\begin{article}
\title{Media Mapping}
\subtitle{Using Georeferenced Images and Audio to provide supporting information for the Analysis of Environmental SensorDatasets}
\author{Phil Bartie, Simon Kingham}
\maketitle

\section{Abstract}
Field based environmental monitoring projects often fail to
gather supporting temporal information on the surroundings, yet these
external factors may play a significant part in understanding
variations in the collected datasets. For example when sampling air
quality the values may change as a result of a bus passing the sampling
point, yet this temporal local information is difficult to capture at a
consistently high resolution over extended time periods. Here we
develop an application which runs on a mobile phone able to capture
visual and audio data with corresponding time and location details. We
also develop a desktop analysis tool which synchronises the display of
this dataset with those captured from environmental sensors. The result
is a tool able to assist researchers in understanding local changes in
environmental datasets as a result of changes in the nearby surrounding
environment.

\section[Introduction]{Introduction}
The analysis of temporal datasets in Geographic Information Systems
(GIS) is often hampered by a lack of supporting relevant information on
local conditions at the time of data capture. Being able to explain
unpredictable variations in temporal datasets may depend on being able
to understand the nature of the local environment at a very local
scale. For example a passing vehicle may be the cause of a noted spike
in airborne particulate matter, but unless this situational information
is recorded the spike may never be explicitly explained. Sensor
networks are able to supply background information revealing the wider
situation, but a co-located synchronized set of sensors are required to
understand the local situation during mobile data capture. Here we
develop an application able to assist researchers in storing
information on the local environment at the time of data capture. 

The solution uses a mobile phone to store audio, visual, and location
details against time such that during analysis the researchers are able
to view the local environment at the time of data capture. A custom
playback and analysis tool was also developed to combine this
situational data with other time stamped data captured from the same
location, allowing researchers fast access to the relevant contextual
information during the analysis of the environmental data.  In this
paper we describe the function of the mobile and desktop applications,
their design, and report on their usefulness in an air pollution
monitoring study conducted in an urban area. The tools proved useful in
explaining local spikes in air pollution data due to local events
documented in the supporting images and audio data. The audio stream
was also useful for allowing the researcher to take spatially and
temporally attributed verbal notes in the field.

\section{Background}
Many studies have examined the relationship between air pollution and
modes of urban commuting (Fruin et al.  2008, Briggs et al.  2008,
O'Donoghue et al.  2007, Gulliver and Briggs  2007,
Van Roosbroeck et al.  2006, Kingham et al.  1998). Air pollution data
is highly temporal, changing across time and space, affected by global
and local events. Local events, such as a bus passing the recording
equipment, are hard to document and traditionally paper based records
are kept. However manual references to such events are hard to
integrate into any analysis, and sampling frequency is often
inconsistent. Yet these factors are important as has been strongly
argued by Briggs et al (2008) who state that local factors could be the
cause of differences reported between studies including such things as
{\textquotedblleft}building configuration, road layout, monitoring
methods, averaging periods, season, meteorological conditions, vehicle,
driving and walking behaviours, and the strength of in-vehicle
sources{\textquotedblright} (Briggs et al.  2008, 20). 

To provide a better understanding of the surrounding environment at the
time of data capture a second set of sensors can be used to
automatically capture contextual information. This contextual
information is not the primary data for research purposes, but a
supporting dataset for the analysis phase. Technological advancements
have made it possible to sense the environment more accurately, at
higher sampling densities than ever before. Sensor networks may consist
of electronic devices (Culler and Mulder  2004, Microsoft Corporation 
2006), or citizens volunteering local environmental information via the
internet (Goodchild  2007). En mass citizens may provide data without
realizing it, such as the monitoring of mobile phones to estimate
population movement (Ratti et al.  2006), or to estimate travel delays
on motorway sections (Astarita et al.  2006). 

The majority of electronic wireless sensor networks are static,
distributed across a region at fixed sites, feeding information to a
central facility which combines the data to build a picture of the
surrounding conditions. For the purpose of mapping a
commuter's exposure to air pollution the local level
changes are also important, therefore a set of sensors should remain
co-located with the air pollution sampling equipment. The data samples
must also be at a high enough temporal resolution to record significant
local events, and the data streams need to remain synchronized during
data capture.

Mobile computing devices and smartphones have been proven useful in
environmental monitoring enabling participants to collect and share
data in real-time (Rudman et al.  2005). The MESSAGE consortium (Polak
and Hoose  2008) have undertaken a number of projects using mobile
phones as personal environmental sensors and data loggers. The mobile
phones were equipped with a payload of environmental sensors able to
record carbon monoxide, carbon dioxide, traffic volume, and nitrogen
dioxide levels. Researchers exploring the city could feed data in real
time to a data centre for processing, revealing current city wide air
pollution trends. The individual trip data could also be replayed and
mapped so that air pollution trails could be reviewed to visualize
areas of poor air quality in the city. However a key aspect missing
from this research was the facility to store video or image data from
the user's surroundings. Therefore any analysis carried
out at a later date would lack documented contextual detail. Although
audio was used, it only provided an estimate of traffic volume and
could not differentiate between vehicle types, or allow field
researchers to take temporally and spatially attributed verbal notes.

Another research group developed a prototype system known as GeoMobSense
(Kanjo et al.  2007). This toolkit allows private users to equip their
own phones with the necessary facilities to log data from connected
sensors. The phones themselves are used to display information, as well
as log sound levels, while separate data loggers are used to record the
environmental data. The resulting datasets can be exported and
displayed on Google Earth, and other GIS applications. Again this
toolkit fails to store continuous image sequences, or to save a
spatially attributed audio file. Therefore any post-capture analysis is
hampered by a lack of documentation on the surrounding situation during
the field study.

Multimedia files provide a useful companion dataset for prompt recall of
events which occurred during data capture. They provide an extra
channel of information useful when linked to GISs (Cartwright et al. 
2007), however the video, image, or sound files are normally linked to
a point, as in Media Mapper (Red Hen Systems  2009). This creates a one
way relationship, only allowing the corresponding multimedia clip to be
found when the user clicks on a map location, essentially using the map
as document retrieval interface. The content of the multimedia file
itself is not spatially attributed, and the ability to jump to the
corresponding position in the video file for a given map location has
to be performed manually.

There have been a number of attempts to more closely link the multimedia
content to space through dynamically geo-referencing multimedia files.
Spatial information is encoded into the file through an appropriate
technique such that at any point in the video or audio the
corresponding location may be referenced. For example specialist
equipment can turn GPS location information into audio data, in a
similar way that a modem is able to turn computer data into audio to
send it across a telephone line, which can then be recorded to the
audio track alongside the video data. An example of this technology is
CamNav Mapper  (Blueglen Ltd  2009). This allows a user to search
through a video file, and at all times be able to display the
corresponding recording location in a GIS. However the connection is
uni-directional, meaning the video is able to provide location
information to the GIS, but it is not possible to initiate a search for
the corresponding part in the video from the GIS.

Jaejun (2002) developed an application which supports bi-directional
searching, permitting the user to find relevant video information from
selecting a GIS location, or for finding the filming location by
searching the video file. Similarly Zeiner (2005) developed an
application able to fuse GPS location and video using timestamp
information collected from synchronized clocks. For still images a set
of points are created in the GIS, however for video recorded while
moving a track denotes which parts of the video correspond to which
geographic location. They also explore the use of data standards in
providing geo-multimedia tools via the World Wide Web, with particular
focus on the overlap between web mapping standards, metadata standards,
and video streaming standards. These tools are not however designed
with the ability to integrate other temporal datasets such as required
for environmental research.

Other studies have used or developed analysis tools to visualize
environmental datasets with local situational data (Kaur et al.  2006,
Terwoert  2009, Arnold et al.  2004). However while these often include
the ability to link photographic images with environmental data, they
appear to lack a tightly integrated mapping facility. 

For our research the requirement was for an application which could
provide a high level of integration between temporal multimedia and
location datasets, with the ability to support additional datasets
collected from synchronized sensors. The capture device needed to be
small, lightweight, and mobile such that it could be carried by a
pedestrian or cyclist easily for extended periods of time. The datasets
for location, audio and image needed to be tightly coupled such that no
synchronisation issues could occur during long field trials. The
analysis tools needed to be easily operated by an untrained GIS user,
such that they could search through the datasets to interact with any
of the captured data streams while maintaining sync with the other linked
data sources. We therefore looked at developing the data logging tool
on a GPS equipped smartphone, which is a highly portable programmable
device available at low cost. As a result of using a single
programmable device the GPS, audio, and image datasets are tightly
coupled, removing the need to synchronize clocks, and guaranteeing data
streams remain in sync indefinitely. Additionally the multimedia files
are georeferenced at the time of data capture removing the need for any
post-capture data processing. In contrast to other applications which
use laptops to capture and process media, the smartphone approach
offers a robust, small, and very portable platform which may be easily
carried by pedestrians. Finally our analysis tool supports a
tri-directional search mechanism, such that users may drive the search
by moving through the audio media, mapping interface, or by
interrogating charts of the additional sensor data. This means the user
is able to easily capture and analyze data using any of three mediums
(location, time, graph value). In the next section we look in more
detail at the applications developed during this research.

\section{Application Development }
In this study we developed two applications to assist in the process of
recording the surrounding situational conditions. The first application
runs at the time of data capture in the field on a smartphone equipped
with Assisted GPS (A-GPS), and stores both an audio and visual record
of the surroundings. A-GPS is particularly useful for urban based
research as it provides a faster start-up location solution throughout
a greater range of urban environments, such that a position could be
found more quickly and maintained more consistently. Furthermore the
phone selected for this research had a high sensitivity GPS chipset,
enabling locations to be calculated across a high proportion of the
city, including inside some single storey buildings. The second
application developed for this research runs on a desktop computer and
assembles independent data streams against a common timeline, such that
the user may easily browse through multiple datasets whilst maintaining
sync between them, at all times being able to refer to the
corresponding situational image and audio data. We discuss each
application in more detail in Sections 3.1 (Mobile Data Capture Application) and 3.2 (Data Analysis and Playback), starting with the
mobile data capture tools.

\subsection{Mobile Data Capture Application}
The main design criterion for the data capture device was that it would
be used in urban studies everyday over an extended period of a few
months. It therefore had to be small, robust, light weight, offer a
large data storage capacity, be able to capture audio, imagery, and run
on battery for at least 90 minutes to ensure an entire urban commute
could be captured in a single session. We decided to use a Nokia N82
smartphone to carry out these tasks as they can be programmed easily
using the Python language, incorporate a high sensitivity A-GPS able to
function adequately in urban canyons, and have a high quality camera.
Furthermore they are able to use micro-SD cards for data storage, are
smaller than any laptop or netbook computer, and have good battery
life.

Nokia Series 60 smartphones can be programmed in three main languages,
which are C, Java, and Python. Python is very suitable for rapid
development and allows the developer to access core phone hardware
through supported Application Program Interfaces (APIs). The hardware
access required for this project included GPS hardware, audio, and
screen display. Our initial application was designed to record a
continuous video and audio feed to the micro-SD card at 15 frames per
second, while logging GPS locations every second. The phone supports
the ability for Python applications to request the position of the
current playhead in the video file during recording, enabling the
application to log the GPS position information along with the current
video position to ensure a tight coupling of the location and video
datastreams. The data capture application performed well, and as it
used MP4 compression a full one hour video with GPS log files occupied
only around 70MB. However the continuous video capture depleted a fully
charged battery in 60 minutes, and made the mobile phone run fairly
hot. After discussions with the air pollution research team we looked
at an alternative solution to record still images at regular intervals.

The next iteration of our application, and the one used in field trials,
records an audio file continuously but captures still images at the
rate of 1 image every 3 seconds.  The audio file is recorded to a WAV
file at 8kHz, again the playhead position is stored with each GPS
update such that for every image the location and position in the audio
file is known explicitly.

To link the audio and image files with GPS a common timeline primary key
is required. GPS time was considered unsuitable for the base timeline
as the user may lose the GPS signals when moving inside buildings.
Therefore the Python time from the phone clock was considered more
reliable, and forms the baseline to which all other datasets are
synced. GPS time is however stored as an additional attribute in the
log file in case it is required later. 

Specialist sensors were used to sample the air quality, recording the
concentration of particulate matter at various sizes (PM10, PM2.5 and
PM1), ultrafine particles (UFP), temperature and carbon monoxide
levels. These devices all have internal clocks which were synced to the
nearest second to the clock on the mobile phone before each journey.
During transit the mobile phone was mounted facing forwards on either
the car dashboard, bike handlebars, or the strap of a rucksack to
ensure the camera could capture a clear view of the oncoming route.

%  [Warning: Image ignored] % Unhandled or unsupported graphics:
%\includegraphics[width=4.4146in,height=1.672in]{a1753361RV-img1}
\begin{figure*}[htb]
\centering
 \includegraphics[width=\textwidth]{foss4g09/bartie/fig1-copy.pdf}
\caption{An Overview of the Data Captured}
\label{fig:1rvfig1}
\end{figure*}
%\label{bkm:Ref233775776}Figure \stepcounter{Figure}{\theFigure}: An
%Overview of the Data Captured

Figure \ref{fig:1rvfig1} shows the data collected with each journey. The smartphone tags
each image with a unique identification number based on the Python time
in seconds, ensuring that images correspond to a single log entry. Log
entries record the position of the playhead in the sound file, Python
time, GPS time, and the GPS latitude and longitude. Additionally we
stored the cell tower identification value so that approximate
locations can be determined if GPS positioning is lost. As well as
recording GPS position we also record speed, GPS accuracy, heading, the
number of satellites visible, and number used for the position
solution. This enables us to carry out analysis on the location
accuracy if required at a later date. 


\subsection{Data Analysis and Playback}
To be able to efficiently analyse the large volume of time series data
collected it was necessary to build a custom application which allowed
non-GIS users the ability to review the data easily from a single
interface. To do this we wrote a custom application using C\# .NET
which made use of a number of open source libraries for charting, map
display, and coordinate projection (Figure \ref{fig:1rvfig2}). One of the key criteria
in our application design was that all the datasets should be synced
and remain in sync across all dataset viewers while the user explored
the data. For example selecting a map point should display the relevant
air pollution data, the corresponding street image, and move the sound
file playback to the correct location so any relevant audio notes and
background noises could be heard. In the following section we discuss
each of these data visualisation elements, and describe the methods
through which the datasets are kept in sync.

%\vspace{0.5cm}

\begin{figure}
\centering
 \includegraphics[width=\columnwidth]{foss4g09/bartie/fig2-copy.pdf}
\caption{ Data Analysis and Playback Tool}
\label{fig:1rvfig2}
\end{figure}

%  [Warning: Image ignored] % Unhandled or unsupported graphics:
%\includegraphics[width=4.4173in,height=3.0244in]{a1753361RV-img2}
 

%\label{bkm:Ref233619823}Figure \stepcounter{Figure}{\theFigure}: Data
%Analysis and Playback Tool

\subsubsection{Mapping}
The mapping element makes use of the Piccolo Zoomable User Interface
(ZUI) graphics framework. This open source library is not specifically
designed for map display but offers powerful functions enabling rapid
development of ZUIs. Piccolo is able to create scene graphs consisting
of both vector and raster nodes, which may be easily animated to change
location, shape or colour. It also provides the functionality to
smoothly zoom and pan around the data, capturing mouse and keyboard
input. Events can be assigned to graphical objects such that a user may
interact with map items and trigger a custom action. In our application
we use this functionality to link map objects to custom search
functions, so for example clicking on a GPS track point moves the
charting tool along to display the air quality values at that location,
and moves the audio playhead forwards or backwards to the corresponding
audio sample.

The mobile phone logs GPS data using the WGS84 coordinate system,
however as Piccolo is unaware of geographic coordinate systems it
requires projected datasets. We used New Zealand Map Grid (NZMG) as our
projected coordinate system for all datasets. The background mapping
layers included Quickbird satellite imagery, and Land Information New
Zealand road centrelines transformed using GRASS to NZMG. The vector
datasets, typically ESRI Shapefiles, were converted to a BNA text
format using OGR libraries before being loaded and displayed by
Piccolo, while the raster datasets (JPEG) were natively supported. ESRI
World files were required with each raster image to provide pixel
resolution and location coordinates values, so that raster datasets
could be loaded into the correct position.

The GPS data, once downloaded from the mobile application to the desktop
application, is transformed into the NZMG coordinate system using the
most accurate NTv2 projection transformations provided by the Proj4
libraries. These points were then displayed as Piccolo vector nodes
over the base mapping, each node with a hidden tag holding the
corresponding capture time primary key. This tag corresponds to the
phone capture time in seconds, and is also recorded in the log file
against the playhead position and location details. These tag data
enable the software to instantly locate corresponding relevant
information in other datasets when a user clicks on a map object
ensuring the system is very responsive.

\subsubsection{Audio}
The audio playback is handled using Microsoft DirectSound libraries,
which enable the rapid development of software able to control audio
datasets. Here we use basic playback control features (i.e. play,
pause, forward, rewind), and the functionality to read the current
playhead position during playback. From this we can calculate the
current play time in seconds from the start of the sampling period, and
therefore find the corresponding log entries which hold the geographic
location and relevant image filenames. As the audio file is captured at
8000 samples per second it has the highest resolution of any of the
datasets. Therefore any searches performed from the user cueing or
reviewing this dataset require an additional step to find the most
relevant (i.e. closest) timestamp in the log files. This was performed
by simply ranking the difference in time from the audio position to all
log entries, the first item being selected as the nearest. This
functionality allows the user to review the audio file and also see
corresponding imagery, location and chart data.

Due to memory issues when loading a 90 minute long audio file, a
buffered playback method was required. Only a small section of the file
is loaded into a memory buffer, and this buffer is constantly filled
from the disk file as audio playback progresses. The performance
impacts of this technique were minimal, and rapid audio reviewing and
cueing are still possible.

\subsubsection{Street Images}
The smartphone captures an image every 3 seconds in JPEG format, and
labels it with the appropriate timestamp. The log file holds a list of
these timestamp filenames along with their corresponding location and
other GPS details. When the user selects a location on the map the tag
(with each Piccolo node object) holds the timestamp details, and
therefore the corresponding image can instantly be loaded without
performing any search other than for the filename in the file system.
When the user controls the audio playback the nearest timestamp
information is found in the log file, and from this the corresponding
picture name can be generated. Similarly when the user moves through
the chart information the nearest timestamp in the log file is found
and used to determine the appropriate image to display.

The image display is supported using native .NET libraries, with
functionality to rotate the image sequence which is useful if the phone
has been placed on its side during image capture.

\subsubsection{Charting}
The environmental sensor datasets are charted using the open source
ZedGraph libraries. These provide sophisticated charting capabilities,
and allow the programmer to link into many key events such as when the
user pans across the chart, clicks in the charting area, or changes the
scale on a chart axis. In our case the x-axis was allocated to time in
seconds since the start of sampling. Ideally the smartphone would be
turned on first to ensure the audio file timeline starts before the
environmental data, but negative time values are also supported. The
y-axis displays the values from the relevant sensor. The display
automatically scales the y-axis according to the values in the entire
loaded dataset, although manual scaling is also possible to zoom into
values for smaller sample periods.

The ZedGraph library was used to create line graphs for each of the
environmental sensor types. These can remain static to allow the
researcher to view the entire datasets while replaying map, image and
audio datasets. Alternatively the graphs can be dynamically linked to
the playback such that they pan along the x-axis (time) automatically
as the data log is replayed. In this case the current playback time is
shown on the far left of the chart.

If the user clicks within the chart area the application retrieves the
corresponding time (x-axis value) and moves the playback position to
that value. This allows the researcher to instantly find the current
geographical location for any spikes noted in the environmental
datasets. Also as the audio and image files remain in sync the
researcher is also able to look and listen to information from the
surroundings at that point.

For reporting purposes the system supports high quality output of the
graphs, by simply double clicking on them. There is also functionality
to allow the user to export snapshots of any interesting results,
effectively using this tool to produce a filtered dataset. To do this
they simply click a button during playback and the GPS, picture link
reference, time and date, and graph data are exported to a text file.
As each environmental sensor operates at a different sampling frequency
the application interpolates values between readings (i.e. straight
line between known values). When the data is output the interpolated
value is used if an actual reading value is not present for the current
playback position.

\section{User Interaction}
One of the key differences in the analysis application developed for
this research to those reviewed earlier, is the ability to initiate a
search from any of three linked interfaces. Figure \ref{fig:1rvfig3} summarises the
processes required to provide this functionality.

\begin{figure}
\centering
 \includegraphics[width=\columnwidth]{foss4g09/bartie/fig3-copy.pdf}
\caption{User Interaction via Audio, Map, or Chart Interfaces}
\label{fig:1rvfig3}
\end{figure}

%  [Warning: Image ignored] % Unhandled or unsupported graphics:
%\includegraphics[width=4.602in,height=4.8465in]{a1753361RV-img3}
 

%\label{bkm:Ref233697378}Figure \stepcounter{Figure}{\theFigure}: User
%Interaction via Audio, Map, or Chart Interfaces

During normal playback the system carries out an audio update event
every 4000 samples (500ms). This ensures that the displays of each
dataset remain in sync, without impacting the performance on slower
computers. 

In the next section we look at a number of examples which demonstrate
the usefulness of this application.

\section{Examples}

\begin{figure}
\centering
\includegraphics[width=0.95\textwidth]{foss4g09/bartie/a1753361RV-img4.png}
\caption{Analysis of Peak Resulting from Bus Pulling Away.}
\label{fig:1rvfig4}
\end{figure}

In the following section we look at data collected in air pollution
studies from Christchurch, New Zealand. The figures illustrate how
useful the contextual information was in explicitly explaining peaks in
the air quality datasets. In Figure \ref{fig:1rvfig4} we can see that the air quality
spikes in the top left graph occur just after the bus (pictured) pulls
away from the field observer. In addition the location information and
map allow the researchers to easily and quickly identify where in the
city these interesting results occurred. 

\begin{figure}
\centering
\includegraphics[width=0.95\textwidth]{foss4g09/bartie/a1753361RV-img5.png}
\caption{Analysis of Air Data while Walking in Pedestrian Precinct.}
\label{fig:1rvfig5}
\end{figure}

In the next example the air quality levels are much better while
exploring a pedestrian precinct, with low particulate matter
concentrations (Figure \ref{fig:1rvfig5}). However following this a sharp rise in
particulate matter can be noted. By clicking on the spike in the graph
display area the researcher is able to see, from the map location and
supporting images, that this spike correlates to when the field
observer entered a multi-storey car park.

In the final example data were collected while travelling on a bus
around the city. A series of spikes can be noted in the carbon monoxide
levels at fairly regularly spaced intervals, as shown in Figure \ref{fig:1rvfig6}. By
reviewing the audio data, just before each spike, it was possible to
hear the sounds of the door opening, ticket machine being operated and
so on, and therefore conclude that these spikes may be related to door
opening events. It is also worth noting that a camera facing forwards
would not have picked up this information, thus demonstrating the value
of collecting audio data. 

%  [Warning: Image ignored] % Unhandled or unsupported graphics:
%\includegraphics[width=4.5957in,height=3.1736in]{a1753361RV-img6}
 
 %\vspace{0.5cm}
\begin{figure}
\centering
 \includegraphics[width=\columnwidth]{foss4g09/bartie/fig6-copy.pdf}
\caption{Bus Trials of Air Pollution Monitoring Equipment}
\label{fig:1rvfig6}
\end{figure}


%\label{bkm:Ref233778621}Figure \stepcounter{Figure}{\theFigure}: Bus
%Trials of Air Pollution Monitoring Equipment

\section{Conclusion}
In this research we have demonstrated that a smartphone may be used as a
suitable data capture tool, able to accurately and consistently capture
location attributed audio and visual data while operating in an urban
space. The onboard Assisted-GPS (A-GPS) was able to rapidly locate the
user when the device was first turned on, and maintain position
throughout exploration of outdoor urban areas. The audio and image
quality was suitable for later analysis to identify key events which
may be associated with changes in the local environment as a result of
short temporal events (such as a bus pulling out) or changes in the
nature of the physical environment (such as entering a car park).
Python provided an excellent programming language for rapid application
development on mobile phone, with the necessary functionality to use
the phone's hardware such that explicit geo-referenced
image and audio could be processed on board the phone, rather than
during later post-processing in the desktop environment. This ensures
that data sync between audio, location, and imagery datasets may be
maintained indefinitely. The smartphone platform also proved to be
rugged enough for daily trials over a period of several months, and had
battery and storage facility to cater for long urban commutes (tested
up to 120 minutes).

We also demonstrated that a simple desktop application able to maintain
sync between mapping, environmental, audio, and visual datasets proved
useful in the analysis phase of the research. The tool allowed non-GIS
researchers the functionality to easily explore environmental datasets
while maintaining links to the corresponding contextual information. 

The project was completed using a number of open source tools and
libraries, without which the development would not have been possible
in the allotted time or within budget. Future versions of the
application should include the ability to store bookmarks against time,
allowing the user to add keywords or notes, which would be particularly
useful when revisiting previous datasets, or to store comments for
other project collaborators to view. More powerful query tools would be
useful too, such that map points may be highlighted or hidden based on
the closest environmental data. Finally the ability to search audio
files for speech would be useful in longer sampling runs such that the
system could automatically identify where audio notes have been taken. 


\section{Acknowledgements}
The authors would like to acknowledge the contributions made by Kreepa
Shrestha, and Woodrow Pattinson who carried out the extensive field
trials. Also Justin Harrison for setting up and supporting the
environmental sensor equipment. This research would not have been
possible without funding support from the Geospatial Research Centre
(NZ) and New Zealand Transport Agency. 

\address{Phil Bartie\\
Phd Student \\
University of Canterbury, New Zealand\\
\email{philbartie@gmail.com}\\
~\\
Simon Kingham \\
Associate Professor of Geography \\
University of Canterbury, New Zealand\\
\email{simon.kingham@canterbury.ac.nz}
}

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\end{article}

