Citizen Science Is Helping Tackle Stinky Cities


Article by Lucrezia Lozza: “Marta has lived with a bad smell lingering in her hometown in central Spain, Villanueva del Pardillo, for a long time. Fed up, in 2017 she and her neighbors decided to pursue the issue. “The smell is disgusting,” Marta says, pointing a finger at a local yeast factory.

Originally, she thought of recording the “bad smell days” on a spreadsheet. When this didn’t work out, after some research she found Odour Collect, a crowdsourced map that allows users to enter a geolocalized timestamp of bad smells in their neighborhood.

After noise, odor nuisances are the second cause of environmental complaints. Odor regulations vary among countries and there’s little legislation about how to manage smells. For instance, in Spain some municipalities regulate odors, but others do not. In the United States, the Environmental Protection Agency does not regulate odor as a pollutant, so states and local jurisdictions are in charge of the issue.

Only after Marta started using Odour Collect to record the unpleasant smells in her town did she discover that the map was part of ‘D-NOSES’, a European project aimed at bringing citizens, industries and local authorities together to monitor and minimize odor nuisances. D-NOSES relies heavily on citizen science: Affected communities gather odor observations through two maps — Odour Collect and Community Maps — with the goal of implementing new policies in their area. D-NOSES launched several pilots in Europe — in Spain, Greece, Bulgaria, and Portugal — and two outside the continent in Uganda and in Chile.

“Citizen science promotes transparency between all the actors,” said Nora Salas Seoane, Social Sciences Researcher at Fundación Ibercivis, one of the partners of D-NOSES…(More)”.

Citizen assembly takes on Germany’s climate pledges


Martin Kuebler at Deutsche Welle: “A group of 160 German citizens chosen at random from across the country will launch an experiment in participatory democracy this week, aiming to inspire public debate and get the government to follow through with its pledge to reach net-zero CO2 emissions by 2050.

The Bürgerrat Klima, or Citizen Assembly, will follow the example set in the last few years by countries like Ireland, the United Kingdom and France. The concept, intended to directly involve citizens in the climate decisions that will shape their lives in the coming decades, is seen as a way for people to push for stronger climate policies and political action — though the previous experiments abroad have met with varying degrees of success.

Inspired by a 99-person Citizens’ Assembly, the Irish government adopted a series of reforms in its 2019 climate bill aimed at reducing carbon dioxide emissions by 51% before the end of this decade. These included recommendations “to ensure climate change is at the centre of policy-making,” and covered everything from clean tech and power generation to electric vehicles and plans to retrofit older buildings.

But in France, where 150 participants submitted bold proposals that included a ban on domestic flights and making ecocide a crime, lawmakers have been less enthusiastic about taking the measures on board. A new climate and resilience bill, which aims to cut France’s CO2 emissions by 40% over the next decade and is due to be adopted later this year, has incorporated less than half of the group’s ideas. Greenpeace has said the proposed bill would have been “ambitious 15 or 20 years ago.”…(More)”.

How we mapped billions of trees in West Africa using satellites, supercomputers and AI


Martin Brandt and Kjeld Rasmussen in The Conversation: “The possibility that vegetation cover in semi-arid and arid areas was retreating has long been an issue of international concern. In the 1930s it was first theorized that the Sahara was expanding and woody vegetation was on the retreat. In the 1970s, spurred by the “Sahel drought”, focus was on the threat of “desertification”, caused by human overuse and/or climate change. In recent decades, the potential impact of climate change on the vegetation has been the main concern, along with the feedback of vegetation on the climate, associated with the role of the vegetation in the global carbon cycle.

Using high-resolution satellite data and machine-learning techniques at supercomputing facilities, we have now been able to map billions of individual trees and shrubs in West Africa. The goal is to better understand the real state of vegetation coverage and evolution in arid and semi-arid areas.

Finding a shrub in the desert – from space

Since the 1970s, satellite data have been used extensively to map and monitor vegetation in semi-arid areas worldwide. Images are available in “high” spatial resolution (with NASA’s satellites Landsat MSS and TM, and ESA’s satellites Spot and Sentinel) and “medium or low” spatial resolution (NOAA AVHRR and MODIS).

To accurately analyse vegetation cover at continental or global scale, it is necessary to use the highest-resolution images available – with a resolution of 1 metre or less – and up until now the costs of acquiring and analysing the data have been prohibitive. Consequently, most studies have relied on moderate- to low-resolution data. This has not allowed for the identification of individual trees, and therefore these studies only yield aggregate estimates of vegetation cover and productivity, mixing herbaceous and woody vegetation.

In a new study covering a large part of the semi-arid Sahara-Sahel-Sudanian zone of West Africa, published in Nature in October 2020, an international group of researchers was able to overcome these limitations. By combining an immense amount of high-resolution satellite data, advanced computing capacities, machine-learning techniques and extensive field data gathered over decades, we were able to identify individual trees and shrubs with a crown area of more than 3 m2 with great accuracy. The result is a database of 1.8 billion trees in the region studied, available to all interested….(More)”

Supercomputing, machine learning, satellite data and field assessments allow to map billions of individual trees in West Africa. Martin Brandt, Author provided

Using Data and Citizen Science for Gardening Success


Article by Elizabeth Waddington: “…Data can help you personally by providing information you can use. And it also allows you to play a wider role in boosting understanding of our planet and tackling the global crises we face in a collaborative way. Consider the following examples.

Grow Observatory

This is one great example of data gathering and citizen science. Grow Observatory is a European citizen’s observatory through which people work together to take action on climate change, build better soil, grow healthier food and corroborate data from the new generation of Copernicus satellites.

Twenty-four Grow communities in 13 European countries created a network of over 6,500 ground-based soil sensors and collected a lot of soil-related data. And many insights have helped people learn about and test regenerative food growing techniques.

On their website, you can explore sensor locations, or make use of dynamic soil moisture maps. With the Grow Observatory app, you can get crop and planting advice tailored to your location, and get detailed, science-based information about regenerative growing practices. Their water planner also allows small-scale growers to learn more about how much water their plants will need in their location over the coming months if they live in one of the areas which currently have available data…

Cooperative Citizen Science: iNaturalist, Bioblitzes, Bird Counts, and More

Wherever you live, there are many different ways to get involved and help build data. From submitting observations on wildlife in your garden through apps like iNaturalist to taking part in local Bioblitzes, bird counts, and more – there are plenty of ways we can collect data that will help us – and others – down the road.

Collecting data through our observations, and, crucially, sharing that data with others can help us create the future we all want to see. We, as individuals, can often feel powerless. But citizen science projects help us to see the collective power we can wield when we work together. Modern technology means we can be hyper-connected, and affect wider systems, even when we are alone in our own gardens….(More)”

Establishment of Sustainable Data Ecosystems


Report and Recommendations for the evolution of spatial data infrastructures by S. Martin, Gautier, P., Turki, and S., Kotsev: “The purpose of this study is to identify and analyse a set of successful data ecosystems and to address recommendations that can act as catalysts of data-driven innovation in line with the recently published European data strategy. The work presented here tries to identify to the largest extent possible actionable items.

Specifically, the study contributes with insights into the approaches that would help in the evolution of existing spatial data infrastructures (SDI), which are usually governed by the public sector and driven by data providers, to self-sustainable data ecosystems where different actors (including providers, users, intermediaries.) contribute and gain social and economic value in accordance with their specific objectives and incentives.

The overall approach described in this document is based on the identification and documentation of a set of case studies of existing data ecosystems and use cases for developing applications based on data coming from two or more data ecosystems, based on existing operational or experimental applications. Following a literature review on data ecosystem thinking and modelling, a framework consisting of three parts (Annex I) was designed. An ecosystem summary is drawn, giving an overall representation of the ecosystem key aspects. Two additional parts are detailed. One dedicated to ecosystem value dynamic illustrating how the ecosystem is structured through the resources exchanged between stakeholders, and the associated value.

Consequently, the ecosystem data flows represent the ecosystem from a complementary and more technical perspective, representing the flows and the data cycles associated to a given scenario. These two parts provide good proxies to evaluate the health and the maturity of a data ecosystem…(More)”.

2030 Compass CoLab


About: “2030 Compass CoLab invites a group of experts, using an online platform, to contribute their perspectives on potential interactions between the goals in the UN’s 2030 Agenda for Sustainable Development.

By combining the insight of participants who posses broad and diverse knowledge, we hope to develop a richer understanding of how the Sustainable Development Goals (SDGs) may be complementary or conflicting.

Compass 2030 CoLab is part of a larger project, The Agenda 2030 Compass Methodology and toolbox for strategic decision making, funded by Vinnova, Sweden’s government agency for innovation.

Other elements of the larger project include:

  • Deliberations by a panel of experts who will convene in a series of live meetings to undertake in-depth analysis on interactions between the goals. 
  • Quanitative analysis of SDG indicators time series data, which will examine historical correlations between progress on the SDGs.
  • Development of a knowledge repository, residing in a new software tool under development as part of the project. This tool will be made available as a resource to guide the decisions of corporate executives, policy makers, and leaders of NGOs.

The overall project was inspired by the work of researchers at the Stockholm Environment Institute, described in Towards systemic and contextual priority setting for implementing the 2030 Agenda, a 2018 paper in Sustainability Science by Nina Weitz, Henrik Carlsen, Måns Nilsson, and Kristian Skånberg….(More)”.

As Jakarta floods again, humanitarian chatbots on social media support community-led disaster response


Blog by Petabencana: “On February 20th, #banjir and #JakartaBanjir were the highest trending topics on Twitter Indonesia, as the capital city was inundated for the third major time this year, following particularly heavy rainfall from Friday night (19/2/2021) to Saturday morning (20/02/2021). As Jakarta residents turned to social media to share updates about the flood, they were greeted by “Disaster Bot” – a novel AI-assisted chatbot that monitors social media for posts about disasters and automatically invites users to submit more detailed disaster reports. These crowd-sourced reports are used to map disasters in real-time, on a free and open source website, PetaBencana.id.

As flooding blocked major thoroughfares and toll roads, disrupted commuter lines, and cut off electricity to over 60,000 homes, residents continued to share updates about the flood situation in order to stay alert and make timely decisions about safety and response. Hundreds of residents submitted flood reports to PetaBencana.id, alerting each other about water levels, broken infrastructures and road accessibility. The Jakarta Emergency Management Agency also updated the map with official information about flood affected  areas, and monitored the map to respond to resident needs. PetaBencana.id experienced a 2000% in activity in under 12 hours as residents actively checked the map to understand the flooding situation, avoid flooded areas, and make decisions about safety and response. 

Residents share updates about flood-affected road access through the open source information sharing platform, PetaBencana.id. Thousands of residents used the map to navigate safely as heavy rainfall inundated the city for the third major time this year.

As flooding incidents continue to occur with increasing intensity across the country, community-led information sharing is once again proving its significance in supporting response and planning at multiple scales. …(More)”.

Mapping urban temperature using crowd-sensing data and machine learning


Paper by Marius Zumwald, Benedikt Knüsel, David N.Bresch and Reto Knutti: :”Understanding the patterns of urban temperature a high spatial and temporal resolution is of large importance for urban heat adaptation and mitigation. Machine learning offers promising tools for high-resolution modeling of urban heat, but it requires large amounts of data. Measurements from official weather stations are too sparse but could be complemented by crowd-sensed measurements from citizen weather stations (CWS). Here we present an approach to model urban temperature using the quantile regression forest algorithm and CWS, open government and remote sensing data. The analysis is based on data from 691 sensors in the city of Zurich (Switzerland) during a heat wave using data from for 25-30th June 2019. We trained the model using hourly data from for 25-29th June (n = 71,837) and evaluate the model using data from June 30th (n = 14,105). Based on the model, spatiotemporal temperature maps of 10 × 10 m resolution were produced. We demonstrate that our approach can accurately map urban heat at high spatial and temporal resolution without additional measurement infrastructure. We furthermore critically discuss and spatially map estimated prediction and extrapolation uncertainty. Our approach is able to inform highly localized urban policy and decision-making….(More)”.

Climate TRACE


About: “We exist to make meaningful climate action faster and easier by mobilizing the global tech community—harnessing satellites, artificial intelligence, and collective expertise—to track human-caused emissions to specific sources in real time—independently and publicly.

Climate TRACE aims to drive stronger decision-making on environmental policy, investment, corporate sustainability strategy, and more.

WHAT WE DO

01 Monitor human-caused GHG emissions using cutting-edge technologies such as artificial intelligence, machine learning, and satellite image processing.

02 Collaborate with data scientists and emission experts from an array of industries to bring unprecedented transparency to global pollution monitoring.

03 Partner with leaders from the private and public sectors to share valuable insights in order to drive stronger climate policy and strategy.

04 Provide the necessary tools for anyone anywhere to make better decisions to mitigate and adapt to the impacts from climate change… (More)”

Geospatial Data Market Study


Study by Frontier Economics: “Frontier Economics was commissioned by the Geospatial Commission to carry out a detailed economic study of the size, features and characteristics of the UK geospatial data market. The Geospatial Commission was established within the Cabinet Office in 2018, as an independent, expert committee responsible for setting the UK’s Geospatial Strategy and coordinating public sector geospatial activity. The Geospatial Commission’s aim is to unlock the significant economic, social and environmental opportunities offered by location data. The UK’s Geospatial Strategy (2020) sets out how the UK can unlock the full power of location data and take advantage of the significant economic, social and environmental opportunities offered by location data….

Like many other forms of data, the value of geospatial data is not limited to the data creator or data user. Value from using geospatial data can be subdivided into several different categories, based on who the value accrues to:

Direct use value: where value accrues to users of geospatial data. This could include government using geospatial data to better manage public assets like roadways.

Indirect use value: where value is also derived by indirect beneficiaries who interact with direct users. This could include users of the public assets who benefit from better public service provision.

Spillover use value: value that accrues to others who are not a direct data user or indirect beneficiary. This could, for example, include lower levels of emissions due to improvement management of the road network by government. The benefits of lower emissions are felt by all of society even those who do not use the road network.

As the value from geospatial data does not always accrue to the direct user of the data, there is a risk of underinvestment in geospatial technology and services. Our £6 billion estimate of turnover for a subset of geospatial firms in 2018 does not take account of these wider economic benefits that “spill over” across the UK economy, and generate additional value. As such, the value that geospatial data delivers is likely to be significantly higher than we have estimated and is therefore an area for potential future investment….(More)”.