My colleagues and I at QCRI partnered with the World Bank several months ago to develop an automated GeoTagger platform to increase the transparency and accountability of international development projects by accelerating the process of opening key development and finance data. We are proud to launch the first version of the GeoTagger platform today. The project builds on the Bank’s Open Data Initiatives promoted by former President, Robert Zoellick, and continued under the current leadership of Dr. Jim Yong Kim.
The Bank has accumulated an extensive amount of socio-economic data as well as a massive amount of data on Bank-sponsored development projects worldwide. Much of this data, however, is not directly usable by the general public due to numerous data format, quality and access issues. The Bank therefore launched their “Mapping for Results” initiative to visualize the location of Bank-financed projects to better monitor development impact, improve aid effectiveness and coordination while enhancing transparency and social accountability. The geo-tagging of this data, however, has been especially time-consuming and tedious. Numerous interns were required to manually read through tens of thousands of dense World Bank project documentation, safeguard documents and results reports to identify and geocode exact project locations. But there are hundreds of thousands of such PDF documents. To make matters worse, these documents make seemingly “random” passing references to project locations, with no sign of any standardized reporting structure whatsoever.
The purpose of QCRI’s GeoTagger Beta is to automatically “read” through these countless PDF documents to identify and map all references to locations. GeoTagger does this using the World Bank Projects Data API and the Stanford Name Entity Recognizer (NER) & Alchemy. These tools help to automatically search through documents and identify place names, which are then geocoded using the Google Geocoder, Yahoo! Placefinder & Geonames and placed on a de-dicated map. QCRI’s GeoTagger will remain freely available and we’ll be making the code open source as well.
Naturally, this platform could be customized for many different datasets and organizations, which is why we’ve already been approached by a number of pro-spective partners to explore other applications. So feel free to get in touch should this also be of interest to your project and/or organization. In the meantime, a very big thank you to my colleagues at QCRI’s Big Data Analytics Center: Dr. Ihab Ilyas, Dr. Shady El-Bassuoni, Mina Farid and last but certainly not least, Ian Ye for their time on this project. Many thanks as well to my colleagues Johannes Kiess, Aleem Walji and team from the World Bank and Stephen Davenport at Development Gateway for the partnership.
