Unique ID: 2015078

Issue Date: February 13th 2019
Last modified: February 22nd 2019
Collaborative

Integrated Environment System (IES): using environmental sensing systems and data analytics for real-time environmental information

Using environmental sensing systems and data analytics for real-time environmental information

Singapore's National Environment Agency (NEA)'s Integrated Environment System (IES) is an integrated platform that enables the centralised collection of various environmental and weather data for processing, analysis, visualization and distribution. It aims to harness environmental sensing systems coupled with data analytics and modelling to provide real-time environmental information and thus improve predictive capabilities in order to provide early warning, pre-emptive and sensing capabilities in support of field operations for NEA officers (e.g., preventive measures can be put in place prior to the arrival of the trans-boundary haze).

Project Objective:

Other

Project Outcomes:

To provide NEA's officers with real-time environmental situational awareness via customisable dashboards. Video, business analytics and predictive models will provide guidance and information for officers both on the ground and in the office to take pre-emptive or corrective actions on a real-time basis.

Statistical Area

Environmental statistics

Project Sources
Project Sources
Type Of Institution: National statistical office
Big Data Source: Other
Region: East Asia & Pacific
Country Area: Singapore
Id Country Regional: country
Accessing Data
Accessing Data
Data Access Rights: Broader access rights
Data Coverage
Data Coverage
Data Coverage: All available data
Coverage Geo Pop: Whole country / high % of market
Cost Comments: There are various costs associated with system development, including licences, interfacing works with the various sensor systems, etc.
Coverage Period: Data from 2014 onwards
Data Quality
Data Quality
Quality Framework: Quality of processing/throughput
Quality Aspects Evaluated: Privacy and Security, Completeness, Usability, Time Factors, Accessibility, Relevance, Institutional/Business Environment, Validity, Accuracy, including selectivity, Coherence, including linkability to other sources
Quality Framework Comments: We are working with a quality framework used for secondary data collection which had been adapted to scanner data.
Data Quality Concerns Comments: Individual source sensor system is expected to scrub the data before it comes to IES.
Methodology
Methodology
Methods Used: Traditional statistical methods, Data visualization methods, Machine learning (Random forest, etc.)
Technologies
Technologies
Technologies: GIS, Relational database, Column store database, Spreadsheet, Cloud services
Technologies Comments: Data mining tools; Data visualization tools
Other
Other
Income Level: High-income
Iso: SG
Timeframe To Produce Indicator: NA
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