Spatio-temporal information is increasingly used for driving a plethora of intelligent transportation, smart-city, and crowd-sensing applications. At the same time, different types of data marketplaces are proposed for de-siloing and monetising individual and enterprise data . In this paper we study the problem of estimating the relative value of  spatio-temporal data sold in wholesale and retail data marketplaces for the purpose of forecasting future demand in a certain area, e. g. a city. Using as case studies large datasets of taxi rides from Chicago and New York, we ask questions such as "When does it make sense for different taxi companies to combine their data?"  and "How should different companies be compensated for the data that they share?". We then turn our attention to the even harder problem of establishing the  value of the data brought to retail marketplaces by individual drivers. Overall, we show that simplistic approaches, such as assuming that the value of the dataheld by companies or drivers is proportional to its volumeare inaccurate, because they fail to consider the complex complementarities that may exist among different datasets. To remedy this ,  more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them . Applying the Shapley value to large datasets from many sources is  computationally challenging. We use structured sampling to overcome such scalability challenges and manage to compute accurately the importance of different data sources, even when their number ranges in the thousands, as in the case of all the taxi drivers in a large metropolis .
Spatio-temporal information is used for driving a plethora of intelligent transportation, smart-city, and crowd-sensing applications. Since data is now considered a valuable production factor, data marketplaces have appeared to help individuals and enterprises bring it to market to satisfy the ever-growing demand. In such marketplaces, several sources may need to combine their data in order to meet the requirements of different applications . In this paper we study the problem of estimating the relative value of different spatio-temporal datasets combined in wholesale and retail marketplaces for the purpose of predicting demand in metropolitan areas. Using as case studies large datasets of taxi rides from Chicago and New York, we ask questions such as "When does it make sense for different taxi companies to combine their data?" , and "How should different companies be compensated for the data that they share?". We then turn our attention to the even harder problem of establishing the relative value of the data brought to retail marketplaces by individual drivers. Overall, we show that simplistic but popular approaches for estimating the relative value of data, such as using volume , or the ``leave-one-out'' heuristic, are inaccurate. Instead, more complex notions of value from economics and game-theory, such as the Shapley value need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms . Applying the Shapley value to large datasets from many sources is , of course, computationally challenging. We resort to structured sampling and manage to compute accurately the importance of thousands of data sources. We show that the relative value of the data held by different taxi companies and drivers may differ substantially, and that its relative ranking may change from district to district within a metropolitan area .
D
increasingly
R
At the same time, different types of data marketplaces are proposed for de-siloing and monetising individual and enterprise data
Since data is now considered a valuable production factor, data marketplaces have appeared to help individuals and enterprises bring it to market to satisfy the ever-growing demand. In such marketplaces, several sources may need to combine their data in order to meet the requirements of different applications
A
different
R
data sold
datasets combined
D
data
R
forecasting future demand in a certain area, e. g. a city.
predicting demand in metropolitan areas.
A
,
A
relative
R
approaches, such as assuming that the value of the dataheld by companies or drivers is proportional to its volumeare inaccurate, because they fail to consider the complex complementarities that may exist among different datasets. To remedy this
but popular approaches for estimating the relative value of data, such as using volume
A
or the ``leave-one-out'' heuristic, are inaccurate. Instead,
R
value-sharing
value
R
used
employed if one wishes
R
effect of mixing
complex effects of mixing different
D
driven by them
A
, of course,
R
use structured sampling to overcome such scalability challenges
resort to structured sampling
R
different data sources, even when their number ranges in the thousands, as in the case of all the taxi drivers in a large metropolis
thousands of data sources. We show that the relative value of the data held by different taxi companies and drivers may differ substantially, and that its relative ranking may change from district to district within a metropolitan area