Urban Computing for Climate and Environmental Justice: Early Perspectives From Two Research Initiatives

• URL: http://arxiv.org/abs/2410.04318v1
• Date: Sun, 6 Oct 2024 00:32:03 GMT
• Title: Urban Computing for Climate and Environmental Justice: Early Perspectives From Two Research Initiatives
• Authors: Carolina Veiga, Ashish Sharma, Daniel de Oliveira, Marcos Lage, Fabio Miranda,
• Abstract summary: Extreme weather events are becoming more frequent and severe, disproportionately affecting low-income and underrepresented groups. We present two multiyear, multidisciplinary projects situated in Chicago, USA and Niter'oi, Brazil. We discuss the essential requirements, as well as existing gaps, for visual analytics tools that facilitate the understanding and mitigation of climate-related risks in urban environments.
• Score: 2.8980151855313387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The impacts of climate change are intensifying existing vulnerabilities and disparities within urban communities around the globe, as extreme weather events, including floods and heatwaves, are becoming more frequent and severe, disproportionately affecting low-income and underrepresented groups. Tackling these increasing challenges requires novel approaches that integrate expertise across multiple domains, including computer science, engineering, climate science, and public health. Urban computing can play a pivotal role in these efforts by integrating data from multiple sources to support decision-making and provide actionable insights into weather patterns, infrastructure weaknesses, and population vulnerabilities. However, the capacity to leverage technological advancements varies significantly between the Global South and Global North. In this paper, we present two multiyear, multidisciplinary projects situated in Chicago, USA and Niter\'oi, Brazil, highlighting the opportunities and limitations of urban computing in these diverse contexts. Reflecting on our experiences, we then discuss the essential requirements, as well as existing gaps, for visual analytics tools that facilitate the understanding and mitigation of climate-related risks in urban environments.

Related papers

• Towards A Comprehensive Assessment of AI's Environmental Impact [0.5982922468400899]
  Recent surge of interest in machine learning has sparked a trend towards large-scale adoption of AI/ML. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations.
  arXiv  Detail & Related papers  (2024-05-22T21:19:35Z)
• The Need for Climate Data Stewardship: 10 Tensions and Reflections regarding Climate Data Governance [0.21756081703275998]
  Article advocates for a paradigm shift towards multi-stakeholder governance, data stewardship, and equitable data practices. It underscores the critical role of data stewards in navigating these challenges.
  arXiv  Detail & Related papers  (2024-03-26T21:16:03Z)
• Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques [0.9387233631570752]
  We use BERT (Bidirectional Representations from Transformers) for Named Entity Recognition (NER) to identify specific geographies within the climate literature. We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area. These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies.
  arXiv  Detail & Related papers  (2024-01-11T16:44:59Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study [94.07737890568644]
  As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
  arXiv  Detail & Related papers  (2023-10-24T15:15:28Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• Remote sensing, AI and innovative prediction methods for adapting cities to the impacts of the climate change [0.0]
  I propose an AI-based framework which might be useful for extracting indicators from remote sensing images. I underline that this is an open field and an ongoing research for many scientists, therefore I offer an in depth discussion on the challenges and limitations of AI-based methods.
  arXiv  Detail & Related papers  (2021-07-06T15:55:26Z)
• Quantum technologies for climate change: Preliminary assessment [0.0]
  Climate change presents an existential threat to human societies and the Earth's ecosystems. Quantum technologies in computing, sensing, and communication could become useful tools to diagnose and help mitigate the effects of climate change. This report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas.
  arXiv  Detail & Related papers  (2021-06-23T18:02:19Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.