Social media as an information system : improving the technological agility

There is an increased focus on research involving social media. This research however has failed to catch up with the pace of the technology development and may prove disadvantageous for both practice and theory. The longitudinal study presented in the paper was conducted over a 3-year period involving Australian banks and popular social media technologies. The paper empirically tests the Honeycomb model as a tool that enhances the technological agility of social media. The paper fills a key research gap and provides dynamism to social media strategy formation, continuous improvement of strategy development in support of greater business agility.

Negotiating meaning: Effects of group dynamics on information system development.

The research explored the impacts that the psychological processes within and between work groups had on software development and IS project failure. It has identified and demonstrated ways to moderate such group dynamics and their causes; in turn providing an approach to reduce IS project failure.

Distribution of disease caused by Phytophthora cinnamomi in Wilsons Promontory National Park and potential for further impact

The extent of disease caused by Phytophthora cinnamomi was determined within vegetation communities of Wilsons Promontory National Park. Aerial survey of visible symptoms by helicopter and systematic survey along all roads and tracks followed by isolation of the pathogen from soil found that in total 551 ha of moist foothill forest, heath and heathy woodland broad vegetation types were affected by the disease. P. cinnamomi was isolated from 93% of sites that, based on the presence of visible symptoms, were expected to yield the pathogen. The species-rich heathy woodland was most affected with 6.5% of the total area of this type showing symptoms of disease. The size of infestation ranged from 229 ha on the slopes of the Vereker Range in the north to less than 1 ha along the Sealers Cove Walking Track in the south. The potential for disease to spread into uninfested vegetation was estimated for all sites from which P. cinnamomi was isolated. Eight of 18 sites where evidence of disease was found were estimated to have a high potential for further disease spread. This study indicates that even though the disease may be waning in some areas of the Park, the pathogen is active and easily isolated from others and provides a continuing threat to susceptible vegetation communities.

Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low   abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and  increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant  independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale  approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

In the coastal region of south-western Victoria, Australia, populations of native small mammal species are restricted to patches of suitable habitat in a highly fragmented landscape. The size and spatial arrangement of these patches is likely to influence both the occupancy and richness of species at a location. Geographic Information System (GIS)-based habitat models of the species richness of native small mammals, and individual species  occurrences, were developed to produce maps displaying the spatial  configuration of suitable habitat. Models were generated using either generalised linear Poisson regression (for species richness) or logistic regression (for species occurrences) with species richness or  presence/absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictor variables. A multi-model inference approach based on the Akaike Information Criterion was used and the resulting model was applied in a GIS framework to extrapolate predicted richness/likelihood of occurrence across the entire area of the study. A negative association between species  richness and elevation, habitat complexity and sun index indicated that richness within the study area decreases with increasing altitude, vertical vegetation structure and exposure to solar radiation. Landform  characteristics were important (to varying degrees) in determining habitat occupancy for all of the species examined, while the influence of habitat complexity was important for only one of the species. Performance of all but one of the models generated using presence/absence data was high, as indicated by the area under the curve of a receiver-operating characteristic plot. The effective conservation of the small mammal species in the area of concern is likely to depend on management actions that promote the protection of the critical habitats identified in the models.

Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence greater-or-equal, slanted0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.

Personal, family, social, and environmental correlates of active commuting to school

Background: Active commuting to school may be an important opportunity for children to accumulate adequate physical activity for improved cardiovascular risk factors, enhanced bone health, and psychosocial well-being. The purpose of this study was to examine personal, family, social, and environmental correlates of active commuting to school among children. Methods: Cross-sectional study of 235 children aged 5 to 6 years and 677 children aged 10 to 12 years from 19 elementary schools in Melbourne, Australia. Self-administered questionnaires were completed by parents, and the older children. The shortest possible routes to school were examined using a geographic information system. Results: Among both age groups, negative correlates of active commuting to school included parental perception of few other children in the neighborhood and no lights or crossings for their child to use, and an objectively assessed busy road barrier en route to school. In younger children, an objectively assessed steep incline en route to school was negatively associated with walking or cycling to school. Good connectivity en route to school was negatively associated with walking or cycling to school among older children. Among both age groups, children were more likely to actively commute to school if their route was <800 meters. There were no associations with perceived energy levels or enjoyment of physical activity, weight status, or family factors. Conclusions: For children, creating child-friendly communities and providing skills to safely negotiate the environment may be important. Environmental correlates of active transport in children and adults may differ and warrant further investigation.

Is availability of public open space equitable across areas?

This study examined whether availability of public open spaces that can be used for recreation varies according to neighbourhood socio-economic disadvantage. Density and area of public open spaces were examined using a geographic information system and postal boundaries were used to define neighbourhoods. Neighbourhood socio-economic status (SES) was stratified into quintiles. Once neighbourhood population and geographic area were considered there were no differences in the number or total area of free-access, restricted access or sporting/recreation open spaces across quintiles of neighbourhood SES. Future research should examine whether the quality of public open spaces differ by neighbourhood SES.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Terrain characteristics influencing aquifer salinisation of the Glenelg-Hopkins CMA region, using GIS

Salinisation of aquifers is an issue of great concern in the Glenelg-Hopkins region. The GlenelgHopkins region is located in south-west Victoria, south of the Great Dividing Range and covers 2.6 million hectares. The area receives an annual average rainfall of 500-910 mm and experiences a Mediterranean climate, with hot, dry summers and cold wet winters and has varied geology and soil types. Terrain characteristics, such as soil type, geology, depth-to-water table, land use and topography have been integrated into a Geographic Information System (GIS). A geostatistical approach, including the use of multiple linear regression is used to analyse the spatial variability of the relationships between aquifer salinity and terrain characteristics across the entire region. Results from this study should greatly improve knowledge of aquifer salinisation across the region. It is expected that this work will enable managers to determine the most appropriate mitigating measures for each specific area affected.

Residents' perceptions of walkability attributes in objectively different neighbourhoods: a pilot study

Physical attributes of local environments may influence walking. We used a modified version of the Neighbourhood Environment Walkability Scale to compare residents’ perceptions of the attributes of two neighbourhoods that differed on measures derived from Geographic Information System databases. Residents of the high-walkable neighbourhood rated relevant attributes of residential density, land-use mix (access and diversity) and street connectivity, consistently higher than did residents of the low-walkable neighbourhood. Traffic safety and safety from crime attributes did not differ. Perceived neighbourhood environment characteristics had moderate to high test–retest reliabilities. Neighbourhood environment attribute ratings may be used in population surveys and other studies.

Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

Distribution and habitat preferences of the rufous bristlebird, Dasyornis broadbenti, in Portland, Victoria

The Rufous Bristlebird Dasyornis broadbenti is a ground-dwelling bird that is listed as nearthreatened (Lower Risk) in Victoria. The species has been observed in a variety of habitats ranging from thickets of shrubs in coastal gullies, shrubland and heathlands on limestone cliffs to sheltered gullies. This study aimed to assess the distribution and habitat preferences of a population of the species in Portland, southwest Victoria. Monthly surveys were conducted on foot in the study area for one hour following sunrise and one hour prior to sunset, and bird presence was recorded on the basis of calls and sightings. Observations outside of the survey times were also recorded to determine habitat utilisation. Vegetation floristics and structure and food availability were measured in areas where birds were present as well as surrounding areas where they were absent to determine habitat preferences. A population size of between 45 and 60 individuals was estimated in the 200ha study area. Bird presence was significantly positively correlated with increasing vegetation density. No significant associations were found between Rufous Bristlebird presence and the floristic associations. Although Rufous Bristlebirds occupy a variety of vegetation communities, results indicate that the key common factor appears to be structure of the vegetation. The findings of this study will be incorporated into a Geographic Information System to develop a spatial model of suitable habitat.

Using community-based monitoring with GIS to create habitat maps for a marine protected area in Australia

In recent years there has been an increase in community-based monitoring programmes developed and implemented worldwide. This paper describes how the data collected from such a programme could be integrated into a Geographic Information System (GIS) to create temperate subtidal marine habitat maps. A differential Global Positioning System was utilized to accurately record the location of the trained community-based SCUBA diver data. These georeferenced data sets were then used to classify benthic habitats using an aerial photograph and digitizing techniques. This study demonstrated that trained community-based volunteers can collect data that can be utilized within a GIS to create reliable and cost-effective maps of shallow temperate subtidal rocky reef systems.

Children's fruit and vegetable intake : Associations with the neighbourhood food environment

Objective : To examine associations between availability of different types of food outlets and children's fruit and vegetable intake.
Method : Parents of 340 5–6 and 461 10–12 year-old Australian children reported how frequently their child ate 14 fruits and 13 vegetables in the last week in 2002/3. A geographic information system (GIS) was used to determine the availability of the following types of food outlets near home: greengrocers; supermarkets; convenience stores; fast food outlets; restaurants, cafés and takeaway outlets. Logistic regression analyses examined the likelihood of consuming fruit ≥ 2 times/day and vegetables ≥ 3 times/day, according to access to food outlets.
Results : Overall, 62.5% of children ate fruit ≥ 2 times/day and 46.4% ate vegetables ≥ 3 times/day. The more fast food outlets (OR = 0.82, 95%CI = 0.67–0.99) and convenience stores (OR = 0.84, 95%CI = 0.73–0.98) close to home, the lower the likelihood of consuming fruit ≥ 2 times/day. There was also an inverse association between density of convenience stores and the likelihood of consuming vegetables ≥ 3 times/day (OR = 0.84, 95%CI = 0.74–0.95). The likelihood of consuming vegetables ≥ 3 times/day was greater the farther children lived from a supermarket (OR = 1.27, 95%CI = 1.07–1.51) or a fast food outlet (OR = 1.19, 95%CI = 1.06–1.35).
Conclusion : Availability of fast food outlets and convenience stores close to home may have a negative effect on children's fruit and vegetable intake.