Unique ID: 2017003
| Division: | eScience Institute |
|---|---|
| Issue Date: | February 13th 2019 |
| Last modified: | February 22nd 2019 |
Cost: FREE
Crowd-sensing Census
A Tool for Estimating and Visualizing Poverty Maps.
SDG: 01 - No Poverty
Statistical Area
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
| 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: | 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
| Data Access Rights: | Broader access rights |
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| Data Access Comments: | Data made publicly available by Telecom Italia as part of their Data Challenge |
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
| 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
| Methods Used: | Traditional statistical methods, Supervised learning, Data visualization methods, Machine learning (Random forest, etc.) |
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Technologies
| Technologies: | GIS, Data visualization tools, Other, Hadoop Clusters, Cloud services |
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| Technologies Comments: | R, Python |
Other
| Income Level: | High-income |
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| Iso: | IT |
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