Robust Estimation using Aggregated Data for Urban policy making (READ-URBAN)
Read-Urban was a first project to investigate whether policy recommendations can be made with the aid of linked data collections and data science and to gain experience with the success factors for such a process.
- 2017 - 2018
- Wessel Kraaij
- Municipality of The Hague
The READ-URBAN project was carried out by Leiden University in a collaboration between LIACS and the Institute of Public Administration. The project was partly funded by the Municipality of The Hague. The visualization was developed by Bloomingdata
Big Data for robust measurement of policy indicators
Monitoring indicators in various policy areas such as employment, accessibility, health and safety is standard practice in public administration. After all, policies can only be made based on reliable data. Traditionally, most data is collected through questionnaires. This approach has disadvantages: the most recent figures often lag far behind current events, so that quantitative analysis of interventions only becomes available at a relatively late stage. In addition, there is always the risk of sample bias.
In a big data approach, in principle all available relevant information can be included in the analysis and much more frequent updates can be made. An example of such an approach is the analysis of open internet sources, including social media, using text mining techniques. This involves techniques such as entity recognition, sentiment mining, event recognition. Company sites can be analyzed for features ranging from job advertisements to cyber crime security methods. Other examples of available big data sources are mobile phone data to map crowds and flows; or traffic loop data and floating card data for traffic flows. Experience with these sources is already available and can be used to develop applications for metropolitan practice. Various other resources can be gradually incorporated as we gain access; think of financial transaction data, smart meters, sensor data, or satellite photos.
In the READ-URBAN project, the poverty reduction policy area was chosen as use case in consultation with the municipality of The Hague, in particular to find out more about the category of households that belong to the 'working poor'. These are households that do not receive social assistance benefits but actually live below the poverty line (income consists of various part-time temporary contracts).
In April 2017, a first case study was started by postdoc José Miotto aimed at mapping this category of households, while a second step was aimed at a better understanding of the conditions for influx and outflux of this population. The use of the ‘Ooivaarspas’, an instrument to make activities accessible to people with a low income and in this way to promote social cohesion, was also examined.
- Paper presentation at the Data for Policy Conference in London, September 2017, Title: The Working Poor in Urban Areas: Effective policy initiatives.
- Paper presentation at the Data for Policy Conference in London, June 2019, title: Challenges of data-driven evaluation of soft policy instruments: The poverty pass.
- Working paper 'The Working Poor in Urban Areas: Effective policy initiatives' - https://static1.squarespace.com/static/560bd9e7e4b067a54c36b111/t/59b18553e5dd5b4656861fa7/1504806228772/D4P_ConferencePaper_Final.pdf
The value of data matching for public poverty initiatives: a local voucher program example Giest, Sarah, Miotto, Jose, and Wessel Kraaij, Data & Policy 3, e5, 2021
An interactive visualization has been developed to inspect the neighbourhood data of the municpality of The Hague. The visualization can be found at read-urban.liacs.nl.