Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Appendix 8: Quantifying the effects of environmental factors on catch rates in South-East fisheries: 1986-2006

The collection of basic environmental data by industry members was successful and offers a way of overcoming the problems associated with differences in scale between the environment and fisheries datasets. A simple method of collecting environmental data was developed that was only a small time burden on skippers, yet has the potential to provide very useful information on the same scale as the catch and effort data recorded in the logbooks. The success of this trial was aided by the natural interest of fishers to learn more about the environment in which they fish. The archival temperature-depth tags chosen proved robust, reliable and easy to use. While the use of large scale environmental data may not yield significant improvements in stock assessments for most SESSF species, fine-scale data collected from selected vessels using methods developed during this project may, in the longer term, be useful for incorporation into CPUE standardisations in the future...

Remote Sensing and Data Collection from Solar Powered Wireless Sensor Network using an Unmanned Aerial Vehicle System

Sensor networks for environmental monitoring present enormous benefits to the community and society as a whole. Currently there is a need for low cost, compact, solar powered sensors suitable for deployment in rural areas. The purpose of this research is to develop both a ground based wireless sensor network and data collection using unmanned aerial vehicles. The ground based sensor system is capable of measuring environmental data such as temperature or air quality using cost effective low power sensors. The sensor will be configured such that its data is stored on an ATMega16 microcontroller which will have the capability of communicating with a UAV flying overhead using UAV communication protocols. The data is then either sent to the ground in real time or stored on the UAV using a microcontroller until it lands or is close enough to enable the transmission of data to the ground station.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Sensor network deployment for agronomical data gathering in semi-arid regions

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

Web-based spatiotemporal visualization of marine environment data

With long-term marine surveys and research, and especially with the development of new marine environment monitoring technologies, prodigious amounts of complex marine environmental data are generated, and continuously increase rapidly. Features of these data include massive volume, widespread distribution, multiple-sources, heterogeneous, multi-dimensional and dynamic in structure and time. The present study recommends an integrative visualization solution for these data, to enhance the visual display of data and data archives, and to develop a joint use of these data distributed among different organizations or communities. This study also analyses the web services technologies and defines the concept of the marine information gird, then focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method. We discuss how marine environmental data can be organized based on the spatiotemporal visualization method, and how organized data are represented for use with web services and stored in a reusable fashion. In addition, we provide an original visualization architecture that is integrative and based on the explored technologies. In the end, we propose a prototype system of marine environmental data of the South China Sea for visualizations of Argo floats, sea surface temperature fields, sea current fields, salinity, in-situ investigation data, and ocean stations. An integration visualization architecture is illustrated on the prototype system, which highlights the process-oriented temporal visualization method and demonstrates the benefit of the architecture and the methods described in this study.

Using continuous plankton recorder data

The continuous plankton recorder (CPR) survey is the largest multi-decadal plankton monitoring programme in the world. It was initiated in 1931 and by the end of 2004 had counted 207,619 samples and identified 437 phyto- and zooplankton taxa throughout the North Atlantic. CPR data are used extensively by the research community and in recent years have been used increasingly to underpin marine management. Here, we take a critical look at how best to use CPR data. We first describe the CPR itself, CPR sampling, and plankton counting procedures. We discuss the spatial and temporal biases in the Survey, summarise environmental data that have not previously been available, and describe the new data access policy. We supply information essential to using CPR data, including descriptions of each CPR taxonomic entity, the idiosyncrasies associated with counting many of the taxa, the logic behind taxonomic changes in the Survey, the semi-quantitative nature of CPR sampling, and recommendations on choosing the spatial and temporal scale of study. This forms the basis for a broader discussion on how to use CPR data for deriving ecologically meaningful indices based on size, functional groups and biomass that can be used to support research and management. This contribution should be useful for plankton ecologists, modellers and policy makers that actively use CPR data.

Bird diversity and environmental gradients in Britain:a test of the species-energy hypothesis

1. We tested the species diversity-energy hypothesis using the British bird fauna. This predicts that temperature patterns should match diversity patterns. We also tested the hypothesis that the mechanism operates directly through effects of temperature on thermoregulatory loads; this further predicts that seasonal changes in temperature cause matching changes in patterns of diversity, and that species' body mass is influential.

2. We defined four assemblages using migration status (residents or visitors) and season (summer or winter distribution). Records of species' presence/absence in a total of 2362, 10 x 10-km, quadrats covering most of Britain were used, together with a wide selection of habitat, topographic and seasonal climatic data.

3. We fitted a logistic regression model to each species' distribution using the environmental data. We then combined these individual species models mathematically to form a diversity model. Analysis of this composite model revealed that summer temperature was the factor most strongly associated with diversity.

4. Although the species-energy hypothesis was supported, the direct mechanism, predicting an important role for body mass and matching seasonal patterns of change between diversity and temperature, was not supported.

5. However, summer temperature is the best overall explanation for bird diversity patterns in Britain. It is a better predictor of winter diversity than winter temperature. Winter diversity is predicted more precisely from environmental factors than summer diversity.

6. Climate change is likely to influence the diversity of different areas to different extents; for resident species, low diversity areas may respond more strongly as climate change progresses. For winter visitors, higher diversity areas may respond more strongly, while summer visitors are approximately neutral.

Automated pre-processing strategies for species occurrence data used in biodiversity modelling

To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

MOLES3: implementing an ISO standards driven data catalogue

ISO19156 Observations and Measurements (O&M) provides a standardised framework for organising information about the collection of information about the environment. Here we describe the implementation of a specialisation of O&M for environmental data, the Metadata Objects for Linking Environmental Sciences (MOLES3). MOLES3 provides support for organising information about data, and for user navigation around data holdings. The implementation described here, “CEDA-MOLES”, also supports data management functions for the Centre for Environmental Data Archival, CEDA. The previous iteration of MOLES (MOLES2) saw active use over five years, being replaced by CEDA-MOLES in late 2014. During that period important lessons were learnt both about the information needed, as well as how to design and maintain the necessary information systems. In this paper we review the problems encountered in MOLES2; how and why CEDA-MOLES was developed and engineered; the migration of information holdings from MOLES2 to CEDA-MOLES; and, finally, provide an early assessment of MOLES3 (as implemented in CEDA-MOLES) and its limitations. Key drivers for the MOLES3 development included the necessity for improved data provenance, for further structured information to support ISO19115 discovery metadata export (for EU INSPIRE compliance), and to provide appropriate fixed landing pages for Digital Object Identifiers (DOIs) in the presence of evolving datasets. Key lessons learned included the importance of minimising information structure in free text fields, and the necessity to support as much agility in the information infrastructure as possible without compromising on maintainability both by those using the systems internally and externally (e.g. citing in to the information infrastructure), and those responsible for the systems themselves. The migration itself needed to ensure continuity of service and traceability of archived assets.

Socio-economic inequalities in women`s fruit and vegetable intakes: a multilevel study of individual, social and environmental mediators

Objective: This study employed a multilevel design to test the contribution of individual, social and environmental factors to mediating socio-economic status (SES) inequalities in fruit and vegetable consumption among women. Design: A cross-sectional survey was linked with objective environmental data. Setting: A community sample involving 45 neighbourhoods. Subjects: In total, 1347 women from 45 neighbourhoods provided survey data on their SES (highest education level), nutrition knowledge, health considerations related to food purchasing, and social support for healthy eating. These data were linked with objective environmental data on the density of supermarkets and fruit and vegetable outlets in local neighbourhoods. Results: Multilevel modelling showed that individual and social factors partly mediated, but did not completely explain, SES variations in fruit and vegetable consumption. Store density did not mediate the relationship of SES with fruit or vegetable consumption. Conclusions: Nutrition promotion interventions should focus on enhancing nutrition knowledge and health considerations underlying food purchasing in order to promote healthy eating, particularly among those who are socio-economically disadvantaged. Further investigation is required to identify additional potential mediators of SES–diet relationships, particularly at the environmental level.

Personal, social and environmental determinants of educational inequalities in walking: a multilevel study

Objective: To investigate the contribution of personal, social and environmental factors to mediating socioeconomic (educational) inequalities in women’s leisure-time walking and walking for transport.
Methods: A community sample of 1282 women provided survey data on walking for leisure and transport; educational level; enjoyment of, and self-efficacy for, walking; physical activity barriers and intentions; social support for physical activity; sporting/recreational club membership; dog ownership; and perceived environmental aesthetics and safety. These data were linked with objective environmental data on the density of public open space and walking tracks in the women’s local neighbourhood, coastal proximity and street connectivity.
Results: Multilevel modelling showed that different personal, social and environmental factors were associated with walking for leisure and walking for transport. Variables from all three domains explained (mediated) educational inequalities in leisure-time walking, including neighbourhood walking tracks; coastal proximity; friends’ social support; dog ownership; self-efficacy, enjoyment and intentions. On the other hand, few of the variables examined explained educational variations in walking for transport, exceptions being neighbourhood, coastal proximity, street connectivity and social support from family.
Conclusions: Public health initiatives aimed at promoting, and reducing educational inequalities in, leisure-time walking should incorporate a focus on environmental strategies, such as advocating for neighbourhood walking tracks, as well as personal and social factors. Further investigation is required to better understand the pathways by which education might influence walking for transport.

Overcoming data constraints to create meaningful ecological models

Many techniques used to model ecosystems cannot be meaningfully applied to large-scale ecological problems due to data constraints. Disparate collection methods, data types and incomplete data sets, or limited theoretical understanding mean that a wide range of modelling techniques used to model physical processes or for problems specific to species or populations cannot be used at an ecosystem scale. In developing an ecological response model for the Coorong, a South Australian hypersaline estuary, we combined several flexible modelling approaches in a statistical framework to develop an approach we call ‘ecosystem states’. This model uses simulated hydrodynamic conditions as input to predict one of a suite of states per space and time, allowing prediction of likely ecological conditions under a variety of scenarios. Each ecosystem state has defined sets of biota and physico-chemical parameters. The existing model is limited in that its predictions have yet to be tested and, as yet, no spatial or temporal connectivity has been incorporated into simulated time series of ecosystem states. This approach can be used in a wide range of ecosystems, where enough data are available to model ecosystem states. We are in the process of applying the technique to a nearby lake system. This has been more difficult than for the Coorong as there is little overlap in the spatial and temporal coverage of biological data sets for that region. The approach is robust to low-quality biological data and missing environmental data, so should suit situations where community or management monitoring programs have occurred through time.

Errors due to the tropospheric and ionospheric effects on the geographic location of data collection platforms

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of tropospheric and ionospheric effects on the geographic localization of data collection platforms

The Brazilian National Institute for Space Research (INPE) is operating the Brazilian Environmental Data Collection System that currently amounts to a user community of around 100 organizations and more than 700 data collection platforms installed in Brazil. This system uses the SCD-1, SCD-2, and CBERS-2 low Earth orbit satellites to accomplish the data collection services. The main system applications are hydrology, meteorology, oceanography, water quality, and others. One of the functionalities offered by this system is the geographic localization of the data collection platforms by using Doppler shifts and a batch estimator based on least-squares technique. There is a growing demand to improve the quality of the geographical location of data collection platforms for animal tracking. This work presents an evaluation of the ionospheric and tropospheric effects on the Brazilian Environmental Data Collection System transmitter geographic location. Some models of the ionosphere and troposphere are presented to simulate their impacts and to evaluate performance of the platform location algorithm. The results of the Doppler shift measurements, using the SCD-2 satellite and the data collection platform (DCP) located in Cuiabá town, are presented and discussed.