Author supplied: "This paper gives a linearised adjustment model for the affine, similarity and congruence transformations in 3D that is easily extendable with other parameters to describe deformations. The model considers all coordinates stochastic. Full positive semi-definite covariance matrices and correlation between epochs can be handled. The determination of transformation parameters between two or more coordinate sets, determined by geodetic monitoring measurements, can be handled as a least squares adjustment problem. It can be solved without linearisation of the functional model, if it concerns an affine, similarity or congruence transformation in one-, two- or three-dimensional space. If the functional model describes more than such a transformation, it is hardly ever possible to find a direct solution for the transformation parameters. Linearisation of the functional model and applying least squares formulas is then an appropriate mode of working. The adjustment model is given as a model of observation equations with constraints on the parameters. The starting point is the affine transformation, whose parameters are constrained to get the parameters of the similarity or congruence transformation. In this way the use of Euler angles is avoided. Because the model is linearised, iteration is necessary to get the final solution. In each iteration step approximate coordinates are necessary that fulfil the constraints. For the affine transformation it is easy to get approximate coordinates. For the similarity and congruence transformation the approximate coordinates have to comply to constraints. To achieve this, use is made of the singular value decomposition of the rotation matrix. To show the effectiveness of the proposed adjustment model total station measurements in two epochs of monitored buildings are analysed. Coordinate sets with full, rank deficient covariance matrices are determined from the measurements and adjusted with the proposed model. Testing the adjustment for deformations results in detection of the simulated deformations."
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Exploratory analyses are an important first step in psychological research, particularly in problem-based research where various variables are often included from multiple theoretical perspectives not studied together in combination before. Notably, exploratory analyses aim to give first insights into how items and variables included in a study relate to each other. Typically, exploratory analyses involve computing bivariate correlations between items and variables and presenting them in a table. While this is suitable for relatively small data sets, such tables can easily become overwhelming when datasets contain a broad set of variables from multiple theories. We propose the Gaussian graphical model as a novel exploratory analyses tool and present a systematic roadmap to apply this model to explore relationships between items and variables in environmental psychology research. We demonstrate the use and value of the Gaussian graphical model to study relationships between a broad set of items and variables that are expected to explain the effectiveness of community energy initiatives in promoting sustainable energy behaviors.
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Urban green and shading are adaptation measures that reduce urban heat. This is evident from meteorological measurements and investigations with surveys and has been described in many papers (e.g. Klemm et al., 2015). The cooling effect of these adaptation measures is reflected by lower air temperatures and an improved thermal comfort. Shading and urban green are also experienced as cooler than impervious urban spaces without vegetation or shading. However, the cooling effect of water bodies in cities, such as rivers, lakes, ponds, canals,fountains, is not clear yet (Steeneveld et al., 2014). Several studies show that the cooling effect of water bodies in cities is small, or can even be a source of heat during nighttime. The effect depends on the characteristics of the water body and the meteorological conditions. Nevertheless, water is often mentioned as an adaptation measure to reduce urban heat.To support urban professionals in designing cooler urban environments by using water bodies, we investigated in more detail how different water types in msterdam contribute to cooling the environment. During five summer days, we measured the cooling effect of five different water bodies: a pond, a fountain, a canal, and two rivers. We used measurements from mobile weather stations (air temperature, relative humidity, wind speed, global radiation and globe temperature) and collected almost 1000 surveys near the water bodies and a reference location. From these data, we could determine the effect of the water bodies on air temperature, thermal comfort and thermal sensation. The research question that we tried to answer with this study is: What is the cooling effect of different water types in the city of Amsterdam during hot days? The study has been carried out within the framework of a Dutch research project ‘Urban climate resilience – Turning climate adaptation into practice’ and supports urban professionals to decide on the right adaptation measures to reduce urban heat.
