6 km north and south from thisborder and included Carinthia, Italy, and Slovenia. Thestudy area is displayed with a shaded relief in Figure 1.The purpose of this research was to locate areas character-ized by high pedestrian porosity within the 12 km borderzone. The width of the border zone was chosen based onthe presence of many potential hiking destinations (e.g.,mountain peaks and ridges, points of interest for tourists)situated within 6 km of the border on each side.

Three topographic parameters were used in the study:land cover, elevation, and slope. The two original datasetsused were CORINE Land Cover data from the EuropeanEnvironment Agency and elevation data provided by theProvincial Government of Carinthia (CarinthiaGeographical Information System KAGIS). Slope wasderived from the elevation dataset. Road data for Austriawas also obtained from the Provincial Government ofCarinthia (Carinthia Geographical Information SystemKAGIS), while road data for Slovenia and Italy wereobtained from Open Street Map. The ground resolutionof all of the original datasets was 25 m/cell, and thisresolution was used for the analysis.

Analysis workflow

The analytical workflow for segmenting the 12 km-widestrip of land paralleling the border into the areas of homo-genous porosity is presented in Figure 2. The workflowbegins with classifying topographic data into clustersusing two methods, the fuzzy K-means clustering methodand Self-Organizing Map in combination with the fuzzyK-means clustering method (step 1).

As a result of the clustering, each pixel from the 12 kmwide border strip is assigned to a class, forming a basis forportioning the border strip into topographic porosity seg-ments. Then, each segment is assigned porosity values toeach class value according to its attributes (step 2).Following the assignment of topographic porosity valueto each border segment, infrastructural data (roads – step3) are used to compute the infrastructural porosity value(step 4). Two porosity measures are then combined andtheir distribution interpreted (step 5). The key step of theworkflow is clustering of topographic input variables (landcover, elevation, and slope) such that each pixel of theraster representing the 12 km wide border strip becomesassigned to one class. The two clustering techniques:fuzzy K-means and Self-Organizing Map (SOM), resultin two alternative classifications, which in turn lead to twoalternative border segmentations according to measures ofporosity. Both clustering techniques are discussed in moredetail below.

Fuzzy K-means Clustering

Fuzzy K-means clustering is a type of unsupervised classi-fication method. Supervised classification requires the userto generate representative parameters for each class beforeclassification can occur, while unsupervised classificationmethods automatically identify the clusters from the dataset(Gonçalves et al. 2008; Jensen 2005). This is desirablewhere little a priori knowledge for classifying the dataexists or when avoiding subjectivity in the classificationprocess (Kelly et al. 2004). An unsupervised classification

Figure 1. Map of the study area.

Figure 2. Workflow of the border segmentation method.

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method was chosen for this study because little was knownon how to classify the border according to the topographiccharacteristics.

The K-means clustering technique is a popular discreteclustering technique where each pixel is assigned a fullmembership to a cluster (Gonçalves et al. 2008; Kuo et al.2002; Viswanathan et al. 2005; Huang 1998). However,this technique may not always accurately represent thephysical world because in reality, each pixel is likely tobelong to more than one class. The fuzzy K-means clus-tering method takes this into consideration and allows fora continuous approach to data classification (Gorsevski etal., 2003; Bezdek et al., 1982). In this approach, a pixelcan belong to more than one class as long as the member-ship value is between 0 and 1 and the sum of thesemembership values is equal to 1 for each pixel. Thismethod is more appropriate than K-means clusteringwhen classifying physical entities such as landforms orsoil because these entities are more continuous than dis-crete. Although other K-means clustering methods exist(e.g., extended K-means, agglomerative hierarchical, andfuzzy maximum likelihood), the fuzzy K-means clusteringtechnique has been demonstrated to produce the mostaccurate results in reproducing the characteristics ofinput data (Duda and Canty 2002).

One disadvantage of fuzzy K-means clustering is thatit needs user input for determining the number of clus-ters, and therefore assumes that this number is knownprior to the process (Kuo et al. 2002; Gonçalves et al.2008). To objectively determine the optimal number ofclasses, the classification can be repeated for a range ofcluster numbers and the two parameters, the fuzzy per-formance index and the modified partition entropy, areevaluated to minimize fuzziness of the classes (Burroughet al. 2001; Gorsevski et al. 2003). This process isdescribed in more detail below. Another disadvantageof the fuzzy K-means clustering is that it can only workwith numeric data or data where variables are measuredon an ordinal scale (Huang 1998). This is a problemwhen working with categorical data such as land cover.One solution to overcome this issue is to reclassify thecategorical data to an ordinal scale prior to using it as aninput for the K-means clustering process. As shown inTable 1, land cover categories obtained from CORINEland cover data were reclassified into an ordinal 1–5scale according to assumed porosity, shown below:

(1) Very low porosity (e.g., bodies of water)(2) Low porosity (e.g., bare rocks and construction

sites)(3) Medium porosity (e.g., forests, grassland)(4) High porosity (e.g., developed open space,

farmland)(5) Very high porosity (e.g., medium-high intensity

development)

Once the land cover data was reclassified, the fuzzy K-means algorithm was run using the FuzME software fromthe Precision Agriculture Laboratory from the Universityof Sydney (Minasny and McBratney 2000). The reclassi-fied land cover, elevation, and slope were used as the inputparameters. Because the FuzME software was unable toprocess the entire border at 25 m resolution, the borderwas split into three parts and processed individually, thenmerged back together again. When running FuzME, theoptimal number of classes is initially unknown, so toobjectively find this number, the algorithm was run for arange of classes from 2 to 20. The number of classes thatminimized the degree of fuzziness and the degree of dis-organization, which are measured by the fuzzy perfor-mance index (FPI) and the modified partition entropy(MPE), respectively, was selected as the optimal numberof classes (McBratney and Moore 1985; Bezdek et al.1982). The FPI estimates the degree of fuzziness gener-ated by a specific number of classes. The MPE estimatesthe degree of disorganization within the classification. Tomeasure the similarity or dissimilarity of pixels and thesimilarity or dissimilarity of clusters, Mahalanobis dis-tance was used as a measure of correlation (similarity)between pixels and clusters (Gorsevski et al. 2003). Infuzzy clustering, each pixel has the possibility to belongto more than one class. Accordingly, each pixel is char-acterized by a list of membership values representing thestrength of membership in each class. For the purpose ofclassifying the border region, only the highest membership

Table 1. Reclassification table for land cover classification.

Originalcode Land cover description

Newcode

112 Discontinuous urban fabric 5121 Industrial or commercial units 5122 Road and rail networks and associated

land5

131 Mineral extraction sites 2133 Construction sites 2142 Sport and leisure facilities 5211 Non-irrigated arable land 4231 Pastures 4242 Complex cultivation patterns 3243 Land principally occupied by agriculture;

significant areas of natural vegetation3

311 Broad-leaved forest 3312 Coniferous forest 3313 Mixed forest 3321 Natural grasslands 4322 Moors and heathland 4324 Transitional woodland-shrub 3331 Beaches, dunes, sands 3332 Bare rocks 2333 Sparsely vegetated areas 4411 Inland marshes 1511 Water courses 1512 Water bodies 1

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value was retained for each pixel. Therefore, each classconstituted of the pixels that attained the highest member-ship value for the given class.

Combination of Self-Organizing Map (SOM) and FuzzyK-means Clustering

The SOM is a type of artificial neural network for thevisualization of high dimensional data. It does this byorderly mapping the high dimensional data onto a lowdimensional grid, thereby converting complex nonlinearrelationships within the high dimensional data into lowdimensional display (Kohonen 1998). While doing so, theSOM preserves the topology and spatial distribution of theinput space (Gonçalves et al. 2008). To accomplish this,the SOM has an input vector for each parameter that isconnected to every output neuron by weights which arerandomly assigned. Then, the Euclidean distances betweenthe weight vector and input layer vector are calculated andthe output layer neuron with the shortest distance is the“winner.” The weights of the “winner” neuron and itsneighboring neurons are altered and the process isrepeated until convergence is reached (Eastman 2009b).At this point, a feature map characterizing the distributionof input parameters using different colors for each class isgenerated.

SOM and fuzzy K-means clustering are similar inmultiple ways. First, both methods can utilize an unsuper-vised classification algorithm (Murtagh and Hernandez-Pajares 1995). Second, SOM and fuzzy K-means cluster-ing result in an output grid where similar objects are closerto one another than dissimilar ones (Kohonen 1998).Third, both methods can easily handle large amounts ofdata (Kuo et al. 2002). The major difference with fuzzy K-means is that in SOM, categorical data can be directlyused as an input parameter and the number of outputclasses is automatically calculated. In addition, SOM pre-serves the topological features of the input datasets, mean-ing that patterns that are close in the input datasets will bemapped to areas that are also close in the output layer. Themost widely used SOM is Kohonen’s algorithm (Kuoet al. 2002). There have been studies that compare theK-means clustering technique with SOM algorithms with-out definite conclusion of which is better (Bação et al.2005).

The ability of the SOM to handle categorical data(unlike the fuzzy K-means method) made it possible todirectly use land cover, elevation, and slope as inputparameters without the need to reclassify land cover. Thedatasets were processed using IDRISI Taiga’s SOM algo-rithm (Eastman 2009a). The unsupervised SOM classifica-tion was used because the representative class parameterswere initially unknown, and the maximum number ofclasses was set to 20. The fuzzy K-means clusteringtechnique was then used to assign pixels to clusters after

the initial SOM process. Integrating a clustering techniquewith SOM classification has been found to perform betterand require less computational effort relative to onlyusing direct clustering (Vesanto and Alhoniemi 2000).When the two methods are combined, the SOM algo-rithm is first used to form prototypes of the classes,which generally produce a larger number of classesthan expected. These prototypes are then further clusteredusing the fuzzy K-means algorithm (Vesanto andAlhoniemi 2000). The output of the SOM-fuzzy Kmeans classification and clustering procedure was a fea-ture map of the classified border region, much like thatof fuzzy K-means’ result of mapping the maximummembership classes.

Both approaches (fuzzy K-means and SOM followedby fuzzy K-means) generated a representation of the bor-der where each pixel belonged to a cluster symbolizedwith a unique color hue. Although some trends werevisible, in order to reveal the overall pattern of the border,the data was generalized to create homogeneous zones. Todo so, the border classification was generalized with thefocal majority function so that the value that appears mostoften in the specified neighborhood window appears in theoutput raster in the corresponding location. The outputwas a generalized version of the border classification.The border region was manually edited to further general-ize the classification where appropriate (i.e. small groupsof pixels were merged with larger zones). Once the zoneswere created, the summaries for each attribute for eachclass were calculated using the original 25 m datasets.

Results

Assigning topographic porosity

Using topographic data visualization and the summarytable of the attributes for the classes, a porosity valuewas assigned for each class for the results of both theSelf-Organizing Map and fuzzy K-means clustering. This“Topographic porosity” includes land cover, elevation,and slope, and is on a scale of 1 to 5. A topographicporosity of 1 depicts low porosity areas, or classes thatare most difficult to cross by foot while a topographicporosity of 5 depicts high porosity areas, or classes thatare most easily crossed by foot. Five levels of porositywere used because some classes belonged to neither oftwo extremes (porosity 1 or 5) and had a medium porosityvalue (i.e. porosity value of 3) and other classes were onlysomewhat difficult or somewhat easy to cross (i.e. porosityvalues of 2 and 4, respectively). Because the land covercategory “mixed forest” dominates most of the border stripand thus was the majority land cover for all the classes,elevation and slope were most influential in deciding theporosity values. Generally, the mean elevation and meanslope were positively correlated.

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Topographic porosity with Fuzzy K-means Clustering

Border segmentation reflecting the topographic porosity com-puted with fuzzy K-means clustering is depicted in Figure 3.Higher elevation and slopes represent areas that are moredifficult to traverse; therefore, classes with such traits wereassigned lower porosity values. Lower elevation and lowerslopes represent areas that are easier to cross; therefore, classeswith these traits were assigned higher porosity values.

Topographic porosity with SOM

Border segmentation reflecting the topographic porosity com-putedwith SOM is depicted in Figure 4. Similarly to assigning

porosity values to the results of the fuzzy K-means clusteringclassification, classes with higher altitudes and slopes wereassigned lower porosities and classes with lower altitudes andslopes were assigned higher porosities.

Assigning infrastructural accessibility

In addition to the topographic characteristics of terrain, theability of a pedestrian to walk through the area is influ-enced by road density. Regardless of topographic traits,roads will provide access to areas that are otherwiseunreachable. Although the road density data was a gen-eralized map of the actual roads, the road pattern is still

Figure 3. Topographic porosity for fuzzy K-means clustering classification and its class summaries.

Figure 4. Topographic porosity for SOM clustering classification and its class summaries.

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clearly visible meaning that it was still too detailed todetect zones of high or low road density. Therefore, inorder to visualize these zones, the road density data wasprocessed with a kernel density function so that the result-ing image was a more continuous raster image, much likea Digital Elevation Model (DEM), with a gradient thatshowed distances from the nearest road. These valueswere then classified into three categories using Jenksnatural breaks and the classes were labeled high, medium,and low road density (Figure 5).

The darker green represents areas with high road den-sity. The important areas for this study are high to mediumdensity regions that are close to the border.

Combining topographic with infrastructural porosity

The final step of the border segmentation workflowinvolves overlaying topographic porosity with infrastruc-tural porosity (Figure 6).

Figure 6 represents the combined (topographic andinfrastructural) porosity for the border region. Areas withhigh to medium infrastructural porosity that traverse theborder are displayed with a cross hatch pattern. Theseareas overlapping with areas of high topographic porosityrepresent the areas with a high potential for pedestriantraffic. Areas with medium topographic porosity alsobecome more suitable to pedestrian traffic by their spatialcoincidence with high infrastructural porosity areas.

Discussion

Comparing the results of Fuzzy K-means Clusteringwith SOM

The two classification methods resulted in two distinctsegmentations with a few similarities as shown side by

side in Figure 7. For example, both results display areaswith very low porosity (i.e. porosity level 1) only on thewest end of the study area in the mountainous regions ofthe Carnic Alps. The two segmentations also show that themajority of the study area has mixed forest land type, meanelevation of approximately 1100 m, and mean slope of 25and 26 degrees. Finally, both results show some high por-osity areas on the north side of the center of the strip nearArnoldstein.

There are, however, many differences between the tworesults. Three specific areas that will be discussed and thatare different between the two results are circled in Figure7 and labeled A, B, and C. As mentioned in the previousparagraph and displayed in area A, the lowest porosity isonly visible on the mountainous west end of the borderstrip for both results but the size of the very low porosityregion (red) is different for the two maps. Looking at atopographic map reveals that the mountainous regionspreads across the border as depicted in the fuzzy K-means red region and SOM’s low porosity regions (redand orange). The difference is that the fuzzy K-meansclassification grouped the entire mountain region intoone class while SOM developed two classes, a low por-osity class (orange) and a very low porosity class (red) forthis area.

Another difference can be seen in area B betweenHermagor and Arnoldstein. This area is a somewhat devel-oped area that begins in the flat area to the north of theborder strip and extends to the lower elevations of themountains that exist on the border. The fuzzy K-meansclustering displays a high porosity area (light green) formost of this area and an even higher porosity (dark green)for a small area closer to the border because it has a lowerelevation and slope and is dominated by farmland. Incontrast, the SOM clustering displays a medium porosity

Figure 5. Infrastructural porosity and its class summaries.

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(yellow) for most of this area except for small patches onthe north side of the border, which has low elevation andslope and also is dominated by farmland. Because thisarea is mostly developed land in the lower elevations ofthe mountain, it seems that it would be easier to traverse,

or that it would have a higher porosity as depicted by thefuzzy K-means clustering.

A third difference between the two results is shown in areaC. The area to the southeast of Jesenice is shown as highporosity (green) in fuzzyK-meanswhile it is shown asmedium

Figure 7. Topographic porosity for both clustering methods with special areas noted.

Figure 6. Topographic porosity for fuzzy k-means and SOM overlayed with areas of high infrastructural porosity.

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porosity (yellow) in SOMclustering. Since the area C overlapswith the flat and developed area of the mountain range classi-fying it as high porosity seems more appropriate.

The above comparison suggests that the results fromthe fuzzy K-means clustering may be more useful than theresults from SOM when determining porosity levels of theborder. That is not to say that the results of SOM algo-rithm are inaccurate; classes from both algorithms reflectthe ground conditions. However, much of the SOM algo-rithm’s border is of the same porosity levels ranging frommedium to low porosity (yellow and orange, respectively).Furthermore, SOM resulted with only one high porosityclass (light green) while fuzzy K-means resulted in twohigh porosity classes (light and dark greens). When look-ing at the attribute values for the high porosity classes, theelevation and slope of the high porosity class for SOM(light green) are higher than both of the fuzzy K-means’high porosity classes (light and dark greens), while thecharacteristics for the lowest porosity (red) are very simi-lar for both results. This shows that fuzzy K-means wasbetter able to detect and distinguish areas with lowerelevation and slope, or with higher porosity.

To further compare the two clustering methods, the meanroad density was calculated for each class for both results.

As displayed in Tables 2 and 3, the values for classes 1,2, and 3 for fuzzy K-means clustering and SOM, respec-tively, are very similar. This is as mostly predicted becausethe class attributes for the three classes are very similar.

Limitations of the analysis

This analysis included only a limited set of parameterscomprised of terrain derivatives, land use categories, andone infrastructure characteristic (roads). However, thereare additional parameters that could potentially affect thesegmentation of the border according to its porosity, suchas the following:

Seasonality. Certain seasonal conditions such as snowcover can increase or decrease porosity depending onfactors such as availability of proper gear. For example,limited accessible areas in summer might be easily

accessible in winter using skiing equipment. In addition,some huts are seasonally operated, meaning that they maybe closed during the winter or the summer.

The existence of alpine pastures. During the summer,farmers take their cattle up to higher elevations to allow themto graze on the land. This contributes to increased infrastruc-ture because of the roads used by people and cattle, and thecottages occupied by the farmers are sometimes opened upto hikers for use as a rest stop and a place to replenish foodand/or water, much like the mountain huts.

Natural hazards. Detailed data on natural hazards canbe used as one of the parameters to refine porosity. Areaswith high potential for natural hazards would havedecreased porosity.

Wildlife protection zones. Some areas of Carinthia areseasonally protected for wildlife, which would limit areasthat the pedestrians can cross. Identifying wildlife protec-tion zones is also important for development purposes.

Ownership of land. Although pedestrians are per-mitted to cross private land, bicyclists are not. Again, ifthere are plans for future development, private and publicownership of land would become important.

Conclusion

The main objective of research presented in this articlewas to develop a method of classifying the southernnational border area of Carinthia, Austria according to itspedestrian porosity. The results have shown that the fuzzyK-means classification may be more effective than theSelf-Organizing Map-based classification in discerningthe change of topographic characteristics along the border.However, as previous research has demonstrated thatSOM can result in higher accuracy of classification,more research is required to determine specific scenariosin which these algorithms can be used optimally (Baçãoet al. 2005). The presented approach uses infrastructuralporosity as a complement to topographic porosity to indi-cate road sections that provide accessibility to areas of lowtopographic porosity. The border segmentation based on afew selected topographic and infrastructure characteristicscould be further developed to assist in the decision makingprocess of tourism development. In the next steps, thestudy should be expanded by including additional para-meters that might refine the spatial distribution of porosityalong the border, such as the already mentioned season-ality, locations of alpine pastures, surface curvature,aspect, natural hazards, wildlife protection zones, landownership, and other types of tourist infrastructure. Amore extensive database of factors contributing to borderporosity could serve as a starting point for developing aspatial decision support system to identify and evaluateborder areas suitable for new developments in bordertourism infrastructure.

Table 2. Class summaries for fuzzy K-means classification.

Fuzzy K-means clustering summary

Class 1 2 3 4 5Road density (%) 10.40 6.22 9.92 10.93 33.51

Table 3. Class summaries for SOM classification.

SOM clustering class summary

Class 1 2 3 4Road density (%) 10.26 7.71 10.53 7.03

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AcknowledgmentsThe comprehensive data provision for this research by theCarinthian Geographical Information System KAGIS (www.kagis.gv.at) is gratefully acknowledged. This research projectwas funded by the Austrian Marshall Plan Foundation.

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