Archaeologists regularly work with maps and data about where sites, samples and objects are found. We ask lots of questions that have a spatial component. Examples of archaeological questions about space and place include: Which Bronze Age cairns are close to the coast in England? In this excavation, is bone found inside a building or outside in the yard? In archaeology space and place matter. It's important to learn to work with spatial data and maps in order to succeed in a variety of careers in archaeology and heritage management.
In this exercise, you will be looking at a group of Medieval burials from the Carmelite Monastery at Linlithgow. Archaeologists studying cemeteries might want to know which way the burials are oriented (north to south vs east to west) and if all the burials are oriented the same way. In studying a cemetery you might also want to investigate whether any burials physically overlap other burials, implying they took place at two different times. If you're thinking about family groups, you might want to check whether adults and infants and men and women are buried near one another. All these questions have a spatial component, so you need to work with spatial data to be able to answer them.
The aim of this exercise is for you to:
To start working with spatial data and maps, you need to put together your toolkit. You're currently working inside something called a jupyter notebook, which will be a key part of your spatial analysis toolkit. It's a place to keep notes, pictures, code and maps together. You can add tools and data into your jupyter notebook and then use them to ask spatial questions and make maps and visualisations that help answer those questions.
Here's what you need to do: Work your way down the page, carefully reading the notes and comments in each cell (each box on the page) and looking through the code. Anything written with a # symbol in front of it is a comment I've included to explain to you what the code is doing. Comments will appear in blue type. Code will be a mix of black, green and purple type. In jupyter notebooks you hit 'Ctrl+Enter' to execute the code in each cell. Once you have read and understood the comments and code hit 'Ctrl+Enter' to execute the code and then think about the results.
You can make changes to the code to ask different questions. Simply double click in the cell to be able to type in it, make your changes, and re-run the code by simply hitting 'Ctrl+Enter' in the cell again. For example, if you started with code that searched for all the burials where the sex is 'female' you could change the bit of the code that says 'female' to 'male' to search for the male burials. If things go wrong, 'ctrl+z' will undo your changes and you can try again!
# Start loading your tools by telling the notebook to 'import' them (from the internet). %matplotlib inline !pip install descartes import descartes import pandas as pd import requests import fiona import geopandas as gpd import ipywidgets as widgets # These are what we call prerequisites. They are basic toosl you need to get started. # Pandas manipulate data. Geo-pandas manipulate geographic data. They're also black and white and like to eat bamboo... # You need these to manipulate your data! # Fiona helps with geographic data. # Requests are for asking for things. It's good to be able to ask for things. # ipywidgets supports interactivity. # Matplotlib is your tool for drawing graphs and basic maps. You need this! # Remember to click inside this box and hit Ctrl+Enter to make things happen!
The ADS is an archaeological archive that provides data on an open access basis. You can learn more about the Linlithgow excavations dataset, which is part of the 'Medieval Monastic Cemeteries of Britain (1050-1600)' Project here.
url = 'https://raw.githubusercontent.com/ropitz/spatialarchaeology/master/data/lintlithgow.geojson' # This is where I put the data. It's in a format called geojson, used to represent geometry (shapes) and attributes (text). # Geojson is a common format for spatial data, especially if it is being shared online. request = requests.get(url) # Please get me the data at that web address (url) b = bytes(request.content) # I will use the letter 'b' to refer to the data, like a nickname. # In this step, I am reading the stuff on the page the url (web address ) points to with fiona.BytesCollection(b) as f: crs = f.crs linlithgow_burials = gpd.GeoDataFrame.from_features(f, crs=crs) print(linlithgow_burials.head()) # In this step I will use the fiona tool to wrap up all the data from 'b' into a tidy package. # Then I check the coordinate system (crs) listed in the file # and print out the first few lines of the file so I can check everything looks ok. # Don't worry if you don't understand all the details of this part!
You should have a bunch of information in a 'geometry' column that describes the shapes of lines. These are the outlines of the shape of each burial and a 'stick figure' type skeleton for some of them of the contexts from Linlithgow. You should also have a bunch of descriptions and information about the burials. Importantly you should be able to spot the column names. Look for 'COMMENTS' and 'AGE_AT_DEATH' and 'GRAVE_SHAPE'. You should also be able to see 'context' which is a unique identifier for each burial. Each column contains a different type of information.
Spatial data by itself isn't that useful. If we just had a bunch of lines and no descriptions of them we couldn't say much about the archaeological features at Linlithgow's cemetery. It's the combination of spatial and descriptive data that is interesting.
Right now you have your spatial and descritive data as a table. It's hard to read spatial data as just a list of numbers and understand the shapes that are being described. Let's visualise the data as a map to better understand the spatial information.
# We are going to run a bit of code that tells the software to draw the shapes of the burials as a map. linlithgow_map1 = linlithgow_burials.plot(column='EARLIEST_DATE', categorical = True, cmap='Accent', edgecolor='grey', figsize=(15, 8), legend = True); # Let's break down that command. # 'plot' means draw me a map showing the geometry of each feature in my data. # We want to control things like the color of different types of burials on our map. # I used the 'Accent' colorscale command (cmap stands for 'colour map') # and asked it to draw the polygons differently based on the type of earliest possible date at which each burial took place. # The 'EARLIEST_DATE' column, you can see in the table, lists the earliest possible date for each burial # I also told it to make my figure 15x8 in size (figsize) and to show us a legend so we can see which date goes with which colour.
Remember, you just double click in the cell to be able to type, change the items you want to modify, and hit 'Ctrl+Enter' again.
Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r
This is good, but what if you only want to look at one kind of burial? We can select specific types of burials from within our dataset by searching (aka querying) for them.
How do we know what kind of burials we have? Looking at what's inside the data describing all those shapes on the map should help.
Start by printing out our data in a tidy way. Just type its name...
linlithgow_burials # Typing the name of any dataset will print it out
In archaeology we often talk about the dates at which different things happened, and when activities started and ended. Sometimes we are not sure when something happened, so we give a range of dates with an early guess and a late guess. Look at the 'earliest_date' and 'latest_date' columns in the table, and you'll see the date ranges guessed for each burial.
# Say you only want to look at the burials with a latest date before 1400 because you are only interested in earlier burials. # Pandas use square brackets  to make selections within a dataset. # Here we select all the rows (.loc) where the column 'LATEST_DATE' has a value less than 1400. < means 'less than' in code linlithgow_burials.loc[linlithgow_burials['LATEST_DATE']<1400]
# You should have a table view of the data and if you look at the 'LATEST_DATE' column, you should only see dates before 1400. # If we want to see this result as a map, we just add the .plot command to the end of what we typed before. # Remember to include the options to control the style of your map, like you did earlier. linlithgow_burials.loc[linlithgow_burials['LATEST_DATE'] <1400].plot(column='EARLIEST_DATE', categorical = True, cmap='Accent', figsize=(15, 8), legend = True) # Note that I've used many of the same commands that I used before to control the color of the features and the map size.
# Try and do the same thing for burials that are earlier than 1500 linlithgow_burials.loc[(linlithgow_burials['LATEST_DATE']<=1500) & (linlithgow_burials['LATEST_DATE'] >= 1400)]
# Remember to draw it as a map! linlithgow_burials.loc[(linlithgow_burials['LATEST_DATE']<=1500) & (linlithgow_burials['LATEST_DATE'] >= 1400)].plot(column='EARLIEST_DATE', categorical = True, cmap='Accent', figsize=(15, 8), legend = True)
# It might be interesting to see if the burials are arranged by sex. # We can plot each of these maps coloring each burial based on whether it is male or female. linlithgow_sexed = linlithgow_burials.loc[linlithgow_burials['SEX'].isin(['UNK','MALE?','MALE','FEMALE','FEMALE?'])] linlithgow_sexed.plot(column='SEX', categorical = True, cmap='Accent', edgecolor='grey', figsize=(15, 8), legend = True);
# There area a lot of 'UNK' unknown sex burials cluttering up the map. # Remove them by not including them in our 'isin' list of valid options for sex. linlithgow_sexed = linlithgow_burials.loc[linlithgow_burials['SEX'].isin(['MALE?','MALE','FEMALE','FEMALE?'])] linlithgow_sexed.plot(column='SEX', categorical = True, cmap='Accent', edgecolor='grey', figsize=(15, 8), legend = True);
Do you think you see male and female burials in their own areas, or do they cluster together? What might a division between genders suggest about burial practices?
What if you wanted to map by age at death? How would you know the available options? We can usually have a good guess at the valid options for gender by looking at the table, but how an archaeologist divides up their 'age at death' category is less clear.
# One quick way to do this is to use the 'groupby' command. # It shows the groups of values in the 'AGE_AT_DEATH' column. linlithgow_burials.groupby('AGE_AT_DEATH').count()
# We can also ask to see the number and breakdown of items in any of the other categories using .count() adult_burials = linlithgow_burials.loc[linlithgow_burials['AGE_AT_DEATH'].isin(['ADULT (26-45)'])] adult_burials.groupby('GRAVE_SHAPE').count()
infant_burials = linlithgow_burials.loc[linlithgow_burials['AGE_AT_DEATH'].isin(['INFANT (0-5)'])] infant_burials.groupby('GRAVE_SHAPE').count()
Which grave shapes are the most common? Are the common grave shapes the same when you look at adults vs. infants or are they different?
#Let's plot the adults and the infants and colour by grave shape linlithgow_aged = linlithgow_burials.loc[linlithgow_burials['AGE_AT_DEATH'].isin(['ADULT (26-45)','INFANT (0-5)'])] linlithgow_aged.plot(column='GRAVE_SHAPE', categorical = True, cmap='Accent', edgecolor='grey', figsize=(15, 8), legend = True);
What do you conclude here. Is there a relationship between age at death and grave shape?
# Let's save these selections of 'pre1400' and 'pre1500' so we can use them again. # I've given them names here. These are now 'named variables' pre1400 = linlithgow_burials.loc[linlithgow_burials['LATEST_DATE'] <1400] pre1500 = linlithgow_burials.loc[(linlithgow_burials['LATEST_DATE']<=1500) & (linlithgow_burials['LATEST_DATE'] >= 1400)]
#Test your named variable by printing it out again, calling it by its name. pre1400
#Test your other named variable by printing it out again, calling it by its name. pre1500
So far these searches have been about the attributes of our data, or the way each context has been described. You could try selecting on different attributes to ask your own questions, following the pattern of commands we used above. For example, you could search for a specific type of burial, or for burials later than a certain date.
We can also ask questions about spatial relationships between contexts or about the real-world location of our contexts. For example, we could try and find out the location of our whole data. To describe the location of the whole dataset, we might draw a box around all the features. This is called a 'bounding box'. Let's find the bounding box, or real world location and extent of our data. We use the command 'total_bounds' to ask this question. Things 'in bounds' are inside the box.
You should see a set of coordinates that represent the location of your data in a local coordinate system. The coordinates are in meters, representing how far away each burial is from a point that the archaeologists working at the site declared to have the value (0,0,0).
This is less than ideal, as what you really want are the OSGB coordiantes. OSGB is one of the most common coordinate systems in the UK, and would give you real world references, like those your GPS in your phone uses to navigate. You can learn more about coordinate systems here and here.
Why does this matter? Well, if you wanted to get out a map and find the location of this cemetery so you could go visit the place, you would need the coordinates. If you wanted to tell someone else where they were, or tell a planner the area where they should not build a road, you would need the coordiantes to do so. Lots of excavations make up their own coordinate system or 'local grid'. If you become an archaeologist, please try to avoid doing this.
# Now do the same thing for the pre-1500 burials. The results should be similar, but not identical. pre1500.total_bounds # Take a minute and think about why this would be the case.
# So far we have been asking questions about groups of features of different types. # We can also ask spatial questions about single contexts or burials. # Now we will select a single burial by the context number assigned to it. pre1500_195 = pre1500.loc[pre1500['context']== 195] pre1500_195 pre1500_195.plot()
#Now we will select another specific burial. pre1500_189 = pre1500.loc[pre1500['context'] == 189] pre1500_189 pre1500_189.plot()
What kinds of spatial questions can we ask by comparing individual burials? We might ask if they are they different shapes or sizes, or facing different directions. Look at the maps and try and spot any differences. Orientation is easy enough, but it's hard to compare size when the burials are on different maps. Putting the individual burials we want to compare on the same map will make it easier.
# To do this we have to provide a list of the values we are interested in seeing on the map, in square brackets  pre1500_both= pre1500.loc[pre1500['context'].isin([195,189])] pre1500_both pre1500_both.plot(column='context', categorical = True, cmap='Accent', figsize=(15, 8), legend = True)
What does this map suggest? Are there any size, shape or orientation differences in these burials? What might similarities or differences mean?
Look in the 'Age_at_Death' column. Infants are defined as 'INFANT (0-5)'. In many archaeological situations infants are buried differently. Perhaps they are in a separate area, or always close to an adult. These are interesting questions to investigate spatially.
linlithgow_infants = linlithgow_burials[linlithgow_burials['AGE_AT_DEATH'].str.contains('INFANT', na= False)] linlithgow_infants # The command .str.contains means that we want all the contexts where the word 'infant' appears anywhere in the AGE_AT_DEATH column. # It doesn't have to be an exact match, which is useful as archaeological data is often a little inconsistent or untidy. # Speaking of untidy data, the 'na=False' bit tells the code to ignore rows where no data has been entered for AGE_AT_DEATH.
# Now create a map of all the burials of infants. linlithgow_infants.plot(column='LATEST_DATE', categorical = True, cmap='viridis', edgecolor='grey', figsize=(15, 8), legend = True)
How close is close? Let's say 0.5meters. This will be a two step process...
# First we use the 'buffer' command to expand the size of each line and make it 0.5m thick # Doing this defines the area within 0.5 of each infant burial. linlithgow_infants_close = linlithgow_infants.buffer(0.5) linlithgow_infants_close.plot(cmap='Accent', edgecolor='grey', figsize=(15, 15))
# Now we plot the intersection between the buffered infant burials shapes and all the other burials shapes. # This result should return all the burials that physically overlap the area within 0.5 of infant burials. ax = linlithgow_burials.union(linlithgow_infants_close).plot(cmap='Accent', edgecolor='grey', figsize=(15, 15), label = 'adult burials') ax.legend();
Hopefully you learned to: