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This study demonstrates the integration of landscape aesthetic quality and probable urban growth patterns in urban landscape modelling. This was performed using SLEUTH as a scenario-based urban growth model in Gorgan City of Iran. Future urbanization was predicted under developing three different scenarios including historical, managed and aesthetically sound urban growth up to the year 2030. Multi-Layer Perceptron neural network model was conducted for mapping the aesthetic suitability of the study area. The aesthetic suitability layer was used in the third scenario of SLEUTH model as the excluded layer to protect the scenic patches in future. The results showed that by correct implementation of urban growth policies, 323 ha in the second scenario and 650 ha in the third scenario would be saved. This integrated model would help the planners for a better management of urban landscapes as a Spatial Decision Support System. [ABSTRACT FROM AUTHOR]

Stresses acting on fault systems before major earthquakes can produce thermal anomalies; these abnormalities can be observed using multi-sensor satellite data. Moderate resolution spectro-radiometer on board the terra and aqua satellites can provide thermal infrared (TIR) imaging data for land and ocean. These TIR data have recorded short-lived thermal anomaly prior to major earthquakes. It is suggested by others that these electromagnetic (EM) phenomena relate to stress build up before earthquakes. The objective of this study is to find an association between spatial extent and temporal evolution of thermal anomalies and known major earthquakes near the boundary of Nazca plate and South American plate. Our approach is to map the TIR transient fields from polar orbiting satellites and analysing those data using time series temperature plots to detect the abnormal thermal trends before the earthquake. This study concentrated on marine earthquakes to detect the changes in both land and ocean before seismic activity. [ABSTRACT FROM AUTHOR]

Geographic clustering of industries for mutual economic benefit has long been recognised. The concept of ‘externalities’ introduced by Alfred Marshall early last century rely on agglomeration of specialised industry within a geographic area. However, only recently has cluster modelling been applied to the tourism and hospitality (T&H) industry. The aim of this paper, therefore, is twofold: first is to develop a cluster-based theoretical framework for delineating geographic boundaries of T&H clusters, and second is to identify the underlying factors that drive their form and shape. Drawing on employment data as the basis for co-location of T&H firms, spatial econometrics techniques are applied to model the spatial clustering of T&H employment in Victoria, Australia. Results show that rural tourism regions have higher levels of employment in tourism operational services whereas employment in city-based regions is more concentrated in hospitality services. Our findings, when normalised as a percentage of total employment, show that rural and regional Victoria ranks most highly as employers in the T&H industry. Adopting a range of spatial metrics, we show that T&H clustering throughout Victoria is largely driven by six location-specific factors: (1) the availability of tourism attractions; (2) proximity to the coast; (3) the road density network; (4) accessibility to employment within the Melbourne CBD; (5) the scale of the regional economy; and, (6) the advantages and disadvantages associated with economic resources. We conclude that the cluster-led strategy pose a number of challenges for tourism planners to promote regional tourism. Nevertheless, results from this study indicate that T&H employment clustering creates a more cohesive spatial structure that could support economic development and better connectedness of tourism destinations. These clusters could act as service hubs to their wider catchment areas where visitors are encouraged to stay overnight and travel to specific sights during the day. [ABSTRACT FROM PUBLISHER]

This paper discusses research aimed at identifying key factors influencing the distribution of residential housing renovation employment in metropolitan Melbourne. Using Geographically Weighted Regression (GWR), employment focused on residential housing renovation is modelled using six parameters representing urban space: distance to the central business district, median household income, distance to highways, the number of nearby shopping centres, distance to public open space and accessibility to railway stations. Of the six different explanatory variables, the estimated value of the Ordinary Least Square model for distance to CBD and open space were statistically significant. Mapping the values of local coefficient estimates of independent variables revealed their extent of influence and variation in residential housing renovation employment. [ABSTRACT FROM AUTHOR]