There is increased interest in building larger and more complex SDIs, which is due to the value and savings of sharing geospatial data for consensus building, business development and decision making. But there are unique challenges to developing large SDI systems. Many of these challenges can be mitigated by subdividing the SDI architecture into focused and manageable components. These components involve a subsystem to input data, a subsystem to produce standard products for online users and a subsystem for deep data analysis. Read this blog post to find out more about why and how to modernize your SDI through subsystem component deployments.
Recently, I was invited to join a panel discussion at an international workshop regarding the modernization of SDIs. The primary topic of the panel was to envision how to increase data interoperability and application development in an SDI. SDIs are all about how to get useful geospatial data from those who have data to those who need data. It was nice to discuss ideas about how this interchange of data can be made easier and more efficient, while still meeting the FAIR (Findable, Accessible, Interoperable and Reusable) data principles.
So, let’s get started by looking at the early technical architecture of Canada’s SDI, termed the Canadian Geospatial Data Infrastructure (CGDI). This architecture was based on the concept of a data supplier publishing data, a user finding this data and then the user accessing or binding this data service. This was the traditional Publish, Find and Bind SDI paradigm.
The early technical architecture of Canada’s SDI was based on the Publish, Find and Bind paradigm. Reference: The Canadian Geospatial Data Infrastructure - architecture description Version 2.0; Page 15; GeoConnections Secretariat, Natural Resources Canada, Ottawa, Ontario .
Contemporary SDIs are much more functional and performant and therefore much more complex than they were a decade ago. Today’s SDIs can do many things and help in diverse applications such as public health, public safety, government services, policy mapping and even snow plow management. Today’s SDI architectures include back-office technology that provides services and information to an organization’s clients, stakeholders and staff.
The key concept is that the geospatial data used in current SDIs is becoming more and more interoperable, multi-purpose and ubiquitous. Current SDI users no longer need to always search for datasets, check if the data is analysis-ready or worry about the map symbology or projection. This is because a GIS professional has already worked out and solved these issues and made the online app available in a simple but functional format and with clear output products.
But lately, there has been a continual progression of the approach to developing SDIs, in that organizations are planning to create larger, multi-function, data-rich SDI systems. This is a good thing, but the approach to developing these large complex systems needs to be an improvement over the approaches used in the past. The approach for developing these more complex SDI systems needs to subdivide the entire architecture into three parts that can be developed and can operate somewhat independently. These components (or subsystems) are a system of record, a system of engagement and a system of insight.
The best way to develop and operate a large or complicated SDI going forward will be to break down the primary functions of the SDI into three subsystems, namely a system of record, a system of engagement and a system of insight.
Each of these subsystems are self-contained pieces of the SDI puzzle that separately perform specific functions. The system of record handles both the local spatial data plus the authoritative and approved spatial data that comes from external sources and must be accessed via web services or downloaded on demand. The system of engagement provides convenient access to and visualization of user-requested spatial data and common applications for supporting diverse internal and external user communities. The system of insight provides analysis functions that allow well-informed and power users to connect to special data and to perform advanced analytics.
Each of the three SDI subsystems has specific requirements, including data, functional requirements and non-functional requirements.
The system of record is the system that accesses, stores and manages the spatial data within your SDI and is implemented in an environment that uses ArcGIS Enterprise. ArcGIS Enterprise seamlessly supports a large number of geospatial data types, formats, access methods and storage capabilities, so it is ideal for a system of record. This system would have immediate access to the various Canadian basemap data, Living Atlas feature layers and open web services plus real-time observation data such as that collected by sensor networks. In addition, support for ArcGIS Survey123 field data, ArcGIS Drone2Map data collections and ArcGIS GeoEvent Server data is available.
The big advantage of having a system of record implementation is that all the data for the SDI system is controlled and managed in one place so that application developers do not need to worry about the details of data ingest; they just need to access the required data that is available within the system of record. Once the data ingest is developed and operational for a particular application, then this data is now also available for other applications. Also, the system of record allows users to access the latest data or to access historical data through temporal data workflows.
The SDI system of record may need to support a distributed, centralized and/or federated approach to data sharing. In addition, it may need to support many varieties and formats of geospatial and non-geospatial data.
The system of engagement is the SDI subsystem that is like the user interface component of the system. The user communities could include internal members of the SDI organization, external users or even the general public, all of who have specific access rights and privileges to use particular data, information products or capabilities. The system of engagement applications are kept up-to-date, focused and ready to use in the front office, in the back office, in the field, with a specific user community or with the general public.
The real value in the system of engagement comes from providing all users with access to the best information and applications to inform them and help them make the best decisions. This interface provides convenient access to relevant spatial data as well as targeted applications that enable collaboration across the organization, within a community or across the globe.
The system of engagement is where most users (internal, stakeholders and the public) will come to use the system. The system of engagement will include a number of maps, apps, data, services and capabilities for meeting the SDI business requirements.
The system of insight is the component of an SDI system that provides analysis software that accesses data in the system of record. It uses techniques such as modelling, statistical analysis, open data science and business intelligence to create new results for users. These results could be research, trends, hypothesis testing or unique observation detection. Bespoke spatial data analytics can be developed by users of all skill levels, across the organization, to directly connect to data, perform advanced analytics and create results.
The system of insight is where knowledgeable users will come to develop specialized products through analysis of the data in the SDI.
SDIs are continuing to evolve and improve and are now getting more complicated to the point where they need to sub-divided into manageable components. The three primary components are:
- a component to get and manage spatial data within the system;
- a component to provide efficient and valuable products to the end-users; and
- a component that allows deep analysis of data.
Each of these SDI components can easily be built and managed using ArcGIS. A modern SDI is an effective and efficient machine-driven solution to better enable true interoperability of all types, collection dates and combinations of spatial data.
This post was translated to French and can be viewed here.