Urban Computing and Environmental Justice: Tackling Climate Risks in Global Cities

The paper explores how urban computing can address climate and environmental justice in cities, focusing on two projects in Niteroi, Brazil, and Chicago, USA. It highlights both the potential and challenges of integrating data and visual tools to manage environmental risks and promote equity.

A study by Carolina Veiga, Ashish Sharma, Daniel de Oliveira, Marcos Lage, and Fabio Miranda, from the University of Illinois, Universidade Federal Fluminense, and University of Illinois Chicago, explores how urban computing can be used to address climate and environmental justice issues. The researchers focus on two distinct urban contexts, Niteroi in Brazil and Chicago in the USA, to understand how data analytics and visual tools can help mitigate the impacts of climate change, especially on vulnerable urban communities. As climate change exacerbates existing disparities in cities, particularly for low-income and underrepresented groups, urban computing offers a way to leverage technological advancements for better decision-making. However, the paper highlights the uneven capacity to apply these technologies across the Global North and South, creating significant gaps in addressing climate-related risks.

The Power of Urban Computing for Climate Action

Urban computing, which involves the collection, processing, and analysis of urban data from various sources, plays a critical role in managing environmental risks like flooding, heatwaves, and other extreme weather events. This approach allows for the integration of data from sensors, public datasets, simulations, and demographic information to inform policymakers and urban planners. The researchers explain that urban computing has already contributed to improvements in disaster management, air pollution tracking, and city infrastructure. However, challenges remain, particularly in ensuring that these tools are accessible and usable across different regions and contexts. The two projects presented in the paper demonstrate both the potential and limitations of using urban computing to address climate and environmental justice.

Niteroi: Managing Floods and Landslides with Data

In Niteroi, Brazil, the project focuses on managing disasters such as landslides and floods. Niteroi, a mountainous region prone to heavy rainfall, frequently experiences climate-related disasters. To address this, the researchers worked with local officials to design and implement a system that analyzes rainfall data and related events, such as landslides, to improve emergency responses. The project involved collecting a wide range of urban data, including rainfall volumes, flood occurrences, traffic camera videos, and demographic information. This data was then used to train a computer vision model to detect flood-prone areas, helping local authorities to anticipate risks and respond more effectively. The system also provides real-time information for emergency vehicles, offering alternative routes based on traffic and flood conditions. Visual analytics, such as heatmaps and line charts, were employed to present the data in an accessible format for city officials, enabling more data-driven decision-making.

Chicago: Highlighting Environmental Justice through Community Involvement

In contrast, the Chicago project focuses on issues of environmental justice, particularly in low-income communities of color, who are disproportionately affected by climate risks. The researchers partnered with Latino and African American communities in Chicago to develop tools that highlight environmental hazards such as pollution and extreme heat. These communities, located near industrial corridors and highways, face significant exposure to air pollution and other environmental risks. The project used a participatory approach, involving community members in the data collection process to ensure that the tools developed addressed their specific concerns. Data was gathered from various sources, including crowdsourced data from platforms like OpenStreetMap, as well as satellite and sensor data on temperature and air quality. This data was then integrated into dashboards that allowed users to explore environmental and climate risks in their neighborhoods. By combining demographic data, such as income levels and racial composition, with environmental data, the dashboards provided a clear picture of how environmental hazards disproportionately affect vulnerable populations.

Addressing Data Gaps and Visualization Challenges

Both projects demonstrate the potential of urban computing to address complex urban challenges, but they also highlight significant limitations. One of the major challenges in both Niteroi and Chicago was the collection and management of data. Urban data is often heterogeneous, coming from multiple sources with different formats and levels of reliability. In Niteroi, for example, there were significant gaps in the flood occurrence data, which made it difficult to create a comprehensive model of flood risks. Similarly, in Chicago, the integration of diverse data sources, such as crowdsourced data and satellite images, required extensive processing and transformation before it could be used in the dashboards. The researchers also noted that the size and complexity of urban datasets can make them difficult to analyze, especially when spatial and temporal patterns need to be compared.

Building a Future of Collaborative and Inclusive Urban Computing

Another challenge was ensuring that the visual tools developed in both projects were accessible to users with different levels of expertise. In Chicago, the researchers worked to create a flexible interface that could be adapted to the needs of different users, from community members to policymakers. This involved designing a grammar-based framework that allowed for quick iteration of visualization designs, ensuring that the tools were both user-friendly and effective in communicating complex data. However, maintaining the connection between the data and the visualizations proved to be a challenge, particularly as new data was added or requirements changed. The researchers emphasized the need for more reusable and adaptable tools that can be easily applied to different urban contexts.

The paper concludes by calling for stronger collaborations between researchers, policymakers, and communities to ensure that urban computing tools are not only technologically advanced but also socially equitable. The authors argue that more effort is needed to move beyond siloed projects and ensure that the tools developed in one context can be reused in others. This would allow urban computing to more effectively contribute to the development of healthy and equitable cities in the face of climate change.