Mallon Technology

Case Study

National Transport Authority

The Client

The National Transport Authority (NTA) is the transport authority for Greater Dublin and public licencing agency for the Republic of Ireland.

The Problem

Once of the services that the NTA runs is a public awareness programme, Smarter Travel Workplaces and Smarter Travel Campus.  These work with large employers and third level institutions to implement voluntary workplace and campus travel plans.  The Smarter Travel programmes undertake various surveys over the course of a year, in an attempt to better understand the commuting habits of its audience.

As a result of this, the NTA required geocoding services to enable the production of maps detailing the mode of transport and home location for all respondents of the surveys.

What We Did

Mallon provided a team of four people to deliver the project to the NTA which required the completion of a number of different tasks, including:

  • Cleaning and validation of the address data provided by the NTA against Geodirectory
  • Matching each point feature using a unique identifier to an Excel database provided by the NTA
  • Geocoding address data to a household using Geodirectory
  • Adding coordinate columns to the Excel database provided in ITM
  • Supplying the data in both Excel and GIS (.shp) format

Additionally to this, Mallon imported and re–categorised Geodirectory into an enterprise database system, by developing a geocoding application which utilised a number of data sources to retrieve the correct XY coordinates for a given address.  This was custom built, in–house and cross checked several address datasets, including:

  • Geodirectory
  • Eircodes
  • Google
  • Here
  • Bing

All results were returned to the NTA within 7 days of receipt of the data.

The Benefits

  • NTA are now able to quickly and easily identify patterns and areas of interest through the visualisation of their data
  • Data collected can now be used as a layer to enhance existing maps
  • Data can be compared with previous datasets to highlight trends or shifts in customer habits
  • Maps show areas not covered by data and can help to identify areas of potential opportunity
  • Address information for each data entry is now correctly formatted having been checked against several databases