@inproceedings{KoenigVaroudis,
  author    = {K{\"o}nig, Reinhard and Varoudis, Tasos},
  title     = {Spatial Optimizations: Merging depthmapX , spatial graph networks and evolutionary design in Grasshopper},
  series = {Proceedings of ecaade 34: Complexity \& Simplicity},
  booktitle = {Proceedings of ecaade 34: Complexity \& Simplicity},
  address   = {Oulu, Finland},
  doi       = {10.25643/bauhaus-universitaet.2604},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20160622-26040},
  pages     = {1 -- 6},
  abstract  = {In the Space Syntax community, the standard tool for computing all kinds of spatial graph network measures is depthmapX (Turner, 2004; Varoudis, 2012). The process of evaluating many design variants of networks is relatively complicated, since they need to be drawn in a separated CAD system, exported and imported in depthmapX via dxf file format. This procedure disables a continuous integration into a design process. Furthermore, the standalone character of depthmapX makes it impossible to use its network centrality calculation for optimization processes. To overcome this limitations, we present in this paper the first steps of experimenting with a Grasshopper component (reference omitted until final version) that can access the functions of depthmapX and integrate them into Grasshopper/Rhino3D. Here the component is implemented in a way that it can be used directly for an evolutionary algorithm (EA) implemented in a Python scripting component in Grasshopper},
  language  = {en}
}