Vletter, W. F. and van Lanen, R. J. (2018). Finding Vanished Routes: Applying a Multi-modelling Approach
on Lost Route and Path Networks in the Veluwe Region, the Netherlands. Rural Landscapes: Society,
Environment, History, 5(1): 2, 1–19, DOI: https://doi.org/10.16993/rl.35
RESEARCH
Finding Vanished Routes: Applying a Multi-modelling
Approach on Lost Route and Path Networks in the
Veluwe Region, the Netherlands
Willem F. Vletter*,† and Rowin J. van Lanen‡,§
Route networks are influenced by cultural and environmental dynamics. Consequently, route networks
themselves often are dynamic as well. This is especially true in lowland areas, such as the Netherlands,
where environmental processes (e.g. geomorphological changes, floods) probably reshaped these networks
numerous times. Many of the existing route networks in the Netherlands were established relatively
recently, and little is known of their historical predecessors. Recent developments in spatial modelling may
improve locating and analysing these old, vanished routes.
In this study we have applied two recently-developed applications for historical-route network modelling
to the Veluwe (the Netherlands) in order to reconstruct the route network in the region around AD 1500.
This region is not densely cultivated and is known to have a long history of routes and paths running
through the landscape. The first method, network friction, uses high-resolution geoscientific and cultural
data to calculate potential movement corridors and probable route zones. The second method uses a more
traditional least-cost path (LCP) model based on surface, groundwater level and slope. The usefulness of
these approaches for reconstructing past route networks and the general added value of these approaches
was assessed by comparing the reconstructions to the few existing spatial overviews of historical-route
networks in this region and hollow ways extracted from Airborne Laser Scanning (ALS) data.
Our findings show that the results of the first method, network-friction modelling, correspond best
with the comparison data regarding known routes in the study area. However, the general results point
towards the necessity of integrating the two applied methods, since a combination of these models best
reflects the multiscale variability within regional route networks.
Keywords: Spatial modeling; routes; history; roads; paths; Airborne Laser Scan
1. Introduction
Route networks both reflect and influence (large-scale)
cultural and landscape processes and therefore are key
to understanding human-landscape interactions. Van
Lanen et al. (2015a, 2015b) developed a new method for
reconstructing large-scale (supraregional) route networks
in the past. In this paper we investigate the applicability
of this method and a more traditional least-cost path
approach in order to improve our understanding of the
layout of partly-vanished historical route networks on a
* Vienna Institute of Archaeological Science, University of
Vienna, A-1190 Vienna, Franz-Klein Gasse 1, AT
†
‡
§
Groningen University, Department of Landscape History, Oude
Boteringestraat 34, Groningen, NL
Utrecht University, Department of Physical Geography,
Heidelberglaan 5, Utrecht, NL
Cultural Heritage Agency of the Netherlands, Landscape
Department, Smallepad 5, Amersfoort, NL
Corresponding author: Willem F. Vletter (willem.vletter@univie.ac.at)
regional scale. Locating vanished and abandoned routes
is important because: (1) information about past routes
derived from ALS data and historical sources probably
only covers a small percentage of the once-existing
networks and (2) route-network development can help
to study human-landscape interactions in the past.
Over time many routes will have disappeared mainly
through dynamic geomorphological (e.g. erosion) and
human-induced processes (e.g. agricultural and building
activities). However, these same dynamics through routenetwork modelling enable us to calculate the probable
location of many of these vanished routes, since not every
region is equally suitable for travel and transport and
therefore for hosting (persistent) route networks (Van
Lanen et al., 2015a, 2016; Van Lanen, 2016).
Our study area is the Veluwe region in the Netherlands.
The Veluwe is an area located in the central part of
the Netherlands (ca. 1100 km2; Figure 1). We selected
this region as research area since high-resolution
environmental (e.g. geomorphology, palaeogeography),
cultural (e.g. settlement patterns) and Airborne Laser
2
Vletter and van Lanen: Finding Vanished Routes
Figure 1: Section of the archaeological landscape map of the Netherlands (1:50,000) depicting the Veluwe region.
Clearly visible are the characterizing high push moraines in this area (light and dark orange sections). For a detailed
description of the individual landscape units, legend and background information please see Rensink et al. (2017).
Scanning (ALS) data are available, making this region well
suited for a more detailed, integrated modelling approach
(Figure 2). The region features many different landscapes
including large sand drifts, woodlands and heaths. The
most striking characteristic of the Veluwe is the presence
of relatively high ice-pushed moraines formed during the
Saalian (ca. 150,000 years ago). Additionally the Veluwe
contains some very long cover-sand ridges and snowmelt
water valleys. Relief in the region nowhere exceeds
110 meters (the highest point of the push moraines) and
slopes are generally gradual.
It has been suggested that some of the routes on the
Veluwe date back as far as the Bronze Age (2000–800
BC) and possibly are marked by prehistoric barrows
(Bakker, 1976). Using visibility analyses and geographical
information systems (GIS), Bourgeois (2013) underlines
that these mounds might have been used as landmarks
for routes, but also notes that convincing physical
evidence for these routes is lacking. The earliest confirmed
remnants of routes within the research area date to the
late Middle Ages.
Route-network modelling is essentially a type of spatial
modelling. Recently Van Lanen et al. (2015a) developed
a network-friction model (NFM) in order to reconstruct
Roman and early-medieval route networks. Following
the definition by Van Lanen et al., “network friction is the
variable that determines potential regional accessibility
based on the comparison of local and surrounding
landscape factors” (2015a, 200–201). This model was
specifically designed to model landscape prerequisites
for Roman and early-medieval route zones in dynamic
lowlands where relief is often not a decisive factor
for route or path orientation. By integrating cultural
(e.g. settlements, burial sites) and environmental (e.g.
palaeogeography, geomorphology) factors in the NFM,
Van Lanen et al. (2015b) modelled possible route zones
on a supraregional level for the present-day Netherlands
(Appendix A). The models’ outcome was validated against
archaeological data on infrastructure and isolated finds
and obtained good results. However, the calculated NFMroute zones were modelled using a relatively straightforward efficient path computation, i.e. the shortest
distance between two settlements following the most
accessible areas (Van Lanen et al., 2015b). As was already
stated by Van Lanen et al. (2015a, p. 214, 2015b, p. 156), the
next necessary steps for the network-friction method are
to: 1) test its applicability on a more detailed regional level
and for a different historical period and 2) to compare the
models’ outcome with results from other route-network
reconstruction methods, such as the extraction of roads
and paths from ALS data, and the study of historically
attested routes.
In this paper we apply two different types of routenetwork modelling: the network-friction method and the
more traditional LCP calculations. The aims of this paper
are: 1) to reconstruct route networks around AD 1500 by
Vletter and van Lanen: Finding Vanished Routes
3
Figure 2: The research area (in red) including settlements overlaid on the Topographic Military Map 1850 (TMK 1850).
Contemporary rivers are visible in blue.
applying both modelling techniques on the research area;
2) to determine the general applicability and usefulness of
both approaches for route-network reconstructions on a
regional scale level by comparing each outcome with data
on known historical routes in the study area.
2. Route networks and GIS modelling
Over the last few decades a substantial number of papers
and books have been written about spatial and predictive
modelling in archaeology. Many of these primarily
are theoretical exercises of exploring (technological)
possibilities (e.g. Van Leusen et al., 2005; Jiang and
Eastman, 2000; Murietta-Flores, 2010). Moreover, many
of these studies generally have produced few results or
have had relatively limited impact (e.g. Gietl et al., 2008;
Verhagen, 2013; Polla and Verhagen, 2014). One of the
approaches we are going to apply, LCP, can be defined
as a predictive model. We define the latter as a method
predicting routes or paths between two specific locations.
The NFM is much more a spatial model. Although the NFM
calculates potential movement corridors for probable
routes based on multiple geoscientific variables, in itself it
does not predict routes or paths. The NFM however does
allow for the integration of large-scale archaeological
data and the calculation of supraregional route zones
(Van Lanen et al., 2015b). Assuming that all spatial and
predictive models are “expressions of a probabilistic
relationship between human behaviour and prior existing
spatial conditions” (Whitley, 2005, 124), the outcomes of
the NFM and LCP models can be compared.
Traditionally, LCP calculations are most common in
route-network modelling. By calculating so-called friction
surfaces this method calculates the most probable routes
by determining which path requires the least effort to
move between two points. In most cases, these friction
surfaces are mainly based on the slope of the terrain.
However, slope generally has not been the single decisive
factor in past movement through the landscape (Howey,
2011). Alternatively, optimal-path calculations are used to
better understand the formative principles of routes and
paths and to compare these to historically documented
routes (Posluschny and Herzog, 2011; Doneus, 2013).
Other less-frequently applied route-network modelling
techniques include circuit modelling (Howey, 2011) and
From Everywhere To Everywhere (FETE) (White and Barber,
2012). Often these route-network modelling techniques
4
neglect the influence of non-environmental factors (e.g.
political, socio-economical, religious) which probably
greatly influenced past route-network development (Bell
and Lock, 2000; Llobera, 2000; Van Lanen et al., 2015b).
Other studies point at the relative importance of other
cost factors such as river crossings and different types
of transport (e.g. by foot or carriage) in the formation of
these past routes (Herzog, 2013). Van Lanen et al. (2015a,
2015b) suggest that in dynamic lowlands relief probably
was not a decisive factor for route orientation, and that
combined local and surrounding landscape conditions
(e.g. soil types, groundwater levels) were much more
decisive. Current models often are not adapted to include
all these different decision-making factors (Citter, 2012).
For this reason a variety of different and complementary
models is needed to reconstruct historical reality
(Verhagen and Whitley, 2012; Citter, 2012; Herzog, 2013;
Fovet and Zakšek, 2014).
Despite the difficulty of incorporating cultural
and environmental variables, predictive and spatial
modelling in GIS are very promising techniques for the
discovery and analysis of prehistoric and historic route
and path networks. This is especially true for map-based
reconstructions, because the chronology and status
of known routes may be uncertain and many major
connections have not yet been identified. Routes have
history, and their course results from a long and complex
evolution combining abandonments, changes in status
and reactivations. Optimal path modelling simulating
the connections between contemporary archaeological
sites helps to comprehend the chronology and hierarchy
of former communication networks (Fovet and Zakšek,
2014).
Past routes in our research area almost always were
unpaved, implying that tracks may have wandered within
route zones following broad movement corridors often
several hundred metres wide. We define movement
corridors as those areas where landscape setting provides
people with favourable connectivity options, e.g. route
zones, to other places of interests, such as settlements,
fortresses, mining areas (cf. Van Lanen and Pierik, 2017;
Van Lanen, 2017). These route zones filled with (seasonally)
shifting tracks reflect generalized routes and should not
be regarded as exact constants (Bell and Lock, 2000;
Van Lanen et al., 2015b). As such, these route zones are
spatially more dynamic than roads (which are much more
fixed features connecting two places), but in orientation
they are very similar (Van Lanen et al., 2016). Because of
this flexibility, cultural and environmental factors play a
decisive role in the formation of route zones. Therefore in
order to accurately model these complex networks, spatial
models integrating both cultural and environmental
dynamics should be produced for specific cultural periods
(Wilcox, 2009; Van Leusen et al., 2005).
3. Material
NFM modelling in the current study was based on
the datasets used by Van Lanen et al. (2015a, 2015b;
Sections 3.1–3.4). LCP modelling was based on
Airborne Laser Scan (ALS) data and groundwater-
Vletter and van Lanen: Finding Vanished Routes
level reconstructions extracted from the soil maps
(Sections 3.5–3.6).
3.1. Palaeogeography AD 1500
Palaeogeographical reconstructions for multiple periods
were first issued in 2011 with the presentation of the
Atlas of the Holocene Netherlands (Vos et al., 2011). These
maps were updated in 2013 when a second generation
became available (Vos and De Vries, 2013; Vos, 2015).
The palaeographical reconstructions by Vos et al. (2011,
2013) and Vos (2015) describe the genesis of the Dutch
landscape over the last 11,000 years. The reconstructions
are multi-disciplinary in origin, combining numerous
datasets from the Humanities and Geosciences, e.g.
archaeology, geology, palaeoecology, onomastics and soil
sciences. Therefore these maps can be used as a nationwide
reconstruction of the palaeolandscape for both Holocene
and Pleistocene areas.
3.2. Geomorphology
A nationwide, digital geomorphological map became
available in 2003 (Koomen and Exaltus, 2003; Koomen
and Maas, 2004). The map was created by combining
field observations, bore-hole data and surveys with
detailed elevation models (Koomen and Maas, 2004). The
dataset not only contains information on the individual
geomorphological units, but also on relief, genesis and ages
of the landscape elements on a 1:50,000 scale. Therefore
the map greatly adds to our understanding of the past
landscape, especially regarding the higher, more stable
Pleistocene regions. As a result, the geomorphological
map of the Netherlands has proven itself invaluable
for archaeological predictive modelling and was used
amongst others for the indicative map of archaeological
values (IKAW) (Van Leusen et al., 2005; Deeben, 2008).
3.3. Soil and groundwater level data
The soil map of the Netherlands has been developed based
on the soil-classification system of De Bakker and Schelling
(1989). It provides an overview of all current soil types
(up to a depth of 1.20 metres) in the Netherlands (Steur
and Heijink, 1991; De Vries et al., 2003). Additionally, the
datasets also contain data on the average groundwater
levels between 1958 and 1999 (De Vries et al., 2003; Van
de Gaast et al., 2010). In contrast to the geomorphological
dataset, the soil map does not provide any chronological
information about the ages of individual soils. Since soils
change through time, the use of the soil map for historical
periods requires expert judgement. This map is especially
useful for the analyses of higher, more stable regions in
the Netherlands, such as the Veluwe.
3.4. Settlement data
Settlement data for the Veluwe region were collected
by using OpenStreetMap data on current places in the
Netherlands.1 Rutte and IJsselstijn (2014) claim that most
towns in the current Netherlands date back to before AD
1300. Therefore present-day data can be used to recreate
16th-century habitation and to determine route-network
persistence (Van Lanen et al., 2016). Maps from part 1 and
Vletter and van Lanen: Finding Vanished Routes
2 of the Atlas van Nederland (1984) made by Prof. dr. Renes,
Histland2 data and the Archaeological Landscapes Map
of the Netherlands3, were used to filter out settlements
located in uncultivated lands (e.g. heathlands, younger
reclamation areas) during the 16th century.
3.5. Historical roads in the Veluwe region
Historical road data for the Veluwe region were extracted
from the AD 1600 route reconstructions by Horsten (2005)
and the Topographic Military Maps 1850 (TMK 1850).
Horsten (2005) reconstructed historical road networks
for the period between the 16th and 19th century. This
historical road atlas is primarily based on old maps, and
reconstructs major interregional roads for the years ca. AD
1600, ca. 1800 and ca. 1848. Horsten (2005) choose these
intervals since no detailed old maps are available dating
before AD 1600, and after AD 1848 railway networks
substituted many of these thoroughfares (Horsten,
2005). In the current study we used the earliest AD 1600
reconstruction for validation purposes (Figure 3).
The TMK 1850 first appeared between 1850 and 1864
(Van der Linden, 1973). The map constitutes the oldest
nationwide map of the Netherlands on a 1:50,000 scale
5
and was compiled for military purposes. Through thematic
colouring the TMK 1850 provides a high-resolution
overview of the mid-19th-century landscape. Since the
map predates the massive industrialisation that began
at the start of the 20th century, which radically changed
many parts of the Dutch landscape, it provides invaluable
information on past routes and other historical landscape
elements that have since disappeared. Although also
older, local maps are available, for example for the
current province of Gelderland, these maps lack sufficient
geographical precision to be used for this study.
3.6. Airborne Laser Scanning (ALS) data
In the Netherlands ALS data are the basis of the digital
elevation model of the Netherlands, which is referred to
as AHN. In 2003 the first generation of this model, the
AHN-1, became available. This model uses a density of one
height measurement per 1 to 16 m2, resulting in a highest
available grid-cell resolution of 5 m2 (Brand et al., 2003;
Swart, 2010). This raster is too crude for detailed analyses,
which limits the usefulness of the AHN-1 on a local scale.
To counter these limits a second generation of the AHN,
the AHN-2, was presented in 2013 (Van der Zon, 2013).
Figure 3: Road network in the Netherlands around AD 1600 reconstructed by Horsten (2005). In the present study
only connection routes on the Veluwe (smaller framework) were included.
6
Vletter and van Lanen: Finding Vanished Routes
Table 1: ALS parameters used for route-network
modelling.
Meta-information ALS
ALS-Project
Actueel Hoogtebestand
Purpose of Scan
Water management
Time of Data Acquisition
April 2010
Point-distribution (pt. per sq. m) 6–10
Table 2: Model design of the Veluwe network-friction
model.
Field name
Description
Grid_ID
Unique identifier for each
individual grid cell
Grid_ID500 m
Unique identifier grid cell in larger
nationwide grid
Unit_AD1500
Unit of grid cell according to
palaeogeographic map of AD 1500
Acc_AD1500_LA
Accessibility AD 1500 based on
land factors
Unit_Arch_La
Unit of grid cell according to
geomorphological map of the
Netherlands
Scanner Type
Riegl LMS-Q680i
Full-Waveform
Scan Angle (whole FOV)
45°
Flying Height above Ground
600 m
Speed of Aircraft (TAS)
36 m/s
Laser Pulse Rate
100 000 Hz
Scan Rate
66 000 Hz
Arch_LA_GeomorfCode Original geomorphological code
map of the Netherlands
Strip Adjustment
Yes
Acc_Arch_La_LA
Filtering
Yes
Accessibility geomorphology based
on land factors
DTM-resolution
0.5 m
Unit_Soil
Unit of grid cell according to soil
map of the Netherlands
Interpolation method
Moving planes
Code_soil
Original code from soil map
Acc_Soil_LA
Accessibility based on land-factors
soil map
Unit_GW
Original code groundwater level
map
Acc_GW
Accessibility based on groundwater
reconstructions
Nf_AD1500_Sum
Combined network friction sum
AD 1500
Nf_AD1500_AvG
Combined network friction average
AD 1500
The new dataset contains up-to-date measurements and
a much higher resolution, with 6–10 measurements per
m2. For the sake of clarity, we use the (raw) ALS dataset
and not the AHN models based on it. We defined the
most important parameters of the ALS data for our routenetwork modelling in Table 1.
4. Method
The presented datasets were used to create a NFM
showing local accessibility and to reconstruct route
zones in the study area based on the network-friction
method and LCP modelling.
4.1. The network-friction model (NFM)
The Veluwe NFM is based on the method presented
by Van Lanen et al. (2015a; see Appendix A for more
background information). It excludes data postdating
AD 1600 and exclusively uses a 100 × 100 m grid-cell
resolution. The new model integrates environmental
data in order to locate landscape obstacles that could
limit accessibility (Sections 3.1–3.3). It covers the Veluwe
region and immediate surroundings (Figure 2). It is
important to include the latter since habitation in this
area is largely clustered on the edges of the Veluwe. The
model consists of 250,447 individual grid cells, roughly
covering a region from the current city of Amersfoort
in the west to the river IJssel in the east. Contrary to
the NFM developed by Van Lanen et al. (2015a) which
used a 500 × 500 m grid-cell resolution, the Veluwe
NFM consists out of 100 × 100 m grid cells. Each cell
was given a unique identifier and location coordinates.
The Veluwe NFM consists of 14 data fields, covering all
imported datasets (Sections 3.1–3.4 and Table 2). Pointlocation data such as settlements were not converted to
the grid.
The Veluwe NFM is designed to reconstruct local
accessibility, which is crucial for route orientation. Since
within the NFM each grid cell can contain only one specific
data unit per imported dataset (e.g. palaeogeography,
geomorphology), based on archaeological and historical
datasets we first imported traditionally accessible
landscape units (e.g. high, dry areas; Appendix A). As
a result, the NFM represents the maximum amount of
possible movement corridors. In line with Van Lanen et al.
(2015a) data was imported by overlaying the geoscientific
datasets on the grid. The geometric intersections were
imported using the following query in MapInfo 12.0.2:
Grid_ID.obj intersects Unit_External_Dataset_X.obj
In this SQL-query the location geometry (.obj) of each
grid cell (Grid_ID) is compared to a specific landscape
unit (e.g. peat, ice-pushed ridge) from each of the
external datasets (Section 3). Grid cells intersecting
these selected geoscientific polygons were then updated
with the content of the external dataset. This import
process was repeated for each of the geoscientific
datasets. Next, within the geoscientific datasets
accessibility values were given to each landscape unit
following the classifications presented and substantiated
Vletter and van Lanen: Finding Vanished Routes
7
Table 3: Network-friction levels as defined by Van Lanen
et al. (2015a).
in Van Lanen et al. (2015a; Appendix A). These values
were then used to calculate network-friction averages
depicting local accessibility based on natural landscape
settings showing obstacles and corridors for movement in
the region (Table 2; Appendix A). Local accessibility was
defined using five network-friction levels: 1–5, ranging
from inaccessible to accessible respectively (Table 3).
Veluwe based mainly on the largest water body in the
region, the Zuiderzee (now IJsselmeer). Nevertheless,
other water bodies like creeks were also taken into
account when setting the threshold. Depressions in
the landscape were also identified based on the DTM in
combination with the suited tools in ArcGIS (fill and cut
fill). These values were then combined into one GIS layer
reflecting the lower, wetter areas. All remaining areas
were classified as higher grounds. In order to make them
suitable for LCP modelling in ArcGIS, the values (costs)
of two classes were based on the terrain coefficients of
Soule and Goldman (1972).
In our LCP calculations, terrain, slope and groundwater
levels have (changeable) weight values, which serve as
input for the cost-path calculation tool in ArcGIS. This
allows us to calculate LCP routes between two places
and to compare these trajectories with data on historical
routes. In order to optimize the comparison with the
NFM-route zones we selected routes that cross the study
area in different directions.
4.2. Modelling route zones based on network friction
4.4. ALS extraction
NFM-route zones were modelled based on the Veluwe
NFM and available settlement data (Section 3.4). Deviating
from the original method by Van Lanen et al. (2015b;
Appendix A) we only modelled land routes for the research
area since no detailed overviews of water routes for the
period around AD 1500 exist. Additionally, we excluded
burial sites from our model, since the location rules
surrounding these areas completely differ from the Roman
and early-medieval periods. NFM-route zones reflect
probable zones where people in the past frequently moved
through the landscape, i.e. areas likely to contain roads,
routes, paths and tracks. These route zones were modelled
based on the assumption that they are largely defined by
the wish to follow movement corridors (pull factors) and
consequently to avoid movement obstacles (push factors).
Following this hypothesis the shortest distance between
two settlements following the best possible networksfriction values was calculated. These calculated lines were
then converted to route zones with a width of 100 m (i.e.
the highest possible accuracy level in a 100 × 100 m gridcell resolution), which were used to compare with data on
extracted hollow ways and known AD 1600 routes.
Remnants of cart tracks and hollow ways were extracted
using ALS data from the Veluwe. Based on the extraction
model developed by Vletter (2014) a semi-automatic
extraction was executed on the data from the Veluwe
(Section 3.6). The micro topography was visualized in grey
scales using the openness module in OPALS developed
by the Technical University of Vienna (Yokoyama et al.,
2002; Pfeifer et al., 2014). Although some visualization
techniques might provide better results for the
reconstruction of microrelief in flat areas (Hesse, 2016),
we chose for openness because other techniques are less
suited for extraction purposes. We used openness in an
extraction model created in the software plug-in Feature
Analyst (FA) in ArcGIS. The original extraction model
for the Leitha area was adjusted to fit the conditions of
the research area. Since it is difficult to differentiate
between man-made linear features, such as (historical)
roads and paths made by cart and wagons, and natural
linear features, some additional manual mapping based
on expert judgement was needed. Further, the merge and
dissolve tools in ArcGIS were used to create road sections.
In order to allow a comparison between these ALSextracted hollow ways (which can spread over hundreds
of meters) and our NFM and LCP routes, we drew a ‘centre
line’ through the extracted zones.
Description
Network-friction
value
Inaccessible
1
Poorly accessible
2
Moderately accessible
3
Reasonably accessible
4
Accessible
5
4.3. LCP model
In order to determine the applicability of the LCP method
we selected four well-known historical roads running
through the study area: 1) the hessenweg between Hattem
and Voorthuizen, connecting Amsterdam over land to
Germany, 2) the road between Arnhem and Harderwijk
(Harderwijkerweg), 3) the route from Dieren to Barneveld
and 4) the route between Apeldoorn and Voorthuizen.
Since in relative lowlands such as the Netherlands
slope often is not a decisive factor (Verhagen, 2013)
(Section 2), we have applied an LCP model incorporating
three factors: terrain, slope and groundwater levels.
Terrain values were based on two factors: the vicinity
to water bodies and depressions in the landscape. We
calculated the vicinity to water bodies by applying a
threshold in the digital-terrain model (DTM) of the
4.5. Modelling validation through comparison data
In order to determine the applicability and usefulness
of the Veluwe NFM and LCP models we compared the
modelling outcomes with several other datasets on
route networks in our study area. First, the NFM and LCP
outcomes were compared with data on known routes
in the study area through a comparison with the TMK
1850, the AD 1600 route network compiled by Horsten
(2005), and the extracted ALS route data. Second, we used
the NFM to calculate the correspondence between local
accessibility values based on landscape setting and the
LCP model, routes visible on the TMK 1850, the AD 1600
route network, and extracted ALS route data.
8
Vletter and van Lanen: Finding Vanished Routes
Figure 4: Network-friction map of the research area around AD 1500. Probable movement corridors are show dark
green, and obstacles in red. Additional percentages of poorly and well-accessible areas are given. Major rivers (in blue)
bordering the research area, the river IJssel in the east and the river Rhine to south.
5. Results
5.1. Network-friction map Veluwe ca. AD 1500
Based on the network-friction values the research area is
divided into several corridors and obstacles for movement
(Figure 4). The central part of the Veluwe appears to have
been relatively well accessible. This is in contrast to the
eastern and southern parts, where the rivers Rhine and
IJssel (and their floodplains) constituted clear obstacles.
In these parts the location of bridges and ferries must
have determined the orientation of routes. Several stream
valleys ran from the central part of the Veluwe to the edges
of the research area. In the lower parts of these valleys the
occurrence of peat and clay severely must have hampered
movement, especially in the western part of the research
area (Figure 4). Accessibility in the north of the research
area was negatively influenced by a large peat area and the
water from the Zuiderzee.
5.2. NFM route zones
Route zones were modelled using settlement distribution
around AD 1600 and the method presented by Van Lanen
et al. (2015b; Appendix A) (Figure 5). Results show that
the majority of the modelled route zones are located in
the western part of the research area. Only a few routes
9
Vletter and van Lanen: Finding Vanished Routes
Figure 5: Route network based on the network-friction approach in the Veluwe area around AD 1500.
connected settlements east and west from the higher
push moraine, which seems to have formed an obstacle.
Route zones in the western and eastern parts of the study
area clearly were influenced by local soil conditions such
as the presence of peat and by the vicinity of waterways
(Figure 5).
routes and to determine the relative influence of specific
landscape factors on route-network development. The
flexibility of the model, i.e. the possibility of assigning
different or new weight values to individual factors, allows
us to easily expand or change the focus of the model.
5.4. ALS-extracted routes
5.3. LCP routes
Based on the LCP model we were able to calculate 4 LCP
routes (Figure 6). The modelled routes point towards an
especially strong influence of the terrain factor on routenetwork orientation. In general, groundwater levels and
slope appear to have been of less influence. The LCP route
between Arnhem and Harderwijk (Harderwijkerweg)
is an exception, since here multiple groundwater-level
differences influenced route orientation. The factor slope
appears to have been least influential on the routes in
the area. Therefore, the LCP model allows us to calculate
Based on the ALS extraction we were able to locate a high
number of hollow ways (Figure 7). Primarily these could
be extracted for the sandy regions, with the exception of
the sand-blown areas where past roads and paths probably
are covered. The western and eastern parts of the study
area show limited signs of hollow ways, which is probably
due to different soil conditions (mainly peat and clay) in
these parts. Looking at the directionality of the extracted
hollow ways, they can be divided into two main groups: 1)
a high number of west-east connections crossing the push
moraine; 2) a lower number of north-south connections
10
Vletter and van Lanen: Finding Vanished Routes
Figure 6: Least-cost path (LCP) calculated routes on the Veluwe. Four routes were preselected and modelled: 1) the LCP
running from Voorthuizen to Hattem, 2) LCP running from Harderwijk to Arnhem, 3) LCP running from Voorthuizen
to Apeldoorn and 4) the LCP running between Barneveld and Dieren.
descending from the push moraine to the coastal plane,
especially in the northern part of the study area.
6. Validation through comparison data
In order to determine the applicability of the NFM and
LCP models we compared the outcomes with existing
route-network datasets pertaining to the study area. As
comparison data we used the ALS-extracted hollow ways,
routes visible on the TMK 1850, and the AD 1600 route
reconstruction by Horsten (2005).
6.1. Validating the NFM
6.1.1. NFM accessibility
In order to determine the usefulness of NFM accessibility
calculations, we computed the agreement between the
ALS-extracted hollow ways, the AD 1600 route network
and the TMK 1850 routes (Table 4). For the comparison
datasets we determined the absolute number (in
metres/grid cells) and the surface percentages of routes
located within either well-accessible or poorly-accessible
areas (NFM values 4–5 and 1–3, respectively). For each
of the comparison datasets a convincing agreement
between local accessibility and the occurrence of routes
can be identified, showing that a combined landscape
setting clearly influences route-network development
(Table 4). The relative high number of ALS-extracted
hollow ways located in well-accessible areas (99.0%) is
best explained by preservation circumstances, i.e. the
hollow ways that still remain today are best preserved in
higher and dryer areas, which often also reflect the wellaccessible movement corridors. For the AD 1600 network,
some routes ran through less-accessible areas, mainly near
the rivers in the south and northeast of the study area
(Figure 7). Although the lowest agreement of the TMK-
Vletter and van Lanen: Finding Vanished Routes
11
Figure 7: Hollow ways (in black) extracted from airborne laser scanner data. Sections where these hollow ways
correspond with the calculated NFM-route zones (red) are highlighted in blue.
Table 4: Agreement between local accessibility based on network friction, the ALS-extracted hollow ways and the AD
1600 route network.
Description
ALS-extracted hollow ways
(in metres)
AD 1600 route network
(in square metres)
TMK-1850 routes
(n grid cells)
NFM value NFM value % NFM value % NFM value
<=3
>=3
<=3
>=3
234
269247
1.0%
99.0%
1397
6283
18.2%
81.8%
35359
117061
23.2%
76.8%
1850 routes, being still quite high at 76.8%, ran through
well-accessible areas, it should be noted that in the entire
NFM 66.9% of the grid cells reflect well-accessible areas
(n = 167,494).
6.1.2. NFM-route zones
The calculated NFM-route zones (100 m wide) were
compared with the ALS-extracted hollow ways, the AD
1600 route network, and routes shown on the TMK
1850 (Table 5). For each of the comparison datasets we
determined the surface area of routes corresponding
with the NFM-route zones. Results show that only 2.3%
of the ALS-extracted hollow ways are located in NFMroute zones (Figure 8). This might be explained by the
fact that hollow ways reflect a different chronological
time frame or a different type of connection (i.e. more
local paths within the network). A strong argument for
this interpretation is the perpendicular orientation of
the NFM-route zones and the ALS-extracted hollow ways.
Agreement with the AD 1600 network is notably higher:
29.2% (Table 5; Figure 9). Looking at the distribution
of the overlap between the two networks, agreement
in the lower parts of the study area is relatively high,
predominantly the western part. Additionally, the
12
Vletter and van Lanen: Finding Vanished Routes
Table 5: Agreement between route zones (100 m wide) based on settlement data and network friction, and the
ALS-extracted hollow ways, the AD 1600 route network and the TMK 1850.
Description
No agreement
Agreement
% Not
% Correlating
with route zone with route zone correlating with
with
(in km2)
(in km2)
route zone
route zones
ALS-extracted hollow ways
(in km2)
1570
37
97.6%
2.4%
AD 1600 route network
(in km2)
44.8
18.5
70.8%
29.2%
782.2
313.2
60.0%
40.0%
TMK 1850 routes
(in km)
Figure 8: AD 1600 road-map sections overlapping with NFM routes (in blue).
dissemination of the overlapping route sections visually
points towards a higher agreement when increasing
route-zone width, i.e. correlating route sections covering
the majority of the network (Figure 9). The largest
deviation between the two networks appears to be
located on top of the largest push moraine in the area.
Here the NFM fails to reconstruct the thoroughfare
reconstructed by Horsten (2005) running on top of
13
Vletter and van Lanen: Finding Vanished Routes
Figure 9: Local-accessibility values integrated in the AD 1600-route network. For each section within the AD 1600
route network local accessibility values based on network friction is given. Green areas depict well-accessible areas,
yellow, orange and red poorly accessible regions.
Table 6: Agreement between LCP-calculated routes (100 m wide route zone), the ALS-extracted hollow ways, the AD
1600 route network and the TMK 1850.
Description
Outside
Inside
% Outside
% Inside
route zone route zone route zones route zone
ALS-extracted hollow ways
(in metres)
150314
11292
92.7%
7.3%
AD 1600 route network
(in metres)
147223
14383
91.1%
8.9%
TMK 1850 routes
(in metres)
108600
53006
67.2%
32.8%
this moraine, which is best explained by more complex
cultural variables (e.g. socio-economic) behind the
development of this route. Comparison results are best
with the TMK 1850, showing that 40.0% of the routes
on this map correspond with the calculated NFM-route
zones. It should, however, be noted that the TMK 1850
depicts a much higher number of routes than the other
two comparison datasets.
6.2. Validating the LCP model
The results of our LCP model, routes converted to
100 m-wide zones, were compared to the same datasets
as those that were used in the validation of the NFM
(Table 6). Given the agreement between the LCP routes
and the ALS-extracted hollow ways, only 7.3% of the hollow
ways correspond to the LCP routes. This relatively minor
overlap might be explained by the partial disappearance
of these routes over time. This is supported by the fact
that 53.0% of the extracted hollow ways correspond with
routes shown on the TMK 1850, which suggests that
local hollow ways are better preserved than long-distance
ones. The agreement between the LCP routes and the
AD 1600 network is slightly higher than with the ALSextracted hollow ways: 8.9% (Table 6). This still relatively
minor overlap is best explained by the fact that Horsten
(2005) in his overview did not reconstruct the complete
14
route network and only focussed on the most important
connections. Additionally, the very small applied route
zone of 100 m and the incompleteness of the model
further hamper results. Since we selected the LCP routes
based on their confirmed existence in historical sources,
agreement with the TMK 1850 is (not surprisingly) the
highest: 32.8% (Table 6). It should, however, be noted
that the TMK 1850 shows more roads and paths than the
other comparison datasets. Therefore, more alternative
routes are likely to fall into the 100 m route zone and
variations between the individuals LCP routes are visible
(for more background data see, Appendix B).
7. Discussion
7.1. Network-friction method
The high percentage of overlap between high NFM
accessibility and the location of ALS-extracted hollow
ways and AD 1600 routes, 99.0% and 81.8% respectively
(Table 4), point towards a strong link between route
networks and (combined) landscape setting. Although
the NFM does not predict the location of individual
hollow ways it does calculate regions where remnants of
these landscape features can be expected. This predictive
potential of the NFM could be further increased by
incorporating detailed information on past-drift sands
into the model. It should however be noted that these
strong agreements potentially are positively influenced by
the general high level of accessibility of the Veluwe region
(66.9% well-accessible areas). Therefore to further test the
applicability of the network-friction approach a similar
NFM should be applied on more dynamic lowland areas,
such as river areas.
In contrast, route zones calculated through the NFM
show relatively little agreement with the ALS-extracted
hollow ways and AD 1600 routes. There are various
explanations for the minor overlap between NFM-route
zones and the ALS-extracted hollow ways (2.3%; Table 5).
First, scale differences between the two methods most
likely play a role. Where the NFM-route zones were
designed to model supraregional connections, the ALSextracted hollow ways (based on orientation) appear
to reflect a more local network of paths and tracks
(Section 6.1). In order to determine whether the NFM
can be used to also model these local paths and tracks,
grid-cell resolution and especially input-data resolution
should be greatly increased in the future. Second, the
NFM specifically was designed to reconstruct route
zones around AD 1500. The ALS-extracted hollow ways
lack chronological differentiation and can only be dated
relatively. Therefore part of the ALS-extracted hollow
ways could actually reflect preceding or postdating time
frames. Third, both the NFM and the ALS-extracted hollow
ways only reflect remnants of the old route networks. ALS
data is only useful for locations where features of these
routes and paths are still preserved in the landscape,
and preservation strongly depends on geomorphological
stability (non-dynamic sandy areas) and cultural
conditions (e.g. non-densely populated or cultivated
areas). Fourth, the current NFM-route zones currently lack
a differentiation between different types of routes (e.g.
route hierarchy); adding such detailed (historical) data
Vletter and van Lanen: Finding Vanished Routes
to the model would probably benefit modelling results
further.
Agreement between the NFM-route zones and the
AD 1600 route network is much higher, but still only
29.2% (Table 5). This is best explained by the spatial
resolution of the NFM and the method behind route-zone
calculations. First, in our agreement calculations we used
100 m wide route zones, despite the fact that many of
these zones could actually be several hundreds of metres
wide (Section 2). In this respect, the overlap percentage
reflects a minimum amount of corresponding routes
and actual overlap percentages might be higher. Second,
NFM-route zones were designed to reconstruct large-scale
connection transport zones (Section 2). The method was
not designed to model routes on a detailed regional scale,
which would require a more dense network with multiple
connections between nodes. In order to determine the
full potential of the network-friction method, NFM-route
zones could incorporate more detailed network analyses
and LCP calculations in combination with more detailed
geoscientific data. Figure 8, however, shows that despite
the low overlap percentage between the NFM-route
zones and the AD 1600 network, many parts do line up,
and increasing route-zone widths would probably lead to
much higher agreements. The most notable exception
is the route running over the high push moraine in the
centre of the study area. Here, the NFM fails to calculate a
route zone since no nearby settlement data are available.
The probably socio-economic origin of this route
reconstructed by Horsten (2005) fundamentally differs
from the (Roman and early-medieval) variables defined by
Van Lanen et al. (2015b). In order to also model these kinds
of routes, other input variables, specifically designed for
this historical period, should be developed for the NFM.
One of the aims of the current study was to determine
the applicability of the network-friction method on a more
detailed, regional scale and a different historical period. The
method originally was designed specifically for the Roman
period and Early Middle Ages, but this study shows that
the approach does have potential in reconstructing more
recent route networks. Since the NFM is an accumulative
model the number of input datasets can be potentially
endless, making the model flexible and especially accurate
in reconstructing past accessibility based on landscape
prerequisites. Although the models’ resolution was
increased by decreasing grid-cell size from 500 × 500 m
to 100 × 100 m, agreement percentages (Table 5) show
that NFM route-zone calculations probably benefit from
a) more detailed geoscientific input data (<1:50,000),
including high-resolution vegetation reconstructions, and
b) modelling techniques from network and least-cost path
analyses.
7.2. Least-cost path method
The LCP-calculated routes agree best with the TMK 1850
dataset. This is not surprising, because a) this map shows
a much higher number of (alternative) roads and paths
and b) the LCP-calculated routes reflect preselected
trajectories of routes known to be in use during the 19th
century (Section 4.3; Appendix B). Based on the LCP model
it has become clear that terrain appears to have been the
Vletter and van Lanen: Finding Vanished Routes
forcing factor in route-network orientation, followed
by groundwater levels and slope. However, the model
also shows that forcing factors can differ per individual
route section (Appendix B). Consequently there is not
one factor dominating route orientation in the Veluwe
and each LCP route actually should be investigated
individually. For example, avoiding lower, wet areas was
especially important for route sections between Arnhem
– Harderwijk which ran on the west brink of the push
moraine and Hattem – Voorthuizen near the coast. If
merely slope would have been the forcing factor for these
routes, these lower areas would have been best suited
for the network and agreement with the TMK 1850 even
lower. In some cases, like the route Dieren – Barneveld
the forcing factor is difficult to assess and cultural factors
especially appear to have been in play (Appendix B). The
diversity in forcing factors do point towards the need of
applying flexible modelling which incorporates changing
local accessibility settings based on a multitude of datasets.
The LCP model applied in this paper calculates routes
based on weight values derived from terrain, slope and
groundwater-level factors. Terrain coefficients were
quantified based on scientific data (Soule and Goldman,
1972). However, many of other weight values were
determined based on expert judgement. For example,
in determining the terrain factor, threshold values were
determined based on a combination of the soil map,
DTM and the TMK 1850 (Appendix B). Therefore these
threshold values depict the present-day situation and
may differ slightly from the historical situation around
15
AD 1500. The same bias applies to the factor slope, which
was calculated based on current elevation data in the
study area. It is currently impossible to determine the
exact differences between the historical and present-day
situation. Therefore the LCP model would benefit from
more detailed geoscientific input data reflecting the
period around AD 1500.
7.3. Methodological integration
This study shows that route-network modelling using
GIS improves our understanding of past route networks
in the Veluwe region. Agreement results are best for the
network-friction approach. Through the accumulative
nature, the NFM integrates multiple geoscientific datasets
and provides dynamic local accessibility values for entire
route trajectories, which allows to compensate for
changing forcing factors. Route zones calculated by the
NFM appear to mainly reflect thoroughfares in the study
area. These NFM-route zones show the best agreement
in the lower areas where movement corridors are most
pronounced. Through the integration of multiple
datasets the NFM also allows to locate omissions in other
datasets. For example by comparing the NFM with the AD
1600 network we were able to locate areas with a highaccessibility level and an abundance of settlements and
therefore increased likelihood of route occurrence not
reconstructed by Horsten (2005) (Figure 10). Agreement
results for the LCP model were lower compared to the
NFM but did show the necessity of incorporating multiple
landscape variables when calculating individual LCP
Figure 10: Network-friction map overlaid on the TMK 1850 and the AD 1600 road map by Horsten (2005) for the
northwestern part of the study area. In this section, for example, it is clearly visible that not all AD 1600 routes connect
settlements, reflecting the fragmentary state of AD 1600 route network. Based on the NFM, movement corridors
are reconstructed which can point towards probable missing route-zone connections. For example connecting
thoroughfares can be expected running from west to east in the northern part of the study area.
16
routes, i.e. different sections within one route can have
different forcing factors. Both modelling approaches show
potential for route-network modelling on the Veluwe and
could be further integrated in the future. Where network
friction provides dynamic local accessibility values, the
LCP modelling allows for the calculation of specific
movement conditions which could benefit the NFM-route
zone modelling on this more detailed regional scale in
order to also include more local paths and tracks in less
pronounced movement corridors.
8. Conclusion
In this study we have applied and compared two different
route-network modelling techniques in order to optimally
reconstruct route networks around AD 1500 in the
Veluwe region. We were able to determine that the central
part of the study area appears to have been relatively well
accessible. In the western, lower parts the presence of peat
and clay must have limited route options, resulting in few
and narrow movement corridors. To the south and east
accessibility was bound by the rivers Rhine and IJssel and
to the north by the sea (Zuiderzee). Although the study
area in general is well accessible, routes appear to have
mainly run along the borders and not through its central
part. This is underlined by the lack of settlements in
this area. Consequently, east-west routes predominantly
appear to have run alongside, and not across, the largest
push moraine.
Both the NFM and LCP model we have applied in this
paper successfully modelled parts of the route networks
around AD 1500. Agreement results with comparison
datasets are highest for the NFM, which shows great
potential in reconstructing past local accessibility and
thoroughfares based on integrating multiple datasets. The
NFM however has difficulties reconstructing more local,
micro-regional connections. This study shows that it is
possible to increase the grid-cell resolution of a NFM to
100 × 100 m, but that much is to be gained by increasing
the resolution of geoscientific and cultural input data.
The more traditional LCP model was especially successful
in determining different forcing factors behind route
development, but agreement results with comparison
data on route network are generally low. However, the
model does show the need for incorporating multiple
factors during LCP calculations. Both models prove quite
useful for route-network modelling on a regional scale,
reconstructing parts of past networks. Because regional
and micro-regional route networks are characterized by
multi-scale variability, i.e. supraregional, regional and
micro-regional connections are all entwined, studying
these spatial structures requires a more integrated multiproxy approach.
Notes
1
For more information on OpenStreet data and mapping,
see www.openstreetmap.org (accessed 17-11-2015).
2
Histland contains data on the reclamation and
dynamics of the Dutch landscape. For more
information, see http://landschapinnederland.nl/
bronnen-en-kaarten/histland (accessed 17-11-2015).
Vletter and van Lanen: Finding Vanished Routes
3
This dataset was developed in 2015 by the
Cultural Heritage Agency of the Netherland. See
http://archeologieinnederland.nl/bronnen-enkaarten/archeologische-landschappenkaart (accessed
on 17-11-2015).
Additional Files
The additional files for this article can be found as follows:
• Appendix A. Network friction. DOI: https://doi.
org/10.16993/rl.35.s1
• Appendix B. Least-cost path (LCP) calculations. DOI:
https://doi.org/10.16993/rl.35.s2
Acknowledgements
This collaborative study was carried as part of the
research program ‘The Dark Age of the Lowlands in an
interdisciplinary light: people, landscape and climate in
the Netherlands between AD 300 and 1000’, which is
funded by the Netherlands Organisation for Scientific
Research (NWO, section Humanities; 2012–2018; project
number 360-60-110). It is also part of the Initiative College
Archaeological Prospection (IC-Archpro) of the University
of Vienna. The authors would like to thank University
Prof. Mag. Dr. Michael Doneas, Prof. Dr. Esther Jansma,
Dr. Bert J. Groenewoudt, and Prof. Dr. Theo Spek for their
comments on earlier drafts of the paper.
Competing Interests
The authors have no competing interests to declare.
Authors Information
Willem F. Vletter and Rowin J. van Lanen are contributed
equally to this work.
References
Bakker, J. A. (1976). On the possibility of reconstruction
roads from the TRB period. Berichten van de
Rijksdienst voor Oudheidkundig Bodemonderzoek,
26, 63–91.
Bell, T. & Lock, G. (2000). Topography and cultural
influences on walking the Ridgeway in later
prehistoric times. Lock, G. R. (Eds.), Beyond the map:
archaeology and spatial technologies, 85–100. IOS
Press.
Bourgeois, Q. P. J. (2013). Monuments on the horizon.
(Dissertation thesis, University of Leiden). Sidestone
press.
Brand, G. B. M., Crombaghs, M. J. E., Oude
Elberink, S. J., Brügelmann, R. & de Min,
E. J. (2003). Predisiebeschrijving AHN 2002,
Rijkswaterstaat AGI.
Citter, C. (2012). Modelli predittivi e archeologia
postclassica: Vecchi strumenti e nuove prospettive.
Redi, F. & Forgione, A. (Eds.), Atti del VI convegno
nazionale della SAMI, 3–6. L’Aquila, 2012, Firenze:
Edizioni All'Insegna del Giglio.
De Bakker, H. & Schelling, J. (1989). Systeem van
bodemclassificatie voor Nederland. De hogere niveaus.
Wageningen, Pudoc.
Vletter and van Lanen: Finding Vanished Routes
Deeben, J. H. C. (Eds.), (2008). De Indicatieve Kaart
van Archeologische Waarden, derde generatie.
Rapportage Archeologische Monumentenzorg, 155.
Amersfoort.
De Vries, F., de Groot, W. J. M., Hoogerland, T. &
Denneboom, J. (2003). De bodemkaart van
Nederland digitaal; Toelichting bij inhoud, actualiteit
en methodiek en korte beschrijving van additionele
informatie. Alterra-rapport, 81. Wageningen, Alterra
Research Instituut voor de Groene Ruimte.
Doneus, M. (2013). Die hinterlassene Landschaft.
Prospektion
und
Interpretation
in
der
Landschaftsarchäologie.
Mitteilungen
der
Prähistorischen Kommission, 78. Vienna, Verl.
D. Österr. Akad. D. Wisss. DOI: https://doi.
org/10.2307/j.ctt1vw0qcb
Fovet, É. & Zakšek, K. (2014). Path Network Modelling
and network of agglomerated settlements: A case
study in Languedoc (Southeastern France). Polla, S.
& Verhagen, Ph. (Eds.), Computational Approaches
to the Study of Movement in Archaeology. Theory,
Practice and Interpretation of Factors and Effects of
Long-term Landscape Formation and Transformation,
43–72. Berlin, de Gruyter.
Gietl, R., Doneus, M. & Fera, M. (2008). Cost Distance
Analysis in an Alpine Environment: Comparison
of Different Cost Surface Modules. Posluschny, A.,
Lambers, K. & Herzog, I. (Eds.), Layers of Perception.
Proceedings of the 35th International Conference
on Computer Applications and Quantitative
Methods in Archaeology (CAA), 10, 336–341. Berlin,
Germany, April 2–6, 2007 (Kolloquien zur Vor- und
Frühgeschichte, Bonn, Dr Rudolf Habelt GmbH).
Herzog, I. (2013). Theory and Practice of Cost Functions.
Contreras, F., Farjas, M. & Melero, F. J. (Eds.), Fusion
of Cultures. Proceedings of the 38th Annual Conference
on Computer Applications and Quantitative Methods
in Archaeology, 375–282. Granada, Spain, April 2010.
BAR International Series 2494. Oxford: Archaeopress.
Herzog, I. & Posluschny, A. (2011). Tilt – Slope-Dependent
Least Cost Path Calculations Revisited. Jerem,
E., Redő, F. & Szeverényi, V. (Eds.), On the Road
to Reconstructing the Past. Computer Applications
and Quantitative Methods in Archaeology (CAA).
Proceedings of the 36th International Conference,
212–218. Budapest, April 2–6, 2008, Budapest:
Archeaeolingua.
Hesse, R. (2016). Visualisierung hochauflösender digitaler
Geländemodelle mit LiVT. Lieberwirth, U. & Herzog,
I. (Eds.), 3D-Anwendungen in der Archäologie.
Computeranwendungen und quantitative Methoden
in der Archäologie. Workshop der AG CAA und des
Exzellenzclusters Topoi 2013, 109–128. Berlin:
Edition Topoi.
Horsten, F. H. (2005). Doorgaande wegen in Nederland,
16etot 19E eeuw. Een historische wegenatlas.
(Dissertation thesis, University of Utrecht, Utrecht).
Amsterdam: Aksant.
Howey, M. C. L. (2011). Multiple pathways across past
landscapes: Circuit theory as a complementary
17
geospatial method to least-cost path for modeling
past movement. Journal of Archaeological Science,
38, 2523–2535. DOI: https://doi.org/10.1016/j.
jas.2011.03.024
Jiang, H. & Eastman, J. R. (2000). Application of fuzzy
measures in multi-criteria evaluation in GIS.
International Journal of Geographical Information
Science, 14(2), 173–184. DOI: https://doi.
org/10.1080/136588100240903
Koomen, A. J. M. & Excaltus, R. P. (2003). De vervlakking
van Nederland; naar een gaafheidkaart voor reliëf
en bodem, Alterra-rapport 740. Alterra research
institute, Wageningen.
Koomen, A. J. M. & Maas, G. J. (2004). Geomorfologische
Kaart Nederland (GKN); Achtergronddocument
bij het landsdekkende digitale bestand. Alterrarapport, 1039. Wageningen: Alterra research
institute.
Llobera, M. (2000). Understanding movement: A pilot
model towards the sociology of movement. Lock,
G. R. (Ed.), Beyond the Map: Archaeology and Spatial
Technologies, 65–84. Amsterdam: IOS Press.
Murietta-Flores, P. (2010). Traveling in a Prehistoric
Landscape: Exploring the Influences that Shaped
Human Movement. Making History Interactive.
Computer Applications and Quantitative Methods
in Archaeology (CAA). Proceedings of the 37th
International Conference, Williamsburg, Virginia,
United States of America, 249–267. March 22–26,
2009. Oxford: Archaeopress.
Pfeifer, N., Mandlburger, G., Otepka, J. & Karel, W.
(2014). OPALS – A framework for Airborne Laser
Scanning data analysis. Computers, Environment
and Urban Systems, 45, 125–136. DOI: https://doi.
org/10.1016/j.compenvurbsys.2013.11.002
Polla, S. & Verhagen, J. W. H. P. (Eds.), (2014).
Computational Approaches to the Study of
Movement in Archaeology. Theory, Practice and
Interpretation of Factors and Effects of Longterm Landscape Formation and Transformation.
Berlin, de Gruyter. DOI: https://doi.org/10.1515/
9783110288384
Renes, H. (1984). Atlas van Nederland, Deel 2,
Bewoningsgeschiedenis. The Hague: Staatsdrukkerij.
Rensink, E., Weerts, H. J. T., Weerts, Kosian, M. C.,
Feiken, H. & Smit, B. I. (2017). The Archaeological
Landscapes Map of the Netherlands. A new map
for inventory and analysis at the archaeologylandscape interface. Lauwerier, R. C. G. M., Eerden,
J. M., Groenewoudt, B. J., Lascaris, M. A., Rensink,
E., Smit, B. I., Speleers, B. P. & Van Doesburg, J.
(Eds.), Knowledge for informed choices. Tools for
a more effective and efficient selection of valuable
archaeology in the Netherlands, Nederlandse
Archeologische Rapporten (NAR), 55, 36–47.
Rutte, R. J. & IJsselstijn, M. (2014). 1000–1500.
Stadswording aan waterwegen: De grote
stedenboom Atlas van de verstedelijking in
Nederland. 1000 jaar ruimtelijke ontwikkeling,
170–185. Bussum: Thoth.
18
Soule, R. G. & Goldman, R. F. (1972). Terrain coefficients
for Energy Cost Predition. Journal of Applied
Physiology, 32(5), 706–708. DOI: https://doi.
org/10.1152/jappl.1972.32.5.706
Steur, G. G. G. & Heijink, W. (Eds.), (1991). Bodemkaart
van Nederland. Schaal 1:50000. Algemene begrippen
en indelingen. Wageningen.
Swart, L. M. Th. (2010). How the up-to-date height model
of the Netherlands (AHN) became a massive point
data cloud. Management of Massive Point Cloud
Data: Wet and Dry. Delft: Nederlandse commissie
voor Geodesie.
Van der Gaast, J. W. J., Vroon, H. R. J. & Massop, H.
Th. L. (2010). Grondwaterregime op basis van
karteerbare kenmerken. STOWA rapportnummer, 41.
Amersfoort: STOWA.
Van der Linden, J. A. (1973). Topographische en
Militaire kaart van het Koningrijk der Nederlanden.
Bussum.
Van der Zon, N. (2013). Kwaliteitsdocument AHN2. Delft:
Rijkswaterstaat en Waterschappen.
Van Lanen, R. J. (2016). Historische routes in Nederland.
Een multidisciplinaire zoektocht naar verdwenen
en langdurig gebruikte routetrajecten, Tijdschrift
voor Historische Geografie (THG), 12–29. jaargang
1, 1, Verloren. DOI: https://doi.org/10.1007/
s12520-016-0431-z
Van Lanen, R. J. (2017). Changing ways. Patterns
of connectivity, habitation and persistence in
Northwest European lowlands during the first
millennium AD, PhD dissertation Utrecht University.
Utrecht Studies in Earth Sciences (USES), 137. Ipskamp.
Van Lanen, R. J., Groenewoudt, B. J., Spek, T.
& Jansma, E. (2016). Route persistence. Modelling
and quantifying historical route-network stability
from the Roman period to Early-Modern Times (AD
100–1600): A case study from the Netherlands,
Archaeol Anthropol Sci. DOI: https://doi.
org/10.1007/s12520–016–0431-z
Van Lanen, R. J., Kosian, M. C., Groenewoudt,
B. J. & Jansma, E. (2015b). Best travel options:
Modelling Roman and early-medieval routes in the
Netherlands using a multi-proxy approach. Journal
of Archaeological Science: Reports (JASR), 3, 144–159.
DOI: https://doi.org/10.1016/j.jasrep.2015.05.024
Van Lanen, R. J., Kosian, M. C., Groenewoudt, B. J.
& Jansma, E. (2015a). Finding a way: Modeling
Landscape Prerequisites for Roman and EarlyMedieval Routes in the Netherlands. Geoarchaeology:
An international Journal, 30, 200–222. DOI: https://
doi.org/10.1002/gea.21510
Van Lanen, R. J. & Pierik, H. J. (2017). Calculating
connectivity patterns in delta landscapes: Modelling
Roman and early-medieval route networks and
their stability in dynamic lowlands. Quatenary
international.
Van Leusen, P. M., Deeben, J., Hallewas, D., Zoetbrood,
P., Kamermans, H. & Verhagen, J. W. H. P. (2005). A
Vletter and van Lanen: Finding Vanished Routes
Baseline for Predictive Modelling in the Netherlands.
van Leusen, M. & Kamermans, H. (Eds.), Predictive
Modelling for Archaeological Heritage Management:
A Research Agenda. Nederlandse Archeologische
Rapporten, 29, 25–92. Amersfoort: Rijksdienst voor
het Oudheidkundig Bodemonderzoek.
Verhagen, J. W. H. P. (2013). On the Road to Nowhere?
Least-Cost Paths, Accessibility and the Predictive
Modelling Perspective. Proceedings of the 38th
Annual Conference on Computer Applications
and Quantitative Methods in Archaeology,
CAA2010, Contreras, F., Farjas, M. & Melero, F. J.
(Eds.), 383–390.
Verhagen, J. W. H. P. & Whitley, T. G. (2012). Integrating
Archaeological Theory and Predictive Modeling: A
Live Report from the Scene. Journal of Archaeological
Method and Theory, 19(1), 49–100. DOI: https://doi.
org/10.1007/s10816-011-9102-7
Vletter, W. (2014). (Semi) automatic extraction
from Airborne Laser Scan data of routes and
paths in forested areas. In SPIE proceedings
Second International Conference on Remote
Sensing and Geoinformation of the Environment,
9229:92291D August 2014. DOI: https://doi.
org/10.1117/12.2069709
Vos, P. C. (2015). Origin of the Dutch Coastal Landscape
(Distertation thesis, University of Utrecht). Eelde:
Barkhuis publishing.
Vos, P. C., Bazelmans, J., Weerts, H. J. T. & Van der
Meulen, H. J. T. (Eds.), (2011). Atlas van Nederland
in het Holoceen, Amsterdam.
Vos, P. C. & De Vries, S. (2013). 2e generatie
paleogeografische kaarten van Nederland (versie 2.0).
Utrecht: Deltares.
White, D. A. & Barber, S. R. (2012). Geospatial modeling
of pedestrian transportation networks: A case
study from precolumbian Oaxaca, Mexico. Journal
of Archaeological Science, 39, 2684–2696. DOI:
https://doi.org/10.1016/j.jas.2012.04.017
Whitley, G. (2005). A brief outline of causalitybased cognitive archaeological probabilistic
modelling. van Leusen, M. & Kamermans, H.
(Eds.), Predictive Modelling for Archaeological
Heritage Management: A Research Agenda,
123–137. Nederlandse Archeologische Rapporten
29 Amerfoorst: Rijksdienst voor Oudheidkungid
Bodemonderzoek.
Wilcox, W. (2009). Archaeological predictive modelling in
East Anglia and Norfolk. In proceedings of Computer
Applications to Archaeology 2009 Williamsburg,
Virginia, USA. March 22–26. http://archive.
caaconference.org/2009/PapersProceedings.cfm.
html.
Yokoyama, R., Shirasawa, M. & Pike, R. (2002).
Visualizing topography by openness: a new
application of image processing to digital elevation
models. Photogrammetric Engineering and Remote
Sensing, 68(3), 257–265.
19
Vletter and van Lanen: Finding Vanished Routes
How to cite this article: Vletter, W. F. and van Lanen, R. J. (2018). Finding Vanished Routes: Applying a Multi-modelling Approach
on Lost Route and Path Networks in the Veluwe Region, the Netherlands. Rural Landscapes: Society, Environment, History, 5(1): 2,
1–19, DOI: https://doi.org/10.16993/rl.35
Submitted: 03 June 2016
Accepted: 10 January 2018
Published: 05 February 2018
Copyright: © 2018 The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
Rural Landscapes: Society, Environment, History is a peer-reviewed open access journal
published by Stockholm University Press.
OPEN ACCESS