Contact tracing, the process by which health authorities can identify those at risk of infection by virtue of proximity to a known case of a virus, has long been a pillar of pandemic response. With radio-enabled mobile devices now globally ubiquitous, it comes as little surprise to see that they are playing a role in the evolution of contact tracing methods.
Contact tracing is only as effective as its scale. The ability to evaluate only small proportions of an at-risk population will ultimately undermine any contact tracing system, manual or digital. As early attempts to develop contact tracing apps launched around the globe, technical limitations, privacy concerns and poor assumptions about adoption rates undermined almost all efforts. Into this mess stepped Apple and Google, together responsible for the software, and to a lesser extent hardware, which power the majority of our mobile devices.
The next generation of mobile broadband data network is being discussed in almost every context you can imagine, from technology to healthcare to sociology to urbanism. 5G is coming. But what is it, what does it enable, and how is it relevant to the citizens of urban places? And therefore, what does it mean to those designing and moulding the cities in which we live?
5G will replace or augment your existing 4G connection, providing exponentially greater bandwidth alongside massively reduced latency – the time it takes for data to get from A to B.
5G operates across a broad range of frequency spectrums. Lower frequency ranges (below 6GHz) provide a more reliable signal but are limited in their bandwidth. These ranges are nearing saturation in many cases from overloading of existing 4G networks. 5G also leverages higher frequency spectrums, which provide massively increased data rates and much lower latency, but which are quite limited in their ability to penetrate buildings, and the coverage area for a single antenna is limited, thus necessitating much larger numbers of antennae to achieve uniform and reliable coverage.
This is just a drill. What is reassuring about the COVID-19 pandemic is the fact that we humans have prior experience of pandemics. We, as a millenia old global society, have learned from the suffering of so many before us, and can reason about the course of this outbreak as it traces the scars of pandemics past. It is this knowledge which allows our scientists to confidently inform our responses to the challenge we presently face. Contagion factors, mortality rates, exponential curves – these are all intuitive factors in the relatively straightforward science of viral transmission.
Despite this prior experience, and despite an intuitive and relatively simple mathematical foundation for modelling the impact of a viral outbreak, society has struggled to respond in a coordinated, cohesive and intellectually sound manner. And as such the disease has killed thousands, spread globally at a rate which continues to grow exponentially, and has caused economic damage running to trillions of dollars.
In this paper Geoff Boeing examines ways in which the Smart Cities paradigm can leverage publicly sourced datasets to enhance traditional forms of urban form analysis and visualisation to further our understanding of urban morphology.
Copenhagen is developing a resilient neighbourhood in the north-eastern district of Østerbro. Climate resilience, particularly the challenges associated with heavy rainfall, is combined with a social objective to create valuable communal spaces which reinforce strong community.
The first adapted space in the area as part of this programme was Taasinge Plads, completed in 2014. It combines a multi-faceted rainwater management solution with a new piazza and elements of nature integrated with both the accessible space and the stormwater management ponds.
This quick sketch, from a spot on the junction between Bernstorffsgade and Vesterbrogade, features Axel Towers and the surrounding street scene. This is a busy intersection very close to Copenhagen’s main station and Tivoli, a popular tourist attraction.
Almost every article, briefing, lecture or essay on matters of urbanisation seems to make reference to the extent of urbanisation in the world today and the expected urban population globally in the future. Often, such material specifically refers to the well established benchmark of the UN World Urbanization Prospects publication1.
So it is perhaps surprising to find that the level of standardisation of the urbanisation figures, or specifically the methodology used to derive them, is very low. In the case of the “official” UN figures, national methods are used and aggregated without a huge effort to ensure there is a degree of alignment between the various approaches.
This recent OECD paper2 goes some way to both understanding the deficiencies in existing metrics and in proposing alternatives which might provide a better baseline for understanding a) what “urban” really means in the context of measuring populations, and b) the methods which can be deployed to effectively measure on this basis.
An extensive review of approaches covers population density by area and by administrative designation, designation by infrastructure provision and finally rural employment. None is “correct”, but for a globally deployable methodology only the first makes sense. Several limitations of a naive approach must be overcome however.
The proposed methodology, a population grid with units of land designated as city, town, suburban or rural, overcomes some of the inconsistencies identified with the existing approaches. The authors address unit size when defining the grid, acknowledging that manipulation of the grid resolution will dramatically effect the output of a given survey. Density, as the primary factor in the assignment of categorisation to each cell, allows urban areas to be identified by applying additional rules about clusters of contiguous urban cells and the total population within these designated clusters.
Dealing with density variation
Ulaanbaatar, the capital of Mongolia, is a city with 1.4 million inhabitants. The area of the municipality is particularly big with 4,700 sq km. As a result, the density of this municipality is very low: less than 300 inhabitants per sq km. Purely relying on municipal densities would inevitably mean that Ulaanbaatar would be classified as rural.4
To overcome the issue illustrated by this example, specifically the designation of urban population in areas with low density, the authors proposed additional categories which allow low density urban clusters to be designated as such. This approach disambiguates such areas from, for example, sub-urban zone around a more densely populated urban centre. In selecting regions for analysis of the potential of experiments or initiatives which make an assumption of density, this additional granularity will enable significantly more accurate forecasting of efficacy.
It is clear that there remains work to be done to establish a recognised means of designating urban areas and differentiating between urban populations with materially different characteristics. However, in this paper I found answers to many of my own questions about the degree of urbanisation in the world today, and a rigorous analysis of the shortcomings of the data being both existing measures and the new proposal. In the conclusions set out in the paper, it is clear that the new method raised few surprises in the Americas, Europe and Oceania. However in Africa and Asia the findings demonstrate a clear discrepancy in the interpretation of “urban” which may, with better data, lead to a much higher measure of urban population today than previously thought.
A stark reminder this week that there are two sides to the renewable energy revolution: supply and demand. Whilst we in Denmark can be proud of our record on the supply side of this equation, the recent increase in interest from big tech in locations with good renewable credentials does suggest that governments may be called upon to impose some control on the demand side also.