Data Quality and Integrity
Continuous Iteration for Building a High-Quality Database
Last updated
Continuous Iteration for Building a High-Quality Database
Last updated
We collect a range of datasets, some of which follow the principle of large numbers, meaning many measurements are taken together to provide valuable insights into trends. We aim to identify these trends with a high degree of confidence.
Noise levels are a prime example of a dataset that follows this rule. The accuracy of smartphone noise level measurements depends on the microphone’s quality and the app’s calibration. While smartphones may not offer the same accuracy as professional dB(A) measurement hardware, our collaboration with renowned acoustic scientists ensures that we continually improve our measurement methods.
We measure noise in dB(A), the sound level adjusted, to reflect the sensitivity of the human ear. We never record or analyze audio content, ensuring user privacy is always protected.
Smartphones and their built-in microphones offer a valuable way to measure sound levels and map hyper-local noise levels. Several apps utilize these microphones to capture and display real-time sound data or store it for analysis. Accuracy depends on the quality of the smartphone microphone and software calibration. A has shown that, although not the most precise, gathering data from many participants can minimize errors and provide valuable insights into noise pollution trends. By leveraging smartphones and large-scale data collection, we aim to map noise levels more accurately and improve our understanding of their environmental and health impacts.
To maintain data integrity, Silencio uses a two-tier system of in-app points that secures the reward process. While anyone can earn in-app Coins, only those with verified accounts that pass our ongoing spoofing tests receive $SLC tokens. This system and user reporting of suspected cheaters ensure a fair and safe environment for all participants.
Data quality and integrity are critical to Silencio. Users contribute by following proper measurement techniques and rejecting attempts to falsify data. We implement strict controls and verification processes to maintain data integrity. Before selling data, we ensure its accuracy and swiftly act on any discrepancies.
As we market our data, maintaining high standards is essential to provide valuable insights to urban planners, policymakers, researchers, and technology companies, who use our data to make better user decisions.