Abstract

Ancient Egypt’s funerary monuments drastically changed in shape and physical prominence during the thousand years between the Old Kingdom and New Kingdom. Despite the changes in the monuments’ construction and location, does their spatial arrangement reveal long-lasting similarities between the Kingdoms? This research focuses on discovering the spatial patterns of funerary monuments in and between the royal necropolises of Thebes and Memphis, the ancient capital cities of these two kingdoms. This analysis leverages Esri’s ArcGIS Pro to digitize the location of tombs from a variety of digital maps. Analysis shifts to R and R Studio to calculate the nearest neighbor index, cumulative distribution (g-function), and kernel density estimations to determine if the funerary monuments are clustered or randomly placed. Results reveal different numbers and densities of clusters of funerary monuments between the necropolises. This suggests that internal and external factors, such as topology, society, and religion, play a greater role in the spatial relationships and placements of the funerary monuments than long-lasting cultural similarities between the Old and New Kingdoms.

Introduction

The shifting nature in ancient Egypt’s funerary monuments is evident when comparing the necropolises of Thebes and Memphis. Thebes, the religious capital of the New Kingdom, is surrounded by underground tombs within the Valleys of the Kings and Queens whereas Memphis, the capital of the Old Kingdom, has a variety of above-ground funerary monuments such as mastabas and pyramids (UNESCO1, 2026; UNESCO2, 2026).

The differences in these monuments reflect a change in topography as well as almost one thousand years of religious developments (Ikram, 2015). For instance, the Memphite necropolis raises from the western desert plains in northern Egypt. These funerary monuments—pyramids and mastabas—served as the stairways to heaven and were meant to be seen and revered by the population (Ikram, 2015). Over 500 kilometers south in central Egypt, the Theban necropolis is buried within the western cliffs. These underground tombs represented the path through the underworld and were to remain hidden from the population (Ikram, 2015).

Despite these differences, organizational, religious, and structural similarities between the necropolises persisted. For instance, the organization of necropolises still reflect the social hierarchies of its occupants. The Kings retained their divine nature in their religions albeit in a reduced state during the New Kingdom. And structurally, the people celebrated deceased Kings in temples that are adjacent or disconnected to the tombs. The pyramidal shape persisted as well; the specific valley that inters the New Kingdom Kings was chosen for its pyramidal peak that connects to the heavens despite the tombs existing underground (Ikram, 2015). Over one thousand years later and five hundred kilometers south, Egyptian tomb placement was still affected by the pyramidal stairway to the heavens. This raises the question, do any similarities in the spatial arrangement of the royal funerary monuments remain between the Old and New Kingdoms?

The discovery of these spatial patterns primarily leverages cluster analysis and kernel density estimation as the main statistical analytic techniques. The application of statistical analysis in archaeology historically focuses on techniques such as cluster analysis, spatial autocorrelation, and kernel density estimation (Conolly and Lake, 2006). More recently, the application of point pattern analysis and kernel density estimation in the study of landscapes in Sudan discovered environmentally and socio-culturally driven clusters for the arrangement of funerary sites and monuments that have been occupied for two thousand years (Costanzo et al, 2021).

The research herein compares the spatial arrangement of royal tombs in the Theban and Memphite necropolises. The main objective is to analyze, characterize, and compare the tombs’ spatial distributions with numerous types of point pattern analysis. Kernel density estimation visualizes the tombs’ spatial distribution within each necropolis. Cluster analysis calculates the nearest neighbor index and cumulative distribution (g-function) to determine whether the tombs are clustered or random. Comparing the results from the necropolises will shed light into whether any spatial similarities exist between the royal funerary monuments of the Old and New Kingdoms.

Data

Four datasets were created and employed for the analysis in this report: two point datasets representing the locations of the funerary monuments in each necropolis and two polygon datasets representing the outline of each necropolis (Figure 1). The dataset of royal tombs in the Theban necropolis was digitized from Open Street Maps and from a series of digital maps created by the Theban Mapping Project (TMP1, 2026; TMP2, 2026). The locations of 175 Theban tombs were identified based on the monument’s entry point. The dataset of royal tombs in the Memphite necropolis was digitized from Open Street Maps, high-resolution basemap imagery, and from a series of digital maps hosted by Almansa-Villatoro (n.d.) and Crystal (n.d.). The location of 67 Memphite tombs was identified based on the approximate center point of the monuments’ remains.

The necropolis boundaries were created in order to meet the analytic window requirement for point pattern analysis in the “spatstat” package. These boundaries take the shape of a convex hull that was expanded with a one-hundred-meter buffer. The most appropriate shape of the necropolis’ boundaries would incorporate the areal extent of numerous factors, such as the local geology, the tombs’ elevation, and religious practices that are known to affect the location of the funerary monuments; acquiring and mapping these factors is outside the scope of this report. Fortunately, the tombs’ locations were built with these factors in mind and so the tombs’ placements already reflect these outside influences. Thus, the convex hull shape was chosen to reflect the necropolis boundaries. Both boundaries were expanded with a one-hundred-meter buffer. For the Theban necropolis, the buffer spatially encompasses the tombs in their entirety because the convex hull only incorporated the tomb entrances. The same one-hundred-meter buffer was applied to the Memphite necropolis in an attempt to better capture the separation between the desert plains and the Nile Valley.

Digitizing information from various sources and media introduces numerous errors to the datasets. These can consist of locational errors appearing from the misalignment or misidentification of monuments between different digital maps or reference images. These can also consist of thematic errors such as tombs belonging to different dynasties or to different interpretations of “royal”. The associated historic period of the tombs is assumed as New Kingdom for Theban locations and Old Kingdom for Memphite locations. The thematic data will be further limited to only incorporating the tombs’ coordinates and designators. The lack of richness in the datasets severely limits the insights that can be gained from this analysis. Finally, archaeological bias, or the inability to know if all funerary monuments are discovered, affects the data inputs and therefore the analytic outputs to an unknown extent.

# ---- Load packages----

library(spatstat) 
library(sf) 
library(leaflet) 
library(htmltools) 
library(leafem)

# ---- Access Shape Files ---- 

# Access and read necropolis data and outlines
# Tomb data per necropolis
memphis <- read_sf("C:/Geog588Root/TermProject/Shapefiles/Memphis.shp") 
thebes <- read_sf("C:/Geog588Root/TermProject/Shapefiles/Thebes.shp")

# Necropolis outlines
memphisOutline <-read_sf("C:/Geog588Root/TermProject/Shapefiles/Memphis_outline.shp")
thebesOutline <-read_sf("C:/Geog588Root/TermProject/Shapefiles/Thebes_outline.shp")

# Access and Read Tomb Data Shapefiles
tombs <- st_read("C:/Geog588Root/TermProject/Shapefiles/All_Tombs.shp")
tombs <- st_transform(tombs, 4326) # WGS84 is 4326

# Access and Read Tomb Data Shapefiles
outlines <- st_read("C:/Geog588Root/TermProject/Shapefiles/Necropolis_Outlines.shp")
outlines <- st_transform(outlines, 4326) # WGS84 is 4326
# ---- Interactive Map ----

# Define the title and subtitle elements
maptitles <- tags$style(HTML("
    .leaflet-control.map-title { 
      background: rgba(255,255,255,0.75); 
      font-weight: bold; 
      font-size: 15px; 
      padding: 5px;}"))

# Provide text to title and subtitle (i.e., caption)
title <- tags$div(maptitles, HTML("Theban and Memphite Necropolises"))
subtitle <- tags$div(maptitles, HTML("Figure 1: Interactive map of royal tombs 
                                          from Egypt's New and Old Kingdoms."))

# Create color palette for the map
pal <- colorFactor(c("tomato3","steelblue3"), domain = c("Memphis", "Thebes"))
palpol <- colorFactor(c("tomato3","steelblue3"), domain = c("Memphis Necropolis", "Thebes Necropolis"))

# Create map extent for zoom buttons: home, Thebes, and Memphis 
home <- c(25,24,37,32)
mem_button <- c(31.10, 29.77, 31.26, 29.98)
the_button <- c(32.58, 25.72, 32.61, 25.75)

# Map the tombs 
leaflet(data = tombs, 
        
        # De-clutter the map by removing zoom and attribution
        options = leafletOptions(zoomControl = FALSE, attributionControl = FALSE)) |> 
  
  # Add base maps for user choice
  addProviderTiles(providers$OpenStreetMap, group = "Street map") |>  
  addProviderTiles(providers$Esri.WorldImagery, group = "Imagery") |> 
  addProviderTiles(providers$Esri.WorldTopoMap, group = "Terrain") |>
  
  # Symbolize necropolis based on location, label necropolis on hover
  addPolygons(data = outlines, 
              color = ~palpol(Name), 
              stroke = TRUE,
              opacity = 1,
              fillOpacity = 0,
              label = paste(outlines$Name)) |>         # label on hover
  
  # Symbolize tombs based on location, label the tomb name on hover, and provide location
  # information on click
  addCircleMarkers(color = ~pal(Necropolis), 
                   fillOpacity = .75,
                   radius = 4,
                   stroke = FALSE,
                   label = paste(tombs$LongName),         # label on hover
                   popup = leafpop::popupTable(           # label on click
                     zcol = c("LongName", "Necropolis"),  # show data
                     st_drop_geometry(tombs),             # remove geometry column
                     feature.id = FALSE,                  # remove feature ID column
                     row.numbers = FALSE)) |>             # remove row numbers
  
  # Add map title and subtitle
  addControl(title, position = "topleft", className = "map-title") |>
  addControl(subtitle, position = "bottomleft", className = "map-subtitle")  |>
  
  # Create a legend in the lower left corner and provide a title 
  addLegend(position = "bottomleft",
            pal = pal,
            values = ~Necropolis,
            title = "Necropolis") |>
  
  # Create a toggle feature for the base maps
  addLayersControl(c("Imagery", "Terrain", "Street Map")) |>
  
  # Adjust map (zoom out) to reduce overlap of map features
  fitBounds(lng1 = 25, lat1 = 24, # Southwest corner
            lng2 = 37, lat2 = 32) |> # Northeast corner

  # Create zoom buttons for home, Thebes, and Memphis
  addHomeButton(ext=the_button, "Thebes", position = "bottomleft") |>
  addHomeButton(ext=mem_button, "Memphis", position = "bottomleft") |>
  addHomeButton(ext=home, "Home", position = "bottomleft") |>
  
  # Add a scale bar
  addScaleBar(position = "bottomright")

Methodology

The analyses in this report leverages various software platforms to create a series of maps and charts that illustrates the tombs’ locations and the results of the cluster analysis. Esri’s ArcGIS Pro was used for its mapping and data digitization capabilities whereas R Studio and the R programming language were used for mapping data and for conducting standard statistical analysis.

Data creation consisted of digitizing the monuments visible on Open Street Maps and on high-resolution imagery as well as by visually comparing monuments from maps of the royal necropolises (Figure 2, left panel). The Theban tombs within the Valleys of the Kings and Queens are compared against the maps created by the Theban Mapping Project (TMP1, 2026; TMP2, 2026). Maps of the Memphite tombs are hosted by Almansa-Villatoro (n.d.) and Crystal (n.d.). The digitization process did not require georeferenced digital maps because the monuments’ locations were sufficiently identified by visually comparing high-resolution imagery and digital maps. Once data was digitized, it was exported as shapefiles for mapping and statistical analysis in R Studio.

Two map graphics were created for this report (Figure 2, center panel). A static map graphic was created with Esri’s ArcGIS Pro and is included in only the MS Word version of this report. This map provides a visual comparison of the size and tomb composition of the two necropolises. An interactive map showing the same information was created with R Studio’s “leaflet” package and titled with the “htmltools” package. This map allows the user to visualize the tombs’ local topology and satellite overview, outlines of some tombs, and the tombs’ designators. This map is shown in Figure 1.

Various analyses, such as data-type conversions, calculations, and plotting, occurred in R Studio (Figure 2, right panel). Kernel density analysis visualized the tombs’ spatial distribution within each component of the necropolis. Cluster analysis calculated the nearest neighbor index and cumulative distribution (g-function) to determine whether the Theban and Memphite tombs are clustered or random. The statistical analysis in R leveraged the simple features “sf” library for its spatial vector data analysis abilities and the “spatstat” library to analyze spatial point patterns (CRAN1, 2026; CRAN2, 2026). The “htmltools” package supported chart titling.

methodology graphic

Figure 2: The methodology and the steps employed for data processing and analysis as well as the respective software platforms.

Results

# ---- Prepare Data for Point Pattern Analysis ----

# Convert the necropolis outlines to "window" data type
thebesWindow <-as.owin(thebesOutline)
memphisWindow <-as.owin(memphisOutline)

# Convert the necropolis shapefiles into point pattern datasets
# First convert to data frame
thebesDF <- data.frame(thebes) 
memphisDF <-data.frame(memphis)

# Then convert to point patterns
thebesppp <- ppp(thebes$XCoor, thebes$YCoor, window=thebesWindow, marks=thebesDF)
memphisppp <-ppp(memphis$XCoor, memphis$YCoor, window=memphisWindow, marks=memphisDF)
Kernel Density Estimation Analysis

The kernel density estimations (KDE) visualize the density of funerary monuments across their respective necropolises (Figure 3). The Theban necropolis contains approximately 175 tombs that span almost two square kilometers (i.e., 87.5 funerary monuments per square kilometer). The KDE plot reveals two main clusters in the Theban necropolis (Figure 3, left map). These two clusters are comprised of the Valley of the Kings in the north and the more concentrated Valley of the Queens in the south. Despite the valleys’ names, at least one Queen, royal children, and numerous high-ranking officials are interred in the Valley of the Kings (Quiles et al, 2025). One can surmise that the Valley of the Queens contains a similar composition of higher social classes.

The Memphite necropolis contains 67 mastabas and pyramids that span almost 70 square kilometers (i.e., about one funerary monument per square kilometer) (Figure 3, right map). The Memphite necropolis is comprised of six clusters of funerary monuments that generally run parallel to the Nile Valley. The Kings’ and Queens’ pyramids tend to be collocated in the same pyramid complexes with high-ranking officials interred in smaller neighboring monuments. It is important to note that the funerary monuments of high-ranking officials in the Memphite necropolis are not captured in the data set; there is a realistic possibility that that the funerary monuments of high-ranking officials are captured in the Theban necropolis. The reason for this discrepancy is that the elite class, i.e., the “royals”, expanded into a larger social class by the New Kingdom (Ikram, 2015).

# ---- Kernel Density Estimation (KDE) Analysis ----

# Create and plot KDE maps
# Prepare space for side-by-side KDE plots, and reduce empty space between elements
par(mfrow = c(1, 2), mgp = c(0, 0.5, 0))

# Density: KDE for Thebes
# Calculate KDE for Thebes
KDE_thebes <- density(thebesppp, sigma = 150) # bandwidth 

# Plot KDE on map
plot(KDE_thebes, main = NULL, las=1, cex.axis = 0.8)

# Overlay Thebes' tombs and add symbology
plot(thebesppp, 
     pch=19, 
     col="white", 
     use.marks=FALSE, 
     cex=.4, 
     add=TRUE) 

# Add title and subtitle
mtext("Theban Necropolis", side=3,line=1) 
mtext("Bandwidth at 150 meters", side=3, line=0) 

# Density: KDE for Memphis
# Calculate KDE for Memphis
KDE_memphis<-density(memphisppp, sigma = 500) # bandwidth

# Plot KDE on map
plot(KDE_memphis, main=NULL, las=1, cex.axis = 0.8)

# Overlay Memphis' tombs and add symbology
plot(memphisppp, 
     pch=19,
     col="white", 
     use.marks=FALSE, 
     cex=0.4,
     add=TRUE)

# Add title and subtitle
mtext("Memphite Necropolis", side=3,line=1)
mtext("Bandwidth at 500 meters", side=3, line=0)

# Add overall title and caption
par(mfrow = c(1, 1))
title(main = "Kernel Density Estimations (KDE)", cex.main = 1.1, line = 3)
mtext("Figure 3: Kernel density estimates of the royal tombs in the Theban 
      (left graphic) and Memphite (right graphic) necropolises. The Theban 
      necropolis spans an area measuring 1.93 KM2. The Memphite necropolis 
      spans an area measuring 72.59 KM2.", side=1, line=2, cex = 0.6)

Nearest Neighbor Analysis

The nearest neighbor index (NNI) is a ratio of the observed mean nearest neighbor distance to the expected mean nearest neighbor distance (Esri, n.d.). Index values less than one indicate clusters within the data and values greater than one indicate dispersed data (Esri, n.d.). The NNI for Theban tombs is 0.278 and the NNI for Memphite monuments is 0.342 (Figure 4). These values indicate that the funerary monuments in both necropolises are clustered with the Theban tombs showing tighter clusters. The Theban tombs’ tighter clustering compared to the Memphite tombs is also evident by the necropolises’ mean nearest neighbor distances. The former necropolis’ mean nearest neighbor distance at 14.62 meters whereas the latter necropolis’ mean nearest neighbor distance at 178.02 meters (Figure 4). Finally, the histograms’ right-skewed distributions indicate that the tombs are clustered in both necropolises (Figure 4). The histograms also show a small number of tombs that are geographically separated from the main clusters.

# ---- Nearest Neighbor Analysis ----

# Calculate nearest neighbor distances (NND) 
thebesNND <- nndist(thebesppp) 
memphisNND <- nndist(memphisppp)

# Calculate the mean NND
thebes_mNND <- mean(thebesNND) 
memphis_mNND <- mean(memphisNND) 

# Calculate the nearest neighbor index (NNI) values
# NNI < 1 are clusters | NNI ~ 1 are random | NNI > 1 are dispersed
theNNI <- thebes_mNND / (0.5/ sqrt(thebesppp$n / area.owin(thebesWindow)))
memNNI <- memphis_mNND / (0.5/ sqrt(memphisppp$n / area.owin(memphisWindow)))

# Create and plot NND histograms
# Prepare space for side-by-side histograms, and reduce empty space between elements
par(mfrow = c(1, 2), mgp = c(1.5, 0.5, 0))

# Create histogram of Thebes NND
theHis <- hist(thebesNND, 
               main=paste0("\n\nThebes\nmean NND: ", round(thebes_mNND,2), " m\nNNI: ", round(theNNI,3)),
               cex.main = 0.9,
               xlab = "Distance to nearest neighbor (m)",
               cex.lab = 0.8,
               cex.axis = 0.7,
               col = "steelblue3", 
               border = "steelblue4") 

# Create histogram of Memphis NND
memHis <- hist(memphisNND,
               main = paste0("\n\nMemphis\nmean NND: ", round(memphis_mNND,2), " m\nNNI: ", round(memNNI,3)),
               cex.main = 0.9,
               xlab = "Distance to nearest neighbor (m)",
               cex.lab = 0.8,
               cex.axis = 0.7,
               col = "tomato3", 
               border = "tomato4") 

# Add overall title and caption
par(mfrow = c(1, 1))
title(main = "Nearest Neighbor Distances (NND) and Indices (NNI)", 
      cex.main = 1.1, line = 3)
title(sub = "Figure 4: The mean nearest neighbor distances (NND), the nearest neighbor
      indices (NNI) values, and NND histograms of the Theban tombs (left chart) and the 
      Memphite monuments (right chart).",
      cex.sub = 0.6, line = 4)

G-Function Analysis

The G-function is the cumulative distribution of the tombs’ nearest neighbor distances and also indicate whether data is clustered or dispersed (Rossiter, 2024). The individual charts indicate that the funerary monuments in both necropolises are clustered (Figure 5). This is evident by the tombs’ cumulative distance rising faster than the cumulative distance of hypothetical tombs with complete spatial randomness. In other words, the tombs are found at closer distances than expected by randomness. This means that the majority of tombs are located near each other as reflected by the large positive slope in both charts (Figure 5). There is a small number of tombs that are located farther apart from other tombs in both necropolises, although this is more prevalent in Memphis. This is represented by the less positive slope for Thebes and the almost flat slope for Memphis in the upper x-axis range (Figure 5).

Analyzing the data near the charts’ x-axis origins indicates that in Thebes, there is a lack of neighboring tombs for about 4 meters, whereas in Memphis, there is a lack of neighboring monuments for about 25 meters (Figure 5). This could indicate that the sizes of funerary monuments are smaller at Thebes because monuments are expected to exist side-by-side and not atop each other. However, the difference in distances is more likely caused by various factors. For instance, this report’s method at representing the funerary monuments such that Theban tombs are captured by their entrances which tend to face each other whereas Memphite monuments are represented by their center point. Another possible reason is that some Theban tombs do exist atop one-another (e.g., KV8, KV9, and KV57) which is possible because they are built underground.

# ---- G-Function Analysis ----

# Create and plot g-function charts
# Prepare space for side-by-side g-functions, and reduce empty space between elements
par(mfrow = c(1, 2), mgp = c(1.5, 0.5, 0))

# Initialize and plot the G estimation: Thebes
g_env_h <-envelope(thebesppp, Gest, nsim=99, nrank=1)
plot(g_env_h, main = 'Theban Necropolis', cex.main=0.9, legend=FALSE)

# Initialize and plot the G estimation: Memphis
g_env_d <-envelope(memphisppp, Gest, nsim=99, nrank=1)
plot(g_env_d, main = 'Memphite Necropolis', cex.main=0.9, legend=FALSE)

# Add overall title and caption
par(mfrow = c(1, 1))
title(main = "G-Function: Cumulative Distribution", cex.main = 1.1, line = 3)
mtext("Figure 5: The cumulative distributions of the distances of funerary monuments to their nearest neighbor 
in the Theban necropolis (left chart) and the Memphite necropolis (right chart). The black line charts the observed distribution. 
The red dashed line and gray envelope chart the distribution of hypothetical funerary monuments that have complete spatial randomness.", side=1, line=4, cex = 0.6)

Reflection

The KDE maps as well as the nearest neighbor calculations and distributions indicate that there are similarities and differences in the spatial distribution of funerary monuments in the Theban and Memphite necropolises. Two similarities are that the majority of the tombs are clustered and that there is a minor number of tombs that exist outside of the main clusters. Three differences are the greater number and elongated shape of clusters at Memphis, and the denser clusters at Thebes.

Although this analysis reveals both similarities and differences between the Old and New Kingdom necropolises, additional data would more appropriately reveal the internal and external factors affecting the placement of the funerary monuments. For instance, the local topography, geology, and geomorphology as well as the funerary monuments’ elevation greatly affect the placement of above- and below-ground funerary monuments which need sturdy bedrock and durable quarries (Bardaji et al, 2017; Ikram, 2015). The Memphite necropolis’ elongated shape running parallel to the Nile Valley and the Theban necropolis’ concentrated shapes within canyon valleys reflect these external influences. Using this data to better define the necropolis’ outlines would affect the nearest neighbor calculations and the KDE visualizations.

Additional analysis into the second-order properties, or the local distribution, of the placement of funerary monuments could better reveal similarities in the spatial distribution of them. For instance, this type of analysis could reveal whether any socio-cultural practices or beliefs continually influenced tomb placement as well as provide insights into the tombs that are geographic outliers (i.e., those that exist outside of the main clusters). Such a cultural change is partially evident when comparing the adjacent pyramids of the Kings and Queens in Memphis to the geographically separated tombs in the Valleys of the Kings and Queens in Thebes. However, these types of local analyses require data enriched with dynasties, familial relations, social classes, elevation, and other thematic properties.

Even without additional thematic or external data, the analysis in the report could be strengthened in two notable ways. First, by employing the same method to represent the funerary monuments in both necropolises (i.e., point representation for entrances versus center points). A second option is to determine how to represent funerary monuments that interred multiple family members or were reused in different dynasties by different families. For example, the Theban KV40 tomb interred royal children and the Theban KV64 tomb was created in the 18th dynasty and reused in the 22nd dynasty (Quiles et al, 2025). In this analysis, one data point represented one funerary monument despite its multi- or re-use. If each of these funerary monuments were counted twice, thrice, or more to reflect the number of instances of its reuse, then the analytic results would more than likely change to reflect a more concentrated spatial arrangement.

Conclusion

Analyzing ancient Egypt’s funerary monuments in the royal necropolises of Thebes and Memphis revealed that the societies in the Old and New Kingdoms both retained and adapted certain aspects of their final resting places. Various spatial statistics, such as kernel density estimation and nearest neighbor index and distributions, revealed two dense clusters of tombs in the Theban necropolis and six less dense clusters of monuments in the Memphite necropolis. Despite multiple point pattern analytic methods indicating that the funerary monuments are clustered, it is difficult to differentiate the underlying causes. Additional analysis with thematic data reflecting information such as familial relations or social classes as well as with data describing the landscape such as topography and resources could begin to differentiate the observed similarities and differences in the spatial distribution of the tombs and monuments. The analysis in this report takes the first step in comparing the spatial distribution of funerary monuments and suggests additional avenues of analytic research to explore factors affecting the placement of tombs in the Old and New Kingdoms of Egypt.

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