Early on the United Nations recognised the need for geospatial information and related analysis to ensure that global sustainable development is promoted and also achieved.  Consequently, reference is made in several publications, including The Future We Want and the 2030 Agenda for Sustainable Development (paragraph 76), a resolution adopted by the UN General Assembly in 2015.  Jack Dangermond, president of esri, also addressed the United Nations’ High Level Political Forum on 7 July 2020 on sustainable development and the value GIS currently offers regarding Covid-19.

However, a longstanding issue for countries to meet the UN Sustainable Development Goals (SDGs) is the lack of access to relevant data and/or resources to collect data. As part of an ongoing response the UN partnered with Google and other organisations to assist on the subject – an initiative known as Data for Now.  Furthermore, esri has worked with the UN and member countries to establish FIS4SDGs (Federal Information System for the SDGs), a global network of SDG data hubs.  The intention is for this system to enable all nations and the UN to monitor and report progress towards the SDGs.  FIS4SDGs also featured at the UN World Geospatial Information Congress in 2018, with an exploration of how countries and agencies are formulating a vision for achieving the SDGs through federated SDG Data Hubs. 

Further to the previous discussion on geospatial science and the complexity of the SDGs, the following discussion provides a brief consideration of the GIS & spatial data science methodologies and international studies applied to achieve some of the 17 SDG goals:

SDG 1 – No Poverty

Included in Ireland’s SDG Data Hub is a StoryMap (Fig. 1) that was generated to focus on unemployment and poverty in Ireland over the last decade.  The study also considers Goal 8 (Decent Work and Economic Growth) in the analysis and illustrates changes in relation to unemployment and poverty over the last decade across Ireland and also at county level.

Similarly, a StoryMap (Fig. 2) was developed for Palestine’s SDG Site, focusing on poverty in Palestine in 2017.  It highlighted the large percentage of poverty in the Gaza Strip (53%), compared to the much lower 13.9% in the West Bank.

Through the open data portal of the Philippines SDG data is also made available through the application of a GIS webmap (Fig. 3). Poverty levels can be visualised at provincial level between 2006-2015. This services allows for a quick overview of the distribution of patterns where poverty either worsened or improved.  Comparative statistics are included for each province and can be consulted by any individual/decision-maker with access to the internet.

SDG 2 – Zero Hunger

The United Nations World Food Programme (WFP) generates a global map (Fig. 4) of the prevalence of undernourishment of a total population in 2017-2019. The WFP defines undernourishment as “the condition in which an individual’s habitual food consumption is insufficient to provide the amount of dietary energy required to maintain a normal, active, healthy life.. Further information is available in The State of Food Security and Nutrition in the World Report 2020.

In addition to the pdf map above, the WFP also maintains a live “HungerMap” at a global scale (Fig. 5a), which now also includes statistics on Covid-19, provided by Johns Hopkins University. Furthermore, more detailed demographic and nutritional statistics can be studied at country level (Fig. 5b). The HungerMap is a global hunger monitoring system and was developed with the Mapbox open source mapping platform. It covers 94 countries, including lower and lower-middle income countries (as classified by the World Bank).

GIS functionality also allows for the generation of “Emergency Dashboards” in an infographic-style map to provide an overview of the most essential statistics by country. Information is also acquired from various sources, including the World Health Organisation and UN High Commissioner for Refugees. Figure 6 presents an example of the August 2020 map for the Democratic Republic of Congo, reflecting a variety of demographic and humanitarian statistics. The QR code in the bottom right provides access to a country brief and various other publications on the specific country.

SDG 3 – Good Health and Wellbeing

Considering SDG 3, Covid-19 is the predominant subject globally of course and a number of web maps and related dashboards are available to consult online. The best example would be the Johns Hopkins University Covid-19 Dashboard. Apart from these toolkits, a number of GIS StoryMaps are also available on the subject and Figure 7 provides a snapshot of a Covid-19 StoryMap produced by staff members at esri. It provides additional information on the pandemic and also trends within the pandemic. These trends were obtained from a map that analyzes daily updates to the Johns Hopkins data.

SDG 6 – Clean Water and Sanitation

Focusing on achieving SDG 6, MUN Impact and the Thirst Project partnered to develop an extensive StoryMap to educate and inform the public of the global water crisis and raise awareness of related issues. Data is obtained from the WHO/UNICEF Joint Monitoring Programme (JMP). Figure 8 shows one of the maps applied, which also offers downloadable data in chart and table format.

SDG 7 – Affordable and Clean Energy

The UN emphasized that investment in solar, wind and thermal power, improving energy productivity, and ensuring energy for everyone is vital for the achievement of SDG 7 by 2030.

As part of esri’s ‘ArcGIS Solutions for Local Government‘ the Calculate Solar Radiation configuration for ArcGIS Pro was developed to assist in calculating solar radiation maps to assess whether buildings have potential for solar panel installation. Figure 9 shows a snapshot of a web scene that illustrates the application.

In 2019, a partnership between USAID and the National Renewable Energy Laboratory was established and a Renewable Energy GIS Tool Guide was produced. The report was effectively a comparative analysis of the following six web GIS applications and includes a qualitative analysis of functionality and capability and an overview of quantifiable attributes related to each tool/application:

Global Solar Atlas

Global Wind Atlas

Multi-Criteria Analysis for Planning Renewable Energy (MapRE)

European Commission Photovoltaic Geographical Information System (PVGIS)

International Renewable Energy Agency (IRENA) Global Atlas

Renewable Energy Data Explorer (RE Explorer)

SDG 11 – Sustainable Cities and Communities

More than half of the world’s population lives in cities and the UN projection indicates that two-thirds of all humanity will be urban by 2050 (UNDP). It has also been said numerous times that the battle against climate change will be won or lost in the cities. Thus, significant transformation in the development and management of urban space is essential to achieve sustainability. The UN’s 2018 SDG 11 Synthesis Report provides an overview of progress made to date, regarding goal 11.

One of the most significant tools recently developed is the Million Neighborhoods Map by the Mansueto Institute for Urban Innovation at the University of Chicago. The map highlights informal settlements globally and assists in identifying communities needing infrastructure, resources and sanitation most.

Prof. Luis Bettencourt (Inaugural Director of the Mansueto Institute of Urban Innovation) stated: “The projected growth of informal settlements, in combination with the challenges of climate change, requires the world’s immediate attention. We hope the Million Neighborhoods Map will create a change in perspective and methods enabling new forms of community-driven urban planning…” “Using this map, we can quickly identify the infrastructure deficits of entire cities and pinpoint areas most in need of improvements.”

SDG 13 – Climate Action

The United Arab Emirates Ministry of Climate Change and Environment developed a National Climate Change Adaptation Program and related targets and strategies (Figure 12) to achieve SDG 13. 

The UAE Ministry of Climate Change and Environment also provides an Open Data service via the ministry’s information center. In addition, the GIS outlined in Figure 13 below has been constructed and is accompanied by the “GeoEnvAe” mobile application, providing spatial information such as environmental, biodiversity, protected areas and agricultural statistics across the UAE.

In the US, a partnership was formed between the Desert Research Institute, University of Idaho and Google to develop a service called Climate Engine (utilising Google Earth Engine).  This application allows for the analysis of climate and earth observations in relation to drought, water usage, agriculture, wildfire and ecology. Figure 14 shows the wildfire ‘Burning Index’ for the country.

SDG 14 – Life Below Water

Some of the main targets of SDG 14 are to reduce marine pollution, protect and restore ecosystems, reduce ocean acidification, ensure sustainable fishing and the conservation of coastal and marine areas.

The UNEP and WCMC (UN Environment World Conservation Monitoring Centre) Ocean Data Viewer offers users the ability to view and download various spatial datasets regarding marine and coastal biodiversity.

Another very interesting application of web mapping is the Plastic Adrift site where users can choose a location in the ocean on a global map to see the potential movement of marine plastics over time (Figure 16).

The scientific methodology applied to calculate the paths of floating debris over a 10 year period is complex and the video below provides a better further information. Further information on the tool can be found in this paper.

SDG 15 – Life on Land

“Forests cover 30 percent of the Earth’s surface, provide vital habitats for millions of species, and important sources for clean air and water, as well as being crucial for combating climate change. Every year, 13 million hectares of forests are lost, while the persistent degradation of drylands has led to the desertification of 3.6 billion hectares, disproportionately affecting poor communities.

While 15 percent of land is protected, biodiversity is still at risk. Nearly 7,000 species of animals and plants have been illegally traded. Wildlife trafficking not only erodes biodiversity, but creates insecurity, fuels conflict, and feeds corruption.” UNDP

The Half-Earth StoryMap provides for the visualisation of biodiversity globally and features embedded web maps to investigate the spatial distribution of various plant and animal species. The Biodiversity web map provides a stand-alone tool (Figure 17) to search the globe for the distribution of various animal species. The Half-Earth Project is an initiative of the E.O. Wilson Biodiversity Foundation, in partnership with esri.

This post does not reflect a comprehensive study of the available geospatial toolkits and methodologies for the promotion of sustainable development and conservation of natural resources and biodiversity. Nonetheless, it provides a sample of important applications relevant to each of the discussed Sustainable Development Goals (SDGs) and highlights the importance of applying GIS and spatial data science to aid the task of achieving sustainability globally.

Resources

GIS for Sustainable Development

UN Global Geospatial Information Management

SDG Monitoring and Reporting Toolkit for UN Country Teams

SDG Country Profile

SDG Indicators and Data

Open SDG Data Hub

UN Environment SDG Hub

Migration Data Portal

The Impact of Digital Infrastructure on the SDGs

Utilizing geospatial information to implement SDGs and monitor their
Progress

I sat in on a panel discussion at the AAG conference (Las Vegas, 2009) between the US State Department, Jack Dangermond from esri and a number of international organisations.  The subject was “Continuing Global Dialogues on Geospatial Science and Sustainable Development”.  Again, the aspiration for unified sustainable development goals was evident.  What was also clear was the level of complexity of such goals and the intricate role systems thinking and geospatial science would play in achieving them. 

The eight UN Millennium Development Goals were replaced 7 years later by 17 Sustainable Development Goals [1].  However, to date the achievement of these goals remains a challenge, whether it is due to systemic barriers [2] or issues around prioritisation and accountability [3].  In addition, I argue that a holistic view of and complex systems approach to these 17 goals are predominantly absent and contribute significantly to these deficiencies.  Considering the familiar image of the Sustainable Development Goals (Figure 1), it is hard not to envisage a list of subjects to be addressed through a reductionist approach:  decomposing these goals into 17 or more pieces, solving them separately and putting them back together as a consolidated solution. 

However, such an approach ultimately overlooks the complex interrelated nature of these 17 goals – multiple causes and effects between them, feedback loops and autonomous actors.  A systems thinking approach would allow for the relationships between these goals to be identified and also prioritised in terms of importance.  Hence, the consideration of network theory (link analysis potentially) would optimise prioritisation. Furthermore, the influence related goals have on each other will reveal leverage points to guide attention and decision-making.  How would Goal 1 (No Poverty) be achieved without consideration of Goal 3 (Good Health and Well-being) or Goal 4 (Quality Education)[4]?  How do different goals compete?  How will ecological sustainability and inequality reduction potentially be influenced by the aspiration of a high level of economic growth?  These goals should thus be viewed in a different manner.  Figure 2 provides one of many examples, showing the relationship between goals and the level of significance [5].  Fu et al. (2019)[6] regarded sustainable development as a societal outcome, produced through the assurance of a balance between human development and environmental protection, and in doing so revealing that goal implementation is an optimisation process within a complex global system.  

The application of Geospatial Science in studying complex geographical systems offers far more than merely the analysis and visualisation of static or even basic temporal geographic dynamics. Increasing convergence of spatial data science and methodologies for studying complex systems enhances the possibility of considering and understanding better the interrelated and non-linear dynamics of phenomena.  This is particularly important, given the eye-watering 169 targets set out for the 17 SDGs.  Advancement in techniques for the incorporation of GIS data into agent-based modelling and cellular automata toolkits resulted in a significant increase in spatially explicit modelling. 

Furthermore, the benefits of GIS and BIM (Building Information Modelling) integration extend far beyond the optimisation of sustainable designs, assurance of collaborative workflows and efficient life cycle management of infrastructure (promoting Goals 9 and 11).  Combining BIM and GIS not only optimises the assessment of urban energy performance in smart city planning [7] for example, but also aids sustainable management of the complex relationship between the built and natural environment.  The practice of GeoDesign applies systems thinking to the collaborative consideration of complex environmental dynamics and subsequent enhancement of sustainable design.  The IGC (International GeoDesign Collaboration) adopted the SDGs as a standard format for assessing the impacts of more than 2 000 scenario-based designs [8].  Hence, all IGC projects should indicate how efficiently design scenario outcomes would address the SDGs.

The intention of this discussion is to promote systems thinking in terms of the SDGs and the consideration of these goals and related targets as interrelated components of a complex system. Thus, a broad overview is provided of subjects for deliberation, rather than an in-depth study of each.

References

[1]  https://sdgs.un.org/

[2]  https://europa.eu/capacity4dev/articles/challenges-implementing-sustainable-development-goals-asia

[3]  https://borgenproject.org/three-challenges-of-the-sustainable-development-goals/

[4]  https://unsdg.un.org/blog/untangling-complexity-sustainable-development-goals-moldova

[5]  https://blog.kumu.io/a-toolkit-for-mapping-relationships-among-the-sustainable-development-goals-sdgs-a21b76d4dda0

[6]  https://academic.oup.com/nsr/article/6/3/386/5381567

[7]  https://www.sciencedirect.com/science/article/pii/S1877705817318167

[8]  https://www.igc-geodesign.org/project-workflow

“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:

• Campus of the Future
• Future of Labs
• Future of Libraries
• Future of Schools

Cities Alive series:

• 100 Issues Shaping Future Cities
• Designing for Aging Communities
• Rethinking Cites and Arid Environments
• Water for People
• Urban Childhoods

Further publications on foresight, the future and related scenarios:

• 2050 Scenarios
• An Introduction to Corporate Foresight
• Exploring the Future of African Cities
• Foresight Advisory Brochure
• Future of Rail 2050
• Future Transport UK
• Future of Urban Water

 

References

DEFRA (2002): Horizon Scanning & Futues Home. URL: https://webarchive.nationalarchives.gov.uk/20070506093923/http://horizonscanning.defra.gov.uk/

 

The following section will be updated continuously with numerous methodologies and toolkits, developed for studying complex systems and related phenomena.

Subjects will include:

• Geographic Information Systems
• Agent-based Modelling
• Cellular Automata
• Mathematical Modelling

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

• 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

• 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

• 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

• 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.

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).

References:

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

“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).

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)

References

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.

 

 

 

 

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.

References

Castellani, B. (2018)  Map of the Complexity Sciences.  Art & Science Factory.  https://www.art-sciencefactory.com/complexity-map_feb09.html

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.