Diagonal: Building open source software for city system analysis

为了应对气候危机，建设有弹性的城市和包容的社区--我们需要我们的城市以大胆的方式改变。我们从来没有像今天这样拥有关于建筑环境的更多信息：交通系统、建筑、人口、空气质量和其他城市系统都可以用数字建模。然而，所有这些数据都无法实现所需的变化规模或速度。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。现有的工具很难在城市系统的交叉口进行分析。对角线重新构思了地理信息系统(GIS)的基础。我们不是在构建一个工具来取代所有的地理信息系统工具。相反，我们的技术是数据科学家工具箱中的一项重要进步，能够在规模上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，以便数据科学家可以开始进行地理空间分析。我们构建了一个定制数据处理框架，将内存(RAM)地理空间数据存储与特定于地理空间的分析功能相结合：包括网络遍历和地理搜索。这项技术是一种新的数据基础设施。我们已经为规模化构建了我们的技术，这样数据大小就不会拖累数据科学家。因为我们的数据模型在内存中，所以数据科学家可以运行基于文件或数据库存储不可行的分析。我们希望将同样的工具引入到地理空间分析中，这种工具存在于非空间领域。我们的组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系统分析。有了快速启动创新基金的资助，我们将能够加速将我们的技术核心作为开源软件发布。我们相信，围绕开放核心模式建立我们的业务有很多好处。最重要的是，我们认为，导致公共领域变化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.