Unique ID: 2017007
Division: | Population and Social Statistics |
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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
Statistical Area
Project Sources
Type Of Institution: | National statistical office |
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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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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 |
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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 |
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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. |
Methodology
Methods Used: | Traditional statistical methods, Data visualization methods |
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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 |