Digital neighborhood analyses at district level

Building design
aerial-view-of-a-town-with-many-houses-sC6IvRuqx-g

An impressive aerial view of a city with numerous houses, taken by Berke Can.

Neighbourhoods in the digital mirror: with modern neighborhood analyses at district level, the city is beginning to understand itself – and often reinvent itself. Between real-time data, citizens’ interests and algorithmic forecasts, planners are facing the biggest challenge since the introduction of the land use plan. Those who fail to recognize the opportunities offered by digital tools are left at an analog dead end.

  • Definition and development of digital neighborhood analyses and their integration into urban planning processes.
  • Technical basics: From geodata to AI – which tools and data types are used?
  • Use and impact: How do digital analyses change neighborhood development, participation and governance?
  • Practical examples from Germany, Austria and Switzerland – successes, stumbling blocks and lessons learned.
  • Opportunities for climate resilience, social mix, mobility transition and sustainable land use.
  • Risks: Data protection, algorithmic distortions and the risk of alienating urban societies.
  • Legal, technical and cultural hurdles in German-speaking planning practice.
  • Strategic recommendations for cities, planners and developers venturing into digital neighborhood analyses.

Digitalization of the neighbourhood: from classic social space analysis to real-time neighbourhoods

Anyone who still thinks of neighborhood analyses in terms of transit traffic counts with clipboards and timesheets has overslept the last few years. In the meantime, digital neighborhood analysis has become a highly dynamic field that does not do away with traditional methods, but takes them to a new level. What used to be painstakingly determined through surveys and observations is now created from a fine mesh of real-time data, algorithmic evaluation and participatory feedback. But what is really behind it all?

At its core, digital neighborhood analysis describes the systematic collection, linking and evaluation of a wide range of data on the condition, use and development of a neighborhood. Unlike a rough social area analysis, today it is no longer just demographic and infrastructural key figures that are collected. Instead, mobility flows, residence times, climatic parameters, energy consumption, noise levels, green space quality and even the residents’ subjective perception of safety are digitized, collated and analysed. Thanks to sensor technology, geoinformation systems and artificial intelligence, a living, multidimensional image of the neighborhood is being created.

This development is not an end in itself. It is the answer to the increasing complexity of urban spaces, where traditional static analyses quickly reach their limits. Neighborhoods today are highly dynamic systems with diverse interactions. A new café, a building site or a heavy rainfall event can change the fabric within hours. Digital neighborhood analysis creates the conditions for reacting flexibly to such changes – or even anticipating them.

What is particularly exciting is that digital analyses can not only capture the purely spatial aspects of a neighborhood, but also the social aspects. By evaluating anonymized mobile phone data, social media feeds or online participation platforms, patterns of use, wishes and problems of residents can be made visible. This creates a holistic picture that goes far beyond the traditional planning perspective.

The digitalization of neighbourhood analysis is therefore not a technical gimmick, but a new form of urban intelligence. It enables planning that is no longer based solely on experience and gut feeling, but on reliable, up-to-date and multi-linked data. And it opens up the opportunity not just to manage the city, but to actively shape it.

Technical foundations: data, sensors and AI – the digital nervous system of the neighborhood

The basis of every digital neighborhood analysis is a data-driven ecosystem, the complexity of which is often underestimated. At its heart is geodata, which is fed from a wide variety of sources. Traditional cadastral data and official statistics only form the foundation. The real magic comes from the integration of real-time data from sensors, mobility providers, energy suppliers, weather stations, public WLANs, sharing services and even smart home systems. This transforms the neighborhood into an “Internet of Neighborhood Things” – a dense network that regularly provides up-to-date information.

Sensor technology is no longer limited to traffic counts or environmental measurements. Modern LoRaWAN sensors record particulate matter, temperature, humidity, noise, light intensity and movement profiles – and do so comprehensively, cost-effectively and with low maintenance. There are also crowd data approaches: Residents themselves provide valuable information via apps, social networks and digital participation platforms, for example on problem areas, quality of stay or conflicts of use.

What happens to this data is decided by the next layer of analysis: powerful algorithms and artificial intelligence. They recognize patterns, calculate forecasts and simulate scenarios. For example, it is possible to model how a new traffic routing will affect noise distribution, how the microclimate will change with additional greenery or how social infrastructure will have to adapt to the development of the neighborhood. The demands are high: it’s about more than just pretty visualization – it’s about well-founded decision support in real time.

Open interfaces, so-called Open Urban Platforms, play a decisive role here. They ensure that data from different systems can communicate with each other – without proprietary isolated solutions or data monopolies. This is the only way to create a holistic, interoperable picture of the neighborhood that can be used and further developed by various stakeholders.

The requirements for data protection, data sovereignty and cybersecurity should not be underestimated. The more granular and up-to-date the data, the greater the responsibility in handling it. The development of legally compliant, transparent and comprehensible analysis processes is therefore one of the key tasks for planners, technology service providers and local authorities alike.

New planning reality: how digital neighborhood analyses are changing districts

The establishment of digital neighborhood analyses is fundamentally changing the rules of the game in neighborhood development. Planning is becoming more dynamic, more interactive and – in the best case – more inclusive. Suddenly, planners can not only document current conditions, but also simulate future developments and weigh up different scenarios against each other. A new residential district? The effects on traffic, infrastructure, microclimate and social mix are no longer a guessing game, but can be estimated based on data.

An illustrative example: In Zurich, all movement data in public spaces was evaluated anonymously as part of a Smart City project. The analysis showed that certain places were avoided despite their attractive design – because they were perceived as unsafe. Only the combination of quantitative movement data and qualitative feedback from a digital participation platform revealed the causes: lack of lighting, poor sightlines, lack of social control. The city was able to make targeted adjustments – and visibly improve the quality of life.

Digital neighborhood analyses are also a key to climate-resilient neighborhoods. In Vienna, for example, particulate matter and temperature data is evaluated in real time in order to identify heat islands and to green them in a targeted manner. In Hamburg, mobility data is used to assess the effectiveness of traffic calming measures and to optimize neighbourhood mobility. These examples show: The possibilities extend far beyond the classic survey of existing traffic.

The influence on governance in the neighborhood is particularly significant. Digital analyses make connections visible that previously remained hidden in the fog of subjective perception. They promote the transparency of planning processes and enable a more precise, fact-based discussion between administration, politicians and residents. Participation thus becomes not only more digital, but also more substantial – as long as the data is open and comprehensibly accessible.

Of course, not all that glitters digitally is gold. The use of digital tools can also lead to alienation if the technology becomes a black box and citizens feel excluded. This is where planners and local authorities are called upon to establish digital neighborhood analyses as an instrument of understanding – not as a substitute for dialogue, but as its catalyst.

Practice and perspective: opportunities, risks and the German-speaking reality

In practice, the picture is quite mixed. While international pioneers such as Helsinki and Singapore have long been using digital city models as a basis for neighborhood decisions, German-speaking countries are often even more cautious. Cities such as Hamburg, Munich and Zurich have set up initial pilot projects, but the big leap towards the widespread use of digital neighborhood analyses has yet to be made in many places. There are many reasons for this: technical hurdles, a lack of standards, uncertainty about data protection and governance and, last but not least, cultural reservations about algorithmic planning.

Nevertheless, successful examples show the potential that can be tapped. In Vienna, for example, digital neighborhood analyses are being systematically integrated into urban development planning. Neighborhood profiles are created there that map climate resilience, social mix, mobility options and energy consumption in real time. The results flow directly into competitions, development plans and investment decisions. In Zurich, the Smart City Lab demonstrates how the combination of real-time data, visualization and citizen participation can not only accelerate planning processes, but also increase the acceptance of new projects.

Risks exist in particular in the danger of algorithmic distortions. If data sources are unrepresentative or algorithms make non-transparent decisions, social imbalances can be exacerbated instead of remedied. The risk of excessive commercialization is also real: if large technology companies gain data sovereignty over neighbourhoods, urban development threatens to become the plaything of private interests.

The legal framework in German-speaking countries continues to be a stumbling block. Data protection laws, the separation of responsibilities between the federal, state and local authorities and the lack of binding standards make it difficult to introduce the system across the board. Added to this is the often small-scale administrative structure, which slows down rather than promotes innovation. But here, too, the following applies: those who invest early on create a strategic advantage – and can help shape standards instead of being overrun by them.

What remains is the realization that digital neighborhood analyses are not a panacea, but a tool – one that offers enormous added value when used wisely, but also creates new responsibilities. The key to success lies in the combination of technical excellence, open governance and a culture of dialog that sees the city and neighbourhood as a living organism.

Strategies for the future: recommendations for planners, municipalities and developers

Any planner, local authority or developer who wants to venture into the world of digital neighborhood analyses faces an exciting but challenging task. The most important recommendation is: technology is never an end in itself. It is crucial to ask the right questions and choose the right tools. Start with a clear analysis of the objectives: Is it about traffic optimization, climate adaptation, social integration or all of the above? Each goal requires its own data, methods and participation formats.

Rely on open, interoperable platforms instead of isolated solutions. This is the only way to flexibly expand data sources and integrate different stakeholders. Invest in the data expertise of your own teams – and create interfaces to external experts from IT, social sciences and communication. Digital neighborhood analyses are teamwork, not an individual discipline.

Don’t forget the people in the neighborhood. Digital participation is not a one-way street, but thrives on transparency and feedback. Explain what data is collected and how it is used. Actively involve residents – for example via digital reporting platforms, participatory workshops or visualizations that even laypeople can understand. If you operate digital neighborhood analysis as a black box, you will lose trust and acceptance.

Establish clear rules for data use, data protection and governance. Define who has access to which information, how decisions are documented in a comprehensible manner and how errors or distortions are identified and corrected. Remember: with every new technology, the responsibility towards urban society and democracy also grows.

Finally: Have the courage to innovate. Digital neighborhood analysis is not a rigid recipe, but a dynamic learning process. Mistakes are unavoidable, but also valuable – as long as they are made transparent and used to improve. Those who close their minds to digital change are planning for the city of yesterday. Those who shape it will shape the neighborhoods of tomorrow.

Conclusion: Digital neighborhood analysis – a compass for the city of the future

Digital neighborhood analyses at district level mark a paradigm shift in urban and open space planning. They create the basis for forward-looking, resilient and participatory development of urban spaces – and are therefore far more than just another technical tool. They make the dynamics of the district visible, promote a new dialog between planning, politics and society and give the city a voice that comes not just from the drawing board, but from real life. They are not a sure-fire success, but require technical expertise, open governance and a good dose of courage to question old ways of thinking. But it’s worth the effort: if you use digital neighborhood analyses wisely, you can turn data into real quality of life – and set the course for the city of the future. With this in mind, welcome to the reality of tomorrow, which begins today.

POTREBBE INTERESSARTI ANCHE

Mobility data for adaptive road design

Building design
a-city-street-with-cars-parking-at-the-edge-of-the-street-V32TUYynmhg

Central city street in St. Gallen with parked cars, photographed by Albatros Aslan

Imagine streets that react spontaneously to traffic flows, green spaces that grow where they are needed most and cycle paths that are created because mobility data demands them. Adaptive street design with mobility data is not a dream of the future, but the big stage for cities that not only move with the times, but determine them themselves. If you want to know how data can be turned into dynamic spaces, read on – and learn why the mobility data revolution has long since arrived on the asphalt.

  • Definition and relevance of mobility data for adaptive road design
  • Technological basics: sensors, data sources and interfaces
  • Adaptive street design: practical examples from German-speaking cities
  • Data-supported planning processes and their challenges
  • Governance, data protection and the question of data sovereignty
  • Opportunities and risks: From better traffic flows to algorithmic bias
  • Influence on sustainable urban development, climate resilience and social participation
  • Outlook: How mobility data is changing the planning culture and job profile

Mobility data: The backbone of adaptive streetscape design

Anyone talking about streetscape design today can no longer ignore mobility data. This data is far more than just columns of figures from traffic counts; it is the pulse of the city, a highly dynamic image of urban mobility that goes far beyond traditional traffic planning. Mobility data encompasses all movement flows of people and vehicles, whether on foot, by bike, car, public transport or modern sharing services. It is generated from a variety of sources: Traffic light controls, WLAN tracking, GPS from cell phones, camera sensors, induction loops, floating car data from vehicle fleets as well as from apps that record traffic and movement profiles anonymously. The trick is to link and interpret these data sources in order to obtain as complete, up-to-date and reliable a picture as possible of the reality of mobility.

The term adaptive road design describes the ability to adapt road spaces to changing requirements in a flexible and demand-oriented manner. This ranges from the temporary reallocation of lanes and dynamic traffic routing to pop-up cycle paths and flexible pedestrian zones. The basis for this is comprehensive, precise mobility data, preferably available in real time. It shows where bottlenecks occur, which routes are particularly busy or when certain means of transport are preferred. Only with this database is a truly adaptive, i.e. responsive, design even conceivable.

However, the use of such mobility data places high demands on the technical infrastructure and the skills of planners. It is not enough to simply collect data; it must be analyzed, interpreted and translated into concrete options for action. This requires modern geoinformation systems, powerful data platforms and interfaces that link different data sources with one another. Artificial intelligence and machine learning come into play to recognize patterns and create forecasts. Adaptive street design is thus becoming a discipline that combines technical expertise, planning creativity and a deep understanding of urban dynamics.

In many German, Austrian and Swiss cities, mobility data is already part of planning practice. Intelligent traffic guidance systems, dynamic traffic lights and real-time information for local public transport are visible results. But adaptive streetscape design goes further: it asks how the streetscape itself can be changed to respond to new mobility patterns. The goal is a city in which space follows demand – not the other way around.

Overall, mobility data is the backbone of a city that sees itself as a learning, flexible and participatory system. It enables planning to no longer be understood as a rigid corset, but as an open, continuous process. The street space becomes a stage on which data and users jointly determine the choreography.

Technology, sensors and data interfaces: The invisible infrastructure

Behind every adaptive road design is a complex network of sensors, data management and digital infrastructure. The collection of mobility data begins with classic induction loops in the roadway that count vehicles and extends to state-of-the-art camera systems with automatic object recognition. Floating car data, i.e. movement data from vehicle fleets that allow conclusions to be drawn about traffic density and congestion trends in real time, is also becoming increasingly relevant. Mobile devices that provide anonymized position data via GPS complete the picture and make it possible to systematically record pedestrian and bicycle traffic for the first time. Particularly exciting is the use of WLAN and Bluetooth tracking, which makes movement patterns in public spaces visible without storing personal data.

All these sensors and data sources provide raw data that must first be collected and processed. This is where so-called urban data platforms come into play, which act as data hubs. They aggregate, filter and harmonize information from a wide variety of sources and make it available for planning, administration and the public. The interoperability of these platforms is crucial, as mobility data only unfolds its full value when it can be interpreted in the context of other urban data – such as weather, construction sites, events or energy consumption. Open interfaces (APIs) and standardized data formats are therefore essential to enable collaboration between different stakeholders, systems and administrative levels.

Another key element is the real-time capability of the data. Adaptive road design depends on being able to react quickly and flexibly to changes. This requires that data is not only collected, but also processed and visualized in fractions of a second. Modern dashboards, coupled with AI-based evaluation tools, enable planners to see at a glance where action is needed. Automatic alarm systems indicate sudden changes, for example if an accident shifts the flow of traffic or a major event leads to congestion on public transport.

The integration of machine learning opens up new dimensions: Systems learn from past patterns, recognize seasonal fluctuations, recurring bottlenecks or mobility behaviour when the weather changes. Forecasting models simulate how certain measures – such as new cycle lanes, temporary play streets or detour – will affect the overall structure. In this way, planning decisions can be made based on data, scenarios can be run through and measures can be tested in a targeted manner.

All these technological possibilities stand and fall with the acceptance and trust of the population. Data protection and data security are therefore not peripheral issues, but an elementary component of the infrastructure. Only if citizens can be sure that their movement data will be used anonymously and responsibly will the necessary basis for legitimizing adaptive, data-based urban design be created.

Adaptive street design in practice: between pop-up cycle paths and real-time traffic

Numerous projects in German-speaking cities show how mobility data can make adaptive streetscapes a reality. The potential became particularly visible during the coronavirus pandemic, when pop-up cycle paths were created in many places. In Berlin, temporary cycle lanes were set up on the basis of current traffic data to provide short-term space for the increase in bicycle traffic. Sensors and counting stations provided the basis for recording demand and capacity utilization and adapting the measures in a targeted manner. The evaluation of the data enabled continuous optimization: where usage remained particularly high, temporary solutions were converted into permanent infrastructure.

Vienna also relies on mobility data to make street spaces more flexible. The “Digital Twin Vienna” project combines real-time traffic data with information on pedestrian flows, public transport utilization and weather conditions. This allows traffic lights to be adjusted dynamically, temporary meeting zones to be created and bottlenecks to be identified at an early stage. In Zurich, on the other hand, floating car data and anonymized movement profiles are used to identify bottlenecks and temporarily rededicate road space – for events, construction site management or to relieve heavily frequented junctions, for example.

Another example is Hamburg, where the Urban Data Platform integrates traffic flows, roadworks information and environmental data. Adaptive traffic management, flexible loading zones for delivery traffic and dynamic parking space management are directly linked to the evaluation of current mobility data. The effects are immediately noticeable: less congestion, faster response times in the event of incidents and more efficient use of limited road space.

However, adaptive road design is not limited to motorized traffic. The focus is increasingly on pedestrians and cyclists. In Basel, for example, data from counting points and movement analyses are used to create temporary pedestrian zones and safe routes to school – adapted to the time of day, weather and number of events. This creates a public space that is not only efficient, but also people-friendly.

These examples show: Adaptive street design is not a technocratic experiment, but a lived practice. It makes public spaces more flexible, safer and more sustainable – and opens up new possibilities for responding to social and climatic challenges. However, data quality, transparency and participation must always be taken into account.

Governance, data protection and participation: The invisible levers

As impressive as the technological possibilities are, the question of governance is crucial. Who controls, who decides and who monitors the use of mobility data? Adaptive road design requires clear responsibilities and a framework that combines data sovereignty, data protection and participation. In Germany, Austria and Switzerland, it is usually the municipalities that retain sovereignty over urban data platforms – often in cooperation with external service providers who provide technical solutions and analysis tools.

Data protection is more than just a bureaucratic obstacle. It is the guarantor of acceptance and trust. Movement data is sensitive, even if it is anonymized. This is why many cities rely on privacy by design: even during the development of the systems, it is ensured that no conclusions can be drawn about individuals. Data is aggregated, pseudonymized and provided with clear deletion deadlines. Regular audits, open documentation and independent control bodies ensure additional transparency.

Transparency is also the key word when it comes to participation. Adaptive street design thrives on the involvement not only of experts but also of the public. Open data portals, interactive visualizations and participatory planning processes make it possible to make measures comprehensible and verifiable. In this way, citizens do not become the object of data-based control, but actors in a joint learning process. Digital participation formats, from online surveys to interactive maps, create new channels for participation and feedback.

But governance does not end with administration. Cooperation between different disciplines – urban planning, traffic planning, IT, law and society – is also key. Adaptive street design is a cross-cutting issue that breaks down silos and requires new forms of cooperation. Interdisciplinary teams, agile working methods and an open error culture are just as important as technical standards and legal clarity.

Ultimately, the question remains: how can adaptive street design be prevented from becoming a playing field for commercial interests? The trend towards the commercialization of urban data models cannot be overlooked. It is therefore essential that municipalities expand their data competence, retain control over critical infrastructure and define clear rules for access to and use of mobility data. Only in this way will the adaptive, data-based city remain a common good – and not a black box of private providers.

Opportunities, risks and the paradigm shift in urban planning

The use of mobility data for adaptive street design opens up enormous opportunities – but also new risks. On the plus side, there are more efficient traffic flows, less congestion, better air quality and more space for active mobility. Cities become more resilient, more flexible and can react more quickly to crises or new trends. The integration of mobility data into planning enables unprecedented precision and dynamism that makes traditional planning tools look old-fashioned. The ability to run through various scenarios, test measures and observe their effects in real time is particularly impressive.

But as the power of data grows, so does the responsibility. Algorithmic distortions, unfair distribution of resources or the disadvantaging of certain groups are real dangers if data is interpreted in an unreflected or one-sided way. Adaptive systems run the risk of reinforcing existing inequalities if they are not consciously counteracted. It is therefore essential that planners, administrators and the public work together to define guidelines that safeguard values such as justice, transparency and sustainability.

Another risk lies in the technocratization of planning. If decisions are only made on the basis of data, there is a danger that local characteristics, social needs or design quality will fade into the background. This is why the role of professional planners remains indispensable: they are the ones who interpret data, place it in a spatial context and harmonize it with other goals – such as climate protection, quality of stay or social participation.

What does all this mean for the profession of urban planning and landscape architecture? It is becoming more digital, more dynamic and more interdisciplinary. Data literacy is becoming a key qualification, and the ability to deal with uncertainties and contradictions is becoming a central challenge. Adaptive street design requires planners who not only use technology, but also critically reflect on and design it.

The end result is a paradigm shift: planning is no longer static, but a process. The street space is not a finished product, but a living organism that is constantly changing. Mobility data makes this dynamic visible and controllable – but it is not an end in itself. It remains the task of planners to turn data into living spaces that function, inspire and connect.

Conclusion: Data-driven urban design – from a technical tool to a new planning culture

The use of mobility data for adaptive street design is far more than just a technical trend. It marks the dawn of a new planning culture in which data, technology and human intuition work hand in hand. Cities that use mobility data intelligently will become more flexible, more sustainable and more liveable. They can control traffic flows, make better use of space, respond to crises and recognize the needs of their residents in real time. But the path is challenging: it requires high-tech and attitude, data protection and dialog, new competencies and clear rules.

Adaptive street design is not a sure-fire success, but a social and planning experiment that requires courage, creativity and a sense of responsibility. It offers enormous opportunities for sustainable urban development, climate resilience and social participation – but also harbours the risk of technocratization and commercialization. It is crucial that mobility data is understood and used as a common good, that transparency and participation are prioritized and that planning remains human even in the digital age.

Planners who now see mobility data as a tool and inspiration are not only designing streetscapes, but also reinventing urban planning. And that’s a good thing – because the city of tomorrow will not only be built, it will be measured, interpreted, adapted and brought to life together. Welcome to data-driven, adaptive urban design – where public space is constantly being created anew.

Gray colossus

Building design

Worth more than a glance: the ceiling painting

Having barely arrived in Rotterdam, Baumeister Academy winner Maxi Graber shares a photo of the Cornucopia painting in the Markthal on the Academy Instagram account. In 2014, Maxi’s internship office MVRDV built the first market hall in the Netherlands. Reason enough for us to take another look at the gray colossus.

Having barely arrived in Rotterdam, Baumeister Academy winner Maxi Graber shares a photo of the Cornucopia painting in the Markthal on the Academy Instagram account. The post literally goes through the roof. In 2014, Maxi’s internship office MVRDV built the first Markthal in the Netherlands and covered it with a large arch and 200 apartments. Reason enough for us to take another look at the gray colossus. Our editor Sabine Schneider traveled to Rotterdam in 2015 and reported on her visit in the Baumeister March issue. Here is an excerpt from her report.

It won’t be easy. I start my journey to Rotterdam with tense anticipation. I know the market hall in Rotterdam well from publications, and my opinion is clear: it’s a monstrous construction that obviously wants to make itself smaller than it is on the outside with its cladding of camouflage gray granite slabs, but screams all the louder on the inside with a kitschy sky of giant fruits. In cross-section, the building forms a half-baked horseshoe, a tunnel that leads nowhere, an oversized fairground stall with apartments on the hump. A new typology, as the architects are promoting the project? Save us from that.

In fact, my criticism of the façade and form is now far less important when I am on site: the ribbon-like square of the Binnenrotte in the center, under which the tracks run and which therefore cannot be built on, appears cheerless, empty, draughty and not well defined on five out of seven days when there is no weekly market. The large, gray market hall has the same problem as the surrounding buildings: it is an island between islands – it lacks urban density. It does not appear permeable, but stands slightly elevated a few steps above the square, its reflective panes closing off the huge gate, sealing it off. It can only be entered through three narrow revolving doors that you have to squeeze through.

MVRDV have set up simple steel scaffolding as market stalls in Hall 96 on an area roughly the size of a soccer pitch. It’s fun to look, try, stroll and buy here. There is everything from currywurst to exclusive steak, from Dutch cheese to Turkish sweets. A good idea is to set up a terrace on the roof of the stalls, creating a “tasting room” on the roof. Something like this is often missing in traditional markets, because you work up an appetite while strolling around. However, it also brings the market closer to one of the usual “food courts” in shopping malls.

Restaurants, cafés, a cookery school, a household goods store and a wine shop have moved into the first two floors of the long sides of the tunnel. The interior façades of the 102 rental apartments and 126 condominiums, all of which have windows overlooking the market and a terrace to the outside, curve above. The higher you climb in the building, the more oblique the view of the market becomes, until at the very top of the 24 penthouses on the eleventh and last floor you can look straight down vertically.

Concept and compromises

But how did this design come about? Rotterdam is planning to renovate the former old town district and held an investor competition in 2004. The developer Provast submitted the design by MVRDV and won first prize, as the architects were able to combine the two specified residential slabs with a market. Priority was given to housing; there was no budget for a market hall. This resulted in the horseshoe shape, as the upper apartments, which close the arch, were too deep for good lighting – so the shape was slanted at the top. Towards the first floor, the storeys widen again in order to enlarge the retail space as required by the developer. In this way, the constraints did not shape the architectural idea, but deformed it like chewing gum.

You can find the full report here!

And you can find out more about Baumeister Academy there!

The Baumeister Academy is supported by GRAPHISOFT, BAU 2019 and Schöck Bauteile GmbH.