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A larger-scale study of the visual dominance at the Gor River megalithic
landscape (Granada, Spain)
Carolina Cabrero Gonz´
alez
a,*
, Juan Antonio C´
amara Serrano
a
, Enrique Cerrillo Cuenca
b
a
Department of Prehistory and Archaeology, University of Granada, Spain
b
Department of Prehistory, Ancient History and Archaeology, Complutense University of Madrid, Spain
ARTICLE INFO
Keywords:
Late Prehistory
Southeastern Iberia
Megaliths
GIS
Visual landscape
Location patterns
ABSTRACT
This paper presents the results of various analyses conducted on the megalithic complex of the Gor River valley
(Granada, Spain) with the aim of exploring the visual landscape of this area on a larger scale during the Late
Prehistoric period. The analyses performed include clustering of burial mounds using DBSCAN, calculation of
Relative Topographic Position, calculation of fuzzy viewsheds, and statistical analysis of the existence or non-
existence of relationships between dimensions, topographic prominence, and visibility. Fuzzy viewshed anal-
ysis is implemented to rene other visibility analyses that had previously been conducted on the complex,
without considering the fuzziness variable, which is obtained by taking into account distance and size. The re-
sults are consistent with previous analyses that indicate no relationship between the size of the megaliths,
topographic position, and visibility. It reveals the importance of the entire complex to dene the related territory
although the existence of possible particularities associated to various ecological niches in the study area can be
also suggested.
1. Introduction
The megalithic group of the Gor River Valley (Granada, Andalusia,
Spain) (Fig. 1) is one of the primary clusters forming the so-called
Megalithic Phenomenon of the Southeastern Iberian Peninsula (García
Sanju´
an 2009),being one of its main characteristics the high density of
megaliths per km
2
. Initially, 238 dolmens were recorded along 17 km of
the valley during early surveys (Siret 2001). However, only 151 mega-
lithic monuments have been found in the most recent systematic survey,
which is explained by the mechanisation of the agricultural lands since
the mid XX century and the lack of legal and practical protection of the
monuments till the 10 s (Cabrero et al. 2021). Other specic charac-
teristics of this ensemble include varied chamber typologies, primarily
small in size, and the use of the tombs over a wide temporal range—from
the late Neolithic to the Chalcolithic period—with frequent reuses in the
Late Bronze Age (Dorado et al. 2023). This aspect has been widely
registered by the study of the objects found in the chambers (Lorrio
2008) and by the 11 radiocarbon dates obtained till the present, with
data between 4307 ±33 and 2690 ±30 cal. BP (Cabrero et al. 2023a:
4). This characteristic is shared by many megaliths located in Hoya de
Guadix (Aranda et al. 2022) and other Southeastern Iberian areas
(Lorrio 2008; Dorado et al. 2023).
Given the scarcity of data for sites related to settlement, megalithic
monuments are virtually the only evidence we have to analyse the
occupation and settlement patterns in this area during Late Prehistory.
Megaliths are considered to never be placed outside the space exploited
by the community that built them (C´
amara 2001; Furholt and Müller
2011; Schmitt et al. 2019). Besides their primary funerary use, they
served as markers of routes and territories of exploitation (García
Sanju´
an et al. 2009; Scarre 2011), although the type of exploitation
(extensive or intensive, pastoral, agrarian, or other) may vary. The ter-
ritory is understood as the space modied and appropriated by human
social activity (H¨
agerstrand 1973, 1975; Carlstein 1983; Tuan 2001,
2004). Thus, the distribution of megaliths forming necropolises would
not be random but would have a specic conguration related to land
ownership, anthropization, and sacralization of the terrain by Late
Prehistoric farming communities through the burial of their ancestors
(Criado 1984; Godelier 1989; Aug´
e 1992; Fabietti and Matera 2000;
L`
evi-Strauss 2000; C´
amara 2001; Shaffer 2005; Littleton 2002, 2007;
Ch´
enier 2009).
If we accept that the landscape is a space modied by human expe-
rience and activity, and that it conditions human life (Ingold 1993;
* Corresponding author.
E-mail addresses: ccabrero@correo.ugr.es (C.C. Gonz´
alez), jacamara@ugr.es (J.A. C´
amara Serrano), enriqcer@ucm.es (E.C. Cuenca).
Contents lists available at ScienceDirect
Journal of Archaeological Science: Reports
journal homepage: www.elsevier.com/locate/jasrep
https://doi.org/10.1016/j.jasrep.2024.104912
Received 10 July 2024; Received in revised form 21 November 2024; Accepted 29 November 2024
Journal of Archaeological Science: Reports 61 (2025) 104912
2352-409X/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-
nc/4.0/ ).
Tilley 1994; Ashmore and Knapp 1999; Bongers et al. 2012; Cruz et al.
2024; Grier et al. 2017; ˇ
Sprajc et al. 2022), the study of the distribution
of megaliths and their relationship with the environment, space, and
geography can be one of the best approaches to understanding the
communities that built the tombs (Schiffer 1987; Hodder 1990; Criado
1997; Lock and Molineaux 2006; Gillings and Pollard 2016; Whittle
2017; Lock and Puncett 2017; C´
amara et al. 2021), especially consid-
ering the absence of other evidence related to these communities in our
study area.
This substantial quantity of megaliths and their proximity to one
another have traditionally been interpreted as a manifestation of an
intense degree of appropriation and demarcation of the territory by Late
Prehistoric communities. Therefore, studying the distribution of
megaliths and their relationship with the environment is key to under-
standing these past communities, especially in light of the scarcity of
other archaeological data (Renfrew 1976; Sherratt 1990; Binford 1999).
In this paper, we present a new approach to understanding the
experience and perception of these communities on the territory, mainly
through the analysis of fuzzy visual basins applied to the preserved
megaliths. Although visibility analysis has a long history in this region
(Cabrero et al. 2024), this research aims to rene previous results and
extend the study radius to analyse the territory on a larger scale.
Fig. 1. A) location of the Gor River in the region of Andalusia (South of Spain). b and c, Hoyas del Conquín 134 and Majadillas 69, two of the most known
monuments of the area.
C.C. Gonz´
alez et al.
Journal of Archaeological Science: Reports 61 (2025) 104912
2
2. Background. Approaches to the visual landscape of the Gor
River
One of the best ways to analyse the relationship between megaliths
and the territory is through visibility analyses ( ˇ
Cuˇ
ckovi´
c 2016). Visi-
bility is a broad aspect that allows exploration of the relationship be-
tween an archaeological item and its surroundings, the existence or
absence of interrelation between various archaeological structures, the
prominence of a site over its surroundings, or the perceptibility of a
particular element, among many other aspects (Wheatley and Gillings
2000; Llobera 2003, 2012). These different issues help us approach the
visual landscape from a specic site or set of sites (Llobera 2007), closely
related to the perception and signicance of the sites for past commu-
nities (Criado 1984, 1999; Gillings and Wheatley 2001; Scarre 2010;
Rodríguez-Rell´
an and F´
abregas 2023).
In the case of the Gor River area, early 21st-century studies were
conducted by a team from the University of Granada focused on aspects
related to the domain of burial mounds over the landscape (Afonso et al.
2006, 2008, 2010; Spanedda et al. 2014). Although these publications
did not analyse the entire cluster or use GIS techniques, they established
interesting hypotheses about the visual landscape. Their conclusions
suggested the existence of a single visual network where the megaliths
were strategically positioned to control the entire territory, emphasizing
routes from the steep-sided valley to the surrounding plateau. However,
individual differences related to construction typologies or the specic
topographic positions of individual megaliths were noted within each
necropolis.
The rst studies using GIS techniques on the entire cluster were
recently conducted as part of a PhD thesis focused on the Gor River
dolmenic complex and their spatial dimension (Cabrero 2023). This
research considered the 151 preserved megaliths and other Chalcolithic
archaeological structures related to valley access defence (Cabrero et al.
2024). Visibility was analysed from each megalith, the megaliths as a
whole, and the visual relationship between the megaliths and other
archaeological sites. The results reinforced initial research conclusions,
showing a well-planned network with no aws in the intervisibility of
the main dolmenic group. Differences appeared in more distant
necropolises like Ba˜
nos de Alicún and El Baúl, which seemed to form
separate groups based on visibility and distance, with differences in
constructive and topographical patterns (Cabrero et al. 2021). Also in
the frame of the cited PhD work, a research focused on the comparison of
the architectonic features between the megaliths and the necropolises
(typology, presence or lack of corridor, measures of the chambers and
corridors) was carried out (Esquivel et al. 2022). This approach served to
emphasize these particularities. These differences have been interpreted
as cultural boundaries related to the exploitation of different ecological
niches, with megaliths near other riverbeds apart from the Gor River.
However, establishing peripheral and resistance areas in Hoya de Gua-
dix has been challenging due to the scarcity of settlement data (Leisner
and Leisner 1943).
Despite consistent visibility results, these studies had limitations due
to partial data regarding variables like grave goods, shape, and size, and
methodological issues, as they used simple binary visibility analyses
without considering distance gradation. This is particularly relevant for
intervisibility studies where megaliths are far apart, potentially yielding
different results with added distance variables. Previous analyses were
limited to a 3 km radius, excluding distant geographical elements (e.g.,
mountain peaks) despite their visibility above the horizon line due to
size, as it has been already pointed out by other studies (Wheatley 1996;
Parcero et al. 1998; Van Leusen 1999).
This paper aims to improve upon previous research through a rened
visibility analysis using fuzzy viewsheds from each megalith, consid-
ering a larger scale and expanded visibility radius to study the rela-
tionship between megaliths and the entire landscape. This will allow to
nuance and to rene the results of the cited previous works, as long as to
contrast them.
3. Materials and methods
Probability in viewshed analysis was introduced to address issues in
simple viewshed analysis, that considered only if a given point is
completely visible or completely invisible, which can hardly be adjusted
to the reality of human experience. P.F. Fisher added the statistical
probability range of visibility between two points, taking into account
that vision decays exponentially and not constantly as a function of
distance (Fisher 1992). D. Ogburn (2006) later rened this by adding the
size component, acknowledging that larger objects remain visible over
greater distances before becoming blurry. Although more realistic for
visibility and human perception, this complex analysis is rarely used in
archaeology (Cerrillo Cuenca and Liceras 2016), contrasting with the
success of simplied analyses (see Criado 1988; Criado and F´
abregas
1989; Wheatley 1995, 1996; Villoch 2000; Ericson 2002; L´
opez-Romero
2007; Scarre 2010; Nash 2013; Llobera 2016; Carrero-Pazos 2018,
2022), which is mostly explained due to the technical complexity or the
frequent difculty in clearly dening the boundaries of archaeological
sites and structures (Davis et al., 2019). In other words, in this case, the
combination of fuzzy logic and viewsheds allows for the representation
of degrees of visibility, instead of the usual viewsheds that present in-
formation in a dichotomous “all or nothing” manner. This procedure
more adequately captures the continuous and ambiguous nature of
human perception.
The data for these analyses were obtained during the last survey
campaign in the Gor River area in summer 2019 (available at https://
zenodo.org/doi/https://doi.org/10.5281/zenodo.8351123). We pri-
marily used the geographical location of the tumuli in UTM ETRS89
coordinates. The base cartography is provided by the National Aerial
Orthophotography Plan by the National Geographic Institute of Spain,
mainly DTMs based on LiDAR data, publicly available at https://pnoa.ig
n.es/web/portal/pnoa-lidar/presentacion. These DTM’s were created
during the second coverage of the national territory (between 2015 and
2021), and provide a minimum point density between 0.2 and 2/m
2
, an
altimetric accuracy of ≤30 and a RMSE Z ≤20.
1
As noted in section 2, isolating a specic moment in the landscape is
difcult due to continuous changes and the changing perception and
signicance by past communities (Tuan 2001, 2004). This challenge is
compounded by the scarcity of radiocarbon dates, with only 11 dates
available between the Early Copper Age and Final Bronze Age
(4300–2700 BP) (Cabrero et al. 2023a). Thus, following previous
research, we consider all megaliths as contemporary at a certain
moment, assuming all were built by the end of the 3rd millennium and
served as visible territorial markers, although their use for new burials at
concrete periods and building date could be uncertain.
The specic methodology used is as follows:
3.1. Vectorization of burial mounds
Firstly, the burial mounds were digitized using the data from the
second LiDAR coverage of the National Aerial Orthophotography Plan
by the National Geographic Institute of Spain as a reference. For pro-
cessing, all non-ground classied points were ltered out. The remain-
ing terrain points were interpolated with a mesh step of 0.5 m using the
Inverse Distance Weighting (IDW) algorithm, implemented in the
WhiteboxTools toolkit for Python. The digitization was performed
considering the visible footprint of the construction on the ground,
which may introduce some inaccuracies due to the preservation state of
the mounds.
1
All technical specications are available at https://pnoa.ign.es/web/port
al/pnoa-lidar/especicaciones-tecnicas.
C.C. Gonz´
alez et al.
Journal of Archaeological Science: Reports 61 (2025) 104912
3
3.2. Clustering of burial mounds using DBSCAN
For a better statistical analysis, the sites were grouped into natural
clusters to evaluate trends. The archaeological data does not allow dis-
tinguishing specic groups of tombs within the necropolis; thus, the
purpose of this analysis is merely to make comparisons within the
extensive group of mounds we have. These clusters were created using
the DBSCAN algorithm from Scipy, congured to ensure clusters con-
tained at least three elements with a maximum dispersion of 200 m
between them. DBSCAN has been used, for example, by Carrero-Pazos
(2019) in the study of Galician megaliths. These distances can be chal-
lenging to establish, especially to objectify in cultural terms, but they
help to characterize the spatial properties of the necropolises and should
be understood only from this perspective. Of all the metrics available in
the DBSCAN implementation in Scipy, we have chosen the Euclidean
distance, as it is closest to the intuitive distance of human space, which
ultimately could have determined the grouping of the tombs. However,
it should be noted that this metric does not consider topographic fea-
tures such as terrain slope or accessibility to certain topographic posi-
tions. It is important to note that the sample is initially biased as not all
the mounds catalogued by G. and V. Leisner (1943) could be recognized
by recent surveys (Cabrero et al. 2021) and as many mounds are not
visible in surface and partially destroyed (Cabrero et al. 2023b). The
groupings, therefore, may be coherent, but it is necessary to keep in
mind that these combined factors can inuence the clustering of the
monuments.
3.3. Implementation of fuzzy visibility
To analyse the fuzzy visibility of the ensemble, the distance decay
function implemented by Ogburn (2006) was applied. The function was
programmed in Python using various scientic libraries such as Scipy,
Geopandas, Rasterio, and WhiteboxTools, among others. The code is
available at https://github.com/ecerrillo/fuzzyviewshed. All calcula-
tions were performed on the 5-meter resolution Digital Terrain Model
provided openly by the National Geographic Institute of Spain (publicly
available at https://pnoa.ign.es/web/portal/pnoa-lidar/modelo-digita
l-del-terreno). The process considered a surface of 45 by 39 km,
covering the entire study area and its surroundings.
The process started by considering the morphology of the digitized
mounds. The centroids of all mounds were automatically found, and the
maximum distance between nodes was calculated, allowing estimation
of the maximum preserved mound size. The Euclidean distance from
each centroid to the rest of the raster cells was calculated. Visibility was
calculated using the “viewshed” command of WhiteboxTools, using the
centroid of each mound as the observation point with an observer height
of 1.65 m, which is the medium high identied by the most complete
anthropological study upon the buried individuals found in past
archaeological campaign in the Gor River valley (García S´
anchez, 1961).
Using the visible area returned by this algorithm as a mask, the fuzzy
visibility map was calculated using the formulae established by Ogburn
(2016, 410):
μ
xij=1fordvp→ij ≤b1
where
μ
xijrepresents the fuzzy membership value for a cell at position
Fig. 2. Fuzzy viewshed of site number 100. The colour scale represents the visibility value attributed to the pixel in the range [0,1].
C.C. Gonz´
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Journal of Archaeological Science: Reports 61 (2025) 104912
4
xij
.
dvp→ij is the distance in meters from the viewpoint to a given cell. b1
represents the limit of the foreground zone where visibility is considered
perfect, meaning that any object within this distance is assumed to be
fully visible. b1 was set to 1 km, as suggested by Ogburn (2006). This
distance is chosen because it represents a foreground zone of high visual
clarity, where object details are still sharp to the human eye, in addition
to being consistent with previous research on visual ranges.
For pixels beyond the foreground limit the modied formula pro-
posed by Ogburn (2006) was used.
μ
xij=1
1+2d−b1
b22fordvp→ij >b1
This formula adjusts the decay function to account for the size of the
target object, using a visual arc of 1
′
. Thus, b2 is the distance from b1 to
the point where an object subtends a visual arc of 1
′
. The factor of 2 in
the denominator ensures that the drop-off in visibility is appropriately
gradual.
For each of the studied sites, the resulting (xij) values from both
Fig. 3. Map representing the RTP values in the study area for a calculation radius of 500 m.
Fig. 4. Map representing the RTP values in the study area for a calculation radius of 5000 m.
C.C. Gonz´
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Journal of Archaeological Science: Reports 61 (2025) 104912
5
formulas were combined into a single raster, which was then reclassied
using the conventional binary visibility raster as a mask, forming the
fuzzy visibility map. Each of these rasters presents probability
μ
values
in the range 0–1, where 0 generally corresponds to non-visible areas
—those excluded from conventional viewshed analysis— and 1 to the
highest fuzzy membership value. It is essential to remember that each
pixel in this raster should be understood as expressing the likelihood of
belonging to the “visible” category. An example of a fuzzy viewshed is
represented at Fig. 2.
3.4. Calculation of relative topographic position
Topography plays a fundamental role in relation to the visibility of
cultural elements in the landscape, which is why it is advisable to
analyse prominence. The use of this variable is already described in
Llobera (2001). To put in a wider perspective the results, an analysis of
relative topographic prominence (RTP) was performed using the Rela-
tiveTopographicPosition command from WhiteboxTools. This function
(Newman et al. 2018) considers a neighbourhood with a given buffer
size and establishes the relative position of cells based on the maximum
and minimum values in the vicinity. If a given pixel is lower than the
neighbourhood mean, the prominence value is calculated by subtracting
the pixel value from the mean, divided by the mean minus the minimum
value of the vicinity. If the value is equal to or greater than the mean, the
last term in the division is replaced by the maximum value of the vicinity
minus the mean. The resulting value ranges from [-1,1], where −1 in-
dicates a depressed value in the surrounding topography and 1 indicates
a high prominence. For this calculation, the 5-meter resolution DEM was
used, and values were obtained with the centroids of the mounds.
Although in previous papers we have used the calculation of topo-
graphic prominence in comparison with fuzzy visibility (Cerrillo Cuenca
and Liceras 2016), in this work we opt for a function already
implemented in a Python library due mainly to its higher degree of
optimization in the calculation of the variable. The differences between
the topographic prominence (Llobera 2001) and the formula used in this
article essentially lie in the fact that the prominence proposed by Llobera
calculates the percentage of points that, within a given radius, are
located lower than the observed position, while the described approach
compares the elevation of a point with the mean and its extreme values.
In itself, the prominence calculation has the advantage of being intui-
tively interpretable, while the RTP, as presented in this article, is more
sensible to extreme values and can provide positive or negative values,
potentially offering more nuanced information about a location in the
landscape.
The neighbourhood was set with radii of 100, 500, 1000, 2000,
4000, and 5000 m, as recommended by other authors (Llobera 2001).
This allows for understanding different behaviors of topographic
prominence at various radii, enabling a more detailed exploration of the
relationship between monuments and topography. An example of an
RTP raster is shown in Figs. 3 and 4.
The analysis of “visualscapes” certainly encompasses other possi-
bilities. The ability to explore the relationship between the most
prominent positions and those most visually exposed is something that
has been previously tested, for example through the analysis of total and
cumulative visibilities (Llobera 2006b, 2007). These approaches are
certainly an appropriate way to contrast the impact of cultural sites on
the landscape, combined with the analysis of topographic features. In
this work, we have chosen to make the contrast with fuzzy visibility, as it
contemplates certain granularity in the analysis of individual tombs and
groups, allowing the introduction of gradual nuances of clarity in
observation. By integrating RTP with fuzzy viewsheds, we can explore
the visual and topographic landscape of the monuments based on their
positions (Cerrillo Cuenca and Liceras 2016), allowing an approach to
hypothetical symbolic logics of megalith location.
Fig. 5. Distribution of clusters generated by DBSCAN in the study area.
C.C. Gonz´
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4. Results
4.1. Clustering of burial mounds using DBSCAN
The DBSCAN analysis identied 13 spatially signicant mound
clusters, ranging from 21 monuments to 3. This information is
summarized in Fig. 5 and Tab. 1. Of a total of 151 recognized mounds,
28 remained isolated from the clusters proposed by DBSCAN. This
means they correspond to groups of at most two tombs are far from the
main distributions and not integrated into these. This lack of integration
may also be due to preservation issues because many tombs, especially
at the plateau area, have disappeared or cannot be identied (Spanedda
Fig. 6. Histogram representing the mound sizes in the study area.
Fig. 7. Boxplot of the distribution of mound sizes by cluster number.
Table 1
Results of the DBSCAN analyses clustering in relationship to the size of the mounds.
Cluster Number of
mounds
Mean size
(m)
Standard
Deviation
Mound Minimum Size
(m)
Mound maximum size
(m)
Signicant mean differences regarding other clusters
(Turkey HSD)
0 17 7.9 2,61 4,9 16 1, 9
1 11 11 3,05 7,1 16,6 0, 3, 4, 6, 7, 8, 9, 10, 11,12
2 11 8.9 1,31 6,8 10,8 9
3 4 6,5 1,02 5,9 8 1
4 12 8,4 1,57 6,8 11,9 1, 9
5 3 10,4 1,58 9,3 12,4 9, 10
6 8 7,2 1,71 4,7 9,6 1
7 21 6,9 1,43 3,9 9,2 1, 9
8 4 6,1 1,47 5 7,7 1
9 17 4,6 1,46 1,6 7,7 0, 1, 2, 4, 5, 7
10 3 5,3 1,81 5,1 5,5 1
11 6 6,9 0,62 5,8 7,5 1
12 6 7,2 0,62 6,2 7,8 1
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Journal of Archaeological Science: Reports 61 (2025) 104912
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et al., 2014; Cabrero et al., 2023b), because of recent alterations, mainly
by farming activities. The resulting separation of some little clusters in
peripheral areas might have inuenced the spatial analysis.
4.2. Characterization of mound size and its Relevance to visibility
The documented mound widths range from 1.6 to 16.6 m (Figs. 6 and
7), with an average maximum width of 7 m (standard deviation of 2.5).
An ANOVA test was performed to evaluate if there were signicant
differences in the size of the mounds between the clusters determined by
DBSCAN, resulting in F(12, N-13) =9.447, p <0.001. This rejects the
null hypothesis, which states that the mean mound sizes are equal for the
different clusters. Given the signicant differences, a post-hoc Turkey
HSD (Honest Signicant Difference) test was conducted to identify
clusters with signicant differences in their mean sizes. Signicant dif-
ferences between clusters can be found in Table 1. The mounds in cluster
1 stand out for their larger sizes, while clusters 9 and 10 have smaller
mean sizes. Clusters 0 and 1 returned higher standard deviation values,
indicating greater variability in mound sizes compared to other clusters.
Conversely, clusters 11 and 12 appear more regular, with sizes ranging
from 5.8 to 7.8 m.
Although recorded mound sizes are inuenced by alteration pro-
cesses, it can be thought that the majority of graves could experience in a
similar way these reductions by erosion and farming activities. Conse-
quently, results can be thought as signicant and can be explained by
social factors (differences between areas and/or social differences inside
every necropolis). In fact, mound sizes also vary in clusters located in
relatively plain areas as shown by cluster one standard deviations and a
social explanation can be searched for these differences.
In addition, mounds are greater in this northern necropolis (cluster
1), whose distance to the central clusters and typological differences had
already been used to referred possible boundaries reected on those
architectural differences (Esquivel et al., 2022).
4.3. Characterization of relative topographic position (RTP)
In absolute terms, the RTP behavior does not show expressive results.
In smaller buffers of 100 m, the value is 0.51, with a standard deviation
of 0.32, indicating a tendency to locate mounds in moderately elevated
positions, though with some variability. In larger RTP calculation scales,
the mean signicantly decreases (1000 m: 0.37, 2000 m: 0.31, 5000 m:
0.12), with more or less homogeneous standard deviations.
The relationship between mound size and relative topographic po-
sition (RTP) was analysed, as these are the two most direct resources
that can increase tomb visibility in the landscape. A Spearman correla-
tion analysis (Fig. 8) for all megaliths (n =123) indicates no relationship
between mound size and their topographic prominence within a 1000-
meter radius of the analysed area (rs(98) =-0.037, p =0.651).
Among the clusters, only cluster 8 shows a perfect negative relationship
between tomb size and topographic prominence (rs(2) =-1, p =0), but
due to the limited sample size (n =4), this result should not be
considered signicant. Signicant results were obtained only for cluster
2 (n =11), showing a moderately strong positive relationship between
size and RTP in 2000-meter (rs(9) =0.7, p =0.01) and 4000-meter
buffers (rs(9) =0.75, p =0.008). This relationship suggests an in-
crease in mound size as they occupy more prominent positions in the
environment. Among the 13 clusters analysed, this is the only signicant
association, based on a slightly larger number of monuments, making it
a noteworthy correlation.
4.4. Evaluation of fuzzy visibility
Considering the total values of fuzzy viewsheds, the average proba-
bility of visibility for all analysed locations is 0.53 (standard deviation
0.27). Excluding the b2 areas, the majority of probabilities are around
0.3. This suggests that neither the site choice nor the mound size aimed
to enhance the visibility of tombs in the distant landscape. Within
clusters, the common trend is consistent: the probability rapidly declines
inside the b2 areas, especially evident in cluster 1 (n =11), which has
the largest mounds. This indicates no clear relationship between mound
size and their perceptibility in the landscape.
As suggested above, differences in mound size exist, but if graves
were not necessarily designed to be seen from long distances, other so-
cial factors should be taken into account to explain their differences in
size, probably related to increasing hierarchy.
Fuzzy visibility was evaluated considering two levels: intra-group
analysis, visibility among tombs within each cluster, and inter-group
analysis, evaluates the quality of intervisibility among the megaliths
within and between clusters.
For inter-group fuzzy visibility, partial dissimilarity matrices were
obtained, and averages below the diagonal were calculated. Clusters 3
(n =4), 4 (n =12), and 11 (n =6) have a mean value of 1, indicating full
Fig. 8. Barplot representing Spearman’s r values for the intra-group relationship between RTP and mounds’ size in clusters.
C.C. Gonz´
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Journal of Archaeological Science: Reports 61 (2025) 104912
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visibility among tombs within these clusters. Cluster 5 (n =3) showed
no visibility among its tombs (
μ
=0). The general trend shows an
average between 0.7 and 0.3, indicating potential visibility loss among
the necropolis tombs.
To analyze inter-group relationships, we extracted submatrices from
the general dissimilarity matrix comparing each pair of clusters and
excluding intra-group comparisons. The mean value was calculated for
each submatrix, resulting in the average probabilities of inter-group
visibility presented in Table 2. Generally, inter-group studies show
low visibility probability among groups. Clusters 3 (n =4) and 4 (n =
12) have the highest visibility probability (0.96), possibly due to their
proximity. Clusters 2 (n =11) and 5 (n =3) show a medium–high vis-
ibility probability (0.73), also likely due to proximity. These data should
be interpreted cautiously, considering the potential articial division by
DBSCAN, which might have split originally coherent groups.
In fact, as previously referred according to intervisibility analysis,
cumulative and total viewshed (Cabrero et al., 2024), the design of a
dense network of connected graves could play an important role in
territorial control, but the results of this analysis suggest that system was
thought for short distances and not for long ones. It can be asserted that
graves lines served more as inner markers, maybe related to different
groups that boundary ones.
Medium-high visibility probability (0.73) between clusters 0 (n =
17) and 9 (n =17) can be explained by their location at the valley’s
edge, highlighting them in the landscape. Other clusters show proba-
bilities close to 0.5 or very low, suggesting that tomb intervisibility was
not a sought-after visual pattern.
5. Conclusions
The results of the cluster analysis using DBSCAN show considerable
heterogeneity, which corresponds to the diversity in the size of the
burial mounds, ranging from 1.6 m to 16.6 m. This reality is undoubt-
edly the result of a constructive evolution linked to the wide temporal
frame during which the tombs were constructed (and, of course, used
and remodelled). This architectural variability was alsoanalysed in a
previous work by taking into account the measurements of the ortho-
stats, rather than the mound size (Esquivel et al. 2022). This study show
also a great variability and size dispersion. In this case, apart from the
grouping based on architectonic features, another one based on loca-
tional factors such as distance to the Gor River, altitude, and UTM X and
Y coordinates was carried out, revealing 13 groups. In this way, it is
Table 2
Dissimilarity matrix representing the means of fuzzy viewshed values between clusters (inter-group).
c0 c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12
c0 0 0 0 0 0 0 0,07 0,01 0,73 0,02 0,19 0
c1 0 00000000000
c2 0 0 0,18 0 0,73 0 0 0 0 0 0 0
c3 0 0 0,18 0,96 0,33 0,37 0,38 0,25 0,32 0 0 0
c4 0 0 0 0,96 0 0,37 0,42 0,16 0,23 0 0,01 0
c5 0 0 0,73 0,33 0 0000000
c6 0 0 0 0,37 0,37 0 0,41 0,09 0,09 0 0 0
c7 0,07 0 0 0,38 0,42 0 0,41 0,2 0,1 0 0 0
c8 0,01 0 0 0,25 0,16 0 0,09 0,2 0,1 0 0 0
c9 0,73 0 0 0,32 0,23 0 0,09 0,1 0,1 0,57 0,59 0
c10 0,02 0 0 0 0 0 0 0 0 0,57 0 0
c11 0,19 0 0 0 0,01 0 0 0 0 0,59 0 0
c12 0 0 0 0 0 0 0 0 0 0 0 0
Fig. 9. Several burial mounds in the Llano de Olivares necropolis. All of them are very small in size and have a wide visibility index due to their position at the edge
of the high plateau.
C.C. Gonz´
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Journal of Archaeological Science: Reports 61 (2025) 104912
9
important to note that the DBSCAN cluster serves as a basis for the an-
alyses presented here, and are not aimed to present a new internal di-
vision of the complex (Cabrero et al. 2023).
The lack of a relationship between tomb size and topographic loca-
tion had also been suggested in larger studies aiming to nd possible
correlations explaining visibility, specically considering tomb size and
their position in prominent areas as key factors (Cabrero 2023). The
absence of a positive relationship between these variables suggests that
there was no intent to achieve broad visibility for individual megaliths.
This idea had already been proposed, emphasizing the concept of the
complex as a network of intervisibility over the terrain, creating a sort of
landscape scenography or monumentality through the appropriation of
the territory with an extensive and dense network of monuments
throughout the area (Cabrero, 2023). In this regard, we cannot overlook
the positive statistical relationship between large size and elevated po-
sitions in cluster 2 (corresponding to the Majadillas necropolis). How-
ever, it should be noted that this cannot be generalized, as other
necropolises in high plateau areas have particularly small sizes, such as
the Llano de Olivares necropolis (Fig. 9), making this an isolated result,
not extensible to the entire complex. In any case, it must be highlighted
that Majadillas is also the only necropolis where a certain relationship
between situation, size and abundant grave goods can be found
(Spanedda et al. 2014). For this reason, as referred above regarding
mound sizes at cluster 1 – Ba˜
nos de Alicún, we can suggest that social
differences were also marked at Las Majadillas necropolis through
graves situation.
An exception that seems particularly interesting appears in the
relationship between tomb size and geography for clusters 1, 11, and 12.
Cluster 1 refers to the Ba˜
nos de Alicún necropolis, the northernmost in
the complex, cluster 11 to three closely located tombs in Hoyas del
Conquín, in the Umbría del Conquín subgroup, and cluster 12 to the
easternmost necropolis, El Baúl. The particularity lies in the fact that the
Ba˜
nos de Alicún and El Baúl necropolises have been identied in other
studies as groups with evident anomalies in their geographical position
and constructive characteristics (dimensions and typology, hypogeic
nature in the case of Ba˜
nos de Alicún) (Fig. 10). Considering the analysis
that regrouped the megaliths into necropolises based on topographic
variables such as distance to the Gor River, altitude, and UTM X and Y
coordinates, these groups appear as distinct zones not belonging to the
megalithic complex of the Gor River area (Esquivel et al. 2022). An
interesting hypothesis was developed, suggesting that these groups are
slightly closer to other watercourses, the Fardes and Baúl rivers,
respectively. Thus, the differences in location and architecture might
reect neighbour communities with some cultural differences, probably
exploiting different ecological niches. These differences or particular-
ities might be subtle, explaining the difculty in tracing them in other
aspects of the archaeological record (generally summarized in the scarce
grave goods found) (Cabrero, 2023).
Finally, regarding the results of the fuzzy viewshed analysis,
although it had previously been noted that there was signicant visi-
bility among the tombs of each group and in relation to neighbour
groups with few differences between the tombs regarding the area
visually controlled from them (Cabrero et al., 2023a, 2024), the results
of the analysis conducted here show that, largely, this pattern derives
Fig. 10. On the top, Llano de la Ermita 5 (left) and 9 (right), in Ba˜
nos de Alicún, hypogeic and presenting a large size. At the bottom, El Baúl 193 and 194, with small
dimensions and presenting a square typology without a corridor.
C.C. Gonz´
alez et al.
Journal of Archaeological Science: Reports 61 (2025) 104912
10
from the proximity of the tombs and their multiplication, given that the
perceptibility of the mounds decreases considerably with distance. This
aspect largely depends on the fact that neither size nor prominence was
emphasized when the tombs were erected. The difculty in perceiving
monuments from certain distances has already been identied in other
cases (Rodríguez-Rell´
an and F´
abregas 2022). This can be interpreted in
cultural terms, since megaliths are only recognizable if the observer is
placed at a short distance and, above all, if they already know their
position or appearance. In this way, megaliths would be an identifying
element of the same community, not identiable by outside and un-
aware groups. This hypothesis goes along the lines already mentioned of
the existence of different groups, probably linked to different ecological
niches or riverbeds. Anyway, this is more related to the perceptibility
(the visibility to the megaliths) than to the visibility from the megaliths,
so specic researches would be needed to explore this line.
It is evident that some of these aspects depend on the preservation
state of the mounds, which in many cases, due to erosive or anthropic
processes, have lost part of their original perimeter or even the tombs
themselves were almost levelled, leading to clear problems of identi-
cation and interpretation (Cabrero et al., 2023b). However, the multi-
plication of tombs that must have existed in the Gor River area during
the Late Prehistoric period, from the 151 now clearly dened (Cabrero
et al. 2021) to the approximately 240 estimated as the minimum number
actually constructed in the area (Spanedda et al., 2014; Cabrero et al.,
2023b), would only facilitate visibility among the closest mounds
without modifying long-distance visibility, visually controlling specic
areas of their immediate surroundings, especially those not favoured by
the inherent visibility of the area, as a way to ensure the “sacralized”
domination of the entire exploitation/circulation territory (Cabrero
et al. 2024).
The results presented here suggest that the Gor River megalithic
group was designed in order to mark the territory owned by one (or
several communities). In any case, it would be interesting to extend
surveys and studies to other areas also identied within the Megalithic
Phenomenon of the Southeast of the Iberian Peninsula, such as the
nearby Fardes River area, so that cultural and constructive differences,
as well as placement or visibility patterns, could be contrasted on a
larger scale.
CRediT authorship contribution statement
Carolina Cabrero Gonz´
alez: Writing – original draft, Investigation,
Data curation. Juan Antonio C´
amara Serrano: Writing – review &
editing, Supervision. Enrique Cerrillo Cuenca: Validation, Methodol-
ogy, Formal analysis.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgements
This work is related to the development of the projects
(1)“Producci´
on artesanal y divisi´
on del trabajo en el Calcolítico del
Sudeste de la Península Ib´
erica: un an´
alisis a partir del registro
arqueol´
ogico de Los Millares (PARTESI) (PID2020-117437 GB-I00/AEI/
10.13039/501100011033)” nanced by the Agencia Estatal de Inves-
tigaci´
on del Ministerio de Ciencia e Innovaci´
on and (2)“Din´
amicas de
continuidad y transformaci´
on entre el Neolítico y el Calcolítico en el
Alto Guadalquivir (DINAGUA) (Proy_Exc00002)” funded by the Con-
sejería de Universidad, Investigaci´
on e Innovaci´
on de la Junta de
Andalucía.
Data availability
The link to the repository of the data used is in the manuscript.
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