The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.
COBISS.SI-ID: 5100641
The Canadian fire weather index system (CFFWIS) has been adopted for fire danger rating throughout Europe as part of the European Forest Fire Information System. However, its performance has not been thoroughly assessed,especially in environments less prone to fire. In this study, we characterised fire activity between 1995 and 2009 in the sub-Mediterranean Karst forest management area of SW Slovenia. Five fire danger classes (very low, low, moderate, high and very high) were derived from percentile analysis of the CFFWIS Fire Weather Index. These classes were found to be related with fire activity descriptors. Logistic regression prediction of fire-days based on CFFWIS indices had low accuracy, and better discrimination was achieved by classification tree modelling. Fire activity was found to be driven by currentweather conditions rather than by drought. Our findings highlight the potential of fire danger rating to guide fire management but also the limitations imposed by relatively low incidence of fire and the spatial scale of analysis.
COBISS.SI-ID: 5503329
The study attempts to identify and estimate the office rents of submarkets in the Helsinki metropolitan area. We applied a non-parametric empirical approach called the CAE method to identify six parameters: highway APD (access point distance), car traffic density, light rail APD, main retail distance, office building density and effective age. Our results suggest that car traffic density is the single most influential parameter. Office rent decreases with effective age and increases with the density of office buildings. Longer distances to highway access points and to the main retail centres decrease office rents, while shorter distances to the light rail access points increase office rents in general and particularly for locations close to highway access points. We identified local peaks by inspecting multiple graphs. The local peaks were considered evidence for the existence of commercial office submarkets within the Helsinki metropolitan area. We identified seven submarkets at different rent levels. Interpreting submarkets from the CAE graphs allowed us to recognise particular business districts in the Helsinki metropolitan area. In addition, it is of great significance that the roles of the given and estimated variables can be exchanged. The method is directly applicable in real estate studies using adapted database and prescribed smoothing parameters.
COBISS.SI-ID: 5500769