Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

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.

How can a knowledge of the past help to conserve the future? Biodiversity conservation and the relevance of long-term ecological studies

This paper evaluates how long-term records could and should be utilized in conservation policy and practice. Traditionally, there has been an extremely limited use of long-term ecological records (greater than 50 years) in biodiversity conservation. There are a number of reasons why such records tend to be discounted, including a perception of poor scale of resolution in both time and space, and the lack of accessibility of long temporal records to non-specialists. Probably more important, however, is the perception that even if suitable temporal records are available, their roles are purely descriptive, simply demonstrating what has occurred before in Earth’s history, and are of little use in the actual practice of conservation. This paper asks why this is the case and whether there is a place for the temporal record in conservation management. Key conservation initiatives related to extinctions, identification of regions of greatest diversity/threat, climate change and biological invasions are addressed. Examples of how a temporal record can add information that is of direct practicable applicability to these issues are highlighted. These include (i) the identification of species at the end of their evolutionary lifespan and therefore most at risk from extinction, (ii) the setting of realistic goals and targets for conservation ‘hotspots’, and (iii) the identification of various management tools for the maintenance/restoration of a desired biological state. For climate change conservation strategies, the use of long-term ecological records in testing the predictive power of species envelope models is highlighted, along with the potential of fossil records to examine the impact of sea-level rise. It is also argued that a long-term perspective is essential for the management of biological invasions, not least in determining when an invasive is not an invasive. The paper concludes that often inclusion of a long-term ecological perspective can provide a more scientifically defensible basis for conservation decisions than the one based only on contemporary records. The pivotal issue of this paper is not whether long-term records are of interest to conservation biologists, but how they can actually be utilized in conservation practice and policy.

Intertidal molluscan and algal species richness around the UK coast

Geographically referenced databases of species records are becoming increasingly available. Doubts over the heterogeneous quality of the underlying data may restrict analyses of such collated databases. We partitioned the spatial variation in species richness of littoral algae and molluscs from the UK National Biodiversity Network database into a smoothed mesoscale component and a local component. Trend surface analysis (TSA) was used to define the mesoscale patterns of species richness, leaving a local residual component that lacked spatial autocorrelation. The analysis was based on 10 km grid squares with 115035 records of littoral algae (729 species) and 66879 records of littoral molluscs (569 species). The TSA identified variation in algal and molluscan species richness with a characteristic length scale of approximately 120 km. Locations of the most species-rich grid squares were consistent with the southern and western bias of species richness in the UK marine flora and fauna. The TSA also identified areas which showed significant changes in the spatial pattern of species richness: breakpoints, which correspond to major headlands along the south coast of England. Patterns of algal and molluscan species richness were broadly congruent. Residual variability was strongly influenced by proxies of collection effort, but local environmental variables including length of the coastline and variability in wave exposure were also important. Relative to the underlying trend, local species richness hotspots occurred on all coasts. While there is some justification for scepticism in analyses of heterogeneous datasets, our results indicate that the analysis of collated datasets can be informative.

Distribution, extent of inter-annual variability and diet of the bloom-forming jellyfish Rhizostoma in European waters

Jellyfish (Cnidaria: Scyphozoa) are increasingly thought to play a number of important ecosystem roles, but often fundamental knowledge of their distribution, seasonality and inter-annual variability is lacking. Bloom forming species, due to their high densities, can have particularly intense trophic and socio-economic impacts. In northern Europe it is known that one particularly large (up to 30 kg wet weight) bloom forming jellyfish is Rhizostoma spp. Given the potential importance, we set out to review all known records from peer-reviewed and broader public literature of the jellyfish R. octopus (Linnaeus) and R. pulmo (Macri) (Scyphozoa: Rhizostomae) across western Europe. These data revealed distinct hotspots where regular Rhizostoma spp. aggregations appeared to form, with other sites characterized by occasional abundances and a widespread distribution of infrequent observations. Surveys of known R. octopus hotspots around the Irish Sea also revealed marked inter-annual variation with particularly high abundances forming during 2003. The location of such consistent aggregations and inter-annual variances are discussed in relation to physical, climatic and dietary variations.

Mutational hotspot in Exon 20 of PIK3CA in breast cancer among Singapore Chinese

The recent identification of somatic mutations in the catalytic region of PIK3 (PIK3CA) in breast cancer and demonstration of their oncogenic function has implicated PIK3CA in mammary carcinogenesis. To investigate possible ethnic differences in patterns of PIK3CA mutations in Singaporean Chinese breast cancer and to characterize these in a panel of cell lines, we sequenced exons 9 and 20 in 80 primary tumors, 19 breast cancer cell lines and 7 normal human mammary epithelial cells (HMECs). Searching for novel hotspots of mutation, we sequenced additional exons ( 1, 2, 6, 7, 14 and 18) in 20 primary tumors and 6 breast cancer cell lines. We detected 33 point mutations in 31 of 80 (39%) breast cancers, and 11 mutations in 10 of 19 (53%) breast cancer cell lines. No mutations were detected in normal breast tissue adjacent to the tumor, or in the 6 normal HMECs. The exon 20 A3140G (H1047R) substitution was identified most frequently (22/31, 71%) and showed a significant association with patient age ( p = 0.043) and stage of the disease ( p = 0.025), but not with ER/PR status or histological grade of the tumor. The incidence of point mutations in PIK3CA, the A3140G substitution in particular, in Singapore breast cancers are among the most frequent reported to date for any gene in breast cancer. The results suggest that mutation of PIK3CA might contribute to development of early stage breast cancer and could provide a potent target for early diagnosis and therapy.

Combined antenna and localized plasmon resonance in Raman scattering from Random arrays of silver-coated, vertically aligned multiwalled carbon nanotubes

The electric field enhancement associated with detailed structure within novel optical antenna nanostructures is modeled using the surface integral equation technique in the context of surface-enhanced Raman scattering (SERS). The antennae comprise random arrays of vertically aligned, multi-walled carbon nanotubes dressed with highly granular Ag. Different types of "hot-spot" underpinning the SERS are identified, but contrasting characteristics are revealed. Those at the outer edges of the Ag grains are antenna driven with field enhancement amplified in antenna antinodes while intergrain hotspots are largely independent of antenna activity. Hot-spots between the tops of antennae leaning towards each other also appear to benefit from antenna amplification.

On thin ice: surface order and disorder during pre-melting

The effect of temperature on the structure of the ice Ih (0001) surface is considered through a series of molecular dynamics simulations on an ice slab. At relatively low temperatures (200K) a small fraction of surface self-interstitials (i.e. admolecules) appear that are formed exclusively from molecules leaving the outermost bilayer. At higher temperatures (ca. 250 K), vacancies start to appear in the inner part of the outermost bilayer exposing the underlying bilayer and providing sites with a high concentration of the dangling hydrogen bonds. Around 250-260 K aggregates of molecules formed on top of the outermost bilayer from self-interstitials become more mobile and have diffusivities approaching that of liquid water. At similar to 270-280 K the inner bilayer of one surface noticeably destructures and it appears that at above 285 K both surfaces are melting. The observed disparity in the onset of melting between the two sides of the slab is rationalised by considering the relationship between surface energy and the spatial distribution of protons at the surface; thermodynamic stability is conferred on the surface by maximising separations between dangling protons at the crystal exterior. Local hotspots associated with a high dangling proton density are suggested to be susceptible to pre-melting and may be more efficient at trapping species at the external surface than regions with low concentrations of protons thus potentially helping ice particles to catalyse reactions. A preliminary conclusion of this work is that only about 10-20 K below the melting temperature of the particular water potential employed is major disruption of the crystalline lattice noted which could be interpreted as being "liquid", the thickness of this film being about a nanometre.

Evidence and Spatial Planning for Sustainable Development: A Model for Spatially Allocating Material Flows

The aspiration the spatial planning should act as the main coordinating function for the transition to a sustainable society is grounded on the assumption that it is capable of incorporating both a strong evidence base of environmental accounting for policy, coupled with opportunities for open, deliberative decision-making. While there are a number of increasingly sophisticated methods (such as material flow analysis and ecological footprinting) that can be used to longitudinally determine the impact of policy, there are fewer that can provide a robust spatial assessment of sustainability policy. In this paper, we introduce the Spatial Allocation of Material Flow Analysis (SAMFA) model, which uses the concept of socio-economic metabolism to extrapolate the impact of local consumption patterns that may occur as a result of the local spatial planning process at multiple spatial levels. The initial application the SAMFA model is based on County Kildare in the Republic of Ireland, through spatial temporal simulation and visualisation of construction material flows and associated energy use in the housing sector. Thus, while we focus on an Ireland case study, the model is applicable to spatial planning and sustainability research more generally. Through the development and evaluation of alternative scenarios, the model appears to be successful in its prediction of the cumulative resource and energy impacts arising from consumption and development patterns. This leads to some important insights in relation to the differential spatial distribution of disaggregated allocation of material balance and energy use, for example that rural areas have greater resource accumulation (and are therefore in a sense “less sustainable”) than urban areas, confirming that rural housing in Ireland is both more material and energy intensive. This therefore has the potential to identify hotspots of higher material and energy use, which can be addressed through targeted planning initiatives or focussed community engagement. Furthermore, due to the ability of the model to allow manipulation of different policy criteria (increased density, urban conservation etc), it can also act as an effective basis for multi-stakeholder engagement.

Combining global climate and regional landscape models to improve prediction of invasion risk

Aim
It is widely acknowledged that species distributions result from a variety of biotic and abiotic factors operating at different spatial scales. Here, we aimed to (1) determine the extent to which global climate niche models (CNMs) can be improved by the addition of fine-scale regional data; (2) examine climatic and environmental factors influencing the range of 15 invasive aquatic plant species; and (3) provide a case study for the use of such models in invasion management on an island.

Location
Global, with a case study of species invasions in Ireland.

Methods
Climate niche models of global extent (including climate only) and regional environmental niche models (with additional factors such as human influence, land use and soil characteristics) were generated using maxent for 15 invasive aquatic plants. The performance of these models within the invaded range of the study species in Ireland was assessed, and potential hotspots of invasion suitability were determined. Models were projected forward up to 2080 based on two climate scenarios.

Results
While climate variables are important in defining the global range of species, factors related to land use and nutrient level were of greater importance in regional projections. Global climatic models were significantly improved at the island scale by the addition of fine-scale environmental variables (area under the curve values increased by 0.18 and true skill statistic values by 0.36), and projected ranges decreased from an average of 86% to 36% of the island.

Main conclusions
Refining CNMs with regional data on land use, human influence and landscape may have a substantial impact on predictive capacity, providing greater value for prioritization of conservation management at subregional or local scales.

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.