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Reflections

A Question of Density?

Tim Nebert
New York

Density is an important measure in Urban Design, do dense places lead to higher numbers of Covid-19 infections? Berlin serves as an example.

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New York City’s trail in the global pandemic has developed from a hotspot for Covid-19 infection-rates in the beginning of 2020, to significantly lower numbers towards the second half of the year. Density has implicitly been declared as one factor of rising infection rates in urban agglomerations worldwide. We want to take a look at another world metropolis – Berlin.

In May 2020, the New York Citizens Housing & Planning Council (CHPC) described an inherent correlation between density patterns in New York City and numbers of Covid-19 infections. When investigating a bit deeper into the “meaning” ofdensity and its importance to urban life, answering the question which role urban density patterns occupy in the characteristics and expressions of infectionrates remains complex.

Density in New York City

Looking at the official renderings by the CHPC, the picture remains unclear in terms of deriving numbers of infection rates to urban density patterns. Population density, measured by the number of people per square mile living in an area, seems not to be associated with higher rates of Covid-19 in New York City, the surrounding area, or the U.S. but rather simplifying the term density to a mathematical scheme, not catching many aspects of urban and social life and contextualizes high infectionrates as an urban problem.

Rather than seeing high rates of Covid-19 in denser neighborhoods and lower rates of the virus in less dense neighborhoods, lower density areas have some of the highest case rates in the city of New York. Some of the lowest rates, meanwhile, are present in the city’s densest neighborhoods in Manhattan. Another example of this “behavior” can be found in other countries, like Germany as well, where the highest infection-rates are not generally to be found in cities, but occur rural areas as well (March, 2021). While it is generally difficult to elaborate on why some places have been impacted more than others, there seems to be a clear lack of association between population density, as it is commonly defined and measured.

Dreaded density?

The assumption that greater density in the sense of greater numbers of people in an absolute or relative sense, is to be equated with increased numbers of infections could lead to consequences in the form of a changing relationship towards cities and their inhabitants. For instance an increased degree of respect or even fear of dense places such as cities themselves, but also of public or social spaces and thus a changing behavior or perception of urban areas. This could lead to problems in the network of our highly interconnected and interdependent coexistence of cities, such as more individual traffic, a change in the use of urban infrastructures, changing social habits (e.g. „Social Distancing“ or the „Hand-Shake“) as well as physical and mental illnesses. It is very important to have this discourse, since in planning sciences, density is considered as an important factor for successful and well-designed cities – as it seems very important to keep the ability of human interaction and exchange through public and urban life.

A simplified derivation of the events of Covid-19, could lead to a general change in discourse in urban design, especially when considering the “healing” process of urban areas after the pandemic including the distribution of open and public spaces, urban infrastructure, medical care, cultural life and all aspects of how we live in the city. Not to forget: still trusting the cities as places to live.

Density in Berlin

Let’s have a look at the situation in another world metropolis, Berlin, Germany.
As Berlin is a large city, when it comes to pure size in distance/area and slightly bigger than New York City (only considering the city boundaries of Berlin/5 boroughs of NYC, not the metropolitan area: Berlin ca. 870 mi2, NYC: ca. 780 mi2), it is considered to be a comparatively green city. This is particularly due to the fact that Berlin, while growing into the outskirts and reshaping former low-density villages and settlements in the past, ultimately was growing into large areas of landscape and rural settlements, also keeping some of these green places alive.

But there is a hook attached to the story: like population density measurement, the green-factor of the city is relative and another mathematical expression since this also includes that Berlin’ s inner city areas are lacking a good amount of open and public-green spaces for high population density areas, which could be an important factor in the spread of a disease – but definately is crucial in a “pandemic lifestyle”, which often means beeing limited to the extents of the own flat. Berlin is counting 4 118 Inh/km² in 2019, New York’ s 5 Boroughs more than double this number, listing about 10.000 inhabitants per square kilometer in average. As mentioned before, these numbers can vary and rise up to 15.000 Inh/km² in some areas.

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Ra­ther than seing hig­her ra­tes of Co­vid-19 in denser neigh­borhoods and lo­wer ra­tes of the vi­rus in less dense neigh­borhoods, lo­wer densi­ty are­as have some of the hig­hest case ra­tes in NYC.

The desease affects everyone – but not everyone equally

The Corona virus is basically a danger to the general public, but figures suggest that certain groups among the (urban) population are more at risk than others. For instance, the age of a population seems to be a crucial aspect in this regard. This could therefore also be an indication of how the infection-figures and rates are distributed spatially in the urban context, considering “older” and “younger” places in the cities.

People beyond age 70, for example, are more than 40 times likely to die from Corona-infections than people under 40 worldwide. While this seems obvious, there is an indication of higher risks of infections among certain ethnic groups.

In the U.S., African Americans and Latinos are about three times more likely to contract the virus than white people. African Americans, Indians, Bangladeshis and Pakistanis are significantly more likely to die than whites in the UK. In an interview with the British „Guardian,“ an expert described the situation as follows: “We are all going through the same storm – but we are not in the same boat.”
This could be an indication that individual groups have a disadvantageous disposition with regard to spatial and density aspects within the socio-spatial fabric of cities.

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Tot­al num­ber of in­ha­bi­tants per di­strict 2019 (si­zes in sca­le) in­clu­ding colo­red tot­al Co­vid-19 inf­ec­tion-num­bers.

Covid-19 distribution – a question of size?

If we take a step back, the search for explanations is complicated. The numerical material is thin and not particularly detailed. The amount of data points within Berlin’ s twelve districts is not sufficient for statistical analysis and full of “zeros”. It is also very likely that several factors play a role at the same time. Thus, it is difficult to establish a context, which data should be considered and which not.

Which data correlates with each other and ultimately provides information about spatial distribution patterns? Accordingly, it is only possible to look for reasons very roughly and with a great deal of caution. But there are probably factors that are more or less suitable as explanations.

In this regard, the population density factor is difficult to read. There is supposedly an obvious connection between population density and the number of infections. More people in less space is, of course, a conditions that makes it easier for the virus to jump from one person to the next. But, as already illustrated by the New York-example, there is growing indication that spatial density alone is not sufficient to adequately describe the distribution of infection-numbers. As spatial density in Berlin’ s districts does not explicitly correlate to the highest infection-numbers.

Initial studies also show that more people become ill on average in urban centers and that the phase of high infection-rates lasts longer. Here, different spatial aspects and set-ups can influence the occurrence of infections. Denser structures seem to promote the occurrence of infections through increased mobility and higher numbers of contacts.

This can be demonstrated (using the example of certain cities in Asia and Italy) by means of concrete measures that were implemented in the study areas. Restricting mobility led to lower incidence rates, but population density still seems to be an independent factor when looking at the incidence of infection between dense and less dense areas. High density areas seem to develop higher incidence rates over a longer period of time, while lower density areas show high rates at a given time. This suggests that single events are “responsible” for high incidences in less populated areas. Thus, in less densely populated areas (study areas in China – Prefectures), more spiky and shorter outbreaks confined to specific neighbourhoods were often observed, while in more densely populated prefectures, protracted outbreaks of larger definitive size could occur, spilling over to more connected neighbourhoods.

Adding to this, certain properties of infectious diseases (respiratory diseases) themselves, also lead to different outcomes in terms of density. For certain infectious diseases (of the respiratory tract), the temporal clustering of cases in an epidemic (i.e. the shortest period of time in which most cases are observed) varies with increasing indoor population and socioeconomic and climatic factors.

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Tot­al num­ber of in­ha­bi­tants bet­ween 18 and 27 ye­ars of age.

Berlin‘s Covid-19 distribution in districts – a question of size?

For the example of Berlin, higher numbers over time in more dense areas seem to be true, but not linear. The district of Reinickendorf for instance is much more sparsely populated than the district of Pankow and yet much more affected, although not a specific event for supporting higher numbers is known. Berlin’ s “incidence leader” Neukölln has only almost half as many people per district area as Friedrichshain-Kreuzberg and yet is clearly ahead. This is not to say that population density does not matter at all. But at least with district-level data available, there is no indication of a trend. However, this may also be due to the fact that there are, of course, already huge differences within the districts.

In terms of spatial qualities, income patterns and presumably availability to work and stay at home and therefore individual mobility patterns, residential aspects like internal residential density among many other indicators.

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Per­cen­tage wel­fa­re in­co­me re­ci­pi­ents 2019 (Pr­o­gno­seräume).

A question of age?

As we have glimpsed on earlier, different age groups are affected by the virus at different levels. Berlin is not a specifically old or young city, but the spatial distribution of different age-groups might be a hint.It seems likely that the younger people in a society tend to have a larger radius of mobility, which (as we mentioned earlier) might support the danger to suffer or pass an infection.

In addition, one often assumes a more carefree relationship with the virus, as well as certain age groups in Germany and thus also in Berlin (especially cities with a high number of students) tend to support types of housing (e.g.”Wohngemeinschaften”) that show a higher occupancy of apartments and thus a higher internal residential density. Another explanation for the pattern of nine infections is the age distribution in the city.

Age groups with the highest incidences here were, in descending order, the 15- to 19-year-olds, followed by the 20- to 24-year-olds and finally the 25- to 29-year-olds. Spatially, the districts of Mitte, Friedrichshain-Kreuzberg and Neukölln emerge as the districts with the highest incidences in these age groups. Here, too, it cannot be deduced that increased infection-figures are promoted by younger age groups, since the most severely affected districts show a different order. Here, too, we can conclude that there are more dynamic reasons for the incidence of infection.

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In the end, there is a lack of data for further analyses. Berlin’ s Senate carefully indicated a correlation between “the young” and rising infection-rates which seems to be rather insufficient to explain the incidence of infection in Berlin. Protecting the old and vulnerable in a society seems to be common ground. However, it has hardly been possible to protect these often called „vulnerable groups“ from being infected with the virus. Incidentally, this is not a significant problem for cities but is also strongly reflected in the distribution of the number of infections in rural areas. The reasons for this are complex, and here, too, the data-availability is not sufficient.

If one proceeds from the point of spatial distribution, the old people‘s and nursing homes as well as other day care facilities/institutions provide an obvious disadvantage. Although the residents of these facilities have the smallest mobility framework, they are still at great risk of contracting the Corona Virus, which is extremely tragic.

Whatever reasons lead to a lack of shield, perhaps because of their internal density, these types of facilities have been and will be hotspots of the Corona Virus spatially.

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Per­cen­tage of so­ci­al hou­sing in 2019: So­ci­al-hou­sing pat­terns tend to spre­ad spa­ti­al­ly into the the fr­in­ges of the city. Sin­ce tot­al inf­ec­tion-num­bers are more spa­ti­al­ly cen­tered, the­re is no ob­vious cor­re­la­ti­on.

A data-crisis?

The crisis around Covid-19 shows that we are not yet able to understand the dynamics and processes that determine our cities and, while the ability to collect large amounts of data is an achievement, its correct interpretation, application and interdependencies need to be further refined. But, it also reveals a disparity, not only in a lack of data or methods, but in how we are living together considering quality and equality in economic, spatial and social aspects.

More information is needed for a variety of aspects, perspectives need to redefined and refocused. What is the role of the ability to collect large amounts of data, which is usually not complete and how will it develop? What does this mean for interdependencies and follow-up decisions? How can cross-cutting data sets be selected and interrelated, and does working with mono-informatic data automatically imply a wrong “decision-chain”? Is this even dangerous? Which role does the living and working situation of the resident’s play? What role do places of social infrastructure, such as schools and nursing homes, play and how are they to be protected? Why do certain groups seem to be at higher risk than others and how can all be put on an equal footing in an urban context?

We must dig deeper to understand and address the elements of our urban environment that have contributed to the spread of Covid-19. Density seems to play a certain role, but it is definately not linear. The highest numbers of infections are not always found where the most people congregate. Neither in the example of New York, nor in the example of Berlin. This can be seen as a good sign for cities, especially in crisis management and urban recovery. It would be fatal for the development of cities and our society if they were to be perceived as places of danger and uncertainty in the public eye of the future.

All images (without further information): © Tim Nebert, shortcutsstudio

Bibliography and sources

Data availability:
All data was collected from publicly available data sources (news articles, press releases and published reports from public health agencies)

Epidemiological and spatial data used in this study are available via Github (https://github.com/Emergent-Epidemics/Covid_crowding). All Data-Sets in regard to Covid-19 numbers in Berlinhas been processed from an official sources as of January 2021.

Data: Covid-19 erkrankungen | Offene Daten Berlin (o. J.): in: Open Data Portal Berlin, [online] https://daten.berlin.de/tags/Covid-19-erkrankungen [20.01.2021].

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