spacetime : Spatio-Temporal Data in R

This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented in the R package spacetime, and provides examples for analyzing them. It builds upon the classes and methods for spatial data from package sp, and for time series data from package xts. The goal is to cover a number of useful representations for spatio-temporal sensor data, and results from predicting (spatial and/or temporal interpolation or smoothing), aggregating, or subsetting them, and to represent trajectories. The goals of this paper is to explore how spatio-temporal data can be sensibly represented in classes, and to find out which analysis and visualisation methods are useful and feasible. We discuss the time series convention of representing time intervals by their starting time only. This document is the main reference for the R package spacetime, and is available (in updated form) as a vignette in this package

Automated mapping of environmental variables from a SEIS or SISE perspective

The INTAMAP FP6 project has developed an interoperable framework for real-time automatic mapping of critical environmental variables by extending spatial statistical methods and employing open, web-based, data exchange protocols and visualisation tools. This paper will give an overview of the underlying problem, of the project, and discuss which problems it has solved and which open problems seem to be most relevant to deal with next. The interpolation problem that INTAMAP solves is the generic problem of spatial interpolation of environmental variables without user interaction, based on measurements of e.g. PM10, rainfall or gamma dose rate, at arbitrary locations or over a regular grid covering the area of interest. It deals with problems of varying spatial resolution of measurements, the interpolation of averages over larger areas, and with providing information on the interpolation error to the end-user. In addition, monitoring network optimisation is addressed in a non-automatic context

Mapping groundwater quality in the Netherlands

Groundwater quality is the suitability of groundwater for a certain purpose (e.g. for human consumption), and is mostly determined by its chemical composition. Pollution from agricultural and industrial origin threatens the groundwater quality in the Netherlands. Locally, this pollution is measured at tens of metres depth. Since groundwater is the main source for fresh water, this pollution causes a decrease in the long-term resources of water suitable for human consumption. In order to get insight into the current situation of groundwater quality and systematic changes of groundwater quality over time, the national groundwater quality monitoring network was established from 1978 to 1984. This network consists of 370 permanent wells, spread fairly evenly over the country (Fig. 3.2, page 27), with screens at 8-10 and 23-25 metre below the soil surface. The well screens are sampled yearly. More recently, the provinces have installed similar monitoring networks that doubled the measurement density. Because the monitoring networks are a major financial investment, the question arises whether the information on groundwater quality, as obtained from the current monitoring networks, is sufficient. This calls for the quantification of what can be inferred from this information about the quality of all the groundwater of interest. For the modelling of the spatial and temporal variation in groundwater quality, using a physically and chemically based deterministic model would call for information on many variables (e.g. initial and boundary conditions, model parameters), that are at present not available on a national scale. However, mapping groundwater quality is possible by using much simpler models, that lump much of the unknown factors into a spatially dependent stochastic term. The objectives of this study are to map groundwater quality in the Netherlands, using available measurements from the national and provincial groundwater quality; monitoring networks and map information on soil type and land use; show the effects of monitoring network density and the effects of using soil type and land use information on the resulting groundwater quality maps; map the systematic, temporal changes in groundwater quality in a way similar to the mapping of groundwater quality; show how groundwater quality maps can be improved by using relevant ancillary information in the estimation procedure, where ancillary information is obtained from deterministic process models or from other measured variables. The primary aim is, with these objectives, to answer the basic questions of describing the current situation and systematic temporal changes of groundwater quality for the whole of the Netherlands.In this study we use fairly simple models that allow an explicit quantification of the accuracy of resulting estimates. This accuracy is taken into account in the resulting maps, anticipating the question about the value of the current monitoring networks for inferring current situation and systematic changes in time of groundwater quality

The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004

Quality of life, big data and the power of statistics

The digital era has opened up new possibilities for data-driven research. This paper discusses big data challenges in environmental monitoring and reflects on the use of statisticalmethodsintacklingthesechallengesforimprovingthequalityoflifeincities

Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

A very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term exposure analysis. This paper introduces a systematic approach for identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing the approach over the study area has shown that it leads to a significant drop of the mean prediction error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of this work can guide the selection of sites while expanding or creating air quality monitoring networks for robust LUR estimations with minimal prediction errors

The uncertainty enabled model web (UncertWeb)

UncertWeb is a European research project running from 2010-2013 that will realize the uncertainty enabled model web. The assumption is that data services, in order to be useful, need to provide information about the accuracy or uncertainty of the data in a machine-readable form. Models taking these data as imput should understand this and propagate errors through model computations, and quantify and communicate errors or uncertainties generated by the model approximations. The project will develop technology to realize this and provide demonstration case studies

Do Monetary Incentives Influence Users’ Behavior in Participatory Sensing?

Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers