To make help makes sense of the vast range of humanities mapping research and projects, spatio-temporal humanities activity can be broadly grouped in the following areas, in the sense that most projects at conferences and in journals have a focus on one or more of these themes.

Time Maps

TemporalEarth will enable a wide range of spatiotemporal visualisations, such as journeys over time, changes over time and cyclical time, even the changing coastline of the distant past.

The our initial prototype: Temporal Earth (TLCM 1.2b) >>>

Temporal Earth will build on Matt Coller's work, to make this functionality and more available to any user with a log in. For a demonstration see

Deep Maps

'Deep' mapping involves over laying information from multiple sources, adding 'depth' to a normally 2 dimensional map, and to our understanding of complex relationships about places. In many cases this can simply involve importing well formatted geodata into a mapping system, but things can get more complicated, requiring relational databases and complex networks, with various kinds of visualisation, and when viewing changes over time.

To aid in deep mapping we provide ways to discover, wrangle, clean, add to or create subsets of spatiotemporal data, with a humanities focus.


Most systems should be developed to allow layering.

Recogito/TMT (TLCM 1.0b) >>>

Text Map Text will merge functionality of a prototype of TextMapText and Recogito. TextMapText was developed by Bill Pascoe and Jack Newley through C21CH at the University of Newcastle, and was inspired by Icelandic Saga Map. Recogito is a well established tool produced by Pelagios, with a community for mapping the ancient world, including place name detection and correction in texts and with images. TLCMap will combine and add to functionality, ultimately contributing to the development of Recogito, as part of the collaborative open source development community, rather than duplicate or compete with it. En

Planned Development

As far as resources permit:

A common aim in humanities mapping is to geolocate collections and archives of images, texts, audio or video files. Many online collection and archive systems already have built in features or plugins to enable this. Our contributions to this area will be:

Heurist is a system for handling complex data, including collections of media associated with relational and linked data. Heurist is available if you do not already store your collection in a web archive management system. TLCMap will ensure mapping compliance and compatibility in Heurist.

Geolocating collections can be a very laborious, monotonous and time intensive process. We aim to provide some tools to make solving common problems as quick and easy as possible. For example, most systems allow export and import of data as spreadsheets (such as .csv files) - a useful tool in this instance would be one that enables quickly adding coordinates to spreadsheets of data, such as a table of image names with metadata. Reducing such menial tasks which none the less require human judgement from 10 seconds to a few seconds can save time and money. This can make projects that were too expensive feasible.

Map To Map

‘Maps’ come in different forms (songs, dances, hand sketches, lienzos, art etc.), which may be stored as images, audio or video. Many humanities requirements involve making connections and associations not only of some media to a place (such as a photograph in a collection) but among points within these media - such as a hand drawn map or a point in a recorded narrative and a modern satellite map, to a map. These associations can be across all kinds of media, not just to a GIS system. For example, an image of an old map might be related to a ship's journal and both might be related to a map. Parts of a recording of a story might be connected to points in a painting which might be connected to sections of a line on a map, showing the path taken while the story was told. Interacting with particular kinds of data often requires an application of some sort - this may be a simple image viewer that highlights the relevant points, or a complex set of transcriptions, translations, glosses and notes arranged in parallel.

Virtual Maps

Building on research at Curtin University, techniques for using maps in virtual reality, augmented and mixed reality will be made easier to use for people working in humanities, and made available on affordable platforms. For example, importing terrain data, overlaying images and adding interactive points to maps in the 3D VR environment.

Data Maps

Spatiotemporal Metrics 1.0a >>> Quick Coordinates >>>

Various tools will enable visualisation of quantitative data in different ways.

Developed for science, engineering and commercial purposes, many mapping systems already have extensive visualisation features for quantitative data. Rather than attempt to replicate all this functionality, we aim to provide some simple tools to:

Some specific requirements we aim to address include:


Vagueness (of points, lines, polygons and time)

A common problem in humanities is dealing with vague and uncertain data. Narratives may contain comments like, "It happened in late Winter, a day's ride north of the creek." A manuscript might be dated to sometime in the life of a medieval poet, and there may be some debate over their birth and death date. They might have written it in one of 2 cities. We might want to indicate that there are no distinct boundaries between languages but mapping systems often constrain us to draw clear and distinct regions. And so on. Yet computers, to work with dates and places, need specifics. Sometimes we can work around this by specifying ranges, such as translating 'late March, 1836' to a range of between 15/03/1836 and 31/03/1836, or by specifying our best guess and adding a 'notes' field with commentary on the accuracy. We hope to ease this situation by finding ways and practices for representing such vagueness - perhaps with blurs, colour coding, thickness and so on.

Statistical comparison of data on ellipsoid surfaces

Often we make maps to see or show patterns. Statistical comparison can give empirical weight to these patterns, reveal patterns we hadn't noticed and provide better quantitative comparisons. They can help with common problems in maps - for example, if we wish to base an argument on the fact that some set of events occurs close in time and space to another set of events, from which we might infer or make a case for some causal connection. Statistics can help us measure how 'close' two sets of data are, and in relation to other sets of data. When dealing with maps we are typically dealing with spatial coordinates, often scattered sets of points, and so the full range of statistical analysis should be open to us. However, basic statistics is typically done on a cartesian plane - a 2D surface, infinite in all directions, whereas geographical data is on a 3D ellipsoid surface. Near the international date line, the longitude -179 and +179 are only 2 degrees apart (very close) not 358 (very far). The same distance measured in degrees longitude is very different in kilometres at the equator than near the poles. Even the most basic statistical calculations need to be calculated with this in mind, which involves equations more complicated than just taking the average. We hope to provide some basic tools for common statistical measures using coordinates such as