Observation Centric Sensor Data Model

Management of sensor data requires metadata to understand the semantics of observations. While e-science researchers have high demands on metadata, they are selective in entering metadata. The claim in this paper is to focus on the essentials, i.e., the actual observations being described by location, time, owner, instrument, and measurement. The applicability of this approach is demonstrated in two very different case studies

Observation number correlation in WMAP data

A remarkable similarity between the large-scale non-Gaussian pattern of cosmic microwave background (CMB) temperatures obtained by Wilkinson Microwave Anisotropy Probe (WMAP) mission and the distribution feature of observation numbers is noted. Motivated from such a similarity, in this work we check the WMAP data for the correlation between pixel temperature t and observation number N. Systematic effect of imbalance differential observation and significant t-N correlation in magnitude, distribution non-Gaussianity and north-south asymmetry are found. Our results indicate that, for precision cosmology study based on WMAP observations, the observation effect on released WMAP temperature maps has to be further carefully studied.Comment: accepted for publication in MNRA

Home Location Estimation Using Weather Observation Data

We can extract useful information from social media data by adding the user's home location. However, since the user's home location is generally not publicly available, many researchers have been attempting to develop a more accurate home location estimation. In this study, we propose a method to estimate a Twitter user's home location by using weather observation data from AMeDAS. In our method, we first estimate the weather of the area posted by an estimation target user by using the tweet, Next, we check out the estimated weather against weather observation data, and narrow down the area posted by the user. Finally, the user's home location is estimated as which areas the user frequently posts from. In our experiments, the results indicate that our method functions effectively and also demonstrate that accuracy improves under certain conditions.Comment: The 2017 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA2017

Missing observation analysis for matrix-variate time series data

Bayesian inference is developed for matrix-variate dynamic linear models (MV-DLMs), in order to allow missing observation analysis, of any sub-vector or sub-matrix of the observation time series matrix. We propose modifications of the inverted Wishart and matrix t distributions, replacing the scalar degrees of freedom by a diagonal matrix of degrees of freedom. The MV-DLM is then re-defined and modifications of the updating algorithm for missing observations are suggested

London Earth : presentation and assessment of field observation data

The London Earth field survey followed a systematic sampling approach to collect a representative suite of soil samples from across London from a variety of land uses, in order to ensure a robust, unbiased dataset which will represent the baseline geochemistry of the city’s environment. Soil geochemical baseline data can be used to investigate soil quality and geochemical processes in the urban environment, as well as determining where the levels of certain chemical elements are potentially hazardous to humans as well as the natural environment (Johnson and Ander, 2008). In addition to the collection of samples, important accompanying information including observations about the soil colour and composition, and land use details for each sampling site were recorded. This data is an important aspect of the survey as it allows us to assess the site and supports interpretation of the geochemical results. The combination of the geochemical survey data and related field observations provides a comprehensive data resource which will provide valuable information to land use planning and development applications such as urban regeneration as well as provide opportunity for science in the interest of national good. The aim of this report is to present and assess the observational data in order to: i. show the spatial distribution of certain properties of the data set, such as the land use types that were recorded for each sample; ii. to discuss their relative proportions; and, iii. to explain, where possible, any trends or patterns that can be seen in the data. This will be done primarily by presenting maps and graphs of the data and by some discussion of the information they contain. This is intended to provide a useful resource to support the ongoing interpretation of the geochemical data

Missing observation analysis for matrix-variate time series data

Bayesian inference is developed for matrix-variate dynamic linear models (MV-DLMs), in order to allow missing observation analysis, of any sub-vector or sub-matrix of the observation time series matrix. We propose modifications of the inverted Wishart and matrix t distributions, replacing the scalar degrees of freedom by a diagonal matrix of degrees of freedom. The MV-DLM is then re-defined and modifications of the updating algorithm for missing observations are suggested

Program on Earth Observation Data Management Systems (EODMS)

An assessment was made of the needs of a group of potential users of satellite remotely sensed data (state, regional, and local agencies) involved in natural resources management in five states, and alternative data management systems to satisfy these needs are outlined. Tasks described include: (1) a comprehensive data needs analysis of state and local users; (2) the design of remote sensing-derivable information products that serve priority state and local data needs; (3) a cost and performance analysis of alternative processing centers for producing these products; (4) an assessment of the impacts of policy, regulation and government structure on implementing large-scale use of remote sensing technology in this community of users; and (5) the elaboration of alternative institutional arrangements for operational Earth Observation Data Management Systems (EODMS). It is concluded that an operational EODMS will be of most use to state, regional, and local agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products