Using Textual Descriptions to Improve Access to Geo-referenced Statistical Data

Current Experiments


A lot of data available to public is geo-referenced. For example, census data is often aggregated over different levels of geographic regions such as counties and wards. Currently such data is presented to the public using thematic maps such as the ones published by National Statistics showing data from the Census 2001. Although such visual presentations of geo-referenced data work great for sighted users they are inaccessible to visually impaired users. Particularly, visually impaired users find it hard to perceive important trends and patterns in the underlying data which sighted users so effortlessly manage using the visual maps. There are a number of emerging technologies to improve accessibility of map data to visually impaired users such as haptic maps and sonic maps. In this project we apply Natural Language Generation (NLG) technology to automatically produce textual summaries of map data highlighting ‘important’ content extracted from the underlying spatial data. For example, on the Census 2001 site mentioned above, the following is an extract from a human-written textual commentary for general health ‘not good’ data set:

“In Wales, the proportion reporting their health as 'not good' is 12.4 per cent and all local authorities in Wales have above average rates. In England, the region with the highest level of not good health is the North East, where 12 per cent of the population did so.”

In this project we want to automatically generate summaries similar to the above extract. We hope that visually impaired users can use existing screen readers to listen to these textual summaries before exploring the data sets in detail using other access methods. We believe that textual summaries of spatial data could be useful to sighted users as well because multi-modal presentations (visual maps + textual summaries) often work better.


  1. To develop NLG techniques for generating textual summaries of spatial data.
  2. To evaluate the utility of the textual summaries with visually impaired users in collaboration with Grampian Society for the Blind
  3. To evaluate the utility of the combination of textual summaries and visual maps in collaboration with HCI Lab, University of Maryland.


  1. Yaji Sripada
  2. Kavita Thomas


  1. Kavita E Thomas and Somayajulu Sripada (2010) Atlas.txt:Exploring Lingustic Grounding Techniques for Communicating Spatial Information to Blind Users, Universal Access in the Information Society. [ONLINE] DOI: 10.1007/s10209-010-0217-5 pdf
  2. Kavita E Thomas and Somayajulu Sripada (2008) What's in a message? Interpreting Geo-referenced Data for the Visually-impaired Proceedings of the Int. conference on NLG. pdf
  3. Kavita E Thomas, Livia Sumegi, Leo Ferres and Somayajulu Sripada (2008) Enabling Access to Geo-referenced Information: Atlas.txt, Proceedings of the Cross-disciplinary Conference on Web Accessibility. pdf
  4. Kavita E Thomas and Somayajulu Sripada (2007) Atlas.txt:Linking Geo-referenced Data to Text for NLG, Proceedings of the ENLG07 Workshop. pdf


This project is part of our ongoing work on developing technology for automatically producing textual summaries of numerical data. Our work on summarising time series data as part of the SumTime project has lead to the development of SumTime-Mousam, an NLG system that was deployed in the industry to generate marine (for the offshore oil industry) weather forecasts from numerical weather prediction (NWP) data. As part of RoadSafe, we are currently extending this technology to generate weather forecasts for winter road maintenance applications. We are also working on summarising scuba dive computer data in the ScubaText project and clinical data from neonatal intensive care units in the BabyTalk project.

Grampian Society for the Blind

Grampian Society for the Blind is a charity providing advice and support to people with visual impairments in the North-East (of Scotland). In the current project we work closely with their members for understanding their requirements and also for evaluating our technology.

Funded by