Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected. 相似文献
International Journal of Earth Sciences - Stratigraphically well-defined volcanic rocks in Palaeozoic volcano-sedimentary units of the Frankenwald area (Saxothuringian Zone, Variscan Orogen) were... 相似文献
After completion of an exploration well, sandstones of the Exter Formation were hydraulically tested to determine the hydraulic properties and to evaluate chemical and microbial processes caused by drilling and water production. The aim was to determine the suitability of the formation as a reservoir for aquifer thermal energy storage. The tests revealed a hydraulic conductivity of 1–2 E-5 m/s of the reservoir, resulting in a productivity index of 0.6–1 m3/h/bar. A hydraulic connection of the Exter Formation to the overlaying, artesian “Rupelbasissand” cannot be excluded. Water samples were collected for chemical and microbiological analyses. The water was similarly composed as sea water with a maximum salinity of 24.9 g/L, dominated by NaCl (15.6 g/L Cl and 7.8 g/L Na). Until the end of the tests, the water was affected by drilling mud as indicated by the high pH (8.9) and high bicarbonate concentration (359 mg/L) that both resulted from the impact of sodium carbonate (Na2CO3) additives. The high amount of dissolved organic matter (>?58 mg/L) and its molecular-weight distribution pattern indicated that residues of cellulose, an ingredient of the drilling mud, were still present at the end of the tests. Clear evidence of this contamination gave the measured uranine that was added as a tracer into the drilling mud. During fluid production, the microbial community structure and abundance changed and correlated with the content of drilling mud. Eight taxa of sulfate-reducing bacteria, key organisms in processes like bio-corrosion and bio-clogging, were identified. It can be assumed that their activity will be affected during usage of the reservoir. 相似文献
ABSTRACTThe challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover). 相似文献
While carbon pricing is widely seen as a crucial element of climate policy and has been implemented in many countries, it also has met with strong resistance. We provide a comprehensive overview of public perceptions of the fairness of carbon pricing and how these affect policy acceptability. To this end, we review evidence from empirical studies on how individuals judge personal, distributional and procedural aspects of carbon taxes and cap-and-trade. In addition, we examine preferences for particular redistributive and other uses of revenues generated by carbon pricing and their role in instrument acceptability. Our results indicate a high concern over distributional effects, particularly in relation to policy impacts on poor people, in turn reducing policy acceptability. In addition, people show little trust in the capacities of governments to put the revenues of carbon pricing to good use. Somewhat surprisingly, most studies do not indicate clear public preferences for using revenues to ensure fairer policy outcomes, notably by reducing its regressive effects. Instead, many people prefer using revenues for ‘environmental projects’ of various kinds. We end by providing recommendations for improving public acceptability of carbon pricing. One suggestion to increase policy acceptability is combining the redistribution of revenue to vulnerable groups with the funding for environmental projects, such as on renewable energy.
Key policy insights
If people perceive carbon pricing instruments as fair, this increases policy acceptability and support.
People’s satisfaction with information provided by the government about the policy instrument increases acceptability.
While people express high concern over uneven distribution of the policy burden, they often prefer using carbon pricing revenues for environmental projects instead of compensation for inequitable outcomes.
Recent studies find that people’s preferences shift to using revenues for making policy fairer if they better understand the functioning of carbon pricing, notably that relatively high prices of CO2-intensive goods and services reduce their consumption.
Combining the redistribution of revenue to support both vulnerable groups and environmental projects, such as on renewable energy, seems to most increase policy acceptability.