Globally, cities are exposed to hazards, such as pandemics or floods, with increasingly great variation, frequency, duration, and severity. Urban resilience is aimed for by many, but is difficult to achieve. This is in part because we lack practical tools to understand and proactively influence urban resilience. What is required is a systems modelling approach that acknowledges local context, complex interdependencies across sectors, and how short-term shocks effect a wide set of longer-term outcomes. Our paper in Nature Urban Sustainability (Modelling systemic COVID-19 impacts in cities) demonstrates such a model to holistically understand impacts to urban systems, in the context of the COVID-19 pandemic in Edinburgh UK from March-October 2020.
The Urban Systems Abstraction Hierarchy (USAH) links tangible Resources (like Roads, Rivers, Hospitals, or Community centres) to the Processes they fulfil (e.g. Provide life-threatening healthcare services), the Tasks they accomplish (e.g. Public health), the Outcomes they intend to achieve (e.g. Minimal vulnerability), and the Purposes for the overall system (e.g. Safety and security). The result is a five level network which explicitly captures pathways for cascading impacts across sectors (within-scale) and from physical resources to more abstract system properties such as outcomes (cross-scale).
The standard template USAH for a generic UK city is large, consisting of 481 nodes and 4463 links. This can be adjusted to reflect specific locations based on resources present in (or absent from) OpenStreetMap data. A specific hazard scenario can then be modelled by changing the weight of affected links. For example, in a COVID-19 lockdown week, Resources which fulfilled the Process Act as community meeting space were effectively weighted as 0 to reflect that this was no longer being fulfilled; for other Resources where workforces were reduced by e.g. 10% due to sickness leave, linked Processes were degraded from 1 (fully functional everyday scenario) to 0.9 (remaining functionality in hazard scenario). A full description of data, and how it is processed into such link weights, can be found in Methods and Supplementary Information. Network analysis is applied in the everyday baseline condition, and the specific hazard condition, and results are compared to track changes across Tasks and Outcomes.
This was performed for 30 individual weeks, from pre-lockdown through Phase 3 of the Scottish Government COVID-19 route map. Key results reported here clearly show different Tasks or Outcomes changing priority in relation to each other throughout the modelled period.
During lockdown, system conditions led to Tasks from the Health & Wellbeing category (e.g. Biological hazard regulation and Food provision) being prioritized while Tasks from the Economy & Society category were traded off (e.g. Social interaction and Tourism). At the end of the modelled period, Emergency services (a Health & Wellbeing Task) remained a higher priority compared to everyday conditions, whilst Historical and cultural value contribution remained de-prioritized, indicating it may require additional support or resources for a full recovery beyond this point. Infrastructure & Ecosystems Tasks (e.g. Hydrometeorological hazard regulation and Sanitation provision) should be considered in future resilience plans as their influence in the overall system increased, compared to ‘normal’ conditions.
The importance of the Outcome Effective leadership and management increased; strong governance and leadership are critical to stop outbreaks and deliver support to communities. Other Outcomes which decreased in rank are areas where governments and communities should look to build support during the short- to medium-term recovery phases. The USAH model can be used to illustrate how this might be achieved and thus consider how recovery support may be usefully designed.
This shows that the new proposed approach (USAH model) can capture systemic interactions crucial to understanding urban resilience. This provides a powerful tool for future scenario testing and resilience planning. In future this may be applied in other locations, contributing a replicable yet place-based approach. In adjacent work it has been tested for a range of hazard types (e.g. flood, drought, heat stress), demonstrating that the model is hazard-agnostic. Applying this type of approach supports understanding of how short-term dynamics on a scale of hours or days or weeks may lead to regime changes on a scale of months or years, if the system remains in the short-term state. Mapping responses from different actors to the USAH and addressing gaps and overlaps in future resilience planning presents opportunities for deeper collaboration and transformational change, thus supporting responsive or even proactive resilience.
This work provides a glimpse of what is possible when applying real-world data from a hazard event to a holistic systems method to explore cascading impacts through the urban system. Future applications could contribute to building effective response, recovery, and resilience strategies not only for COVID-19 but all future urban challenges.