The Covid-19 pandemic triggered governments and designers to revalue and redesign public spaces. This paper focuses on the various design responses to Covid-19 proposed and implemented in public spaces. In particular, we identify the kinds of challenges that such design responses address and the strategies that they use. We selected 56 design examples, largely collected from internet sources. By analyzing the design examples we identified five Covid-related challenges that were addressed in public space: sustaining amenities, keeping a distance, feeling connected, staying mentally healthy, and expanding health infrastructures. For each challenge, we articulated 2 to 6 design strategies. The challenges highlight the potential of public space to contribute to more resilient cities during times of pandemic, also in the future. The design strategies show the possible ways in which this potential can be fulfilled. In our next steps, we will use our findings to develop a program of possibilities; this program will contain a wide range of design strategies for responding to future pandemics and will be made publically accessible in an online database. The program contributes to more resilient post-Covid cities, by offering a variety of possibilities for coping with, and adapting to, pandemic-related shocks and stressors.
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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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