Unique ID: 2017007
| Division: | Population and Social Statistics |
|---|---|
| Issue Date: | February 13th 2019 |
| Last modified: | February 22nd 2019 |
Statistics on commuting: merging big data and official statistics
Using traffic sensor data targeting for commuting times and distance
The project produced new commuting time estimates based on many data sources. Models are based on comparative studies and built for both commuting time and distance by driving, cycling and using public transport within the national route network.
Project Objective:
Pilot intended to go to production to supplement existing data, For the production of statistics, Scientific / research
Project Outcomes:
The new database includes the following variables: Commuting distance and time by private vehicle, Cycling distance and time, Public transport commuting distance and time (in the whole country), and Capital Region Public Transport commuting distance and time.
Publications Comments:
http://www1.unece.org/stat/platform/display/bigdata/Statistics+Finland+-+Traffic+sensor+data+for+commuting+statistics
Project Publications:
Statistical Area
Project Sources
| Type Of Institution: | National statistical office |
|---|---|
| Big Data Source: | Road sensor data, Public transport usage data |
| Region: | Europe & Central Asia |
| Country Area: | Finland |
| Id Country Regional: | country |
Partnerships
| Data Providers: | Cloud server provider |
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| Other Partners: | Government institute, Other |
SDG Indicators
| SDG: | 11 - Sustainable Cities & Communities |
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| SDG Relevance: |
Accessing Data
| Data Access Rights: | Broader access rights |
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| Data Access Comments: | The access to the sensor data is given to the organisation itself. |
Data Coverage
| Data Coverage: | Only a portion of all data |
|---|---|
| Coverage Geo Pop: | Whole country / high % of market |
| Cost Implication: | Free |
| Coverage Period: | It depends on the data in question. |
Data Quality
| Quality Aspects Evaluated: | Privacy and Security, Completeness, Usability, Time Factors, Accessibility, Relevance, Validity, Accuracy, including selectivity, Coherence, including linkability to other sources |
|---|---|
| Validation Comments: | In the drive time analysis Google Map's routing information has been used as if it were 'ground truth' or the best information available. However, models are not directly based on Google Map. |
| Data Quality Concerns Comments: | The public transport data for the whole country does not cover all. |
| Data Quality Concerns: | |
| Validation With Training Data: |
Methodology
| Methods Used: | Traditional statistical methods, Data visualization methods |
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| Developed New Methods: |
Technologies
| Technologies: | GIS, Relational database, Data mining tools, Data visualization tools, Cloud services |
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Other
| Income Level: | High-income |
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| Iso: | FI |
| Timeframe To Produce Indicator: | NA |