November 27, 2020


The emergent data-driven paradigm, epochal of the second digital turn, marked by an era of data-abundance with reducing costs in storage and processing has a transformative influence on architectural practices. (Carpo, 2017) The paper identifies various data-driven processes such as data-informed structures for establishing project intentions, data-empowered AI and machine learning tools utilised formulti-objective optimisation etc. It then subsequently utilizes the critical examination of these processes to inform and propose a data-driven workflow for the formation of generic social housing. It argues that data-driven systems are well placed at the junction of mass-customization, standardised systems and democratic inclusive design structures, to tackle the design of social housing.The thesis argues that, most of the common housing built is not built to provide the best possible living conditions for its occupants but is a merestandard box, Data here can enable personalisation and customisation at the scale of a housing block. The paper also aims to tackle some of the major issues surrounding data-driven processes such as security, data-privacy and data-ownership.



Architecture is often accused of resisting any attempts at uncovering its ambiguous definitions. While this is true, it can colloquially be defined as both a product and a process of generating the built environment. Although architecture; the product, as the built form is tangible, the architectural process itself often remains shrouded in mysticism and intuition, suspicious of any outside investigation into its inner workings. With the proliferation of the internet in the 1990s and data centres in the 2000s, Architecture has seen a gradual propagation and acceptance of big data and evidence-based processes. The dialectic relationship between architecture’s intuitive process and the contemporary data-driven strategy offers an interesting point of investigation.

The Architectural practice is principally concerned with structuring the built-environment to facilitate intellectual, psychological and utilitarian human purposes. It must be conceded that the designer through value-additive processes promotes an environment that stimulates ‘the good life’, but cannot fully establish what constitutes the said ‘good life’.Consequently, in the absence of quantitative factors, the Architectural process can more accurately be described as problem worrying rather than problem-solving (Negroponte, Toward a Theory of Architecture Machines, 1969)

The current Architectural entanglement with hyper-capitalist tendencies, evident through the rise of starchitects and digital-formalism in the late 20th century, is indifferent to the structuring of domestic spaces. This has led to the stagnation of housing design, where most of the housing that gets built is built for generating profit rather than to house people. More than any museums, offices or theatres, a city is made of chunks of domesticity. It thus provides incredible untapped opportunities for the design discourse to utilise digital technologies to experiment and evolve.

In an un-self-conscious culture, the architecture process adapts to the conditions of its occupants through gradual changes over a period spanning generations, However, when the designer in a self-conscious culture creates a form, they finds themselves unsuccessful, because the preconceived ideas an architect bases their design on do not correspond to the inherent components of the problem, and therefore lead to arbitrariness and lack of understanding (Alexander, 1964). The architect must then post-rationalize and justify these often-arbitrary decisions. Christopher Alexander’s ratiocination draws this correlation between vernacular buildings such as igloos, which self-correct and evolve over many generations and single-authored designs, this inference can also be extended towards Data and AI-driven architecture, where authorship of design is contentious, designs can evolve over various iterations and self-correct if provided with enough data. Design system such as John Frazer’s universal constructor (Frazer, 1995) attempt computation frameworks where unassumed natural data can be utilized to generate what Christopher Alexander states as the ‘best fit’.

The contemporary design process is going through a radical process of digitization and automation, with AI and generative design increasingly becoming part of the architectural discourse. It can be established that at least a part of a successful contemporary design process involves gathering information, Data and filling the gaps in data through prediction and analysis. As the architectural paradigm shifts towards digitization and automation, it is ever more important to comprehend the impact of data influencing architectural decisions and to establish a framework around the ethical data-driven design process.



The 21st century has seen an unprecedented exponential computational progression and an explosion of high-resolution data. While till the end of the last century, processes were strangled by an apparently inevitable scarcity of data, the current age of data-abundance has enabled us for the first-time access to more data than we can utilize. The proliferation of data-centres and cheap cloud storage along with computing have allowed us to collect, store and process an increasing amount of data at ever-reducing costs (Carpo, 2017). Architecture has been opaque and unyieldingly unwilling to outside interrogation. Data and data analysis may be the tools for the demystification of the design process, it thus becomes imperative to investigate the role of data in the design process. It is also with caution that one must approach data-driven design strategies as even though we have an abundant quantity of data and data storage, the quality of data required for spatial decision making remains wanting (Duvier C, 2018) Data-rich operations such as those of Alphabet, Microsoft and WeWork are already utilizing available data sets to customize workspaces to increase productivity, comfort and shape spaces for user needs.  



To analyse the various ways in which data may influence architecture, it is imperative to first define ‘data’ itself. Data, when understood idiomatically, are characteristics or information sets of qualitative or quantitative variables, that are collected through observation. However, within architecture and construction data may be defined in various ways which are dependent upon the intentionality, and the understanding of data for individual architectural practices. The concept of data-driven design assists innovation and decision-making and can enhance performance and productivity. The cross-disciplinary accumulation of Datasets in a data-driven architectural process can be categorised as (1) Inherent geometrical data, intrinsic to the generation of geometry. (For example the inherent data of a NURBS surface, the combinatorial data for a voxel grid, control points, edges, measures of length and curvature)(2) External generative data, values which can be utilized to modify existing geometries or generate new ones, (for example solar performance data, spatial adjacency constraints, user usage preferences etc.) and (3) SupplementalBIM data, data for construction and building processes. (Data-Driven Design and construction, 2015)



Negroponte (1970)in his work “The Architecture Machine” envisions computational tools as architectural assistants able to process data from the architect and elucidate its meaning in a given context. Contrary to what many fear and believe within the architectural field, the current state of AI and data processing doesn't necessarily enable an automated design system to replace the architectural designer.

But what then can generative design systems and AI do with data? If there is some data set to be optimized or any automated process to generate variations of design instances, then an AI system is enabled to aid us in processing the fittest design instance from the design space.  Data can be utilized through digital design tools of parametric optimization, combinatorial logic, algorithmic mathematical models, machine learning, evolutionary design etc. If an AI system is viewed as computational functions for calculating objective values, then the best design can be articulated as a search process for the design instance with maximum objective values in the design space. The volume of such a design search space in real-world applications, with comprehensive and unbiased data representation, tends to be exponentially large. When faced with exponentially large design space, search becomes practically impossible, however, if the search domain is limited and narrowed the system can generate design instances with maximum target values. This can be considered optimization rather than automated design (Chandler Ahrens, 2019).


AI and Machine learning

Data with AI can also be utilized to identify certain patterns in existing collections of data to extract new information, to educate and inspire value judgment and intention in the design process. This extraction of patterns can be achieved either by clustering unsupervised deep learning algorithms out of a collection of data instances or by the logical reasoning of QA systems out of the knowledge data set. This enables designers to obtain a deeper understanding and gain insights into existing data, which can aid designers to conceive ideas (Chandler Ahrens, 2019).

Speculating on the current pace of technological progression, We might infer that certain design processes may be automated, however, the nature of AI and machine learning requires an active initiative in pursuit of creativity and innovations, due to the fact that machine learning systems always need to be provided with base data sets to be trained on for pattern classification and design evaluation by characterizing certain values.

While the architectural practices appear to be bound to ‘creation of things’, from the artefacts of the design process to the assumed products of the design itself, building, But architects, in fact, are providers of value judgment services rather than creators of artefacts and objects (Wendy W. Fok, November 2016). Consequently, even when design instances are generated via automated processes for the AI to evaluate, it is designers who must take the initiative to set the generative design principles and value goals. This active intervention helps overcome the AI “frame problem”, defined as the difficulty to define logical rules for AI comprehensively without having an exponential number of rules and is further extended as a problem in determining relevant properties for a specific task. A designer’s ability to utilize ideation to decide the types of data sets and scope of the generative system, by defining relevant properties is the main advantage we have over AI (Chandler Ahrens, 2019).



In their research on computer-generated residential layouts, Merrell et al. (2010), reminiscent of graph-based architectural process adapted by Yona Freidman and NicholasNegroponte’s “Architecture by yourself”, utilize real-world training data to drive function adjacency, plan optimization, etc. through Bayesian networks to generate generic suburban villa layout plans and 3d-models. In their research, they present a method for automated generation of building layout for graphic applications. Utilizing a Bayesian network trained on real-world existing plans, they were able to synthesize an architectural program from a given input set of high-level functional requirement such as the number of bedrooms required (Merrell, 2010). Although the aforementioned digital workflow can produce generic sub-urban villas indistinguishable from similar designs regularly produced by those architects who participate in designing such spaces, this process can only generate a singular typology of designs similar to the input data-set the system has been trained on. Unintentionally then the system inherits any flaws and biases embedded within the set of designs the system was trained on. It is then perhaps more prudent to widen the training set and approach the problem from a slightly different perspective.

The Bayesian graph-based approach to Plangeneration is built upon by Stanislas Chaillou, et al (2020) in their research on architecture as a graph and their work on Spacemaker AI. They utilize existing floorplans to attempt to access the relevance of adjacencies between different spaces. By cross-referencing adjacency and importance matrix data, they enable a designer to tune the AI to the context-specific expectations from a plan. These examples show us the existing possible implementation of data for design generation (Chaillou, Architecture as a graph: A Computational Approach) .

The data workflow outlined by Stannis Chaillou while providing a more general optimization of spatial adjacency and solving plans does not provide any further scope for space-making, or computational autonomous tweaking of design criteria based upon user needs or design intentions beyond that of spatial adjacency and resolving plans, which admittedly the AI does proficiently. Nevertheless, these algorithms can be utilised to resolve complex spatial adjacency at large scales with varied typologies, for example, it can be used to resolve plans where different users within a house can have different requirements.  A successful data-enabled digital design workflow must be facilitated to have design criteria which are generally intuitive to the designers such as thermal comfort, social engagement, daylight requirement etc.



DanilNagy (2017) in collaboration with Autodesk produced a generative design tool for the application of multi-objective optimization towards solving complex design problems, which takes a range of data to generate multiple design variations through the application. Although they tasked the computational algorithm to explore the design space semi-autonomously, the designer still has to sift through these manually. The proposed digital workflow has three stages:1) A pre-generative design stage where data-informed constraints and requirements are selected by a multi-disciplinary collaboration between designers, clients and engineers. 2) The generative design system and 3) a post-generative design selection of the optimal solution-driven by design intuition and refinement of the design output by the designer. The generative design framework is dependent on three main components: 1) a generative geometry model that defines a ‘design space’ of possible design solutions; 2) a series of measures or metrics that describe the objectives or goals of the design problem; 3) a metaheuristic search algorithm such as a genetic algorithm which can search through the design space to find a variety of high-performing design options based on the stated objectives. (Danil Nagy, 2017)

They utilized six discrete design metrics to evaluate each design within the initial contextual constraints of the site boundary, programmatic requirement, occupation requirement etc, these six metrics were:

1.      Adjacencypreference, to measure travel distance between spaces.

2.     Workstylepreference, to measure the suitability of an assigned neighbourhood’s daylight and distraction measure.

3.     Buzz, which measured the distribution of high activity zones.

4.     Productivity, measuring concentration levels at individual work-stations

5.      Daylight

6.     Views to outside

Generative design workflow established by Daniel Nagy enables designers to discover designs while navigating trade-offs between high-performance optimization, design constraints and goals. The process offers higher control to the system designer to change and add parameters to the optimization process. It is important to understand the algorithms developed as a workflow rather than mere tools if the process needs to be conceptualized as part of a data-driven social housing workflow. The metrics which define such an optimization would optimally be generated via a democratic process that allows user-input and concerns to be part of the design constraints. Be that as it may, vigilance is warranted as such data-driven approach might lead to similar over-simplification and abstraction similar to the high-modernist methods, which the proponents of the digital architecture from the first digital turn sought to oppose. Therefore, the myth of optimization needs to be carefully balanced against the intuition of the designer.



Historically on an urban scale, Doxiadis vied for a design methodology rooted in data, statistics, and objectivity. For this, he had to construct a new discipline, that of ekistics. In this approach, the first stage is not that of the sketch or the diagram but of systematic data collection making the problem clearer. Cityanalytics or Urban analytics is perhaps the current iteration of work started by Doxiadis. This entails analysing big data to improve policy implementation and planning. Smart city Metropolises have large data sets which can be utilized to improve the quality of life for the citizens (TMD STUDIO LTD, 2016). At the urban and planning scale, one can already witness the widespread acceptance of data-driven decision making. Urban data offers immense potentialities towards a more social and liveable social housing neighbourhood. They can be utilized to identify sites and potential functions that the built environment needs to embody.



Charlotte, NorthCaroline city council with increasing pressure due to gentrification and housing demand, chose to turn to data-driven services to review site selection processes. With the help of charlotte city data analytics, they developed a mapping tool. This tool works by giving a multi-value score, that is a combination of four qualitative assessments: proximity to amenities, car and public transit, access to jobs, neighbourhood change score (for an increasing gentrification problem) and neighbourhood diversity. These data measures were adopted from the metrics that city council would discuss through their traditional site selection process but now backed by the city’s publicly available open-source data, allowing the council to consider the metrics they wanted in a transparent and data-enabled way (Gardner, 2019).



Jeanne Arnoldcollected data such as family’s movement data through a house and compiled it in “Life at Home in the Twenty-First Century: 32 Families Open Their Doors,”.For example, the map in figure 3 maps a family’s movement and almost all activity is centred around the family room and kitchen, while the dining and living room which occupy half of the total area is virtually untouched. These findings highlight the frequent discord between use and design. Before the advent of Big data, it would have been impossible to track 24-hour movement data of a family, this research elucidates the spaces we congregate in and allocate the limited space more efficiently. Through this research, Arnold offers insight and questions about what houses might look like if houses are designed to fit stuff that we frequently use. It instantiates how spaces can be moulded according to how they are used (Friedlander, 2014). Data collection and analysis of designed spaces offer an insight into the misplaced architectural process, which is often disconnected with the real space requirement and usage. It is of the utmost importance to have abundant spatial data about pre-existing spatial usage as the data would enable designers to establish intentionalities and alter designs to better fit with real-world observations. Data gathering and data interface systems thus, become active investigators into the gaps in the intuitive creative architectural process. Such systems are an unavoidable pre-requisite for future data-driven design workflows, taking the form of sensors, platforms, satellite maps etc. These systems already flood our built environment. It is now perhaps inevitable that the pervasive presence of these data-gathering systems become inextricably entangled with the spatial design process, both as embedded surveillance sensors within the built environment and as providers of indispensable insights into our existing living patterns.



The Quantifiedcommunity is an infomatics research initiative, which contains a network of neighbourhoods, that collect and analyse data on physical conditions and human behaviour to comprehend how the built environment impacts individual and social well-being. The Quantified community is based on the idea of the quantified-self movement, participatory sensing where individuals collect detailed data about their life, such as sleep pattern, food consumption, health status and physical activity to improve and optimize their well-being. Utilizing mobile phones to collect and transmit audio, visual and location data creates an opportunity to actively engage residents in the process of policy and spatial decisions. Localized measures and data collection can help to untangle observed problems and reveal unobserved opportunities for improvement obscured by spatial and temporal aggregation. The quantified community aims to use technology and data analytics for the collective needs and shared challenges (Kontokosta, 2016). The quantified-self movement is an example of how data feedback can affect real behavioural change.  



"Sidewalk Toronto will combine forward-thinking urban design and new digital technology to create people-centred neighbourhoods that achieve precedent-setting levels of sustainability, affordability, mobility, and economic opportunity,"

Sidewalk Labs’ now scuppered initiative at Quayside project in Toronto, aimed at creating an urban-scale experiment for the future of data managed cities. The project publicized as an urban scale experiment of the future in data-driven smart cities cites proposals to collect data, such as sensors that measure curb space availability (space available for scooters, bikes, trucks, ride-hailing vehicles, etc.), data on the movements and volume of pedestrians, restricted data such as licence plates, data on local temperature and other weather conditions, tenant turnover rates, leasing and rent data, plant health, air quality, waste bin/trash volumes(collected for its proposal on a pay-as-you-throw garbage collection), mail delivery, ride-hailing, energy use in apartments, noise levels and odours. The SWL(sidewalk labs) initiative was marred in controversy due to its early indeterminate, opaque and suspicious documentation of data-ownership and usage. SWL has since then proposed that an independent entity approve proposed collections and uses of urban data in the project area by all parties, including Sidewalk Labs. It further proposes de-identification, de-personalisation or anonymising the data but even so, the project has still not managed to convince all residents. The project was probably doomed to failure due to its lack of input from the residents. Data can enable large-scale urban housing projects to be democratized, however, SWL utilized its neo-liberal machine to propose a self-serving, land grab surveillance machine. Although it must be conceded that urban sensors are essential to create high-quality data sets, the process must be voluntary and the ownership of data must strictly remain with the users. Even beyond ownership, It is essential that the citizens are aware of the implications of their data being utilised.  Sidewalklabs’ opaque silence and inability to disclose how and where such data would be utilised is of concern.



“Rather than as a pre-determined fixed plan, our urban vision describes a flexible grid that is developed per the users’ demands.” – UNStudio

The Project research envisions a future where neighbourhoods are more equipped with sensors at street, house and building scale to form a layer of data that will enable a wide array of services that can add socio-economy value directly to the community. The residents remain owners of their data and can decide whether to participate in the research or not, such data independence is essential for utilising data. The residents can then utilize this data to exert influence on how mobility and energy are organized.

The research is working towards the realisation of The Brianport Smart District, which will support a combination of living and working within the framework of circular economy and self-sufficiency. Underlying the project is a data platform that will collect data about the performance of building systems of all 1500 houses within the community. The project wants to explore data-sharing as ‘labour that requires citizens to be rewarded and compensated for their data. Data collected becomes a tool/ commodity controlled by the residents and provides a variety of services in exchange for participating in the data-sharing process (UNSense, 2019).  The 100 homes project as a case-study provides important insights into fair and transparent data-usage.



One Shared House 2030 by Space10, is a Playful online research project that aims to get insights on the future of co-living through a collaborative online survey. The survey seeks to understand how people would like to live together and what services and amenities people would be willing to share. The project seeks to inform better design decisions when creating housing for the future by taking into account people's preferences and concerns before ever beginning to draw the blueprints for the future of domesticity. The project has enabled space10 to draw various inferences within the framework of co-living such as that most people would prefer to live tight-knit communities of 4-10 people from varied backgrounds and ages. Even so, there’s an obvious embedded bias in a project, that the data is probably gathered from people who are already interested in co-living. But nevertheless, One shared house survey establishes a precedence of how initial data can be utilized to establish intentionality for future projects.



“In the age of big data, everything is quantifiable, even happiness. At long last, an elusive subject like architecture can be held accountable: good architecture makes people happy, bad architecture does not.” – Reiner De Graaf



Research by Coburn et al. based on the theoretical framework of the aesthetic triad collects data about neuro-responses to spatial variables. With the development of mobile neural data collection tools such as Mobile EEG and VR simulations, specifics of human reaction to certain spatial dimensions can be quantified and understood more comprehensively. The aesthetic triad frames the human aesthetic experience in neural terms. The aesthetic experience according to it is generated by three systems, sensorimotor, knowledge-meaning and emotion-value systems. (Alex Coburn, 2017 )   Non-architectural attempts at quantifying human reaction to spatial features might make architects unaccustomed to debating the intricacies of their work with an outsider uncomfortable. Even so, the elusive subject of architecture might finally be held accountable. However, the problem of establishing objective parameters to evaluate human behaviour in architecture is the reduction of architecture to the optimisation of said parameters. Once such parameters are measured, they can be classified, compared and vectorised, impossible to be debated, it creates the standardized model for“happy architecture”. One must tread carefully this line between data and creativity.



In “The Social Logic of Space”(1984), the authors set out a new theory of space as an aspect of social life. Since it was first published in 1984 the theory has been developed into an extensive research programme clubbed under what is now known as ‘space syntax’ theories and tools. The book describes and analyses the different kinds of spatial data, such as visual isovists and justified spatial graphs,  produced by buildings and town, and then examine what it is about different types of societies that leads them to adopt varying spatial forms.

Space Syntax as a theory posits that spaces can be configured into individual components, analysed as a network of choices made by the people occupying them and then represented as data, maps and graphs. If built environments are considered to be organized systems, then their primary nature is configurational, principally because it is through spatial configurations (patterns) that the social purposes for which the built environment is created are expressed. In “SpaceIs the Machine”, Hillier(2007) uses spatial configuration data as a principle to provide a comprehensive theory of architecture and urban design. While paramount to a data-driven system, the evaluation of human behaviour poses ethical questions, such as, if an evaluation is desirable and who does such data belong to. Parameters which can be evaluated can be further quantified and can then be controlled. Within the neo-liberal paradigm, such control over behavioural data may alter our urban environment to favour those with power, who own the capital and the data. The infamous Facebook-Cambridge Analytica data scandal provides us with cautious insights, where political actors in possession of voters behavioural data could target political advertisements and propaganda to shift public opinions.



Data-driven processes are ostensibly convincing because data is numeric and tangible; one can’t argue with it. One presumes that data is inherently objective, but the rhetoric for data objectivity is dependent on multiplicity, the larger the quantity, the more information can be extracted. This argument, however, is less relevant once algorithmic processes are engaged, and data is curated. Any actors engaging with data are challenged with ensuring the quality and reliability of the data one operates on. At the critical moment when data-sets are adumbrated and information is harvested, a pre-existing bias has already been introduced, the design intent. At this moment subjectivity is part of the interpretation. In the discipline of design, there is no innocence in data use. There is always a hypothesis, by means, of which innocence ceases to exist. Being aware of this enables designers with greater freedom, providing them with the ability to be less speculative, less deterministic and more experimental.

Data sets are embedded with faults and biases, that they inherit from the human operators who assembled, parsed and label these sets. Machine Learning and AI often enhance these existing biases as the building structures that these systems are substrate to, loop, reinforce and effect real-world data and exacerbate social hierarchies.  Data must be questioned at all time and in all states as data that is true now doesn’t necessarily remain true forever. For example: behaviour patterns of people occupying certain spaces may change over time due to cultural shifts, it is therefore necessary to have updated data-sets. The solution to biases in data-sets lie entirely with designer’s decision to take those data sets forward in the design process, designer’s discretion here thus plays a major role in determining accurate conclusions from a given data and to uphold it a moral code.

“Computation is typically a prompt to the illusion of determinacy, yet our interaction with itis indeterminate.” (Chandler Ahrens, 2019).

Beyond efficiency, optimization and even beyond creativity, Data and digital processes can be utilized to debate values. Any process of multi-objective data optimization reinforces the need for defining design objectives. (Marble, 2012) Design requires flexibility to respond to non-linear multi-value objectives, unbiased data is restrictive and specific. Design intentions are biases that affect one design objective, which have subsequent consequences on other design objectives. These objectives are design values, goals and desires of a project outside of efficiency, objectivity and computation.



The difficulty of social housing data spawns from the fact that most data is spread across different platforms and different housing providers, the data itself is poorly maintained, unintelligible and expensive to process. Leach’s work with Microsoft and Housing Association’s charitable trust (HACT) elucidate the challenges of utilising data within the housing, the lack of coordinated data standard and the sheer lack of data.

The lack of data standards across the industry means that it is almost impossible to share and compare data across different housing organisations, this implies that utilisation of Big data is nowhere to even being comparable with other data-driven industries (Leach, 2016).

Despite the dearth of data, there is at least one method employed by architects, “POE” or post-occupancy evaluation. POE’s can take on the form of in-person interviews, phone calls and surveys, enquiring the residents about building acoustics, thermal comfort, lighting, general aesthetics etc and asking them to either be rated on a scale or provide more open-ended responses. It is also not just the amount of data, but the contextualizing, parsing and labelling of the data that makes it valuable and usable. Data-visualisation and data-parsing are essential to distinguish between useful information and noise to enable the designer to make informed decisions, as too much data can rather easily paralyze decision making.



Data-driven workflows are marred by concerns around data privacy and security. Political institutions are often responsible for establishing appropriate data protection frameworks, The European general data protection regulation, for example, was established after four years of bureaucratic deliberation, setting seven principles for fair collection and processing of personal data, Lawfulness and transparency, purpose limitation, accuracy, data minimisation, storage limitation, integrity and accountability. System designers need to be conscious of the benefits and risks of establishing a transparent sharing of data. When datasets are made publicly available, concerns around privacy can be managed by keeping the data anonymous through de-personalization of data. While a large amount of non-personal, environmental, structural and building data doesn’t face the same challenges, it is crucial that personal data remains entirely anonymized and that the data ownership is decentralized through transparent data-sharing. It is also paramount that the power to enable data-collection must lie with the users. As Jeremy Bentham points out in his theory on the panopticon, where the central tower contains possible data on the occupants of cells around, information and power are interlinked, those who possess information, wield power over those who do not.



"98% of what gets built today is shit" - Frank Gehry

While Frank Gehry and other starchitects are content with designing for the 2%, The paper argues that the 98% of architecture, what frank calls shit is where most of humanity is fated to live their lives. The processes which govern the construction of the generic house and its parts are not a product of architecture, rather they are shaped by guidelines, by-laws, Neufertist building standards and profit for neo-liberal institutions. These processes, far removed from the intricacies and complexities of the human condition are merely the efficient and optimized systems of standard mass-produced containers for the human existence, inevitably creating alien abstract structures bearing only the most general relationships to the real needs, demands and the daily details which members of the household experience.

These centralizedFordist production chains of modernist efficiency still saturate the construction industry today. While at the same time the star architect of the early 21st century has chosen to disassociate and disengage themselves from the design of domesticity, the most fundamental unit of architecture. The hyper-formal aesthetic produced by the neo-liberal expression of architecture producing exploitative, wasteful and beautiful cultural artifacts is perhaps here to stay and would face relatively no disruption from data-driven processes apart from perhaps the optimisation of the construction process itself. However, data technology is placed at a uniquely effective crossroads between a standard scalable system and customization, to disrupt and reform the generic, monotonous, homogeneous and repetitive social housing architecture.

However one can not be too optimistic, architects had access to digital tools since the advancements in personal computing in the early 90s, these disruptive technologies, from the first digital turn had the similar potential to be utilised towards realising liveable spaces for the masses, where stakeholders would be called on to intervene and participate in the design process, to customize or co-design (Carpo, 2017). Instead, these resources were promptly utilised towards propagation and optimisation of the same design methodologies that had been prevalent since the rise of modernism to produce simple, ugly, dreary boxes, mere containers for the masses to inhabit, not spaces to live in.


Stirred by an absence of a comprehensive theoretical framework for data-driven workflows towards the design of mass social housing, this paper proposes a network of stacked data-enabled processes to question if the metropolis of tomorrow is the same replicated container for living in, or if architects can finally provide the masses with a mass housing methodology that can manage to create liveable spaces. The stacked network challenges the conventional linear top-down design to build design methodology.


At the centre of this proposed data-driven design methodology is a robust and ethical data collection and data usage framework. The thesis proposes publicly owned decentralized data. Ownership of personal data must strictly remain with the citizens; this can be achieved through decentralized platforms that allow citizens to make informed decisions to leverage data for utilities and services.

A transparent and fair open-source data sharing structure is crucial for a rapid technological development and growth of data-driven systems within the construction industry. Public access to data allows for the development of ideas, experiments and investigations which are the bedrock of a healthy discourse.

In a digital-economy where data has been commodified, it is unfortunate that the legislative framework around digital data for most of the world is outdated or non-existent. In the absence of a politically established institution for fair dissemination of data, the paper proposes an independent, vendor-neutral,easy-t0-access, platform to capture, collect and share data.


Datatization of initial constraints and data-visualisation, to empower data-informed formation of intentions is the first proposed step.

The one shared house 2030, The data smart city solution’s Charlotte, North Carolina project, data from city analytics, LandInsight and UrbanIntelligence fit within this first layer informing the site selection, function allocation and planning stage. Data about rent prices, rates of gentrification, displacement due to default on rent, homelessness etc can be utilized to identify neighbourhood which most urgently requires social housing.


It has been established that data is not innocent, designers introduce biases through computation systems by establishing intentions. Algorithmic Design system codify intentions established in the first layer to process data.


While conventional digital tools are authored by computer scientist, architects must play a proactive role in designing the algorithms and code allowing a greater degree of control and balance towards the resolution of the design intentions. Graph-based AI tools developed by StanislasChaillou and multi-objective optimisation Design methodology developed by DanilNagy qualify for this layer within the network which allow the system to resolve spatial configuration for the various occupants of the proposed housing, these individual chunks of spaces may then be further re-arranged and optimized according to factors such as circulation, solar-requirements etc.

Multi-objective optimization here is utilized to combine objectives such as aesthetics, user spatial requirement, privacy requirements, energy usage, structural performance etc. These digitally intelligent architectural systems form an emergent and self-organizing structure that generate a variety of solutions that form the design space. After each algorithmic step, design intention is re-introduced, this narrows the design space as the design space goes through an iterative process.


Smart-city infrastructure requires data-collection, it can be argued that data fuels the innovations promised by the smart-city project. In the absence of a constant stream of new data, the cities cease to be adaptive and responsive urban spaces that they promise to be. Embedded sensors and platforms for data collection proposed in projects by UNSense, Side walklabs are the bedrock of good unbiased spatial data. While citizen surveys provide valuable and essential feedback, they are messy and possibly littered with falsehoods and biases. Tangible responsive real-world interface, which respond to real changing requirements can be utilised to collect real-time data.

A transparent and decentralized data collection, can have major impact on the performances of buildings and alter the face of domesticity. Data and smart technologies already exist as inseparable parts of the contemporary life. in exchange for personal data smart devices allow users apparent free access to various utilities and services, mobile platforms can allow citizens to have control over which data they would like to protect and what data they would not mind being shared.


Creating data standards across the whole industry will enable architects to utilize Big data across all phases of the design-build process and across multiple platforms. An established data standard would also enable data collected from new construction to subsequently provide better designs in the future. New data informs and changes the existing algorithms, which in turn lead to improved designs. This feedback loop is analogous to the vernacular architecture of the unconscious culture, mentioned by Christopher Alexander which over various generations would find the best-fit.


Building data can simultaneously be shared with all members involved within the design-construction system, enabling data-driven construction along with data-driven design, where construction methodologies are part of the design discourse since the inception of design. Simulations and BIM data can ensure that building data is leveraged to allow for accurate and precise implementation of the design.


The algorithm constraints defined by the architect need to be informed by data, collected through public surveys and in consultation with the intended users of the space, the citizens. All algorithms have embedded values, if these values could be exposed and stated in plain terms, they can become a platform for debate and discussion. This empowers the digital model to be utilised for interdisciplinary public debate on values for the project.

The quantitative optimization of performance and metrics need to be carefully measured against broader qualitative design goals, which can be discerned utilizing open debates about spatial and aesthetic values. The designer needs to constantly evolve design criteria to include public feedback, this process is the most tedious step within the network. The network of interconnected stacks of the data-driven system constantly updates as changes redefine the structure.

Within data-driven digital design workflow, standardization does not deliver any economy of scale, designing a single space occupies as many resources as a whole neighbourhood. It must be noted that space-making in the age of big data can be a participatory endeavour. Rather than a centralized authorship, data cases designer in a supervisory role to align multiple-objectives for all involved. This participatory process adds granularity to architecture, where attempting to sort architecture into single typologies is made redundant, data-driven design can enable designs where the same floor of a building is occupied by aloft, a 3-bedroom apartment and a co-living cluster of individual studio apartments.

With the emergence of new data-enabled technologies, the designers must establish their own workflows, a sort of meta-design of designs. While this paper has attempted to answer some of the looming concerns with utilising data for the design of mass housing, Data-driven design requires a much more robust and comprehensive methodology underpinning architecture to empower both the citizens and the Designers. It can be concluded that if the potential of data to democratize the architectural process is utilized, this new paradigm may well change the nature of our cities.


Alex Coburn, O. V. (2017 ). Buildings,  Beauty and the Brain: A Neuroscience of Architectural Experience. Journal of Cognitive Neuroscience, 29(9), 1521–1531. doi:10.1162/jocn_a_01146

Alexander, C. (1964). Notes on the  Synthesis of Form. Harvard University Press.

Autodesk research. (n.d.). Project  Discover. Retrieved from

Carpo, M. (2017, October 17). The  Second Digital Turn: Design Beyond Intelligence. CAMBRIDGE,  MASSACHUSETTS; LONDON, ENGLAND: MIT Press.

Chaillou, S. (n.d.). AI &  Architecture: An Experimental Perspective.

Chaillou, S. (n.d.). ArchiGAN: a  Generative stack for apartment building design.

Chaillou, S. (n.d.). Architecture as a  graph: A Computational Approach.

Chandler Ahrens, A. S. (2019). Instabilities  and Potentialities : Notes on the Nature of Knowledge in Digital  Architecture. Routledge.

Christopher Alexander, S. I. (1977). A  Pattern Language. Oxford University Press.

Cross, N. (1977). The Automated  Architect. Pion: Viking Penguin.

Danil Nagy, D. L. (2017). Project  Discover: An Application of Generative Design for. The Living, an Autodesk  Studio. Retrieved from

Daphne Dragona, P. D. (2017). TOMORROWS:  Urban fiction for possible futures.

Data-Driven Design and construction.  (2015). In A. L. Randy Deutsch, 25 Strategies for Capturing, Analyzing,  and Applying Building Data. Hoboken, New Jersey: WILEY.

Doxiadis, C. A. (1968). ECUMENOPOLIS:  Tomorrow’s city.

Doxiadis, C. A. (1970). Ekistics, the  Science of human settlements.

Drymiotis, A. (2007). Constantinos A.  Doxiadis and his work. Athens: The Association of Collaborators and Friends  of Constantinos A. Doxiadis.

Duvier C, A. P.-D. (2018). Data quality  and governance in a UK social housing initiative: Implications for smart  sustainable cities. Sustainable Cities and Society(39), 358–365.

Frazer, J. (1995). An Evolutionary  Architecture. London: Architectural Association.

Friedlander, D. (2014, Nov 21). Data-Driven  Architecture: Learning how we really use our homes so we can use them better.  Retrieved from

Gardner, B. (2019, June 19). How  Data-Driven Decision-Making Can Inform Affordable Housing. Retrieved from

Hillier, B. &. (1984). The Social  Logic of Space. Cambridge: Cambridge University Press.

Hillier, B. (2007). Space is the  Machine. London: Space Syntax Laboratory.

Hillier, B. (n.d.). Space syntax as a  theory as well as a method. London: Space Syntax Laboratory.

Howe, M. K. (2017, April 5). The  Promise of Generative Design. Retrieved from

Kontokosta, C. E. (2016). The Quantified  Community and Neighborhood Labs: A Framework for Computational Urban Science  and Civic Technology Innovation. Journal of Urban Technology, 23(4),  67-84. doi:10.1080/10630732.2016.1177260

Kotsioris, E. (2015, February 9). Architecture  and the Computer: A Contested History. Retrieved from

Leach, M. (2016, June 07). The problem  with housing and data. Retrieved from

Lui, A. (2015). Command LIne: The  Computer-Aided Office. In M. K. Eva Franch i Gilaber (Ed.), OfficeUS Atlas  (pp. 824-825). Zurich: Lars Muller.

Marble, S. (2012). Digital Workflows in  Architecture: Design–Assembly–Industry.

Merrell, P. &. (2010).  Computer-Generated Residential Building Layouts. ACM Transactions on  Graphics, 29.

Morris, C. G. (1992). Academic Press  Dictionary of Science and Technology. Academic Press.

Negroponte, N. (1969, March). Toward a  Theory of Architecture Machines. Journal of Architectural Education  (1947-1974), Vol. 23(no. 2), 9-12.

Negroponte, N. (1970). The  architecture machine: towards a more human environment. London: M.I.T.  Press.

Nick Srnicek, A. W. (2015). INVENTING  THE FUTURE: Postcapitalism and a World Without Work. LONDON: VERSO.

Pyla, P. (2007). Cities of the future,  heroes of the past: Doxiadis'Vision of post WW-II modernism. Ekistics,  442-447(74), 252.

SidewalkLabs. (2020, June). Retrieved  from

TMD STUDIO LTD. (2016, February 12). Designing  With Data:Big Data is impacting the ways in which cities, buildings are  designed and constructed.

UNSense. (2019). the 100 homes living  lab. Retrieved 5 20, 2020, from

Wendy W. Fok, A. P. (November 2016). Digital  Property: Open-source Architecture.


Figure 1: steps of thedesign interface by Yona Freidman and Nicholas Negroponte, source:ARCHITECTURE-BY-YOURSELF, An Experiment with Computer Graphics for House DesignSiggraph 1976. 9

Figure 2: Architecture as a Graph, AComputational Approach. 10

Figure 3 design metrics (from left toright: adjacency preference, work style prefernce, buzz, productivity,daylight, view to outisde) and timeplot showing lineages of designs throughgenerations. 11

Figure 4:Generative design for architecture workflow. source:Autodesk.comGenerative-Design-Architectural-Space-Planning-2020. 12

Figure5:  Map showing a family's movement,courtesy of UCLA Cotsen Institute of Archaeology Press. 14

Figure 6: Quaside project modular timberconstruction. source: 16

Figure 7: Data and smart technologiesintertwined with daily lives. source: 18

Figure 8: Results from One shared house2030 survey. source: 19



November 27, 2020


A study for a comparative analysis of two different typologies of the same functional buildings using space syntax as a tool for analysis of impact of spatial structure on human behaviour within the space. The first part of the research paper dwells in the human behaviour and how the space occupied impacts it. The second chapter deals with theories of space syntax and tools of analysis required to quantify behavioural impact of space of its occupants. The third part inquires about the different typology of institutions of incarceration and theories pertaining to confinement and prisons.


Architecture is primarily concerned with design of habitable spaces, these spaces can vary from built to open, from solitary rooms to large cities. It is influenced by forces such as utility, structure and aesthetic. It can be contended that aesthetic is intuitively an extension of society and individual outlook on pleasantness of form. Spaces can hence be said to influence how one feels (pleasantness being a behaviour response), it can be concluded that architecture can influence behaviour. To some extent, it seems, buildings can be used to control individuals and society at large can be controlled through design of houses, institutions and ideal communities.  It then becomes necessary for architects to study spatial behaviour (spatial configuration and its impact on behaviour), to be able to design spaces with a positive influence on its occupants.

One is then drawn to contemplate about spaces which fundamentally contradict the idea of positive influence on behaviour, as architects we must strive towards basic human standards for all; even those condemned to be unfit for society. In confined environments such as prisons, one often feels that the environment is hostile and renders any possibilities of reform void. The plan and the symbolic forms with which buildings of incarceration are invested, are certainly of interest. The prison can be seen by society as an instrument of social policy. But such features are evident surface phenomenon, there lies deeper features to these buildings of spatial structure and configuration.

Confinement is, in the penal sense, a restriction of physical activity within a defined boundary. It has been, but should never have been a restriction on mental activity. The imposition of inhumane physical hardship within the prison as a ‘punishment’ in addition to the necessary confinement for public safety, is cruelty to the mind. Society has lost its ability to provide a tolerant and caring environment to such degree, that to create such an environment within prisons will be a cause of great outrage and resentment for most people, but nonetheless necessary. This thesis looks forward to analyse spatial factors that influence positive behavioural reform in penal custodial environments,

Reformative behaviour is a function of spatial configuration in prison environments.



Architecture is the art, which combines expression, technology and the contentment of human needs. It has long been recognized that architecture is influenced by many forces, among them those articulated by Vitruvius in the first century B.C. - utilitas, firmitas and venustas or utility, stability and beauty are known most widely. But how often do architects pay attention to the needs of the user to the behavioural, social and cultural determinants of design and to the role of good design in affecting human behaviour. Architecture can influence people’s behaviour towards an environment or building. Although architecture can influence behaviour, I do not believe that architecture can determine behaviour of people but that it only makes it possible. It is hence imperative to understand environmental factors which affect human behaviour, how they perceive space and how they react to it. Perception of one's environment is affected by sociological needs, psychological state, and individual differences. The environment itself also influences human behaviour. Both mental and physical stimuli affect behavioural responses.


A study of behavioural response to the physical and mental stimuli of built space becomes essential for understanding the mind of the inmates held in institution of study these responses involve some basic knowledge of the process involved in behaviour responses.

Behaviour responses to the environment are complex functions and can be optimally understood through classification of the psychological stages of human behaviour: perception, cognition and Spatial behaviour.

PERCEPTION of the environment is the process of recognizing and becoming aware of space by acquisition of information and interpreting sensory stimuli.

COGNITION is the mental processing of this sensory stimuli and it includes the processes of reasoning and thinking about, remembering, or evaluating this information.

SPATIAL BEHAVIOUR refers to responses and reactions of an individual or a group to the immediate environmental information acquired through perception and cognition.


In the traditional setting of incarceration various basic sociological human requirement are trampled upon, which has larger negative psychological consequences on those held captive at such institutions. It thus becomes necessary to understand how these needs help in functioning and normalization of confinement setting.

The way a person perceives or views their environment can influence their social interaction within that environment. These social interaction within this space are discussed in terms of four concepts: privacy, personal interaction levels, territoriality, and crowding.

PRIVACY is a governing behaviour mechanism by which an individual controls his or her accessibility to others. In a control environment, privacy may be manipulated through the use of partitions which protect the individual from physical, visual and acoustical intrusion. The plan of a space establishes the privacy level at which it functions.

2.2.2 INTERACTION LEVELS are defined as various levels of desired transactions for various tasks. Each individual travels within a domain that expands and contracts depending upon individual needs and social circumstances. The size of the space system one moves through determines perception, experiences and uses of that environment. People inherently distinguish their relationship with others in terms of distances, or spaces, between them.

Edward T. Hall outlines four distinctive space-distances at which interpersonal transactions typically occur. These are categorized as intimate, personal, social, and public

INTIMATE SPACES are the most private spaces immediately surrounding the individual and are reserved for physical and emotional interactions.

PERSONAL SPACE is the space within whose boundaries an individual allows only friends, family or co-workers with whom personal conversation is mandatory.

SOCIAL SPACE is space where a person is expected to conduct short lived temporary social interactions.

PUBLIC SPACE is the space realm where social interactions aren’t expected to occur.

2.2.3 TERRITORIALITY is an approach to maintain a desired level of privacy by an individual involving exclusive control of a space by an individual or a group. The space under control implies privileges for the individual and may result in aggressive measures for its defence. Territorial control provides security and identity and is communicated through personalization and definition.

2.2.4 CROWDING occurs when territoriality mechanism functions ineffectively and privacy and personal space is intruded upon resulting in an excess of undesired unwanted amount of external social contact. Individual response to crowding may vary, often it will be tolerated even though it is unpleasant if it is temporary. Yet, psychological anxiety and discomfort may be experienced if the crowding is perceived as too confronting or occurs for a prolonged period of time.


Each individual responds uniquely to environmental stimuli, still these responses are influenced by various factors which fall into three categories influenced by factors of the built environment - sociological, psychological and physiological

2.3.1 SOCIOLOGICAL DETERMINANTS relate to the social requirements and difficulties of the individuals occupying the particular space system. Factors that affect these sociological responses, including group dynamics and communication, are functions of spatial configuration. GROUP DYNAMICS are the interpersonal relationships amongst individuals in a group which are functions of individual personality, culture and the shared environment.  Spatial configuration of a space system influence environment, task and personality. Various individuals will respond differently to spatial arrangement and tasks within the same space system. The interaction distances and specifications of the task being performed are very important in determining the spatial configuration of the space system. COMMUNICATION is an essential social requirement for humans. Spatial measures such as the size and volume, its relative dimensions to the occupants influence communication distance. As a space size is reduced individuals tend to reduce the distance and form smaller conversational groups. A large number of individuals in a relatively smaller space risks crowding and hindering communication.


The spatial arrangement of partitions, visual barriers and spatial configuration plays a crucial role in determining the psychological effects of space on individuals. The key psychological factors influenced visual privacy, acoustic privacy and aesthetics appreciation. VISUAL PRIVACY is a process through which individuals limit other’s view of oneself. Inherent in human behaviour is the tendency to avoid being observed without being aware of the observer’s identity or presence. That is humans instinctively feel uncomfortable under surveillance. Individuals prefer to sit against walls with their backs protected, controlling the area they cannot directly see. ACOUSTIC PRIVACY in a space system is a result of effective treatment of the acoustic environment as an interrelationship of components such as ceiling, partitions and floor. A successful acoustic system should provide adequate speech privacy. Speech privacy is achieved when there is sufficient acoustic shielding to allow conversation to be unheard beyond the participants of the conversation. A high quality of speech privacy will contribute significantly to a desirable level of communication, social interaction, and productivity. An appropriate relationship between background noise and that produced within the activity space is conducive to speech privacy. AESTHETIC APPRECIATION is expressed in and influenced by the environment. The concepts of aesthetic vary from time and place, purpose and context. It is the responsibility of a designer to understand the values of aesthetic that is understood at a universally comprehensible level. Aesthetic values go beyond the functional aspects of a space system and are associated with the way spaces are perceived by individuals. It concerns itself with individual user experience rather than mere utility.



What is architecture? Reflecting upon the general definition

ARCHITECTURE, N. (Morris 1992)

1. The art and science of designing or constructing a building.

2. The buildings of a particular place or time period.

Architecture must surely be different from a building, the most commonly held belief is that it adds “art” or “style” to the building, but if that was the case it would imply that architecture is simply an add-on , which would make it lesser than the building, conversely so it is known that it is considerably more. Architecture seems to mean both an object or thing and an activity. On one hand it means architectural attributes applied to a building on the other it means what architects do. Likely then architecture as a product and as a process are correlated and co-dependent. Architecture is at once attribute of things and attribute of activity. Architecture as a process can be understood as a process for designing space, which is different from the built in the sense that space is vacancy of object as opposed to the built which is physical mass. A building is thus a space system and architecture the process of spatial configuration (configuration being the relation between two spaces). Configuration is in general ‘non-discursive’, meaning that we do not know how to talk about it and do not in general talk about it even when we are most actively using it. To make this process discursive we require architecture to be predictive in nature that is the ability to ascertain outcomes of certain spatial configurations. To be able to do this requires a framework for spatial analysis through which one would be able to analyse the existing and predict the new. Architecture is therefore not part art, and part science, in the sense that it has both technical and aesthetic aspects, but is both art and science in the sense that it requires both the processes of abstraction by which we know science and the processes of concretion by which we know art. The architect as scientist and as theorist through predictive analysis seeks to establish the laws of the spatial and formal materials with which the architect as artist then composes. (Hillier, Space is the Machine 2007)


In the social logic of space the authors set out a new theory of space as an aspect of social life. Since it was first published in 1984 the theory has been developed into an extensive research programme clubbed under what is now known as ‘space syntax’ theories and tools. The book describes and analyses the different kind of spatial patterns produced by buildings and town, and then examine what it is about different types of societies that leads them to adopt different spatial forms.


“…Architecture, through the design of space, creates a virtual community…If space is designed wrongly, then natural patterns of social co-presence in space are not achieved. In such circumstances, space is at best empty, at worst abused and a source of fear.” (Hillier, Space is the Machine 2007)

Space Syntax as a theory proposes that spaces can be configured into individual components, analysed as a network of choices made by the people occupying them and then represented as maps and graphs. If built environments are considered to be organized systems, then their primary nature is configurational, principally because it is through spatial configurations (patterns) that the social purposes for which the built environment is created are expressed. In Space Is the Machine, Hillier uses spatial configuration as a principle to provide a comprehensive theory of architecture and urban design. Architecture is defined both as a product and a process; the product is the space, which is vacancy (void-an absence of physical form) rather than a thing and the process is taking into thought the non-discursive, the configurational aspect of space and form.  The transition of this non-discursive into discursive requires an analytical theory. This proposed analytical theory evolved as space syntax. Space syntax uses various concepts for space analyses.


The environment is defined as a collection of visible real surfaces in a space. An isovists is the visible space volume or the set of all points visible from a given vantage point in that space or environment. The shaded region in figure above outlines the isovist for the space from the marked vantage point.

Isovists conventionally three-dimensional, can be interpreted in two dimensions as well, either in horizontal section "the plan" or in other vertical sections drawn through the three-dimensional isovist.

The fringe-silhouette of an isovist may vary within an enclosed space. For a convex space  the fringe-silhouette, volume and area of every isovist in that space remains the same but the location of the viewpoint relative to the edge may vary. However, if the space is non-convex or concave (for example, an L-shaped or a partitioned room), then there may be more than a single isovists, each of whose volume and area would be less than that of the whole space and each of these individual isovist silhouettes might have different, perhaps unique edges: large or small, narrow or wide, centric or eccentric, whole or shredded.

Isovist graphs can be visualized as the space volume illuminated by a point source of light radiating light in all directions placed within the space.


An axial line is a straight sightline dealing with linear extension and are represented by an axial map. An axial map is composed of the least number of sight lines that must be drawn in order to cover all the available connections from one convex space to the other. They represent the longest views across spaces whose full area may not be visible. The axial map captures the sense of connections in a space.

A connection between two axial lines is said to be either shallow or deep depending upon the number of intermediate lines that have to be traversed to get from one axial line to the other.

A space is integrated to whole system when all the other spaces in the system are relatively shallow to it, it is thus function of the mean number of axial lines and connection that are required to be traversed from one space point in the system to all the other. Hence, in a space with a high integration value necessitates fewer changes in path direction to move within a whole space system. In this manner integration value is a measure of the relative position of any space point with respect to the entire configuration. (Klarqvist 1993)

Core is the whole set of the most integrated spaces of a spatial system. The configuration of that core, whether it is fully connected or split, whether it assumes a form of a spine or a wheel, whether it penetrates into all parts or remains clustered in one area, is a significant property of layouts. The spatial measures can be related to behavioural indicators, to develop predictive models of the “behavioural effects” of spatial layout.


Depth between two spaces in a space system is defined as the least number of syntactic steps in a graph that are required to reach one space from the other. Syntactic step is defined as the direct connection or permeable relation between a space and its immediate neighbours and is different from a metric step in the way that it is only representational in nature. In the figure above space A, D and E are one syntactic step away from C or are at depth 1 away from C. Similarly spaces B and F are at 2.


Visibility graph analysis (VGA) is a method of analysing the inter-visibility connections within buildings or urban networks. Visibility graph is applied through construction of a visibility graph within the open space of a plan.

A visibility graph is a graph of inter-visible locations (that is points in space system which have a clear line of sight between them), typically for a set of points and obstacles in the Euclidean plane. Each node in the graph represents a point location, and each edge represents a visible connection between them. That is, if the line segment connecting two locations does not pass through any obstacle, an edge is drawn between them in the graph. When the set of locations lies in a line, this can be understood as an ordered series.


Architecture is fundamentally the process of designing spaces with a vision of making the world a more humane and better place to live in. Architecture breathes the idea that the spaces shape the behaviour of its occupants and that through spaces architects can shape individuals and societies. Subsequently the question that arises is what we can do about spaces which are inherently contradictory to this utopian idea. As individuals with conscience devoted to a profession dedicated to improving built for all people, architects cannot participate in the design of spaces that violate human rights and dignity. Prisons are institutions which inadvertently violate human freedom and civil rights. Prisons have evolved from simple places for incarceration (where protection of the public is paramount) to instruments of punishment (where deprivation of liberty is the penalty for breaking the law), to settings for reform (where attempts are made to mould the guilty to conform with society's norms).it is perhaps true that certain buildings should’ve never have been built, buildings which constitute basic human rights violation by their very existence. These are buildings of torture and social malaise.

It’s peculiar to say, but it’s nevertheless true: we punish people with architecture. The building is the method. We put criminals in a locked room, inside a locked structure, and we leave them there for a specified period of time . . . so should architects remove themselves from prison design entirely? No, quite the contrary. Architects need to strive for better system for reform in penal architecture towards models of incarceration that incline towards reform and rehabilitation.


An architecture phenomenon conceived by Jeremy Bentham in the 18th century is a type of confinement facility, ‘an Inspection House’ envisaged as a circular cage with the confined prisoners occupying the circumference and the watchmen or the guard occupying a concealed position in a central tower like position allowing virtually a single guard to watch over the entirety of the facility.

The concept revolves around the inability of the confined to ascertain if they are being watched at that moment as the position of the guard is concealed ; forcing them to assume that they are being watched at all times. Hence even in the absence of a guard the subjects regulate themselves.

Bentham calls it “a new mode of obtaining power over mind...” A psychological prison, where the watched watch themselves.

Power is a relationship between individuals in which one affects another’s actions, differing from force or violence, which affects the body physically. Power involves a free subject to act against his or her will by restricting or alternating it.


The power to punish in the modern carceral (Peatross 1994) system is based on supervision and organization of bodies in time and space. According to Foucault the power and techniques of punishment depend on knowledge that creates and classifies individuals, and that knowledge derives its authority from certain relationships of power and domination.

"He who is subjected to a field of visibility, and who knows it, assumes responsibility for the constraints of power; he makes them play spontaneously upon himself; he inscribes in himself the power relation in which he simultaneously plays both roles; he becomes the principle of his own subjection" (Foucault 1977)

In Discipline and Punish, Michel Foucault elaborates the function of disciplinary mechanism in such a system and illustrated the function of discipline as an apparatus of power. He notes, the Panopticon thus ideally functions as a disciplinary tool- a mechanical eye. The ever-visible inmates are always the object of information, never a subject in communication. Prison architecture inspired by this conceptualization of centralized surveillance with pragmatics of repetitive approach produced plans which were radial in nature, the space was determined such that it narrowly defined decisions, space, movement and responsibility; properties of layout offered direct control as they imposed, eliminated or deliberately structured how things could be done.


After an uneventful first day, prisoners in cell-one broke out in a rebellion on the second day. Taking the unprepared guards by surprise, they barricaded their cell doors with beds, although the guards were able to subdue them by blasting them with fire extinguishers; they found it hard to control nine prisoners in shifts of three guards at a time and decided to use psychological tactics to control the prisoners, such as setting up a privilege cell to invoke distrust amongst the prisoners.

After a mere 36 hours prisoner #8612 had to be released as he began to act extremely distressed, to scream, to cry, to go into a rage that was out of control.

As the guards came to terms with their new role and powers they became increasingly cruel and creative in ways to humiliate, harass and oppress the prisoners.

The guards forced the prisoner to repeat their assigned prisoner ID numbers and started using physical punishment such as push ups for errors. Sanitary conditions declined rapidly, exacerbated by the guards’ refusal to allow prisoners to urinate or defecate. Some prisoners were forced to sleep un-clothed on concrete.

The experiment was ended prematurely on August 20, 1971 just 6 days after it began on moral grounds. (Social Psychology Network 1999)


“All individuals, including those have been condemned as prisoners, should have the same fundamental rights and there living conditions approximate as closely as possible the positive aspect of life in the community, strongly emphasizing the need for the protection of human rights and humane prison conditions.”

The concept of behavioural normalization is based largely on the principle that people get influenced by their physical surroundings. One corollary of this hypothesis is that as individual control over space decreases, the environment assumes increasing importance in determining behaviour. A normalized environment is therefore a "prosthetic", or physically and socially supportive, one. (Peatross 1994)

It is fully recognized that the environment, in isolation is unable to induce normalized behaviours; it can merely act as a prosthesis to enhance the small increments of improvement in behaviour that may be helpful in increasing independence, decreasing negative behaviours, or provoking a positive outlook. It is therefore assumed that the physical environment can play a role in shaping and facilitating more normalized behaviours.

Concept of control centred on normalized settings which offered Opportunities for resident control over arousal/stimulation, information, and privacy helps mediate the experience of institutional environments.

Increasing opportunities for awareness and socialization is critical to way to achieve a more social environment is to provide public, semi-public and private spaces in close proximity to one another, and to avoid long "institutional" corridors. (Peatross 1994)


The release of institutionalized individuals from institutional care to care in the community or the reform or modification of an institution to remove or disguise its institutional character.


Psychiatry. The chronic tendency towards repetition of criminal or antisocial behaviour patterns. The habit of relapsing into crimes by the criminals is known as Recidivism. A recidivist is a person who relapses into crime again and again. The rate of recidivism is a defining factor for a confinement institution to measure and evaluate its effectiveness in reforming its inmates.



The model is based on a profound understanding of human behaviour and takes human need for socialization and comfort into consideration.  The objective of this type of prison model is to allow to re-integrate the inmate in society after their release, to better the lives of these inmates. The inmates are treated with respect and trust and are prepared for life after prison. The prison design is based on harmony with nature and the design of a rehabilitation environment. The guards and the prisoners aren’t put in antigenic positions but work together for the common goal, the rehabilitation. The day areas are dedicate for treatment and work, to social interaction and contact with others. The night areas are dedicated to solitude and introspections. The cells are design in a comfortable manner, for one inmate.


The safety model prisons are fortressed with a succession of layers whose only rationale is to keep the inmates contained within this fortress. The boundless struggle against possible violence and escape leads to reduced quality of life in the prison. Often socialization and learning spaces are sacrificed to create more housing space. This type of model is the bases for the construction of super-max prisons, based on isolation of inmates in a single bed matchbox-cell for 23 hours a day. This can cause severe and permanent psychological problems. Safety prison models lack the consideration for human psyche and human needs. These prisons often sacrifice quality of living condition for economic and functional efficiency. The cells are designed to prevent escape and vandalism. Ironically, measures taken to reduce violence often are source of intense hostility towards the prison staff.


The repressive prison model are spaces for cruelty and inhumanity, marked by intentional violation of human rights. The prisons are characterized by secrecy and introversion, horrors and tortures within these spaces are hidden from the view of the world .these spaces are often in a constant state of deterioration and acute shortage of basic facilities. These spaces often manifest a severe phenomenon of overcrowding.


The hybrid prison model combines the rehabilitation and safety model and have an institutional and correctional ambience. The concept focuses around rehabilitation with strong safety measures. The cells are designed for efficiency and lack privacy.


4.8.1 THE PURE TREE (Architecture of Incarceration 1994)

This plan resembles the traditional nineteenth-century prison and represents the inverted type in its undiluted form. Entrance is through a secure ‘gate’, a series of outer courts, control buildings and inhabitant spaces. Three of these lie on the only ring in the building. In this example cells and communal building are entered from a cloister-like internal space one of which leads to a deeper space which in turn leads to a communal day hall that will have prison staff on duty when it is in use. It is from here that cells open. They are on the tips of branches at the deepest point of the spatial structure. Communication between them is not possible directly, but only through the root of the branches- a space shared by prisoners and staff and hence always under surveillance. There would be no possibility of freely chosen friendship being developed in a private space. The fact that both visitors and inhabitants share a single entrance sequence implies both a degree of solidarity between them- they are both cut off from the outside world in exactly the same way – but also a very strict regime of surveillance and control, the overall depth is at seven layers.

4.8.2 THE DILUTED TREE (ibid.)

This has the same spaces and formal features as the previous design. The only change is that there is extra secure ‘gate’ from the outside and some of the communal spaces are directly interconnected making communication possible without entering the ‘cloister’. The result is a structure with a number of rings; some of which allow some freedom for choosing encounter-generating and encounter-avoiding routes. In this structure prisoners may identify one gate as being their own and also belonging to their visiting friends and relatives and the other as belonging to the staff and official visitors. As a result the overall depth of the tree at four layers is much less.

4.8.3 THE CLOSED NET (ibid.)

Here again there is a single point of controlled entry. But internally the space is now fragmented into separate clusters, in each of which is a group of cells and associated shared communal space. These form clustered branches in the depth of the structure, some group identity will exist and variation in group sizes. Spaces connect to each sub-group and integrate the entire prison. Moreover the connections are rich and result in a complex system of rings. The overall depth lies between the first and the second example.

4.8.4 THE OPEN NET (ibid.)

Like the previous design, this has a shallow, ring like structure, but with several ‘gates’ through security boundary. Two of these – but it could be any number – lead straight into one of the communal spaces which connect a small cluster of cells. In other words, each unit has its own entrance. There is still a ‘main’ gate for the inhabitants’ use. Many possible routes exist between units. This might be the characteristic layout of prison whose inmates go out to work, make visits to their homes, have weekend or longer paroles, receive conjugal visits and participate in local community sports, cultural or social events. The association of an entrance with each cluster gives each of these much identity and independence.

4.8.5 THE HAMLET (ibid.)

This structure represents the idea of small relatively isolated units in which prisoners and staff can form communities with a degree of internal cohesion. The links between units are relatively sparse and if need be, highly controlled. Each unit, as well as the central prison facilities and administration, has its own link to the outside. The resulting spatial structure is shallow and relatively ring-like. But the trees constituted by the cell groups of each unit are clearly evident, as is the much greater connectivity of the central block.

Chapter 5.


While choosing the plans for spatial analysis it was necessary to take into account that the security class of each prison was same.

Within the ‘high security’ prison class were chosen prisons with strikingly contradictory views on penal reform and rehabilitation. They can be seen as the two ends of a spectrum.

5.1.1 ALCATRAZ FEDERAL PENITENTIARY  or United States Penitentiary

Alcatraz Island was a maximum high-security Federal prison on Alcatraz Island, 1.25 miles (2.01 km) off the coast of San Francisco, California, USA.

The three-story cell house included the main four blocks of the jail, A-Block, B-Block, C-Block, and D-Block, the warden's office, visitation room, the library, and the barber shop. The prison cells typically measured 2.7 m by 1.5 m and 2.1 m high. The cells were primitive and lacked privacy, with a bed, a desk and a washbasin and toilet on the back wall, with few furnishings except a blanket. D-Block housed the worst inmates and five cells at the end of it were designated as "The Hole", where badly behaving prisoners would be sent for periods of punishment, often brutally so. The dining hall and kitchen lay off the main building in an extended part where both prisoners and staff would eat three meals a day together. (OceanView Publishing 2015)

Figure 13. PLAN OF ALCATRAZ FEDERAL PENITENTIARY (OceanView Publishing 2015)


Generated using the depthmapX, the space syntax analysis tool by Bill hillier.

It draws multiple axial lines through the input map.

The map colour codes the output according to the density of axial lines passing through a concave space.

Hotter colours such as red and orange represent more dense axial lines, whereas cooler colours such as green and blue represent less dense axial lines. VISIBILITY GRAPH ANALYSIS

The most visually connected points in the structure can be seen as end of the aisles which are used for purposes of surveillance and not as community spaces. While the visual connectivity is low, visual privacy in cells is still violated due to large openings and matchbox shape of the cells, lack of which can cause stress to the inmates.

The overall temperature of the graph (its tone in reference to its value, where red means a higher value and blue means a lower one) for integration is low. That is to say inmates in this setting will find it hard to interact or will have less chances occurring naturally to meet and form bonds of friendship or any social relationship.

Figure 16 Depth map J-graph DEPTH MAP ANALYSIS

The depth map structure of the prison is a typical “pure tree” structure where the cells are placed at the deepest level, the only places at the same or deeper levels are the social places such as library or the mess hall, leading us to believe that these were inaccessible.  Although the Alcatraz federal penitentiary differs from the 18th century prison type in its plan arrangement it is obvious that the spatial structure and configuration of it remains the same and borrows heavily from the same idea of segregation and surveillance. One can also visualize the obvious crowding in the depth map as a marker of actual crowding in the prison.


The prison provides inadequate integration and connectivity of the only community spaces that is the library and the mess hall. As it can be seen from the axial map graph the most integrated space or the core of the spatial structure is the aisle between cell block-B and cell block-C.

Cell Block-D reserved for solitary confinement lacks integration, visual connectivity and choice and is located deep in the depth map. Cell Block-A is just as bad as the one mentioned above.

The most visually connected points in the structure can be seen as end of the aisles which are used for purposes of surveillance and not as community spaces. While the visual connectivity is low, visual privacy in cells is still violated due to large openings and matchbox shape of the cells, lack of which can cause stress to the inmates.

The overall temperature of the graph (its tone in reference to its value, where red means a higher value and blue means a lower one) for integration is low. That is to say inmates in this setting will find it hard to interact or will have less chances occurring naturally to meet and form bonds of friendship or any social relationship.

The Alcatraz federal penitentiary provides inefficient spatial configuration for a reformatory environment and can be classified as a cross between safety and repressive prison model.

5.2.1 HALDEN PRISON or Halden fengsel

At Berg in the municipality of Halden, Østfold, Norway, near the border with Sweden, a new high security prison has been built, with a closed section Comprising 227 cells and an open section consisting of 24 cells. The site contains an administration building, stores, a cell block, the open prison building and, of course, the perimeter wall.

the prison was designed by the Danish group Erik Møller Architects and the British HLM Architect.It was created with a focus on rehabilitation that is reflected in its design. Its design is projected to help inmates reintegrate into society easily by simulating life outside the prison.

The prison's cells are 10 square metres and have a flat-screen television, a toilet, a shower, a mini-fridge, and unbarred vertical windows that let in more light. Prisoners share kitchens and living rooms every 10–12 cells, and while the prison provides the food the prisoners can also buy ingredients and make their own meal. The direction encourages the inmates to take as much time as possible out of their cells



Generated using the depthmapX, the space syntax analysis tool by Bill hillier.

It draws multiple axial lines through the input map.

The map colour codes the output according to the density of axial lines passing through a concave space.Hotter colours such as red and orange represent more dense axial lines, whereas cooler colours such as green and blue represent less dense axial lines.

The most integrated part or the core of the spatial structure is the common corridor for access each individual wing or block. The whole spatial layout seems to have a generally high integration. While the rooms for private visits and washrooms are the most private spaces, the spatial configuration helps formation of interpersonal relationships and social community bonding.


Visually the community gathering area for each wing are the most highlighted area, implying that chances of random encounter are much higher in these area while the community areas are the most visually connected , the overall core of the spatial layout has decent visual connections as well. Furthermore it can be observed that open spaces in the aisle function as spaces of chance encounter as cell doors open towards a single point and increases the chances of bonding amongst inmates with their neighbours. While the visual graph analysis shows great connectivity to social spaces it can be observed that visual privacy is still maintained in the cells due to the odd shape of the cell block.



Figure 19 Depth Map and its J-graph. Halden Fengsel.

The depth map is a cross between the hamlet and the open-net type tree. Staff and inmates can form communities with a degree of internal cohesion. While the total depth of the configuration is deep, it is viewed that the main circulation core is both well integrated and visually connected.


Visually the community gathering area for each wing are the most highlighted area, implying that chances of random encounter are much higher in these area while the community areas are the most visually connected , the overall core of the spatial layout has decent visual connections as well. Furthermore it can be observed that open spaces in the aisle function as spaces of chance encounter as cell doors open towards a single point and increases the chances of bonding amongst inmates with their neighbours. While the visual graph analysis shows great connectivity to social spaces it can be observed that visual privacy is still maintained in the cells due to the odd shape of the cell block.

Halden Prison is an amazing example of how spatial configuration can influence behaviour positively and can be classified as rehabilitation prison model. The intent of the architects of the halden fengsel can be observed through the analysis tools and the success of the intent can be quantified using space syntax and comparative case studies.

Chapter 6.


-Although architecture can influence behaviour it can however not determine it, only provide spaces for the possibility of certain behaviour to occur. It can however enhance this possibility.

-Certain behaviour traits can be predicted using comparative analysis of existing spatial configuration, a database can be built which can then make a framework of spatial configuration for the architect to work within.

-This method of comparative spatial analysis can aid in quantification of the effective implication of the intent or aim of the architect. It however cannot quantify the architecture itself as it still remains a function of the architect’s intent.

- Spatial configuration can influence social relations which can influence positive –reformative behaviour changes in inmates in a prison environment which has been designed with consideration. Although this can be done non-discursively or intuitively it is imperative to form a database of comparative analysis of spatial structures to quantify the degree of this change.

- Reformative behaviour may not entirely be a function of spatial configuration but it is definitely influenced by it.



November 27, 2020


The idea of the machine has permeated the architectural design philosophy of the 20th century, from le Corbusier's “A house is a machine for living in” to the “the architecture machine”, The first concerned with creating architecture in the image of the machine and the latter with the process of architecture, A machine as a designer. Architects have primarily concerned themselves with the design of habitats, from the scale of a room to the urban. The Profession adheres to the collective axiomatic that the role of an architect is defined as the spatial configurator, and their value ascertained by his/her ability to solve a problem in a given context. This casuistry while has helped the profession stake dictatorial claim to the spatial design process, it is increasingly being questioned by algorithmic and data-driven design, advancements in AI and generative design. As generative design tools such as project dreamcatcher by Autodesk have already started to impact the design fields, It becomes essential to investigate the role of the architect in a possible post-automation society, the relationship between the architect and the machine, and if the machine would perhaps ever be able to automate the role of an architect.


while computation has aided the architectural designer in the 21st century by augmenting his design abilities, debates and concerns regarding the utility and role of computers have existed since the earliest mentions of computers in architecture. These conferences in the middle of the 20th century, over 60 years ago are still relevant and must be looked at if answers surrounding the role of the architect and the machine are to be discerned.  In the mid-1960s academic conferences speculated on the implications for the future as new computational changes proliferated through architectural practice. The conference titled Architecture and the computer convened at The Boston Architectural Center in 1964. Attracting participants in numbers that far exceeded expectations, those attending included not only architects and planners but also architectural historians, computer engineers, mechanical engineers, cartographers, representatives of large American offices such as IBM and large architectural offices such as Doxiadis associates, whose inclination towards scientific theory, mathematics and data-driven architecture made them interested in computers.

Opinions at the conference could be largely divided into two groups: First, technophiles who saw computers as tools with immediate utilitarian value to architecture practice, in making the design process more efficient and productive, and Second techno-intellects who saw the current technology as just the mere starting point and pushed for critical theory for computation. Belonging to the latter, cognitive scientist Marvin Minsky prophesized that soon “Architects will have to face the automation of design” (Lui, 2015). Minsky believed that if allowed to develop computation in creative fields would reach unknown limits, through the development of AI, he says” [L]et’s not worry about […] how computers are going to help us with small things. For in no more than 30 years, computers may be as intelligent, or more intelligent than people.”. By the early 1990s, architecture practices across the US had substantially confirmed the predictions of Minsky some thirty years earlier about the computer as a willing design partner. Of the predictions arising out of the conference, many prophesied tools such as CAD, BIM computer rendering and robotics in fabrication have been long fulfilled. While the more ambitious role for computational systems as design collaborators are just starting to be realized with the advent of generative design and evolutionary algorithms.


The research shall first dwell into the work of Constantinos Doxiadis, situated in a post-industrial era of the 1960s, where his contemporaries would have been critical of what we now retrospectively consider the modernist dystopian urbanism, the industrial city, and issues of urban blight and pollution. Doxiadis in his work as an urbanist draws analogies between the urban environment and a biological being, and the city center and the heart. As a designer, he’s responsible for the town-planning of Islamabad, Riyadh and works in Philadelphia, but here we are more interested in his theoretical and speculative works as a social scientist. Furthermore, Doxiadis remains one of the earlier adaptors of computational technology and is known to be responsible to introduce one of the first computers in Greece, which shall be examined later.

Doxiadis proposed the theory of ekistics, a neologism derived from Greek roots, which means “the science of human settlement”. In his, work Doxiadis argues that there are five principles that man has intuitively been following to design and configure settlements. These five principles can be simplified as follows.

The first principle stating that man designs for maximization on one’s potential contacts with the elements of nature, here Doxiadis includes nature not only as that which exists naturally such as trees and water but man-made environments such as roads and building and even other humans as well. The second principle calls for minimization of one’s effort to achieve the aforementioned potential contacts, where all designed structures and routes selected are optimized to minimize effort. The third principle is the optimization of man’s protective spaces, an ethereal bubble that prevents any physical or psychological discomfort. The scale for the third principle varies in scale from, the clothes one wears, to the room one inhabits till the urban and public realm. The fourth principle is the optimization of the quality of one’s relationship with the environment which includes society, built shells, natural environment, and networks. The fifth principle is an attempt to achieve an optimum synthesis between the previous four principles so that they may work in harmony. (Doxiadis, 1970) These principles define Doxiadis' theoretical work on existing urban environments but he also speculates on the future of cities. He creates a classification system for human inhabitation that ranges from the human itself, moves to the room, the building, the village, the mega-city all the way to a planetary-scale city of the future, always connected and covering the whole globe. Doxiadis calls this enormous network of human settlement the Ecumenopolis. He proposed the theory of Ecumenopolis as a part of a research project for “the city of the future”. As cities grow and humans find faster and ever more efficient ways to connect and transport themselves through ever more complicated networks, cities grow like a web, eventually growing and starting to interconnect to form a singular global city, which Doxiadis calls the ecumenic city. (Doxiadis, 1968)Here in his speculative urban works, we see his changing role as a social-scientist, which compliments his position on the current state of designers being unable to cope with the increasing complexity and sheer dimensions of cities of the Anthropos. Doxiadis also observes the separation of man and machine regarding transportation, where he claims that increasingly all transportation would be automated except for those meant for leisure. In his envisioned ecumenopolis, man increasingly automates all tasks which do not present any interest or challenge to him. Here Doxiadis is referring to automation through the electric power, industrialization, and mechanization but it holds true in today’s data-driven era, where more and more tasks are being automated.

In Doxiadis’s theoretical work with the theory of ecumenopolis can be observed his affiliation for design based on mathematics, data, and statistics. (Doxiadis, 1968) Therefore, his early adaptation of computers for data processing comes as little surprise. Doxiadis started using a computer in 1962 to develop mathematical models of settlements. Doxiadis established DACC/Doxiadis Associates computer center becoming the first information technology company in Greece and had a computer, A UNIVAC 1004 as early as 1964, although this was an earlier rudimentary form of computation device where programming was carried out through plugs and should not be considered a computer as we understand it today. By 1969, DACC had a computer system, The UNIVAC 1107 (also known as the thin film computer) that could be coded with earlier programming languages such as FORTRAN and COBOL. DACC would continue to evolve and keep up with the advancements in computing, eventually taking on the role of consultancy for other architectural and urban projects for data and statistical analysis. DACC served various non-architectural purposes such as writing programs for fair lottery auction of land in The ArtCity. (Drymiotis, 2007)

Doxiadis redefines the role of the architect by expanding the scope of an architect, augmented by computational tools. Doxiadis proposed that the architect collaborate more extensively and systematically with scientists, technocrats and institutions. Doxiadis continued to embrace the technocratic model, to objectify, chart and analyze needs, resources, and social relationships.


Constant was a graphic artist and author who proposed an anti-capitalist planetary-scale city with parallels that can be drawn with Constantinos Ecumenopolis. In his proposition Constant questions, the concept of what would be extreme hospitality, what it means to welcome everybody, that rejects millennia of hierarchal operation. Based on the era of the emergence of radio, sharing of ideas and the idea of inter-connectivity. He called this city “new Babylon”. Constan’s new Babylon is speculation on future cities and society. In his future, all work has been automated and the cities are inhabited by homo ludens, the man at play. The nomadic humans inhabiting this city only engage with creative activities based on their disposal, interests, and desires. It is a paradigm of free space and time afforded by automation. New Babylon stands on the foundation of collective ownership, automated labor, and exchange of ideas.  In his proposition exploration and creation coincide as everybody takes on sustained creativity, they create and explore the environment of what has been created simultaneously. While meant to be prophetic and provocative, new Babylon has taken on a more descriptive role where the world is now connected through the internet and all citizens participating engage in the creation of an artistic representation of their lives as they invite others to explore it with them. A life of continuous interconnectivity, drifting from event to event, relations being formed over the internet, it is a society where nobody is a stranger and everybody has the same status. A horizontal architecture for horizontal society is formed. Constant takes on the persona of an architect and in doing so adopts and exaggerates features of architecture, becoming what Mark Wigley calls a hyper-architect. Constant utilizes architectural tools, such as models. He realized the polemic value of the model and uses it to support his theory. In the speculative future of New Babylon then all humans engage in architectural creativity, constantly creating, exploring and recreating spaces.


Alex Williams and Nick Srnicek speculate within the contemporary framework by marking market tendencies towards a post-work society where labor has been fully automated. The authors approach the issue of labor from the standpoint of capital and argue that to achieve full automation, we must first demand higher wages to reduce labor supply and increase the cost of labor. The problem is also approached from the direction of reducing working hours which draws parallels to constant’s new Babylon where more all time is spent in exploration and play. These include tasks which traditionally have not been automated, but are now increasingly possible with algorithms and deep learning This work again offers an insight of today’s age more than it does for the future. Alex Williams and Nick Srnicek here are being critical of the neo-liberal paradigm, and of the current work-culture, of the failure of the left to make any changes to the society and the decline of the utopian ideal and it being replaced with cynicism. Here one can see a slight contrast between our current disposition towards a cynical outlook towards a utopian ideal against the historical context in which the previous authors were situated, where revolutionary ideas are often regarded as naïve. When all work has been automated, humans can then go on to define their labor, not by the requirements of a hierarchical structure meant to extract capital but rather driven by the desires of humans. This would also mark the shift from the selfish capitalist individual to a more communal form of creative expression. These architectural works have largely been situated in a theoretical and speculative framework, but yet they have been socially and politically revolutionary.


Negroponte’s the architecture machine is one of the earliest books on human-computer interaction and the possibilities of computer-aided design, at the time already established idea, different from computer-aided drawing a mere digital drafting tool. “to the first machine that can appreciate the gesture”, Negroponte states postulating the advancements in artificial intelligence to reach a point where the machine switches from a simple aiding tool in the design process to a collaborator.

On the role of an architect and the design process, Negroponte characterizes the architecture process as problem worrying rather than problem-solving. He suggests that architecture is concerned with structuring man's environment to facilitate human purposes (intellectual, psychological and utilitarian). He questions what a good human designer must do in order to achieve good design and concedes that the designer somehow promotes an environment that stimulates the good life, but we cannot fully establish what constitutes a good life. As it has no functionality and cannot be optimized. He also recognizes how our own cognitive systems visualize shapes and geometry leading to the subjective interpretation of good designs. (Negroponte, 1970)

Negroponte states that at least a part of the design process involves gathering information, doing research and filling the gaps in information through prediction, induction and in part playfully and whimsically. He assumes that machines and automation would provide for some of the omitted and difficult to acquire information. In Negroponte’s vision once machines know how to design, they would acquire a collaborative role with the architect. They would be required to interact with and talk to a wide array of people and architects. That such a machine must be able to recognize context and changes in goals and meaning brought about due to changes in context. (Negroponte, 1969)


Nigel questions the nature of CAD tools and their usefulness to the design process. He argues that the CAD as a drafting tool has simply moved to make the work of the architect more efficient and increases productivity without having any impact on the design. He is drawn to investigate the nature of design once the process itself has been mechanized and automated. Nigel while seems to be techno-optimistic about the emergence of a computer designer, says that the architectural profession may be one of the last sectors to undergo ‘computerization’, a prophetic thought which we can today realize probably to be true.

Nigel devises experiments and simulations to try and predict the possible impact on the design process and the human designers themselves of computer aids.

“The problem is, therefore, to devise a suitable simulation of a computer-aided design system. Current prototype systems consist essentially of a teleprinter console, usually plus a graphical device, through which the designer 'converses' with the computer—which may actually be some miles from the console. All that the user perceives of the system is this remote-access console, and the remainder is a black box to him.

Viewing the computer-aided design system in this way leads to an obvious suggestion for a simulation technique—one may as well fill the black box with people as with machinery. Doing so provides a comparatively cheap simulator, with the remarkable advantages of the human operator's flexibility, memory, and intelligence, and which can be reprogrammed to give a wide range of computer roles merely by changing the rules of operation. It sometimes lacks the real computer's speed and accuracy, but a team of experts working simultaneously can compensate to a sufficient degree to provide an acceptable simulation. This was the basic hypothesis of the research described here.” (Cross, 1977)

He predicts that the impact of CAD systems on designer would be increased stress (due to the increased requirement for accuracy and increased data and facts that could no longer be rationalised with hand-waving), Intensification of work rate, reduction of staff and the surfacing of new tasks such as program writing, debugging, tape or card punching, etc.(tasks which are now becoming increasingly commonplace in contemporary architectural practices).

Nigel delineates two possibilities for CAAD systems one that is promised by techno-optimists and one that threatens the role of the architect.

The Promises of computer-aided architectural design he states are the increased efficiency of time and detail, the reduced grunge work of detailing and scheduling, reinforced role of architect as a leader by his enhanced knowledge of information and data handling via computational system, emergence of smaller democratic groups in the design office as each member takes on more work, the designer is offered various possibilities of data manipulation leading to production of variety of information beyond conventional drawings, improved quality of building design as more exhaustive design factors are considered, de-professionalization of the profession, allowing the common-folk access to the design process.

Equally possible are the threats, Increased pressure and stress on the architect, the machine becomes the primary designer with architects role reduced to the periphery, the architect is dominated by those who create the computer programs and the design solution is restricted and constrained, the capital cost of machine equipment is offset by reduced staff employment leading to unemployment for architects, the computer disrupts and destroys the architect’s traditional skills in the preparation and interpretation of drawings, quantitative data drive out qualitative, reduced data set which can be processed by the machine leads to a narrow range of optimized monotonous building types, computer power will lead to design power, design power becomes centralized in large organizations which can afford computation systems reducing scope of individuals to influence design.

The current state of influence of computation on architecture perhaps lies somewhere in the middle of these two realities.


Cross, N., 1977. The Automated Architect. Pion: Viking Penguin.

Daphne Dragona, P. D., 2017. TOMORROWS: Urban fiction for possible futures. s.l.:s.n.

Doxiadis, C. A., 1968. ECUMENOPOLIS: Tomorrow’s city. s.l.:s.n.

Doxiadis, C. A., 1970. Ekistics, the Science of human settlements. s.l.:s.n.

Drymiotis, A., 2007. Constantinos A. Doxiadis and his work. Athens, The Association of Collaborators and Friends of Constantinos A. Doxiadis.

Kotsioris, E., 2015. Architecture and the Computer: A Contested History. [Online]

Available at:

Lui, A., 2015. Command LIne: The Computer-Aided Office. In: M. K. A. M. A. S. Eva Franch i Gilaber, ed. OfficeUS Atlas. Zurich: Lars Muller, pp. 824-825.

Negroponte, N., 1969. Toward a Theory of Architecture Machines. Journal of Architectural Education (1947-1974), March, Vol. 23(no. 2), pp. 9-12.

Negroponte, N., 1970. The architecture machine: towards a more human environment. London: M.I.T. Press.

Nick Srnicek, A. W., 2015. INVENTING THE FUTURE: Postcapitalism and a World Without Work. LONDON: VERSO.

Pyla, P., 2007. Cities of the future, heroes of the past: Doxiadis'Vision of post WW-II modernism. Ekistics, 442-447(74), p. 252.

wigley, M., 1998. Constant's New Babylon: The Hyper-Architecture of desire. s.l.:010 Publishers.



A Cross