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:

- make common scenarios and needs easy
- focus on humanities needs which are not adequately met
- focus on change over time
- handle vagueness

Some specific requirements we aim to address include:

- Mean, Median, Mode, Standard Deviation (not a trivial matter of taking the numeric average of lat and long, given the dateline and convergence of longitude)
- Measures of ‘closeness’ among datasets.
- Metrics on point, line and/or polygon data.
- Transforms between point, line and polygon data.
- Time as a factor across all metrics.
- Altitude and terrain as a factor across all metrics at varying resolutions.
- Spatial distortion by travel 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.

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