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5.3 Contributing to Ambient Spatial Intelligence
Ever smarter smart phones and prospects like Google Glass change the public percep-
tion of spatial information. Using spatial information became as common as writing
an SMS or downloading one's favorite music—anywhere, any time. This (brave)
new world is a very dynamic world. Not only do highly mobile human users inter-
act in dynamic networks and profit from ubiquitous access to spatial information,
but spatially distributed autonomous computing nodes increasingly invade various
application fields of immense socio-economic significance, including applications
in ICT, transportation, and logistics.
In this world of “spatial everyware”, the days of spatial data processing in a
monolithic desktop GIS are numbered. Today's and forward-looking CMA algo-
rithms must comply with highly dynamic multiparty networks, where data volumes,
privacy issues, and constantly changing communication networks dictate that spatial
data collected at different sites be analyzed in a decentralized way without collecting
all data to a central GIS or database. In such distributed and integrated systems the
boundaries between data capture and data processing are increasingly blurred, nur-
turing the vision of ambient spatial intelligence. CMA has an opportunity here by
contributing in an early stage to this emerging field through a combination of classic
GIScience strengths with forward-looking decentralized spatial computing.
5.4 Balancing Benefits and Privacy
Safeguarding user privacy remains a technical challenge and a key ethical respon-
sibility for researchers in CMA. Clearly, animal movement is an excellent use case
for stimulating and interesting CMA problems. But the movement of people and the
related applications and services bears a much larger socio-economic potential. It is
here where CMA must seek its primary contribution, this is a huge opportunity not
to be missed. But people care about privacy. I see two main challenges with respect
to privacy: First, develop strategies for getting access to the really interesting large
volumes of people movement data. Second, develop analytical frameworks that can
produce useful information but at the same time safeguard peoples' privacy.
Access to GSM network data is still very limited, studies where access to very
large numbers of individuals is granted are still rather the exception or don't hap-
pen in the public or scientific domain. Clearly, information and transparency about
a study's goals and privacy precautions help building up trust with users and GSM
providers. Rein Ahas' Estonian case may serve as a successful example here. An
alternative promising strategy for accessing trajectories of large numbers of individ-
uals comes in the form of apps that track their users in informed consent in some
application context. For example, Wirz et al. ( 2012 ) propose a system for real-time
crowd monitoring where a mobile phone app is used that supplies the user with
event-related information, but periodically logs the device's location along the way.
 
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