Unique ID: 2017003

Division: eScience Institute
Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Crowd-sensing Census

A Tool for Estimating and Visualizing Poverty Maps.

SDG: 01 - No Poverty

Statistical Area

Economic and financial

Project Sources
Project Sources
Type Of Institution: research institute
Big Data Source: Mobile phone data, OpenStreetMaps, iSTAT Census data
Region: Europe & Central Asia
Country Area: Italy
Id Country Regional: country
Partnerships
Partnerships
Data Providers: Mobile Phone operator
Other Partners: Research or academic institute
Partnership Comments: This project is conducted as part of DSSG (Data Science for Social Good) in University of Washington.
SDG Indicators
SDG Indicators
SDG: 01 - No Poverty
SDG Comments: This project aims to predict poverty based on big data thus eliminating the need for costly household surveys and census.
Accessing Data
Accessing Data
Data Access Rights: Broader access rights
Data Access Comments: Data made publicly available by Telecom Italia as part of their Data Challenge
Data Coverage
Data Coverage
Data Coverage: Only a portion of all data
Coverage Geo Pop: Part of country / high % of market
Cost Implication: Free
Coverage Geo Comments: Aggregated calls based on Cell Towers rather than individual's records
Coverage Period: 2 months
Data Quality
Data Quality
Quality Aspects Evaluated: Validity, Accuracy, including selectivity
Validation Comments: Ground truth data about poverty collected from last Italian census available from iStat
Quality Framework Comments: Both quantitive and qualitative. Quantitive evaluation of the prediction model and qualitative model of the poverty maps.
Data Quality Concerns Comments: As iStat does not provide a single composite index of poverty we needed to construct this value based on multiple variables such as unemployment, education etc. by using Principle Component Analysis.
Methodology
Methodology
Methods Used: Traditional statistical methods, Supervised learning, Data visualization methods, Machine learning (Random forest, etc.)
Technologies
Technologies
Technologies: GIS, Data visualization tools, Other, Hadoop Clusters, Cloud services
Technologies Comments: R, Python
Other
Other
Income Level: High-income
Iso: IT
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