Geo-spatial visualization

I recently attended a Workshop on Challenges in Geo-spatial Visualization run by the OeRC at Pembroke College, here in Oxford.  The workshop was organised by Prof Min Chen and his colleagues in order to consider challenging problems in the visual analytics of spatial data and to discuss potential solutions.

Jason Dykes and Jo Wood of the giCentre at City University London presented particularly interesting ideas and visualizations, based around cartograms and visualising spatial ‘flows’.  They also emphasised the critical element of visual salience: this is the concept that (spatially) large objects tend to dominate on a map, whereas (interpretatively) important objects ought to be what our attention is drawn to.

I was also particularly taken with the ideas Simon Walton (of the OeRC) in regard to the importance of spatial frequency to visual perception (e.g. if we look at a Google Earth image of a city from space, we arguably tend to think that we distinguish between city and countryside based upon colour [i.e. greens vs greys], but we are in fact more influenced in this regard by the complexity of what we are seeing, with countryside being quite plain and cities complex).

Overall, the workshop was very engaging and challenged my thinking on how I might approach the spatial analysis of EngLaId’s datasets.  In particular, I think I am rather too wedded to the conventional map and, as such, have been experimenting with some alternative visualizations since the workshop.

One idea raised in discussion by Jo Wood was that of making graphs where one axis represents space (in some way) and the other an attribute associated with data located within that space.  It occurred to me that one common concept seen in much archaeological interpretation on the scale of England / Britain was that of difference between the lowland zone of southeastern England and the highlands of the west and north.  Conceptually, we can thus think of this as a trend from south east to north west.

In order to organise our data in such a way as to make it possible to graph data along this axis, I first defined an (arbitrary) point off the south east of England and then created a Euclidean distance raster radiating out from this point:

Euclidean distance raster from point marked by X.

I then generalised this into 10km width bands and joined the results to the vector grid tessellation that I am using to analyse data on the scale of England:

10km distance bands from point marked by X.

It is then possible to use this banding to plot other attributes recorded in the grid square layer as a graph, such as mean elevation or terrain ruggedness (TRI).  As our datasets are not yet quite complete, I do not currently have the ability to query these down to subsets based upon archaeological site type / period.  Therefore, I experimented with creating some graphs based upon the entire dataset, thus showing patterns along this SE-NW axis for England as a whole.

crazy graphs 1 - elev
Graph of mean elevation of grid squares: x-axis = distance band; y-axis = mean elevation. Points are individual data; heat map shows clustering. Deformed England below x-axis to show approximate spatial element.
crazy graphs 2 - TRI
Graph of mean TRI of grid squares: x-axis = distance band; y-axis = mean TRI. Points are individual data; heat map shows clustering. Deformed England below x-axis to show approximate spatial element.

These two graphs (created in Veusz from a .csv table exported from ArcGIS) are constructed so that the distance bands run from left (SE) to right (NW), with the mean elevation / TRI being shown on the y-axis (with the TRI, the higher the number, the more rugged the terrain).  The dots show individual records and the ‘heat map’ behind shows the frequency /clustering of those dots.  The deformed England map below each graph is intended to show an approximation of where these bands fall spatially, although obviously this is an imperfect relationship.  These graphs both show how the English landscape becomes more elevated / rugged at its extremes as you head north or west from the south east, albeit with its main clustering remaining at fairly low elevations and at fairly low degrees of ruggedness.

crazy graphs 3 - obsc
Graph of percentage obscuration of ground surface of grid squares: x-axis = distance band; y-axis = percentage. The red colouring is for “human” factors; the grey colouring superimposed includes soil / geological obscuration in addition.

This final graph shows the frequency / clustering of the percentage of grid cells in each band that are obscured from the air for the purposes of aerial photography.  The red shading shows ‘human’ factors only (see previous post), with the greyscale shading also including geological / soil type factors (see this post).  This graph is a little harder to read, so probably requires more thought.

If we compare these three graphs, we can see that the areas of the country most obscured by human activity (which in this instance includes woodland and lakes) cluster in the same bands as the areas of England which are predominantly of low elevation / ruggedness.  This suggests that there is a relationship between landscape morphology and human activity (as we would expect), with humans tending to prefer to settle in areas which are arguably easier to live in (i.e. lower, flatter terrain).

This is all very experimental at the moment and the conclusions reached are not yet particularly relevant to archaeological study, but it does prove that there is potential for a methodology such as this to elucidate patterns in our data.  Once we are able to query down this grid square dataset to only include cells with particular types of archaeological feature in them, we will be able to create many different graphs such as these and, as such, attempt to quantify the difference / similarity in the distributions of different archaeological features, based upon several attributes (i.e. elevation, TRI, ground obscuration).

Clearly, the banding chosen for this experiment reflects a particular concept of how distributions might vary across England, albeit one that is very common in archaeological interpretation (e.g. the three zones seen by Roberts & Wrathmell [2000 / 2002] and by Jeremy Taylor [2007] in their respective works): it is thus desirable to test different axes across the country to see whether different patterns might emerge.  It would also be possible to do something similar for bands created around all instances of a particular type of site, although this might be argued to be a little too processualist perhaps…

In conclusion, I do think the methodology outlined has potential for studying patterns in our data, but it will require a lot more thought and experimentation to be certain.

Chris Green


Roberts, B. and S. Wrathmell. 2000. An Atlas of Rural Settlement in England. London: English Heritage.

Roberts, B. and S. Wrathmell. 2002. Region and Place. A Study of English Rural Settlement. London: English Heritage.

Taylor, J. 2007. Atlas of Roman Rural Settlement. London: English Heritage.

Author: Chris Green

Postdoctoral Researcher (GIS)

2 thoughts on “Geo-spatial visualization”

Leave a Response

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s