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Behind the scenes: Big Data analysis for public policy

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Este artículo está también disponible en / This post is also available in: Spanish


In the previous blog Analyzing and improving access to culture we told the story of the Pase Cultural program evaluation. Behind that story is an open code tool that was key in analyzing the big, complex datasets generated by the program. The tool, called Georeferenced Program Evaluation (GPE), was designed for detailed geospatial analysis to measure the effectiveness of a program that links location and poverty with access to cultural goods and services.

Figura 1. RStudio (interfaz gráfica para trabajar con el lenguaje R), con datos del Pase Cultural en pantalla
Fig 1. Screenshot: the data from the Cultural Pass assessment in RStudio (graphical interface to work with the R language).

GPE is written in R, an open-source language and programming environment for statistical analysis. The fact that the R language (and the tools written in it) is open-source means that they are open to anyone to access and use at no cost, promoting the dissemination of analyses and reuse of tools – either as-is, or with collaborative improvements. This article will provide a simplified reading of the coding and highlight the potential use of the geo-referenced analysis of public policies.

In the case of the Pase Cultural (Cultural Pass) program, the geographical evaluation included the following sources of information:

  • Database of beneficiaries enrolled in the program
  • Database of educational establishments adhered to the program
  • Database of businesses or institutions partnering with the program
  • Program transactions, respecting privacy protocols, aggregated by gender, area of residence, area of educational establishment, area of cultural supply
  • Connectivity via Google maps
  • Socio-economic data from the 2010 Census of Argentina.

Considering the sources of information, we would like to prepare different outputs. The coding programs, whether custom-made or available online, will be the framework in which the transaction data of this program will be used. Simplistically, this process involves the crossing of different data tables and coordinates to be translated into an analysis image on a map, table, or graph.

Fig. 2. Simplified graphic summary of the sources of information for an analysis map. (data from the schools and homes of the beneficiaries, cultural institutions, transactions, census, and connectivity patterns)
Fig. 2. Simplified graphic summary of the sources of information for an analysis map. (data from the schools and homes of the beneficiaries, cultural institutions, transactions, census, and connectivity patterns)

When none of the existing tools fulfilled the evaluation, analysis needs of this program, so a new coding program was developed in order to:

  • Georeference postal addresses
  • Obtain base maps with the urban grid of any city
  • Estimate a matrix of distances between origin and destination
  • Create metrics for frequency, group distribution and distance metrics of transactions / access among beneficiaries / consumers and supply points.
  • Creating visualizations that allow exploring the difference between frequency and type of consumption by attribute[1] of the beneficiaries

Thus, we built the GPE tool, to run a georeferenced analysis of the cultural pass program or Pase Cultural. Starting with the address database and corresponding points of cultural consumption, we were able to visualize our findings, such as the fact that the activity of the most vulnerable cultural pass beneficiaries, commonly living further from points of cultural offering, is very similar to that of users living closer and with higher incomes (see figure 3).

Fig. 3. Number of transactions per user by socioeconomic group (NSE). In the Y-axis the number of transactions and in the Y-axis the 10 groups, with yellow being the highest income.
Fig. 3. Number of transactions per user by socioeconomic group (NSE). In the Y-axis the number of transactions and in the Y-axis the 10 groups, with yellow being the highest income.

The GPE tool allows the assignment of a color per characteristic. In this case, blue would represent the most vulnerable segment of the population, while yellow would represent the highest-income group. The map (Fig. 4) shows a cross-analysis between socioeconomic level and the location of schools with active users of the Cultural Pass.

 

Fig. 4: Example of the results, location of schools with active users by socioeconomic group (NSE).
Fig. 4: Example of the results, location of schools with active users by socioeconomic group (NSE).

Several specific analytical tricks complemented the GPE tool[2]. For example, to assess connectivity, the surface of the City of Buenos Aires was divided into 200 cells with a radius of approximately 5 blocks each (average walkable distance[3]). Through automated queries to the routing system provided by Google Maps , an origin-destination matrix was compiled with 40,000 possible routes between the cells that contained the cultural supply points and those that contained the addresses of the participants. This allowed for the calculation of travel time between the point of cultural supply of the pass from a participant’s home or school. The overall average travel time on public transportation was 37 minutes. Table 1 shows travel times (average and maximum) by cultural category. Cinemas receive the closest audience, while museums and art bookstores require longer trips.

 

Fig.5. Ejemplos de las rutas en transporte público desde celdas con alta y baja conectividad respecto al resto de la ciudad.
Fig. 5. Examples of public transport routes from cells with high and low connectivity to the rest of the city.
Table 1. Travel times from homes to cultural supply.
Table 1. Travel times from homes to cultural supply.

This detailed analysis was possible thanks to open source tools – incorporating existing tools into custom-built ones. In addition to meeting the needs of the continuous assessment of the Cultural Pass, the GPE tool will serve for the georeferenced analysis of similar programs in the future. It allows us to adjust the supply of benefits in relation to the conditions of the city, moving us towards real accessibility to public programs and fairer cities.

Fig.6. Más detalles de la herramienta EGP en el blog asociado, abierto al Público [https://blogs.iadb.org/conocimiento-abierto/es/open-urban-planning-toolbox-planificacion-urbana/]
Fig. 5 More details on the GPE tool can be found on the associated blog, Abierto al Publico. Presentamos el Open Urban Planning Toolbox: una caja de herramientas digitales para la planificación urbana

[1] An attribute is the characteristic of a beneficiary according to profile, such as gender, age, neighborhood, behavior in cultural transactions, etc.

[2] For more information and examples of this code, go to [https://gpe.netlify.com/articles/using_gpe]

[3] Buenos Aires public transport passengers walk an average of 4.5 blocks to get to their transport stop, according to INTRUPUBA [http://ondat.fra.utn.edu.ar/?p=1044]

Cover source: Buenos Aires Ciudad. Facebook. Musicales Baires Argentina. 10 de octubre, 2018. Edited by the IDB


Isidora Larrain de Andraca

Isidora joined the Inter-American Development Bank to work multidisciplinary on innovative urban projects related to cultural heritage, inclusion, eco-efficiency, and creative and cultural industries. She is also part of the Cities Lab team, experimenting and evaluating new solutions for central areas across the region. Previously, she designed and managed urban and architectural projects in the city center of Santiago-Chile and coordinated the adaptation of the Neighborhood Improvement Program for historic urban landscapes in the Ministry of Housing and Urbanism in Chile. Isidora has designed place-projects in diverse contexts in Malta, UK, Patagonia, Brazil and Surinam, among others. She has been teaching in undergrad and graduate studies for Architecture, Urban Design, and Heritage management at the Catholic University of Chile. Isidora is MSc Sustainable Heritage at the Bartlett, University College London and Architect from the Catholic University of Chile, both with distinction.

Jordan Fischer

Jordan Jasuta Fischer trabaja en inteligencia artificial y análisis cognitivo en la división de sector público de IBM. Previamente, se especializaba en proyectos de código abierto, tecnología cívica y análisis geoespacial en Latinoamérica con el BID. Su experiencia en soluciones tecnológicas, gerencia de datos, y análisis avanzado en el campo del desarrollo internacional ha cubierto temas tan diversos como la administración pública, la salud pública, y los derechos humanos. Jordan tiene una maestría en Análisis de Negocios de la Universidad de George Washington y un bachillerato en Economía de la Universidad de Utah.

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