This Github project contains a Esri CityEngine project with ground plane, streets, parcels and 3D models of Berlin Mitte (2x2km) based on the open data provided by Berlin’s Senatsverwaltung für Stadtentwicklung und Wohnen (https://www.stadtentwicklung.berlin.de/).

According to the copyright of the source data (see link above) you may use and modify this project as you like, but commercial use is prohibited.

The CityEngine project was originally created for a participatory urban planning application “Enabling DEMO:POLIS”, exhibited at the Berlin Academy of Arts in 2016. The source code for the application is available freely as well (see https://robotized.arisona.ch/enabling-demopolis/ for more information).

Compared to the source data, the 3D models in the CityEngine project have been cleaned up with great care (removing duplicate faces, fixing normals, merging vertices etc.). Thus, the model can easily be used for visualization, urban planning scenarios, etc.

Available on Github: https://github.com/arisona/berlin_3d

“Enabling DEMO:POLIS” is a participatory urban planning installation, presented as part of the DEMO:POLIS exhibition at the Berlin Akademie der Künste (https://www.adk.de/demopolis - 11.3.2016 - 29.5.2016). The installation engages the public in the design of open space and consists of six terminals that run a custom, interactive software application.

The software leads the user through a number of typical urban design tools (space allocation, streets, buildings, landscape, etc.) and concludes with a fly-through through the generated 3D scenario, in this case, the Rathausforum / Alexanderplatz area in Berlin.

The following video demonstrates a full cycle of a possible design.

https://youtu.be/sWgARvrcgxk

Open Source

Source code, data and a binary build are available at: https://github.com/arisona

Credits

Concept: Stefan Arisona, Ruth Conroy Dalton, Christoph Hölscher, Wilfried Wang

Data & Coding: Stefan Arisona, Simon Schubiger, Zeng Wei

Support: Akademie der Künste Berlin, FHNW Switzerland (Institute of 4D Technologies), ETH Zürich (Future Cities Laboratory and Chair of Cognitive Science), Northumbria University (Architecture and Built Environment).

Data & Software Workflow

Enabling DEMO:POLIS builds on Open Data, in particular the publicly available 3D models of central Berlin provided by the Staatssenat für Stadtentwicklung und Umwelt (https://www.stadtentwicklung.berlin.de/planen/stadtmodelle/))

The original 3D models were initially imported into Autodesk AutoCAD for layer selection and coordinate system adjustments, then imported into Autodesk Maya for data cleaning and corrections. In a final step the data was imported into Esri CityEngine for final data adjustments & cleaning, merging, labelling, etc. The data was then exported as OBJs. The software application is written in Java, based on the 3D graphics library/engine ether. As indicated above, all source code and data is available as open source.

Good news: CityEngine is under consideration for the 2015 Academy Awards - the so-called “Tech-Oscars”. Fingers crossed!

Update: while we didn’t win, we’re still proud of the nomination!

https://www.oscars.org/news/20-scientific-and-technical-achievements-under-consideration-2015-academy-awards

Journal: International Journal of Geographical Information Science

Authors: Chen Zhong, Stefan Arisona, Xianfeng Huang, Michael Batty, Gerhard Schmitt

Abstract: Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a ‘big’ data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation. In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population’s activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan. To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.

Title: Detecting the Dynamics of Urban Structure Through Spatial Network Analysis
Authors: Chen Zhong, Stefan Arisona, Xianfeng Huang, Michael Batty, Gerhard Schmitt
Journal: International Journal of Geographical Information Science
Publisher: Taylor & Francis
Year: 2014
DOI: 10.1080/13658816.2014.914521
Link: https://www.tandfonline.com/doi/abs/10.1080/13658816.2014.914521#.U8P6N1bsd8E

Conference: 19th International Conference on Computer-Aided Architectural Design Research in Asia CAADRIA 2014

Authors: Chen Zhong, Stefan Arisona, Gerhard Schmitt

Abstract: The advancement of sensor technologies makes it possible to collect large amounts of dynamic urban data. On the other hand, how to store, process, and analyze collected urban data to make them useful becomes a new challenge. To address this issue, this paper proposes a visual analytics framework, which is applied to transportation data to manage and extract information for urban studies. More specif- ically, the proposed framework has three components: (1) a geographic information system (GIS) based pipeline providing basic data processing functions; (2) a spatial network analysis that is integrated into the pipeline for extracting spatial structure of urban movement; (3) interactive operations allowing the user to explore and view the output data sets at different levels of details. Taking Singapore as a case study area, we use a sample data set from the automatic smart card fare collection system as an input to our prototype tool. The result shows the feasibility of proposed framework and analysis method. To summarize, our work shows the potential of geospatial based visual analytics tools in using ‘big’ data for urban analysis.

Award: Won best presentation award.
Title: A Visual Analytics Framework for Large Transportation Datasets
Authors: Chen Zhong, Stefan Arisona, Gerhard Schmitt
Proceedings: 19th International Conference on Computer- Aided Architectural Design Research in Asia CAADRIA 2014
Publisher: The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Year: 2014
Pages: 223 - 232
Link: https://cumincad.scix.net/cgi-bin/works/Show?caadria2014_014