Communicating climate-smart scenarios with data-driven illustrations

Abstract

To communicate how planning investment scenarios might look on the ground, a new graphics workflow generates streetscape perspectives from planning scenario maps. The workflow connects planning forecasts (incorporating economic, transportation and land development data) to a parametric urban modeler to efficiently generate visual representations according to evolving forecasts. This paper explains the decision-making context, how regional to pedestrian-scale information is integrated, and the necessity of diverse expertise in creating the workflow. It compares custom programming in ArcGIS, Python and CityEngine for urban infill tasks, and describes approaches for connecting the specialized software to a broader palette of tools. To generate future urban massing, undervalued lots with high development potential are first identified. The lots are then matched to grid cells in the associated GIS planning scenario map. The cell’s assigned Development Type is used to select possible building types and heights to model on the lot. The undervalued properties are iteratively “redeveloped” until target numbers are met (for example, the number of jobs linked to commercial properties and the number of housing units satisfy development forecasts). The project explored procedures for refining the model graphics, including developing a streetscape model toolkit, so that different designers could generate consistent and compelling perspectives.