Unique ID: 2017001

Division: Center for Spatial Information Science
Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Dynamic Census Project

Estimation of demographic attribute and human mobility at higher granularity using mobile phone data

Develop a method to create a human mobility dataset by analyzing mobile phone data with various secondary data such as land use and transportation networks. For collecting training and validation data, field surveys were conducted. Results obtained through the method are called Dynamic Census that is the human trajectory data and gridded-map data, representing the spatiotemporal distribution of both mobile phone users and non-mobile phone users. The data are labeled with predicted demographic attributes. We believe it can be good supplement data for conventional population and housing census data with the information on population movement at the high granularity and high frequency. Considering that the data structure of mobile phone data does not vary a lot according to the region and country, developed method will be scalable in other parts of the world. We just finished a pilot project in Bangladesh and are preparing for Sri Lanka and Mozambique.

Project Objective:

Pilot intended to go to production to supplement existing data, For the production of statistics, Scientific / research

Project Sources
Project Sources
Type Of Institution: academic institution
Big Data Source: Mobile phone data, Satellite imagery
Region: South Asia, Africa
Country Area: Bangladesh, Sri Lanka, Mozambique
Id Country Regional: country
Partnerships
Partnerships
Data Providers: Satellite or aerial imagery provider, Mobile Phone operator
Other Partners: Government institute, Research or academic institute
Accessing Data
Accessing Data
Data Access Rights: Only for this project
Intermediary:
Data Coverage
Data Coverage
Data Coverage: Only a portion of all data
Coverage Geo Comments: Depends on the country
Data Quality
Data Quality
Quality Aspects Evaluated: Institutional/Business Environment, Coherence, including linkability to other sources
Validation Comments: We are comparing with existing statistics.
Data Quality Concerns Comments: Data cleaning
Data Quality Concerns:
Validation With Training Data:
Methodology
Methodology
Methods Used: Traditional statistical methods, Data visualization methods, Machine learning (Random forest, etc.)
Technologies
Technologies
Technologies: GIS, Relational database, Hadoop Clusters, Cloud services
Other
Other
Income Level: Lower-middle-income
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