Towards intelligent Green and Blue infrastructure

Many Interlocking Parts

Synthesis of the links between pressures, condition and ecosystem services in urban ecosystems. Adapted from EU’s Mapping and Assessment of Ecosystems and their Services [1] and visualised by me.

Learning Urban Environments

  • How do we optimally place and use sensors and other methods to gather data?
  • How do we best store, curate and openly share this data?
  • How do we create standards and ensure quality?
  • How do we identify what data and knowledge are available, who the providers are, and ensure it is accessible for users?
  • How do we catalogue and integrate data?
  • How do we create safe environments/havens/APIs to share ready for analysis data?
  • How can we democratise access to HPC?
  • How do we better expose existing catalogs of datasets and create collaborative environments that make this data easy to access and visualise?
  • How can we fuse information from multiple data sources with uncertainties and biases that vary over space and time;
  • How can we ensure privacy, fairness, accountability, robustness, transparency for environmental analytics and systems?
  • How can we achieve physical consistency via hybrid simulation and data science approaches?
  • How can we integrate probabilistic forecasting methodologies into decision-making?
  • How can we better understand risk and model extreme events?
  • How do we best translate abstract mathematical outputs into actionable, contextual and relevant policy insights?
  • How do we develop open-source tools and user-friendly interfaces?
  • How can better support the climate adaptation activities of local communities, including citizen scientists?
  • How can we better incentivise and involve users and local communities in the development of such interventions?
  • How can we develop better tools and resources to cover social and economic implications across timescales?
  • How do we equip individuals with the skills, information, and training to enable them to generate, communicate and use decision-relevant data?
  • How do we create new processes and tools that enable effective ecosystem services governance, not only within organizations but between the different organizations and sectors?

Where can data science intervene

a) Expand our understanding of space and time

48-hour predictions of air quality (NO2) Central London and a running route that changes shape to minimise air pollution. Source: Alan Turing Institute
Archive footage of the Tour of Flanders obtained by Flemish broadcaster VRT — Flanders Classics
Example visualization built for Indonesian policymakers to display the ‘bare ground’ metric for the pilot study, Source: Green City Watch

b) Proactively steward and maintain

Synthetic Aperture Radar analysis of Amsterdam’s Noorderpark during a flooding event. Source: Green City Watch

c) Collectively sense and act

Source: Kings College London

d) Create new planning and design affordances

Highline aerial view in NYC. Source: David Shankbone (creative commons license)

f) Create post-human encounters

Source: Natalie Jeremijenko
Source: Natalie Jeremijenko
Source: Marshmallow Laser Feast
Source: AnneMarie Maes
Source: Semiconductor films

Concerns

Source: Dark Matter Labs

Intelligent Urban Metabolic Systems for Symbiotic Cities (and violets)

  1. Maes J, Teller A, Erhard M, Grizzetti B, Barredo JI, Paracchini ML, Condé S, Somma F, Orgiazzi A, Jones A, Zulian A, Vallecilo S, Petersen JE, Marquardt D, Kovacevic V, Abdul Malak D, Marin AI, Czúcz B, Mauri A, Loffler P, BastrupBirk A, Biala K, Christiansen T, Werner B (2018) Mapping and Assessment of Ecosystems and their Services: An analytical framework for ecosystem condition. Publications office of the European Union, Luxembourg.
  2. Van Velzen, J., Modderkolk, P., Bouma, J., 2018. Nederland zucht onder de droogte [The Netherlands sighs under drought]. https://www.trouw.nl/groen/nederland-zucht-onderde-droogte~a4e0ab5f/
  3. Nitoslawski, S.A., Galle, N.J., van den Bosch, C.K. and Steenberg, J.W., 2019. Smarter ecosystems for smarter cities? A review of trends, technologies, and turning points for smart urban forestry. Sustainable Cities and Society, p.101770.
  4. Nitoslawski, S.A., Galle, N.J., van den Bosch, C.K. and Steenberg, J.W., 2019. Smarter ecosystems for smarter cities? A review of trends, technologies, and turning points for smart urban forestry. Sustainable Cities and Society, p.101770.
  5. Barrile, V., Bonfa, S. and Bilotta, G., 2017, August. Big Data Analytics for a Smart Green Infrastructure Strategy. In IOP Conference Series: Materials Science and Engineering (Vol. 225, №1, p. 012195). IOP Publishing.
  6. Rai, A., Minsker, B., Sullivan, W. and Band, L., 2019. A novel computational green infrastructure design framework for hydrologic and human benefits. Environmental Modelling & Software, 118, pp.252–261.
  7. Waldmann-Selsam, C., Balmori-de la Puente, A., Breunig, H. and Balmori, A., 2016. Radiofrequency radiation injures trees around mobile phone base stations. Science of the Total Environment, 572, pp.554–569.
  8. Thielens, A., Bell, D., Mortimore, D.B., Greco, M.K., Martens, L. and Joseph, W., 2018. Exposure of insects to radio-frequency electromagnetic fields from 2 to 120 GHz. Scientific reports, 8(1), p.3924.
  9. Dijst, M., Worrell, E., Böcker, L., Brunner, P., Davoudi, S., Geertman, S., Harmsen, R., Helbich, M., Holtslag, A.A., Kwan, M.P. and Lenz, B., 2018. Exploring urban metabolism — towards an interdisciplinary perspective.
  10. Rapoport, E., 2011. Interdisciplinary perspectives on urban metabolism. Development.
  11. Heynen, N., Kaika, M. and Swyngedouw, E. eds., 2006. In the nature of cities: urban political ecology and the politics of urban metabolism. Routledge.
  12. Donges, J.F., Lucht, W., Müller-Hansen, F. and Steffen, W., 2017. The technosphere in Earth System analysis: a coevolutionary perspective. The Anthropocene Review, 4(1), pp.23–33.
  13. Keil, R., 2003. Urban political ecology: a progress report. Urban Geography, 24(8), pp.723–738.

--

--

--

http://emalliaraki.com/

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

How to calculate Pareto distribution and Zipf’s law in Python

If you are using Python and Google Cloud Platform, this will Simplify Life for you (Part 1)

Case Study — Logistic Regression

Proving the Gordon Growth Model: Geometric Series and Their Applications

Unlocking the long-term potential of clinical trial data

3 Types of Higher-Order Notes to Provide a Bird’s Eye View to Your Knowledge

Wanted: Data Stewards: (Re-)Defining The Roles and Responsibilities of Data Stewards for an Age of…

Introduction

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Eirini Malliaraki

Eirini Malliaraki

http://emalliaraki.com/

More from Medium

Networked-Cognitive Cooperative Institute (NC2I) Model

Reading Note: A newcomer to AI data labeling, Encord looks to ride a rising tidal wave

Commercialization of Agriculture Brought Altruistic Research Economy

AI to the rescue in the fight against Lantana Camara

Lantana Camara