Geography Reference
In-Depth Information
Conclusions
Complex processing of uncertainties for noise maps created by information
volunteered from mobile phones is the result of efforts to enable adequate
interpretation of the map with significant lack of data. It is clear that creating
of such noise maps for standard European city is very lengthy process and
already stored data may become obsolete. Therefore it is not assumed that
complete maps of all time periods and with sufficient accuracy would be
available. It is also necessary to prevent the reader impression that person-
centered data can be used interchangeably with data obtained by objective
measurements.
Both of these problems can be resolved by displaying noise measurement
uncertainty. However, introduction of uncertainty theory requires a fairly
extensive theoretical base. The presented analysis revealed that measurement
noise using mobile phones is facing following main groups of problems:
(1) Accuracy of environmental noise measurement—this problem is
solved to a certain extent and can be quantified in terms of uncertainty;
further refinement depends on quality of calibration. Visualization of the
overall uncertainty reflects different accuracy of different types of devices.
(2) Accuracy of positioning—quality of position data can be improved by
adequate post processing techniques, but additional data is necessary. Due to
the double error (inaccuracy to actual position measurement usually disagrees
with lines in the street network model), it is therefore still necessary manually
determine locations on map sometimes. (3) Temporal variability of noise
levels during different time periods (hour/day/week/year)—short-term mea-
surements are often just a random sample, so it is necessary to combine
different measurements. Uncertainty is estimated comparatively reliably, at
least in traffic noise. It depends mainly on knowing the source of the noise
(4) The dependence of measurements on weather conditions. These data can
be only indirectly estimated from meteorological records and uncertainty
cannot be quantified directly, only cataloged. So it depends mainly on how
users in the field evaluate the situation. (5) User knowledge, motivation and
behavior—users can be motivated by gamification, both by fully morphing
data acquisition into interactive games and simpler forms suitable for serious
contributors, similar to applications such as Foursquare.
An adequate number of measurements remains as the most pressing
problem. If enough data is available, we would expect to counterbalance
random errors as different characteristics of devices (sensors), operator error,
spatial error and random environmental effects. Such data would also monitor
changes during the day, because different types of days or periods during the
day are not comparable. Collecting a sufficient amount of relevant data to
produce adequately accurate maps is mainly dependent on user motivation.
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