Category Archives: Cartography

Matters relating to the composition and production of cartographical map forms

Merry Christmas 2015

As another year draws to a close at Cranfield University, sure enough we have another Christmas map for you. As with previous years, we’ve collected a sample of tweets from Twitter that match a number of Christmas related keywords and mapped them using the same process we outlined last year.

The colour range from green to red indicates the density of Christmas related tweets from low to high in that county, relative to the normal density of twitter activity in that area (taken from a random sample of all tweets in the UK).

We’ll let you draw your own conclusions from the map. This time we thought we’d use the opportunity to focus on some of the web mapping technologies we’ve started using this year in other projects and hope to develop our use of heading into 2016. The biggest difference between the map you see above and some of the other maps we’ve published in the past is that this one doesn’t makes use of any GIS server or map hosting platform. Traditionally we’ve used either Geoserver or ArcGIS Server to publish our map tiles and other geospatial data for consumption with JavaScript web mapping APIs. Alternatively, web map hosting services can be used if one doesn’t have access to their own GIS Server, these include ArcGIS Online, Mapbox, CartoDB and others. The example here doesn’t use any of these services, but instead is running from a set of map tiles hosted on this very webserver. Interactivity (roll your mouse over the map to view the county names) is provided via a set of UTF-Grid tiles, also hosted on this webserver.

UTFGrid tiles use a combination of JSON encoding and ASCII grid files that sit alongside the map’s image tiles. For each PNG image tile there is a corresponding ASCII tile with a one pixel to one ASCII character mapping. An accompanying JSON lookup table provides the full set of attributes so that you can go beyond a simple raster map and offer full identify style interactivity.

UTFGrid functionality is available in many of the popular JavaScript web mapping APIs, either out of the box or as easily downloadable plugins. This particular map makes use of the Mapbox JS API, an extension of the Leaflet API.

The tiles used by this map are generated using the TileMill software and stored as a .mbtiles file (which is actually an SQLite database). A small PHP file, acting as a tile server, exposes this SQLite/MBTiles database to web mapping APIs as a large nested folder of image and UTFGrid files in the usual {z}/{x}/{y}.png or {z}/{x}/{y}.json fashion.

We like this approach to web mapping as it is reasonably lightweight and portable. The whole application, including web pages, JavaScript, map tiles and PHP tile server can be picked up and dropped onto any web server that supports PHP and is ready to go. It might not provide some of the advanced features you get with more heavyweight solutions, but a simple interactive map with query-able attributes is often all that’s needed for many web mapping applications. It’s also extremely fast and can be built using entirely open source software and tools.

More information on some of the packages and technologies used can be found here:

Merry Christmas and a happy New Year from all at Geothread!

Merry Christmas from all at Geothread

Once again, we’re preparing to wrap up for the year here at Cranfield University. Before we sign off, we’ll leave you with something that’s become a bit of a tradition with the Geothread team over the past couple of years; our Christmas Twitter map. Last year we mapped the spread of festive cheer across the country, according to Twitter users. Once again, we’ve collected a sample of 60,000 georeferenced tweets mentioning the word Christmas, along with a handful of other related keywords. These have been grouped by county and then normalised against a random sample of tweets taken earlier in the year to eliminate the effects of population density.

Comparing against the same map last year, it’s clear that there are several areas that are consistent in their anticipation of the festive season; Central Wales and North Yorkshire in particular. Anglesey and Conwy in North Wales certainly seem to be getting excited about things this year, whilst Cumbria and the Scottish Highlands don’t seem to be feeling the same level of enthusiasm that they did 12 months ago.

Christmas tweets 2014
Apologies to anyone we missed out in Ireland. Unfortunately the random sample of tweets we used for normalisation did not include coverage of all of the British Isles, which is why there are some holes in the final map.

As a bonus this year, we’ve included an interactive map of the raw data collected from twitter, for you to explore below.

Merry Christmas and a happy New Year from all at Geothread!

Flood Risk Modelling of Rail Infrastructure

A recent MSc student group project, recently concluded at Cranfield University, Bedfordshire, and run on behalf of Network Rail, has investigated novel methodologies for integrated flood risk modelling of rail infrastructure.

Delays are costly for Network Rail. 2012/13 was the second wettest year in the UK national record and resulted in significant disruption to rail services and infrastructure. Some £136 million in compensation was paid to train operators in consideration of unplanned delays and cancellations in that year. Winter 2013/14 saw more challenging weather conditions and impacts on delays. In February 2014 the Department of Transport announced it would provide £31 million to fund rail resilience projects in the South West including the installation of rainfall, river flow and groundwater monitoring at key risk locations.

Flooding is a major contributor to rail delays. To help develop a proactive approach to flood risk assessment, a project was commissioned at Cranfield University to develop methods and tools to help Network Rail. The project was conducted by students from the Masters courses in both Environmental Informatics and Geographical Information Management. The project set out to address a number of key objectives. First, to evaluate existing flood risk assessment methods and flood models to identify techniques applicable to Network Rail’s infrastructure; second to develop approaches for flood risk modelling utilising datasets provided by Network Rail, as well as other available data within 3 selected study areas (fluvial, coastal and surface run-off); thirdly to implement the approach within a GIS framework; and fourthly to develop a web tool to enable visualisation of risk assessments by non-GIS experts.

Given the size and scale of Network Rail’s operations, it is unlikely that there is a single solution to predicting flood risk to Network Rail’s assets. However, this project saw the development and use of a data analytical technique from the world of ‘Big Data’, called CART, or ‘Classification and Regression Tree’. Use of CART ‘inference algorithms’ has helped ascertain the key contributory factors for helping explain the flooding events in the case study areas selected. CART profiles were used both to examine static ‘legacy’ data, as well as more dynamic time-series data. The use of these techniques has helped identify a customised data-oriented approach to flood risk modelling that shows considerable promise, and which could now potentially be extended to other parts of the network beyond the case study areas, as well as to other types of incident (for example, landslips or embankment failures). The approach adopted should be seen as complementary with traditional hydrological modelling approaches that would need to be undertaken for specific site requirements. However, further development of the data driven method, and a systematic approach to reviewing incidents and communicating flood risk to stakeholders, may provide further opportunities to reduce the costs of delays.
As the project concluded, a number of key recommendations emerged that would improve information used for strategic decision-making, as well as providing a platform for cost effective data driven flood risk mitigation. Firstly, the importance of clean and categorised incident data has become evident. Appropriate future mechanisms are therefore required to develop operational processes to ensure recording of new incidents capture and codify locations and, where known, the root causes of flooding. The data driven approach adopted in this study has delivered impressive and promising results, but further studies should now be undertaken to develop data driven prediction of asset flood risk further. Such work could commence, for example, with a target route network and use an iterative approach. Another outcome of the work has been in identifying the importance of adopting the means to visualise and communicate visually the modelling results. The web-based portal developed for dissemination of the flood risk profiles, flooding alerts and other data sources, direct from GIS, has proved a powerful means to communicate risk. Further to this, the project has also usefully trialled the use of 3D ‘virtual reality’ visualisation and projection techniques for analysing flood incidents, and educating stakeholders in improved flood risk management. The benefits of a range of software tools were evaluated. Overall, it is seen that the techniques and tools developed during this project can contribute usefully to managing the rail network and related national critical infrastructure.

Dr Stephen Hallett, whose students undertook the project, said: “This project has provided Network Rail with a powerful methodology for undertaking integrated flood risk assessment, made all the more timely after the recent extreme flooding events. The approach adopted highlights how a data-driven approach can help account for contributory factors to flooding, both proximal to the track, but also in the surrounding catchment areas, such as soil type, landuse, land cover and meteorological conditions.”

Student Project Leader: David Medcalf
Student Team Members: Usman Muhammad Buhari, David Cavero Montaner, Jose J. Cavero Montaner, Santiago Gamiz Tormo, Life Magobeya, Kerry Mazhindu-Page, Alan Yates.
Academic Supervisors: Dr Stephen Hallett (, Tim Brewer (

About Cranfield University
Cranfield University is a globally significant centre of expertise and enterprise in science, technology, engineering and management. The University is an exceptional environment for strongly business-engaged research and innovation and for postgraduate and post-experience education and training.
‘Environment’ is a key strategic theme at Cranfield. We have been contributing to the ‘green economy’ for over 40 years with deep expertise in environmental governance and sustainability, natural resource management, agriculture and land management, energy and the environment, environmental engineering for the treatment of water, wastes and contaminated soils and environmental health and food.

Mapping of the landforms of Cambridgeshire and Bedfordshire

Much of our work here at Cranfield University involves the combination of various geospatial datasets.

In this project, we were aiming to more effectively understand the landscape and geomorphology of Bedfordshire, Cambridgeshire and Milton Keynes. We produced a hillshaded digital terrain model from Ordnance Survey Panorama data, and draped this with BGS’s geology maps and our own soils data. The results have just been published in Geoscientist, the popular monthly colour magazine for Fellows of the Geological Society of London.

Timothy Farewell, Peter Friend & Martin Whiteley (2013). Lie of the Land. Geoscientist, 23(2), 14-19.

Elevation Bedrock Surface geology

Bedrock, Marston Surface geology, Marston Soils, Marston

If you’re not a member of the Geological Society you can access the article via their website. The larger maps are also available here, as a PDF