Arup Foresight & the Future of the Built Environment

“Foresight work is mostly about anticipating how the world is changing and we use those anticipations to create alternative futures.  What are our assumptions of the future and based on those assumptions, what do we change?”

Sohail Inayatullah

UNESCO Chair in Futures Studies

As with many other systems, the complexity of the built environment results in constant change and emergence over time.  It is thus essential that methodologies are in place for identifying factors and trends that would ultimately shape the future of the built environment.  The practice of foresight encapsulates approaches, tools and skill sets to help individuals or organisations to explore, visualise and shape the future.

Arup Foresight consists of a multi-disciplinary team of consultants and designers that assists clients in future-proofing their businesses by understanding change and the opportunities that emergence may hold.  This team understands that key drivers of change exists and that there are implications of change, requiring a business or project to be future-proof.  Working across a broad range of sectors, Arup Foresight contextualises the impact of global trends and formulate decisions for the development of resilience for the future.  The key methodologies applied for future-proofing businesses or projects include horizon scanning, trend research, scenario planning and visioning.  Consequently, the Foresight team’s focus is formulated around four main subjects:

  • Strategy and Visioning: How designs, strategies and solutions could be made future-proof through co-creating visions that provide directional frameworks for organisations
  • Insights and Trends: How emerging risks and opportunities could be identified by scaling global sources for “new trends, insights and cutting-edge thinking”.
  • Scenario Planning: How preparation for an uncertain future can be more efficient, by “challenging our tendency to favour ‘business as usual’” through scenarios to help the framing of strategic thinking and options.
  • Design and Innovation: How new business models and solutions could be developed through a range of approaches – from digital transformation to product and system implementation

Horizon scanning provides for a search process for the identification of emerging issues and events, which may pose as opportunities or threats.  The UK Department for Environment, Food and Rural Affairs (DEFRA) defined Horizon Scanning in 2002 as “The systematic examination of potential threats, opportunities and likely future developments which are at the margins of current thinking and planning. Horizon scanning may explore novel and unexpected issues, as well as persistent problems or trends.”  See also: Urban Futures and the Complex Systems Approach

Foresight Tools & Platforms:

  • The Drivers of Change programme is a toolset for identifying and investigation a wide range of key global issues and trends driving change in the built environment – from climate change to urbanisation and poverty. These key topics can also be explored in a Drivers of Change app and through five “STEEP lenses” (social, technological, economic, environmental and political).
  • The Inspire insights database and platform comprises of more than 1 500 examples of emerging innovation and change to facilitate foresight and strategic thinking. Inspire is effectively an online tool for researching innovation across the built environment.

Further publications include the following:

Future of:

Cities Alive series:

Further publications on foresight, the future and related scenarios:



DEFRA (2002): Horizon Scanning & Futues Home. URL:


The Evolution of GIS

Copyright:  Geocom Ltd.

While celebrating the 50th anniversary of Earth Day, it is virtually impossible not to consider the contribution of geographic information systems and science in studying and solving complex problems in Earth Sciences.  Given the fact that the first mention of a “geographic information system” occurred only 8 years before the first Earth Day celebration, in a paper by Roger Tomlinson, I decided to revisit the evolution of GIS over half a century.  The following is by no means a comprehensive discussion on the history of GIS, but rather a summary of what I personally deem as evolutionary highlights over time.

1960’s – The Field and a System

Roger Tomlinson.  Copyright:  Esri Canada
  • As computers developed and the earliest concepts of quantitative and computational geography emerged, the field of geographic information systems (GIS) originated.
  • In 1963, Roger Tomlinson was commissioned by the Canadian government to create a natural resource inventory that can easily be managed. Tomlinson envisaged the use of computers to undertake this task and he designed an automated computing process that became the very first GIS, the Canada Geographic Information System (CGIS)
  • In 1964, Howard Fisher developed one of the first mapping software programmes (SYMAP, see p.2) at Northwestern University
  • In 1965, Fisher established the Harvard Laboratory for Computer Graphics, which not only created the first computer mapping software, but also became the first research centre for spatial analysis and visualisation.
  • In 1966, the Urban and Regional Information Systems Association (URISA) was established by a group of urban planning and information systems professionals and promotes various aspects of GIS to this day
  • In 1969, Jack and Laura Dangermond founded the consulting firm, Environmental Systems Research Institute, Inc. (Esri), which applied computer mapping and spatial analysis to assist land use planners to enhance decision-making

1970’s – Product Development

Harvard Laboratory for Computer Graphics and Spatial Analysis
  • In 1970, the US Census Bureau produced the first geocoded census by applying a topological model. The topological structure of street segments was coded with ID’s and addresses with X,Y coordinates
  • The Harvard Laboratory developed and distributed (in 1974) the POLYVRT program for the conversion of various data formats, using the topological model that was adopted by the US Geological Survey and Census Bureau
  • In 1977, this work secured a grant from the National Science Foundation and also a symposium for international research scholars, which resulted in the development of the ODYSSEY system (see p.7-8).

1980’s – Going Commercial

ARC/INFO 3.4.2 (5 disks 3.5″)
  • With the enhancement in computing power, Esri improved software tools and the continuous undertaking of projects to solve real-world problems resulted in the development of robust GIS tools that could be applied more widely
  • Consequently, Esri gained recognition from academia regarding spatial analysis methodologies and the need for tools resulted in the development of the first commercial GIS product, ARC/INFO (ArcInfo)
  • In 1982, this product was released and Esri’s evolution into a software company was initiated
  • In 1984, Geographic Resources Analysis and Support System (GRASS GIS) was released as open source software suite
  • In 1986, development was undertaken of the first MapInfo desktop software
  • In 1987, the UK’s Economic and Social Research Council (ESRC) established four regional research laboratories for four main purposes: data management and provision of spatial data archive, software development, spatial analysis and research training and professional development
  • In the same year the Committee of Inquiry into the Handling of Geographic Information recommended that the British Ordnance Survey should transition to a full digital environment (Waters, 1998)

1990’s – Desktop GIS and a Science

Copyright:  Joe Dignam
  • Esri released the desktop solution Arcview throughout the 1990’s
  • Further development of the Internet and enhance computing power resulted in a widespread adoption of GIS
  • In 1992, Michael Goodchild made a major contribution to the field of GIS by stating in a publication that the discipline should transition from a systems to a science-orientated position (Goodchild, 1992)
  • Hence, focus should now shift from ‘how to get geographic information into the system’ to ‘how to handle and exploit this data held in the system’
  • Consequently, the discipline of GIScience was born and resulted in the enhancement of spatial data analysis and visualisation tools and techniques

  2000 – Desktop GIS, Open and Online Developments



  • In 2002, Gary Sherman started development of the open source Quantum GIS software, now known as QGIS
  • In 2004, OpenStreetMap was founded on the foundation of voluntary GIS, which gained momentum
  • In 2005, Google Maps and Google Earth were launched, providing interactive online mapping and a digital representation of the globe respectively
  • In 2007, Google launched Street View as web application and component of Google Maps and captured more than 10 million miles of imagery across 83 countries in the first 10 years
  • In 2009, Ordnance Survey data became freely available to the public

The last two decade have seen an immense development drive in the field of GIS and the integration of related processes, methodologies, techniques and toolkits.  From Building Information Modelling and Digital Twins to Smart City development and Urban Analytics.  As we embark on this journey to an ultimately digital world, the geographic information system and science will continue to play a substantial role.

Copyright:  Urbanizehub

Finally, I believe that the application and integration of GIS with toolkits for modelling dynamical systems (ABM and cellular automata) and generating virtual urban scapes (CityEngine & ArcGIS Urban with Unity or Unreal) will continue to play a pivotal role in sustainability and climate action (the theme of Earth Day 2020).


Goodchild, Michael F. 1992. “Geographical Information Science.” International Journal of Geographical Information Systems, 6(1): 31–45.

Waters, Nigel. 1998. “Geographic Information Systems.” Encyclopedia of Library and Information Science, 63: 98–125.

1000 GIS Applications

StoryMap on Earth Challenge 2020

Digital Twin:  Amaravati

ArcGIS Urban

High-End 3D Visualisation with CityEngine, Unity and Unreal

GIS & Agent-based Modelling:  Urban Growth Model by Andrew Crooks

Enabling Smart Cities and Communities with GIS

John Conway and the Game of Life

Photo Credit:  Thane Plambeck – Wikipedia

“He is Archimedes, Mick Jagger, Salvador Dalí, and Richard Feynman, all rolled into one.”

(The Guardian, 23/07/2015)

As many would know, the PhD journey kicks off with a dive into a vast ocean of subjects and information and theories and ideas.  Although I knew from the start that my research would concern the subject of urban complexity with a focus on the city of Cape Town, I had no idea whether this would entail a study of a socio-economic dynamics or land-use change over time.  Consequently, I started off by investigating methodologies for studying complex systems:  agent-based modelling and cellular automata.  Three names frequented my voyage into cellular automata:  Michael Batty, Stephen Wolfram and John Conway.

John H. Conway was an English mathematician from Liverpool who spent three decades studying mathematics (incl. symmetry) at Cambridge University, before he moved to Princeton University and held the title of John von Neumann Professor Emeritus (Applied and Computational Mathematics) for more than a quarter of a century.  His induction saw him writing his name down in the book that contained the names of Isaac Newton, Albert Einstein and Alan Turing.  John Conway was widely praised as genius by prominent mathematicians and known to be constantly thinking about anything.  “Most mathematicians are analysts or group theorists or number theorists or logicians.  John has contributed to every single one of those areas…”. (Roberts, 2015)

Most notable works by John include On Numbers and Games, The Symmetries of Things and The Book of Numbers.  However, John Conway is perhaps most famous for the Game of Life.

The Game of Life

Although cellular automata’s origins can be traced back to the 1950s, extensive popular interest only developed after John Conway’s Game of Life cellular automaton was introduced in a 1970 Scientific American article (Gardner, 1970).  The Game of Life takes place in a two-dimensional grid in which cells can either be alive/ON or dead/OFF and is defined by a set of rules which jointly determine the state of a cell, given the state of its neighbours (Moore neighbourhood of radius 1).

Intellectual Property of Eugene M. Izhikevich et al. (2015) Game of Life. Scholarpedia, 10(6):1816.

The rules are the following:

  1. Any ON cell with less than 2 ON neighbours at a certain time step changes to OFF at the next time step.
  2. Any ON cell with 2 or 3 ON neighbours remains ON at the next time step
  3. Any ON cell with more than 3 ON neighbours changes to OFF at the next time step
  4. Any OFF cell with exactly 3 ON neighbours change to ON at the next step


These rules were carefully chosen by Conway to satisfy the following criteria (Gardner, 1970; Game of Life):

  • “There should be no initial pattern [configuration] for which there is a simple proof that the population can grow without limit.
  • There should be initial patterns that apparently do grow without limit.
  • There should be simple initial patterns that grow and change over some time, before coming to end in three possible ways: fading away completely (from overcrowding or becoming too sparse); settling into a stable pattern that remains unchanged thereafter, or entering an oscillating phase in which they repeat an endless cycle of two or more periods.”

To Play the Game of Life

Although the Game of Life is quite simple, it provides great examples of the phenomena of self-organisation and emergence.  Both these concepts are important and applicable to a range of biological and non-biological systems.  The game was designed to explore ecological communities and evolution.  “Conway’s organization of rules reflects the epigenetic principle, that genetic action and developmental processes are inseparable dimensions of a single biological system, analogous to the integration processes in symbiopoiesis.” (Caballero et al., 2016)

The Game of Life was also chosen by Google for one of its ‘Easter eggs.  Type “Conway’s Game of Life” and notice the light blue cells in the top right corner, which will gradually crawl across the whole page.

In Memory of John Horton Conway (26/12/1937 – 11/04/2020)


Caballero, L., Hodge, B., Hernandez, S., 2016. Conway’s “Game of Life” and the Epigenetic Principle. Front. Cell. Infect. Microbiol. 6.

Gardner, M., 1970.  The fantastic combinations of John Conway’s new solitaire game ‘life’. Sci. Am. 223, 120–123.

Roberts, S., 2015. Genius At Play: The Curious Mind of John Horton Conway. Bloomsbury USA, New York.





The History of Complexity Science

Copyright:  Jun Park

When considering the history of complexity science and related theory, it is difficult to bypass the wide-ranging narrative Melanie Mitchell (2009) provides on the subject.  Arguably, complex systems have been studied by humanity for thousands of years.  Mitchell (2009) traces this journey back to Aristotle (384-322 B.C.) and the emergence of Dynamical Systems Theory and how this influenced thinking and scientific discovery in the ages to come, until the sixteenth century and the contradicting studies of Galileo on motion.  Nonetheless, Mitchell (2009:17) points to Isaac Newton as the “most important person in the history of dynamics” and the inventor of the science of dynamics.  Based on Newtonian mechanics, Laplace proclaimed in 1814 that it is possible to predict anything.

However, the twentieth century saw the emergence of contradictory discoveries to this notion of absolute prediction, with an increasing understanding of chaos and chaotic systems and the concept of “sensitive dependence on initial conditions”.  However, the first experience of a chaotic system occurred in the late nineteenth century already, when the French mathematician Henri Poincare modelled weather behaviour.  This occurrence paved the way for the aspiration of predicting weather over a longer period and Edward Lorenz found in 1963 that even simpler computational weather models are subject to the sensitive dependence on initial conditions, with these systems manifesting nonlinearity.

From this theory of chaos emerged complexity theory and although complex systems were researched explicitly since the 1970’s (Vemuri, 1978), the studying of complex systems gained much traction with the establishment of the Santa Fe Institute, the first research institute dedicated to research of complex systems and especially complex adaptive systems.  The institute was founded by a group of 24 scientists and mathematicians, with a number of these individuals being scientists with Los Alamos National Laboratory.  In later years a number of other institutions were formed, dedicated to the study of complexity in systems which range from biological to social and economic systems.

I acknowledge that this discussion provides the briefest of overviews on the subject of complex systems and the Further Reading section presents a number of resources on the subject.  Furthermore, this map by Castellani (2018) provides a great visual overview of the development of the complexity sciences over a number of decades.


Castellani, B. (2018)  Map of the Complexity Sciences.  Art & Science Factory.

Laplace, P. S. (1814). Essai Philosophique Sur Les Probabilites. Paris: Courcier.

Mitchell, M. (2009). Complexity: A Guided Tour. Oxford, U.K.: Oxford University Press.

Vemuri, V. (1978). Modeling of Complex Systems: An Introduction. New York: Academic Press.