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Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined that resulted in a small impact on rents. The unexplained spatial variation of rents may be attributed to neighborhood characteristics, socioeconomic conditions and special characteristics of the rentals. [ABSTRACT FROM AUTHOR]

In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran's I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area. [ABSTRACT FROM AUTHOR]

The DEM to mountain transformation was implemented in the Globe DEM representation of Zagros Ranges in south Iran, where collision of the Arabian shield with Iran has produced a spectacular mountainous physiography. An object partitioning framework of the landscape was defined on the basis of the mount and the non-mount terrain classes as well as the isolated mountain features. A parametric representation was achieved on the basis of geomorphometric parameters related to elevation and gradient. Descriptive statistics, frequency distributions and the linear regression between attributes were used in an attempt to characterise landscape. Finally, a terrain classification scheme based on cluster analysis was implemented and the landscape of the study area was mapped on the basis of mountain geomorphometry.From the geomorphometric point of view, Zagros Ranges was proved to be an uplifted landscape, equally developed in the whole elevation range with only one major peneplain at the sea level. The terrain was sloping vertically to the main axis of the mountain ranges while the asymmetry observed towards SW was interpreted as the result of the collision of Arabian shield to Iran. The linear regression between local relief, gradient and size of the mountains captured the geometric signature of the study area. The centroid clustering method revealed clearly the SE–NW stair-step topography observed in Zagros Ranges while the steepest and more massive mountains were also observed along this direction. The zones derived by the mapping of clusters were associated to the existing morphotectonic zones of the study area while geomorphometric processing proved capable of segmenting morphotectonic zones to sub-regions with different geomorphometry. [Copyright &y& Elsevier]