Are low CO2 emitters in rural areas also socially excluded? An empirical investigation of travel diary data from rural Northern Ireland

The reduction of CO2 emissions and social exclusion are two key elements of UK transport strategy. Despite intensive research on each theme, little effort has so far been made linking the relationship between emissions and social exclusion. In addition, current knowledge on each theme is limited to urban areas; little research is available on these themes for rural areas. This research contributes to this gap in the literature by analysing 157 weekly activity-travel diary data collected from three case study areas with differential levels of area accessibility and area mobility options, located in rural Northern Ireland. Individual weekly CO2 emission levels from personal travel diaries (both hot exhaust emission and cold-start emission) were calculated using average speed models for different modes of transport. The socio-spatial patterns associated with CO2 emissions were identified using a general linear model whereas binary logistic regression analyses were conducted to identify mode choice behaviour and activity patterns. This research found groups that emitted a significantly lower level of CO2 included individuals living in an area with a higher level of accessibility and mobility, non-car, non-working, and low-income older people. However, evidence in this research also shows that although certain groups (e.g. those working, and residing in an area with a lower level of accessibility) emitted higher levels of CO2, their rate of participation in activities was however found to be significantly lower compared to their counterparts. Based on the study findings, this research highlights the need for both soft (e.g. teleworking) and physical (e.g. accessibility planning) policy measures in rural areas in order to meet government’s stated CO2 reduction targets while at the same time enhancing social inclusion.

Journeys to health services in Great Britain : an analysis of changing travel patterns 1985-2006

This paper examines changing patterns in the utilisation and geographic access to health services in Great Britain using National Travel Survey data (1985-2006). The utilisation rate was derived using the proportion of journeys made to access health services. Geographic access was analysed by separating the concept into its accessibility and mobility dimensions. Regression analyses were conducted to investigate the differences between different socio-spatial groups in these indicators over the period 1985-2006. This study found that journey distances to health facilities were significantly shorter and also gradually reduced over the period in question for Londoners, females, those without a car or on low incomes, and older people. However, most of their rates of utilisation of health services were found to be significantly lower because their journey times were significantly longer and also gradually increased over the periods. These findings indicate that the rate of utilisation of health services largely depends on mobility level although previous research studies have traditionally overlooked the mobility dimension.

An evaluation of the pattern and structure mathematics awareness program in the early school years

This paper reports a 2-year longitudinal study on the effectiveness of the Pattern and Structure Mathematical Awareness Program (PASMAP) on students’ mathematical development. The study involved 316 Kindergarten students in 17 classes from four schools in Sydney and Brisbane. The development of the PASA assessment interview and scale are presented. The intervention program provided explicit instruction in mathematical pattern and structure that enhanced the development of students’ spatial structuring, multiplicative reasoning, and emergent generalisations. This paper presents the initial findings of the impact of the PASMAP and illustrates students’ structural development.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Spatial and temporal foraging patterns of Queensland fruit fly, Bactrocera tryoni(Froggatt) (Diptera: Tephritidae), for protein and implications for management

Fruit flies require protein for reproductive development and actively feed upon protein sources in the field. Liquid protein baits mixed with insecticide are used routinely to manage pest fruit flies, such as Bactrocera tryoni (Froggatt). However, there are still some gaps in the underpinning science required to improve the efficacy of bait spray technology. The spatial and temporal foraging behaviour of B. tryoni in response to protein was investigated in the field. A series of linked trials using either wild flies in the open field or laboratory-reared flies in field cages and a netted orchard were undertaken using nectarines and guavas. Key questions investigated were the fly's response to protein relative to: height of protein within the canopy, fruiting status of the tree, time of day, season and size of the experimental arena. Canopy height had a significant response on B. tryoni foraging, with more flies foraging on protein in the mid to upper canopy. Fruiting status also had a significant effect on foraging, with most flies responding to protein when applied to fruiting hosts. B. tryoni demonstrated a repeatable diurnal response pattern to protein, with the peak response being between 12:00–16:00 h. Season showed significant but unpredictable effects on fruit fly response to protein in the subtropical environment where the work was undertaken. Relative humidity, but not temperature or rainfall, was positively correlated with protein response. The number of B. tryoni responding to protein decreased dramatically as the spatial scale increased from field cage through to the open field. Based on these results, it is recommend that, to be most effective, protein bait sprays should be applied to the mid to upper canopies of fruiting hosts. Overall, the results show that the protein used, an industry standard, has very low attractancy to B. tryoni and that further work is urgently needed to develop more volatile protein baits.

First flush behaviour in urban residential catchments

The current state of knowledge in relation to first flush does not provide a clear understanding of the role of rainfall and catchment characteristics in influencing this phenomenon. This is attributed to the inconsistent findings from research studies due to the unsatisfactory selection of first flush indicators and how first flush is defined. The research study discussed in this thesis provides the outcomes of a comprehensive analysis on the influence of rainfall and catchment characteristics on first flush behaviour in residential catchments. Two sets of first flush indicators are introduced in this study. These indicators were selected such that they are representative in explaining in a systematic manner the characteristics associated with first flush. Stormwater samples and rainfall-runoff data were collected and recorded from stormwater monitoring stations established at three urban catchments at Coomera Waters, Gold Coast, Australia. In addition, historical data were also used to support the data analysis. Three water quality parameters were analysed, namely, total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN). The data analyses were primarily undertaken using multi criteria decision making methods, PROMETHEE and GAIA. Based on the data obtained, the pollutant load distribution curve (LV) was determined for the individual rainfall events and pollutant types. Accordingly, two sets of first flush indicators were derived from the curve, namely, cumulative load wash-off for every 10% of runoff volume interval (interval first flush indicators or LV) from the beginning of the event and the actual pollutant load wash-off during a 10% increment in runoff volume (section first flush indicators or P). First flush behaviour showed significant variation with pollutant types. TSS and TP showed consistent first flush behaviour. However, the dissolved fraction of TN showed significant differences to TSS and TP first flush while particulate TN showed similarities. Wash-off of TSS, TP and particulate TN during the first 10% of the runoff volume showed no influence from corresponding rainfall intensity. This was attributed to the wash-off of weakly adhered solids on the catchment surface referred to as "short term pollutants" or "weakly adhered solids" load. However, wash-off after 10% of the runoff volume showed dependency on the rainfall intensity. This is attributed to the wash-off of strongly adhered solids being exposed when the weakly adhered solids diminish. The wash-off process was also found to depend on rainfall depth at the end part of the event as the strongly adhered solids are loosened due to impact of rainfall in the earlier part of the event. Events with high intensity rainfall bursts after 70% of the runoff volume did not demonstrate first flush behaviour. This suggests that rainfall pattern plays a critical role in the occurrence of first flush. Rainfall intensity (with respect to the rest of the event) that produces 10% to 20% runoff volume play an important role in defining the magnitude of the first flush. Events can demonstrate high magnitude first flush when the rainfall intensity occurring between 10% and 20% of the runoff volume is comparatively high while low rainfall intensities during this period produces low magnitude first flush. For events with first flush, the phenomenon is clearly visible up to 40% of the runoff volume. This contradicts the common definition that first flush only exists, if for example, 80% of the pollutant mass is transported in the first 30% of runoff volume. First flush behaviour for TN is different compared to TSS and TP. Apart from rainfall characteristics, the composition and the availability of TN on the catchment also play an important role in first flush. The analysis confirmed that events with low rainfall intensity can produce high magnitude first flush for the dissolved fraction of TN, while high rainfall intensity produce low dissolved TN first flush. This is attributed to the source limiting behaviour of dissolved TN wash-off where there is high wash-off during the initial part of a rainfall event irrespective of the intensity. However, for particulate TN, the influence of rainfall intensity on first flush characteristics is similar to TSS and TP. The data analysis also confirmed that first flush can occur as high magnitude first flush, low magnitude first flush or non existence of first flush. Investigation of the influence of catchment characteristics on first flush found that the key factors that influence the phenomenon are the location of the pollutant source, spatial distribution of the pervious and impervious surfaces in the catchment, drainage network layout and slope of the catchment. This confirms that first flush phenomenon cannot be evaluated based on a single or a limited set of parameters as a number of catchment characteristics should be taken into account. Catchments where the pollutant source is located close to the outlet, a high fraction of road surfaces, short travel time to the outlet, with steep slopes can produce high wash-off load during the first 50% of the runoff volume. Rainfall characteristics have a comparatively dominant impact on the wash-off process compared to the catchment characteristics. In addition, the pollutant characteristics also should be taken into account in designing stormwater treatment systems due to different wash-off behaviour. Analysis outcomes confirmed that there is a high TSS load during the first 20% of the runoff volume followed by TN which can extend up to 30% of the runoff volume. In contrast, high TP load can exist during the initial and at the end part of a rainfall event. This is related to the composition of TP available for the wash-off.

Pavlovian Fear Conditioning Activates a Common Pattern of Neurons in the Lateral Amygdala of Individual Brains

Understanding the physical encoding of a memory (the engram) is a fundamental question in neuroscience. Although it has been established that the lateral amygdala is a key site for encoding associative fear memory, it is currently unclear whether the spatial distribution of neurons encoding a given memory is random or stable. Here we used spatial principal components analysis to quantify the topography of activated neurons, in a select region of the lateral amygdala, from rat brains encoding a Pavlovian conditioned fear memory. Our results demonstrate a stable, spatially patterned organization of amygdala neurons are activated during the formation of a Pavlovian conditioned fear memory. We suggest that this stable neuronal assembly constitutes a spatial dimension of the engram. © 2011 This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

Transit passenger segmentation using travel regularity mined from Smart Card transactions data

Transit passenger market segmentation enables transit operators to target different classes of transit users to provide customized information and services. The Smart Card (SC) data, from Automated Fare Collection system, facilitates the understanding of multiday travel regularity of transit passengers, and can be used to segment them into identifiable classes of similar behaviors and needs. However, the use of SC data for market segmentation has attracted very limited attention in the literature. This paper proposes a novel methodology for mining spatial and temporal travel regularity from each individual passenger’s historical SC transactions and segments them into four segments of transit users. After reconstructing the travel itineraries from historical SC transactions, the paper adopts the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm to mine travel regularity of each SC user. The travel regularity is then used to segment SC users by an a priori market segmentation approach. The methodology proposed in this paper assists transit operators to understand their passengers and provide them oriented information and services.

A spatial-indexing model for measuring neighbourhood-level land-use and transport integration

Significant attention has been given in urban policy literature to the integration of land-use and transport planning and policies—with a view to curbing sprawling urban form and diminishing externalities associated with car-dependent travel patterns. By taking land-use and transport interaction into account, this debate mainly focuses on how a successful integration can contribute to societal well-being, providing efficient and balanced economic growth while accomplishing the goal of developing sustainable urban environments and communities. The integration is also a focal theme of contemporary urban development models, such as smart growth, liveable neighbourhoods, and new urbanism. Even though available planning policy options for ameliorating urban form and transport-related externalities have matured—owing to growing research and practice worldwide—there remains a lack of suitable evaluation models to reflect on the current status of urban form and travel problems or on the success of implemented integration policies. In this study we explore the applicability of indicator-based spatial indexing to assess land-use and transport integration at the neighbourhood level. For this, a spatial index is developed by a number of indicators compiled from international studies and trialled in Gold Coast, Queensland, Australia. The results of this modelling study reveal that it is possible to propose an effective metric to determine the success level of city plans considering their sustainability performance via composite indicator methodology. The model proved useful in demarcating areas where planning intervention is applicable, and in identifying the most suitable locations for future urban development and plan amendments. Lastly, we integrate variance-based sensitivity analysis with the spatial indexing method, and discuss the applicability of the model in other urban contexts.

Feral pig populations are structured at fine spatial scales in tropical Queensland, Australia

Feral pigs occur throughout tropical far north Queensland, Australia and are a significant threat to biodiversity and World Heritage values, agriculture and are a vector of infectious diseases. One of the constraints on long-lasting, local eradication of feral pigs is the process of reinvasion into recently controlled areas. This study examined the population genetic structure of feral pigs in far north Queensland to identify the extent of movement and the scale at which demographically independent management units exist. Genetic analysis of 328 feral pigs from the Innisfail to Tully region of tropical Queensland was undertaken. Seven microsatellite loci were screened and Bayesian clustering methods used to infer population clusters. Sequence variation at the mitochondrial DNA control region was examined to identify pig breed. Significant population structure was identified in the study area at a scale of 25 to 35 km, corresponding to three demographically independent management units (MUs). Distinct natural or anthropogenic barriers were not found, but environmental features such as topography and land use appear to influence patterns of gene flow. Despite the strong, overall pattern of structure, some feral pigs clearly exhibited ancestry from a MU outside of that from which they were sampled indicating isolated long distance dispersal or translocation events. Furthermore, our results suggest that gene flow is restricted among pigs of domestic Asian and European origin and non-random mating influences management unit boundaries. We conclude that the three MUs identified in this study should be considered as operational units for feral pig control in far north Queensland. Within a MU, coordinated and simultaneous control is required across farms, rainforest areas and National Park Estates to prevent recolonisation from adjacent localities.

Bluetooth vehicle trajectory by fusing Bluetooth and loops : motorway travel time statistics

Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth MAC Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveller. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways loops are closely spaced, whereas BMS are placed few kilometres apart. In this research, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilised to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific motorway, Brisbane. Comparing the model with the linear interpolation based trajectory provides significant improvements.

Benefit evaluation for information providing system by WebGIS in view of users' travel costs

This paper estimates the benefit of a plan for information providing system on road administration by WebGIS. The system will reduce travel costs of visitors from their business establishments to a road administration section of a city office. The authors had individual interviews with the visitors at the section of the Ichikawa City Office. Annual total sum of travel costs was estimated at 37 million yen at most. This paper also proposes formulas which expect the frequency of visits or the total sum of travel costs from the spatial distribution of the business establishments without questionnaires.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012

BACKGROUND: Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. METHODS: Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. RESULTS: 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f.  = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. CONCLUSIONS: Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Cerebellar grey matter deficits in first-episode schizophrenia mapped using cortical pattern matching

Cerebellar dysfunction has been proposed to lead to “cognitive dysmetria” in schizophrenia via the cortico-cerebellar-thalamic-cortical circuit, contributing to a range of cognitive and clinical symptoms of the disorder. Here we investigated total cerebellar grey and white matter volumes and cerebellar regional grey matter abnormalities in 13 remitted first-episode schizophrenia patients with less than 2 years’ duration of illness. Patient data were compared to 13 pair-wise age, gender, and handedness-matched healthy volunteers using cortical pattern averaging on high-resolution magnetic resonance images. Total cerebellar volume and total grey matter volumes in first-episode schizophrenia patients did not differ from healthy control subjects, but total cerebellar white matter was increased and total grey to white matter ratios were reduced in patients. Four clusters of cerebellar grey matter reduction were identified: (i) in superior vermis; (ii) in the left lobuli VI; (iii) in right-inferior lobule IX, extending into left lobule IX; and (iv) bilaterally in the areas of lobuli III, peduncle and left flocculus. Grey matter deficits were particularly prominent in right lobuli III and IX, left flocculus and bilateral pedunculi. These cerebellar areas have been implicated in attention control, emotional regulation, social functioning, initiation of smooth pursuit eye movements, eye-blink conditioning, language processing, verbal memory, executive function and the processing of spatial and emotional information. Consistent with common clinical, cognitive, and pathophysiological signs of established illness, our findings demonstrate cerebellar pathology as early as in first-episode schizophrenia.