Unique ID: 2017001
| Division: | Center for Spatial Information Science |
|---|---|
| Issue Date: | February 13th 2019 |
| Last modified: | August 1st 2019 |
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
Statistical Area
Tourism, Transportation, Demographic and social, Geo-spatial statistics, Mobility statistics
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
| Data Providers: | Satellite or aerial imagery provider, Mobile Phone operator |
|---|---|
| Other Partners: | Government institute, Research or academic institute |
Accessing Data
| Data Access Rights: | Only for this project |
|---|
Data Coverage
| Data Coverage: | Only a portion of all data |
|---|---|
| Coverage Geo Comments: | Depends on the country |
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 |
Methodology
| Methods Used: | Traditional statistical methods, Data visualization methods, Machine learning (Random forest, etc.) |
|---|
Technologies
| Technologies: | GIS, Relational database, Hadoop Clusters, Cloud services |
|---|
Other
| Income Level: | Lower-middle-income |
|---|