910 Geografie, Reisen
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Keywords
- (dis-)embeddedness (1)
- Accountability (1)
- Data Fusion (1)
- Digital platforms (1)
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- Optical remote sensing data (1)
- Platform economy (1)
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Digital platforms, understood as multi-sided matchmakers, have amassed huge power, reimagining the role of consumers, producers, and even ownership. They increasingly dictate the way the economy and urban life is organized. Yet, despite their influential and far-reaching role in shaping our economic as well as sociocultural world, our understanding of their embeddedness, namely how their activities are embedded in systems of social and societal relationships and how they conceptualize their main functions and actions in relation to their wider setting, remains rudimentary. Consequently, the purpose of this frontier paper is threefold. Firstly, it reveals the need to discuss and evaluate (dis-)embedding processes in platform urbanism in order to understand the underlying dynamics of platform power and urban transformation. Secondly, it aims to reveal the main reasons in regard to the difficulties in pinpointing digital platforms embeddedness. Thirdly, it seeks to propose future research unravelling the (dis-)embeddedness of the platform economy.
This paper argues for three main reasons namely unawareness, unaccountability and non-transparency of digital platforms that drive the lack of embeddedness and reaffirms platform power. This is mainly based on the configuration of new commodities, platforms’ strategic avoidance of labour protections and other regulatory frameworks as well as platforms’ secrecy in which they operate. This frontier paper argues that transferring the concept of embeddedness to the platform economy might serve as a valuable tool to understand and pinpoint essential dynamics and relationships at play, therefore proposing embeddedness as a basis for future research on the platform economy. It strongly argues that a more detailed understanding is urgently needed, in order to be able to understand, accompany and actively influence the development of the platform economy in regulatory terms.
Purpose
Rapid accessibility of (intensive) medical care can make the difference between life and death. Initial care in case of strokes is highly dependent on the location of the patient and the traffic situation for supply vehicles. In this methodologically oriented paper we want to determine the inequivalence of the risks in this respect.
Methods
Using GIS we calculate the driving time between Stroke Units in the district of Münster, Germany for the population distribution at day- & nighttime. Eight different speed scenarios are considered. In order to gain the highest possible spatial resolution, we disaggregate reported population counts from administrative units with respect to a variety of factors onto building level.
Results
The overall accessibility of urban areas is better than in less urban districts using the base scenario. In that scenario 6.5% of the population at daytime and 6.8% at nighttime cannot be reached within a 30-min limit for the first care. Assuming a worse traffic situation, which is realistic at daytime, 18.1% of the population fail the proposed limit.
Conclusions
In general, we reveal inequivalence of the risks in case of a stroke depending on locations and times of the day. The ability to drive at high average speeds is a crucial factor in emergency care. Further important factors are the different population distribution at day and night and the locations of health care facilities. With the increasing centralization of hospital locations, rural residents in particular will face a worse accessibility situation.
Remote sensing time series is the collection or acquisition of remote sensing data in a
fixed equally spaced time period over a particular area or for the whole world. Near
daily high spatial resolution data is very much needed for remote sensing applications
such as agriculture monitoring, phenology change detection, environmental
monitoring and so on. Remote sensing applications can produce better and accurate
results if they are provided with dense and accurate time series of data. The current
remote sensing satellite architecture is still not capable of providing near daily
or daily high spatial resolution images to fulfill the needs of the above mentioned
remote sensing applications. Limitations in sensors, high development, operational
costs of satellites and presence of clouds blocking the area of observation are some
of the reasons that makes near daily or daily high spatial resolution optical remote
sensing data highly challenging to achieve. With developments in the optical sensor
systems and well planned remote sensing satellite constellations, this condition
can be improved but it comes at a cost. Even then the issue will not be completely
resolved and thus the growing need for high temporal and high spatial resolution
data cannot be fulfilled entirely. Because the data collection process relies on satellites
which are physical system, these can fail unpredictably due to various reasons
and cause a complete loss of observation for a given period of time making a gap
in the time series. Moreover, to observe the long term trend in phenology change
due to rapidly changing environmental conditions, the remote sensing data from
the present is not just sufficient, the data from the past is also important. A better
alternative solution for this issue can be the generation of remote sensing time series
by fusing data from multiple remote sensing satellite which has different spatial and
temporal resolutions. This approach will be effective and efficient. In this method
a high temporal low spatial resolution image from a satellite such as Sentinel-2 can
be fused with a low temporal and high spatial resolution image from a satellite such
as the Sentinel-3 to generate a synthetic high temporal high spatial resolution data.
Remote sensing time series generation by data fusion methods can be applied to
the satellite images captured currently as well as the images captured by the satellites
in the past. This will provide the much needed high temporal and high spatial
resolution images for remote sensing applications. This approach with its simplistic
nature is cost effective and provides the researchers the means to generate the
data needed for their application on their own from the limited source of data available
to them. An efficient data fusion approach in combination with a well planned
satellite constellation can offer a solution which will ensure near daily time series of
remote sensing data with out any gap. The aim of this research work is to develop
an efficient data fusion approaches to achieve dense remote sensing time series.