Diagonal: Building open source software for city system analysis

为了应对气候危机，建设有韧性的城市和包容的社区，我们需要大胆地改变我们的城市。我们从未像今天这样拥有更多关于建筑环境的信息：交通系统、建筑、人口、空气质量和其他城市系统都可以数字化建模。然而，所有这些数据都没有实现所需变革的规模和速度。虽然技术不是全部解决方案，但我们目前缺乏有效利用这些信息的正确工具。现有的工具难以处理城市系统交叉点的分析。对角线重新构想了地理信息系统（GIS）的基础。我们不是在构建一个工具来取代所有GIS工具。更确切地说，我们的技术是数据科学家工具箱中的一个重要进步，可以大规模地分析城市系统之间的相互作用。我们已经建立了准备、处理和管理大数据集的技术，这样数据科学家就可以开始进行地理空间分析。我们建立了一个定制的数据处理框架，将内存（RAM）地理空间数据存储与地理空间特定的分析功能相结合：包括网络遍历和地理搜索。这项技术是新的数据基础设施。我们已经建立了规模化的技术，所以数据大小不会影响数据科学家。由于我们的数据模型在内存中，数据科学家可以运行基于文件或数据库存储的不可行的分析。我们希望将同样的工具引入地理空间分析，它存在于非空间领域。我们的综合数据管理和处理工具支持城市规划领域的机器学习、高级分析和大型系统的系统分析。有了快速启动创新基金的资助，我们将能够加速我们的技术核心作为开源软件的发布。我们相信围绕开放核心模式建立我们的业务有很多好处。首先，我们认为，导致公共领域变革的证据应该是可审查和可复制的。

To tackle the climate crisis, build resilient cities, and inclusive communities - we need our cities to change in bold ways. We have never had more information about the built environment than today: transport systems, buildings, populations, air quality, and other city systems can all be modelled digitally. Yet, all this data is not enabling the scale or pace of change needed. While technology is not the whole solution, we currently lack the right tools to make use of this information effectively. Existing tools struggle to handle analysis at the intersections of city systems. Diagonal has reimagined the foundations of a geographic information system (GIS). We are not building one tool to replace all GIS tools. Rather, our technology is an important advancement in data scientists' toolbox, to enable analysis of interactions between city systems - at scale. We have built technology that prepares, processes, and manages big data sets so data scientists can get to work on geospatial analysis. We have built a custom data processing framework that combines in-memory (RAM) geospatial data storage with geospatial-specific analysis functions: including network traversal and geographic search. This technology is new data infrastructure. We have built our technology for scale, so that data size doesn't slow down data scientists. Because our data model is in-memory, data scientists can run analysis that isn't feasible with file-based, or database storage. We want to bring the same tooling to geospatial analysis, which exists in non-spatial fields. Our combined data management and processing tooling enables machine learning, advanced analytics, and large system-of-systems analysis in city planning domains. With the funding from the Fast Start Innovation grant, we will be able to accelerate the publishing of our technology core as open source software. We believe there are many benefits to building our business around an open-core model. At the forefront, we believe that evidence which leads to change in the public realm should be scrutinizable and reproducible.