e-Architects "Rezoning New York City through Big Data AI: Phase II" 2018
"Rezoning New York City through Big Data AI: Phase II" 2018 Design
Principal, Research and Development: Pablo Lorenzo-Eiroa, Designers and
Research: Frank Fengqi Li, Julian Chu-Yun Cheng,
e-Architects Rezoning NYC through Big Data AI is a comprehensive multidimensional information interface and design model project that entails several information topological levels. These multidimensional topological levels of information includie: environmental simulation; natural and artificial ecoloical identification, mapping and mitigation; traffic optimization and delivery optimization using superblocks through neural networks AI; participatory city wide 3d scanning; thermal readings. The multiple parallel projects process paralle information through a multiplicity of algorithms, simulations and computations to propose a dynamnic participatory authority interface to regulate real time through a blockchain technology how the City of New York may inform demolition to recover latent ecologies, and to promote ecological sustainable growth for NYC 2050's target. In parallel and in a correlational mode, the mutiple processes inform each other as well as identify bifurcating projects, from interfaces, virtual reality navigations, to participatory citizenship information.
The project using Artificial Intelligence AI Neural Networks, Big Data, Simulation and API, Applied Research is to be included in a peer review paper publication. It was spoken about in multiple media. The project has been published partially in several peer review publications. The project was presented to the Department of Transportation New York City Presentation to the DOT Commissioner, August 2018. 1 month later Amazon Headquarters were allocated in a site similar to were e-Architects identified as optimized delivery node for NYC. More information to follow.
e-Architects "Rezoning New York City through Big Data AI: Phase II" is a 3d Scanning project idea using participatory design, by retrieving photos from Instagram API and developing a 3d scanning point cloud of the city through millions of data points, retrieving the infomration from the camera position. Some professionals now have developed interfaces following this idea, implementing a real 3d scanning of cities by retrieving throughsands of images simultaneously, activating a Big Data set and processing. This phase articulates in relation to Phase I thermal readings of the city from a pedestrian point of view and also identify denser areas in the city.