A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.

Motivations to interact with brands on Facebook – Towards a typology of consumer–brand interactions

In recent years, Facebook and other social media have become key players in branding activities. However, empirical research on consumer–brand interactions on Facebook is still in its infancy. Therefore, the aim of this research is to provide additional insights to brand managers on how to adapt their approaches to increase consumers’ interactions with brands on Facebook. In this study, we apply the uses and gratification theory proposed by Katz to develop a new typology of consumers based on consumer motivations to interact with brands on Facebook, and explore the type and intensity of these interactions. We identify five main motivations that might influence consumers’ interactions with a brand on Facebook: (i) social influence, (ii) search for information, (iii) entertainment, (iv) trust and (v) reward. Building on these five motivations, a classification using clustering techniques reveals four different groups of consumers: (i) ‘brand detached’, (ii) ‘brand profiteers’, (iii) ‘brand companions’ and (iv) ‘brand reliants’. Our results provide valuable and applicable insights for social media marketing activities, which will assist brand managers to develop strategies for effectively reaching and influencing the most desirable groups of consumers.

Motivations to interact with brands on Facebook – Towards a typology of consumer–brand interactions

In recent years, Facebook and other social media have become key players in branding activities. However, empirical research on consumer–brand interactions on Facebook is still in its infancy. Therefore, the aim of this research is to provide additional insights to brand managers on how to adapt their approaches to increase consumers’ interactions with brands on Facebook. In this study, we apply the uses and gratification theory proposed by Katz (1959) to develop a new typology of consumers based on consumer motivations to interact with brands on Facebook, and explore the type and intensity of these interactions. We identify five main motivations that might influence consumers’ interactions with a brand on Facebook: (1) social influence, (2) search for information, (3) entertainment, (4) trust and (5) reward. Building on these five motivations, a classification using clustering techniques reveals four different groups of consumers: (1) “brand detached”, (2) “brand profiteers”, (3) “brand companions” and (4) “brand reliants”. Our results provide valuable and applicable insights for social media marketing activities, which will assist brand managers to develop strategies for effectively reaching and influencing the most desirable groups of consumers.

Assessment of the spatial variability in Tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones

Little information is available on the degree of within-field variability of potential production of Tall wheatgrass (Thinopyrum ponticum) forage under unirrigated conditions. The aim of this study was to characterize the spatial variability of the accumulated biomass (AB) without nutritional limitations through vegetation indexes, and then use this information to determine potential management zones. A 27-×-27-m grid cell size was chosen and 84 biomass sampling areas (BSA), each 2 m(2) in size, were georeferenced. Nitrogen and phosphorus fertilizers were applied after an initial cut at 3 cm height. At 500 °C day, the AB from each sampling area, was collected and evaluated. The spatial variability of AB was estimated more accurately using the Normalized Difference Vegetation Index (NDVI), calculated from LANDSAT 8 images obtained on 24 November 2014 (NDVInov) and 10 December 2014 (NDVIdec) because the potential AB was highly associated with NDVInov and NDVIdec (r (2) = 0.85 and 0.83, respectively). These models between the potential AB data and NDVI were evaluated by root mean squared error (RMSE) and relative root mean squared error (RRMSE). This last coefficient was 12 and 15 % for NDVInov and NDVIdec, respectively. Potential AB and NDVI spatial correlation were quantified with semivariograms. The spatial dependence of AB was low. Six classes of NDVI were analyzed for comparison, and two management zones (MZ) were established with them. In order to evaluate if the NDVI method allows us to delimit MZ with different attainable yields, the AB estimated for these MZ were compared through an ANOVA test. The potential AB had significant differences among MZ. Based on these findings, it can be concluded that NDVI obtained from LANDSAT 8 images can be reliably used for creating MZ in soils under permanent pastures dominated by Tall wheatgrass.

A spatial data warehouse to predict lithic sources of tombs from South of Portugal: mixing geochemistry, petrology, cartography and archaeology in spatial analysis

MEGAGEO - Moving megaliths in the Neolithic is a project that intends to find the provenience of lithic materials in the construction of tombs. A multidisciplinary approach is carried out, with researchers from several of the knowledge fields involved. This work presents a spatial data warehouse specially developed for this project that comprises information from national archaeological databases, geographic and geological information and new geochemical and petrographic data obtained during the project. The use of the spatial data warehouse proved to be essential in the data analysis phase of the project. The Redondo Area is presented as a case study for the application of the spatial data warehouse to analyze the relations between geochemistry, geology and the tombs in this area.