Is Mortality Spatial or Social?

Mortality modelling for the purposes of demographic forecasting and actuarial pricing is generally done at an aggregate level using national data. Modelling at this level fails to capture the variation in mortality within country and potentially leads to a mis-specification of mortality forecasts for a subset of the population. This can have detrimental effects for pricing and reserving in the actuarial context. In this paper we consider mortality rates at a regional level and analyse the variation in those rates. We consider whether variation in mortality rates within a country can be explained using local economic and social variables. Using Northern Ireland data on mortality and measures of deprivation we identify the variables explaining mortality variation. We create a population polarisation variable and find that this variable is significant in explaining some of the variation in mortality rates. Further, we consider whether spatial and non-spatial models have a part to play in explaining mortality differentials.

Coupling ground and airborne geophysical data with upscaling techniques for regional groundwater modeling of heterogeneous aquifers: Case study of a sedimentary aquifer intruded by volcanic dykes in Northern Ireland

In highly heterogeneous aquifer systems, conceptualization of regional groundwater flow models frequently results in the generalization or negligence of aquifer heterogeneities, both of which may result in erroneous model outputs. The calculation of equivalence related to hydrogeological parameters and applied to upscaling provides a means of accounting for measurement scale information but at regional scale. In this study, the Permo-Triassic Lagan Valley strategic aquifer in Northern Ireland is observed to be heterogeneous, if not discontinuous, due to subvertical trending low-permeability Tertiary dolerite dykes. Interpretation of ground and aerial magnetic surveys produces a deterministic solution to dyke locations. By measuring relative permeabilities of both the dykes and the sedimentary host rock, equivalent directional permeabilities, that determine anisotropy calculated as a function of dyke density, are obtained. This provides parameters for larger scale equivalent blocks, which can be directly imported to numerical groundwater flow models. Different conceptual models with different degrees of upscaling are numerically tested and results compared to regional flow observations. Simulation results show that the upscaled permeabilities from geophysical data allow one to properly account for the observed spatial variations of groundwater flow, without requiring artificial distribution of aquifer properties. It is also found that an intermediate degree of upscaling, between accounting for mapped field-scale dykes and accounting for one regional anisotropy value (maximum upscaling) provides results the closest to the observations at the regional scale.

Integrating Multiple Point Statistics with Aerial Geophysical Data to assist Groundwater Flow Models

The process of accounting for heterogeneity has made significant advances in statistical research, primarily in the framework of stochastic analysis and the development of multiple-point statistics (MPS). Among MPS techniques, the direct sampling (DS) method is tested to determine its ability to delineate heterogeneity from aerial magnetics data in a regional sandstone aquifer intruded by low-permeability volcanic dykes in Northern Ireland, UK. The use of two two-dimensional bivariate training images aids in creating spatial probability distributions of heterogeneities of hydrogeological interest, despite relatively ‘noisy’ magnetics data (i.e. including hydrogeologically irrelevant urban noise and regional geologic effects). These distributions are incorporated into a hierarchy system where previously published density function and upscaling methods are applied to derive regional distributions of equivalent hydraulic conductivity tensor K. Several K models, as determined by several stochastic realisations of MPS dyke locations, are computed within groundwater flow models and evaluated by comparing modelled heads with field observations. Results show a significant improvement in model calibration when compared to a simplistic homogeneous and isotropic aquifer model that does not account for the dyke occurrence evidenced by airborne magnetic data. The best model is obtained when normal and reverse polarity dykes are computed separately within MPS simulations and when a probability threshold of 0.7 is applied. The presented stochastic approach also provides improvement when compared to a previously published deterministic anisotropic model based on the unprocessed (i.e. noisy) airborne magnetics. This demonstrates the potential of coupling MPS to airborne geophysical data for regional groundwater modelling.

Improving signal reliability for indoor off–body communications using spatial diversity at the base station

In this paper, we investigate the potential improvement in signal reliability for indoor off-body communications when using spatial diversity at the base station. In particular, we utilize two hypothetical indoor base stations operating at 5.8 GHz each featuring four antennas which are spaced at either half- or one-wavelength apart. Three on-body locations are considered along with four types of user movement. The cross-correlation between the received signal envelopes observed at each base station antenna element was calculated and found to be always less than 0.5. Selection, maximal ratio, and equal gain combining of the received signal has shown that the greatest improvement is obtained when the user is mobile, with a maximum diversity gain of 11.34 dB achievable when using a four branch receiver. To model the fading envelope obtained at the output of the virtual combiners, we use diversity specific, theoretical probability density functions for multi-branch receivers operating in Nakagami-m fading channels. It is shown that these equations provide an excellent fit to the measured channel data.

Historical data reveal power-law dispersal patterns of invasive aquatic species

Understanding how invasive species spread is of particular concern in the current era of globalisation and rapid environmental change. The occurrence of super-diffusive movements within the context of Lévy flights has been discussed with respect to particle physics, human movements, microzooplankton, disease spread in global epidemiology and animal foraging behaviour. Super-diffusive movements provide a theoretical explanation for the rapid spread of organisms and disease, but their applicability to empirical data on the historic spread of organisms has rarely been tested. This study focuses on the role of long-distance dispersal in the invasion dynamics of aquatic invasive species across three contrasting areas and spatial scales: open ocean (north-east Atlantic), enclosed sea (Mediterranean) and an island environment (Ireland). Study species included five freshwater plant species, Azolla filiculoides, Elodea canadensis, Lagarosiphon major, Elodea nuttallii and Lemna minuta; and ten species of marine algae, Asparagopsis armata, Antithamnionella elegans, Antithamnionella ternifolia, Codium fragile, Colpomenia peregrina, Caulerpa taxifolia, Dasysiphonia sp., Sargassum muticum, Undaria pinnatifida and Womersleyella setacea. A simulation model is constructed to show the validity of using historical data to reconstruct dispersal kernels. Lévy movement patterns similar to those previously observed in humans and wild animals are evident in the re-constructed dispersal pattern of invasive aquatic species. Such patterns may be widespread among invasive species and could be exacerbated by further development of trade networks, human travel and environmental change. These findings have implications for our ability to predict and manage future invasions, and improve our understanding of the potential for spread of organisms including infectious diseases, plant pests and genetically modified organisms.

Spatial allocation of material flow analysis in residential developments: a case study of Kildare County, Ireland

Studies of urban metabolism provide important insights for environmental management of cities, but are not widely used in planning practice due to a mismatch of data scale and coverage. This paper introduces the Spatial Allocation of Material Flow Analysis (SAMFA) model as a potential decision support tool aimed as a contribution to overcome some of these difficulties and describes its pilot use at the county level in the Republic of Ireland. The results suggest that SAMFA is capable of identifying hotspots of higher material and energy use to support targeted planning initiatives, while its ability to visualise different policy scenarios supports more effective multi-stakeholder engagement. The paper evaluates this pilot use and sets out how this model can act as an analytical platform for the industrial ecology–spatial planning nexus.

Community structure, diversity and spatial organization of enchytraeids in Mediterranean urban holm oak stands

There is a strong northern bias in Europe as regards enchytraeid community ecology, particularly in urban settings. We approached the enchytraeid assemblages of urban holm oak stands in Naples and Siena adopting a high intensity sampling that, for the first time in the Mediterranean climate zone, would ensure that the data collected be representative of the target populations. Structural parameters (diversity and evenness, biomass, size classes, aggregation) were compared across different spatial (regional, urban district, within habitat) and temporal scales (season and year). Species richness was found to change significantly only at regional scale; background data suggest that this may depend on the higher environmental heterogeneity occurring at Naples. Differences in size class structure were significant only on a seasonal scale and within either city separately. With one exception (Fridericia bulbosa s.s.), the patterns of spatial aggregation of the common species were fairly robust and the total range of patchiness was consistent with previous studies, despite the different sampling methodologies. The size of the sampling unit, the number of replicates per plot and the number of plots proposed in this study appear suitable to obviate the difficulties of evaluating Mediterranean enchytraeid communities.

Ranking Spatial and Temporal Hotspots for Road Safety Interventions Using a Nonlinear Aggregation Approach

In recent years, the concept of a composite performance index, brought from economic and business statistics, has gained popularity in the field of road safety. The construction of the Composite Safety Performance Index (CSPI) involves the following key steps: the selection of the most appropriate indicators to be aggregated and the method used to aggregate them.

Over the last decade, various aggregation methods for estimating the CSPI have been suggested in the literature. However, recent studies indicates that most of these methods suffer from many deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, as well as their high “degree of freedom” which enables one to readily manipulate them to produce desired outcomes.

The purpose of this study is to introduce an alternative aggregation method for the estimation of the CSPI, which is free from the aforementioned deficiencies. In contrast with the current aggregation methods, which generally use linear combinations of road safety indicators to estimate a CSPI, the approach advocated in this study is based on non-linear combinations of indicators and can be summarized into the following two main steps: the pairwise comparison of road safety indicators and the development of marginal and composite road safety performance functions. The introduced method has been successfully applied to identify and rank temporal and spatial hotspots for Northern Ireland, using road traffic collision data recorded in the UK STATs19 database. The obtained results highlight the promising features of the proposed approach including its stability and consistency, which enables significantly reduced deficiencies associated with the current aggregation methods. Progressively, the introduced method could evolve into an intelligent support system for road safety assessment.

Measuring beta diversity for presence-absence data

1. Little consensus has been reached as to general features of spatial variation in beta diversity, a fundamental component of species diversity. This could reflect a genuine lack of simple gradients in beta diversity, or a lack of agreement as to just what constitutes beta diversity. Unfortunately, a large number of approaches have been applied to the investigation of variation in beta diversity, which potentially makes comparisons of the findings difficult.

2. We review 24 measures of beta diversity for presence/absence data (the most frequent form of data to which such measures are applied) that have been employed in the literature, express many of them for the first time in common terms, and compare some of their basic properties.

3. Four groups of measures are distinguished, with a fundamental distinction arising between 'broad sense' measures incorporating differences in composition attributable to species richness gradients, and 'narrow sense' measures that focus on compositional differences independent of such gradients. On a number of occasions on which the former have been employed in the literature the latter may have been more appropriate, and there are many situations in which consideration of both kinds of measures would be valuable.

4. We particularly recommend (i) considering beta diversity measures in terms of matching/mismatching components (usually denoted a , b and c) and thereby identifying the contribution of different sources of variation in species composition, and (ii) the use of ternary plots to express the relationship between the values of these measures and of the components, and as a way of understanding patterns in beta diversity.

The geographical structure of British bird distributions:diversity, spatial turnover and scale

1. Using data on the spatial distribution of the British avifauna, we address three basic questions about the spatial structure of assemblages: (i) Is there a relationship between species richness (alpha diversity) and spatial turnover of species (beta diversity)? (ii) Do high richness locations have fewer species in common with neighbouring areas than low richness locations?, and (iii) Are any such relationships contingent on spatial scale (resolution or quadrat area), and do they reflect the operation of a particular kind of species-area relationship (SAR)?

2. For all measures of spatial turnover, we found a negative relationship with species richness. This held across all scales, with the exception of turnover measured as beta (sim).

3. Higher richness areas were found to have more species in common with neighbouring areas.

4. The logarithmic SAR fitted better than the power SAR overall, and fitted significantly better in areas with low richness and high turnover.

5. Spatial patterns of both turnover and richness vary with scale. The finest scale richness pattern (10 km) and the coarse scale richness pattern (90 km) are statistically unrelated. The same is true of the turnover patterns.

6. With coarsening scale, locations of the most species-rich quadrats move north. This observed sensitivity of richness 'hotspot' location to spatial scale has implications for conservation biology, e.g. the location of a reserve selected on the basis of maximum richness may change considerably with reserve size or scale of analysis.

7. Average turnover measured using indices declined with coarsening scale, but the average number of species gained or lost between neighbouring quadrats was essentially scale invariant at 10-13 species, despite mean richness rising from 80 to 146 species (across an 81-fold area increase). We show that this kind of scale invariance is consistent with the logarithmic SAR.

Deriving meteorological variables across Africa for the study and control of vector-borne disease:a comparison of remote sensing and spatial interpolation of climate

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) onboard the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study anti control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

PREDICTING THE SPATIAL-DISTRIBUTION OF CLIMATE - TEMPERATURE IN GREAT-BRITAIN

1. The prediction and mapping of climate in areas between climate stations is of increasing importance in ecology.

2. Four categories of model, simple interpolation, thin plate splines, multiple linear regression and mixed spline-regression, were tested for their ability to predict the spatial distribution of temperature on the British mainland. The models were tested by external cross-verification.

3. The British distribution of mean daily temperature was predicted with the greatest accuracy by using a mixed model: a thin plate spline fitted to the surface of the country, after correction of the data by a selection from 16 independent topographical variables (such as altitude, distance from the sea, slope and topographic roughness), chosen by multiple regression from a digital terrain model (DTM) of the country.

4. The next most accurate method was a pure multiple regression model using the DTM. Both regression and thin plate spline models based on a few variables (latitude, longitude and altitude) only were comparatively unsatisfactory, but some rather simple methods of surface interpolation (such as bilinear interpolation after correction to sea level) gave moderately satisfactory results. Differences between the methods seemed to be dependent largely on their ability to model the effect of the sea on land temperatures.

5. Prediction of temperature by the best methods was greater than 95% accurate in all months of the year, as shown by the correlation between the predicted and actual values. The predicted temperatures were calculated at real altitudes, not subject to sea-level correction.

6. A minimum of just over 30 temperature recording stations would generate a satisfactory surface, provided the stations were well spaced.

7. Maps of mean daily temperature, using the best overall methods are provided; further important variables, such as continentality and length of growing season, were also mapped. Many of these are believed to be the first detailed representations at real altitude.

8. The interpolated monthly temperature surfaces are available on disk.

Spatial Distribution of Soil Geochemistry in Geoforensics

Two common scenarios in Geoforensics (definition in text) are considered: the provenance, or localization of unknown samples and the question of sample variability at scenes of crime/alibi locations. Both have been discussed in forensic and soil science publications, but mostly within a theoretical or non-forensic context. These previous publications provide context for the two case study scenarios (one actual, one based on a range of criminal casework) that consider provenance and variability. A challenging scientific question in geoforensics is the provenance question: ‘where may this sample have come from?’ A question the Tellus data can assist in answering. The question of variation between samples maybe less of a challenge, yet variation between a suspect sample within a scene of crime requires detailed sampling. Variation on a larger (tens to hundreds of kilometres) scale may provide useful intelligence on where a sample came from. To summarise, databases such as Tellus and TellusBorder may be used as effective tools to assist in the search for the origin of displaced soil and sediment

Knowledge Exchange, Spatial Analysis & Healthy Urban Environments (KESUE): Project Report

The Knowledge Exchange, Spatial Analysis and Healthy Urban Environments (KESUE) project has extended work previously undertaken by a QUB team of inter-disciplinary researchers engaged with the Physical Activity in the Regeneration of Connswater (PARC) project (Tully et al, 2013). The PARC project focussed on parts of East Belfast to assess the health impact of the Connswater Community Greenway. The KESUE project has aimed to extend some of the tools used initially in East Belfast so that they have data coverage of all of Belfast and Derry-Londonderry. The purpose of this has been to enable the development of evidence and policy tools that link features of the built environment with physical activity in these two cities. The project has used this data to help shape policy decisions in areas such as physical activity, park management, public transport and planning.

Working with a range of local partners who part-funded the project (City Councils in Belfast and Derry-Londonderry, Public Health Agency, Belfast Healthy Cities and Department of Regional Development), this project has mapped all the footpaths in the two cities (covering 37% of the NI population) and employed this to develop evidence used in strategies related to healthy urban planning. Using Geographic Information Systems (GIS), the footpath network has been used as a basis for a wide range of policy-relevant analyses including pedestrian accessibility to public facilities, site options for new infrastructure and assessing how vulnerable groups can access services such as pharmacies. Key outputs have been Accessibility Atlases and maps showing how walkability of the built environment varies across the two cities.

In addition to generating this useful data, the project included intense engagement with potential users of the research, which has led to its continued uptake in a number of policies and strategies, creating a virtuous circle of research, implementation and feedback. The project has proved so valuable to Belfast City Council that they have now taken on one of the researchers to continue the work in-house.

Spatial and temporal variability of the freshwater reservoir effect (FRE) in lacustrine sediments in a cold climate zone, Northwest Territories (Canada)

Climate variability along the 600 km Tibbitt to Contwyoto Winter Road (TCWR) in central Northwest Territories is poorly understood. With the transportation of goods from Yellowknife to the mines projected to increase significantly as new mines open, it is critical that planners and mine developers have reasonable data on the future viability of the road, as alternative transportation costs (e.g. air transport) are prohibitively high.

The research presented here is part of a paleoclimate study based on the analysis of multiple proxy data derived from freeze cores in lakes along the TCWR.