Planning for resilient cities requires an evidence-based understanding of flood risk and the involvement of stakeholders and local actors. The paper addresses research developed within the URCA!—Urban Resilience to Climate Change: to activate the participatory mapping and decision support tool for enhancing sustainable urban drainage—project. A top-down/bottom-up participatory and flexible methodology for the conception of participatory mapping aimed at the planning and installation of sustainable urban drainage systems (SUDS) on the territory is then developed. The innovative methodology is applied and tested in the case study of the Sampierdarena district in Genoa, northern Italy. This research paper illustrates the development of a participatory map (Pmap) that can support the implementation of SUDS as mitigation/adaptation strategies, integrating technical assessment and containing community visions and expectations. Findings concerning the connections between proposed SUDS locations and their frequencies confirm the relevance of the commercial area and the main traffic lanes along, confirming that all zones characterized by intense vehicular and pedestrian flow are suitable for SUDS as a solution to contribute to urban flood resilience. The georeferenced and intergenerational Pmap may be integrated into a decision support system to be developed as a guidance tool for the public administration. 

Participatory Mapping for Enhancing Flood Risk Resilient and Sustainable Urban Drainage: A Collaborative Approach for the Genoa Case Study

This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed patterns analyses. While previous approaches lack predictive air quality options, this interface provides back-and-forth dialogue with the designed multiple Machine Learning (ML) models and comparisons for better visual predictive assessments. These models can be dynamically selected in real-time, and the user could visually compare the results in different time conditions for chosen parameters. Moreover, AQTA provides data selection, display, visualisation of past, present, future (prediction) and correlation structure among air parameters, highlighting the predictive models effectiveness. AQTA has been evaluated using Stuttgart (Germany) city air pollutants, i.e., Particular Matter (PM) PM10, Nitrogen Oxide (NO), Nitrogen Dioxide (NO2), and Ozone (O3) and meteorological parameters like pressure, temperature, wind and humidity. The initial findings are presented that corroborate the city’s COVID lockdown (year 2020) conditions and sudden changes in patterns, highlighting the improvements in the pollutants concentrations. AQTA, thus, successfully discovers temporal relationships among complex air quality data, interactively in different time frames, by harnessing the user’s knowledge of factors influencing the past, present and future behavior, with the aid of ML models. Further, this study also reveals that the decrease in the concentration of one pollutant does not ensure that the surrounding air quality would improve as other factors are interrelated.

Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments

The study set out to investigate how the experience of creating a map-based participatory system might help identify what is needed to support the production of relevant volunteered geographic information (VGI) about urban areas exposed to impacts of adverse weather events in Trondheim, Norway. This article details the systematic approach used to collect VGI, starting from the active engagement of end users during the design and development process of the CitizenSensing participatory system, through using the system in two VGI campaigns, up to the examination of the collected data. Although the VGI examination identified exposed areas in Trondheim, for instance, those that are likely to accumulate surface water from heavy rains or meltwater, the experience gained from the use of the CitizenSensing system helped to identify some critical points regarding the production of relevant VGI. Potential practical implications justify the need for VGI. For instance, in the case of Trondheim, relevant VGI may result in better planned municipal interventions regarding city infrastructure for pedestrians, cyclists and drivers, increased public awareness and access to local knowledge about areas exposed to inundation. The study also confirmed the need for adequate system components for VGI vetting and exploration in the post-collection stage to obtain a comprehensive insight into collected VGI.

/pdf/identifying-relevant-volunteered-geographic-information-3fujsj6e.pdf

Identifying relevant volunteered geographic information about adverse weather events in Trondheim using the CitizenSensing participatory system

Extreme weather events, such as flooding, are expected to increase in frequency and intensity. Therefore, the prediction of extreme weather events, assessment of their local impacts in urban environments, and implementation of adaptation measures are becoming high-priority challenges for local, regional, and national agencies and authorities. To manage these challenges, access to accurate weather warnings and information about the occurrence, extent, and impacts of extreme weather events are crucial. As a result, in addition to official sources of information for prediction and monitoring, citizen volunteered geographic information (VGI) has emerged as a complementary source of valuable information. In this work, we propose the formulation of an approach to complement the impact-based weather warning system that has been introduced in Sweden in 2021 by making use of such alternative sources of data. We present and discuss design considerations and opportunities towards the creation of a visual analytics (VA) pipeline for the identification and exploration of extreme weather events and their impacts from VGI texts and images retrieved from social media. The envisioned VA pipeline incorporates three main steps: (1) data collection, (2) image/text classification and analysis, and (3) visualization and exploration through an interactive visual interface. We envision that our work has the potential to support three processes that involve multiple stakeholders of the weather warning system: (1) the validation of previously issued warnings, (2) local and regional assessment-support documentation, and (3) the monitoring of ongoing events. The results of this work could thus generate information that is relevant to climate adaptation decision making and provide potential support for the future development of national weather warning systems.

Towards a Volunteered Geographic Information-Facilitated Visual Analytics Pipeline to Improve Impact-Based Weather Warning Systems

Despite the urgent need to take action on climate adaptation, public engagement remains relatively low. This low engagement poses significant challenges to the effective adaptation process, highlighting the need for innovative approaches in governance. In this paper, we explore the importance of the affective dimension in enhancing citizen engagement in climate adaptation. Drawing from a review of recent literature, we identify the primary challenges of fostering citizen engagement by integrating research on climate adaptation with the exploration of the affective dimension and the conceptualization of affective practices. We aim to identify specific “affective facilitators’’ that have the potential to enhance citizen engagement and drive actionable outcomes. We advocate for the use of Citizen Science as a means to support citizen engagement, due to its potential to harness the identified facilitators. We illustrate the practical application of our conceptual framework with the Citizen Sensing Project. 

Exploring the affective dimension in citizen science to support urban climate adaptation: a conceptual framework

This paper describes the progressive development of three approaches of successively increasing analytic functionality for visually exploring and analysing climate-related volunteered geographic information. The information is collected in the CitizenSensing project within which urban citizens voluntarily submit reports of site-specific extreme weather conditions, their impacts, and recommendations for best-practice adaptation measures. The work has pursued an iterative development process where the limitations of one approach have become the trigger for the subsequent ones. The proposed visual exploration approaches are: an initial map application providing a low-level data overview, a visual analysis prototype comprising three visual dashboards for more in-depth exploration, and a final custom-made visual analysis interface, the CitizenSensing Visual Analysis Interface (CS-VAI), which enables the flexible multifaceted exploration of the climate-related geographic information in focus. The approaches developed in this work are assessed with volunteered data collected in two of the CitizenSensing project’s campaigns held in the city of Norrkoping, Sweden.

/pdf/a-progressive-development-of-a-visual-analysis-interface-of-4o58svjawi.pdf

A progressive development of a visual analysis interface of climate-related VGI

Visual Exploration of Climate-Related Volunteered Geographic Information

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A progressive development of a visual analysis interface of climate-related VGI

Identifying relevant volunteered geographic information about adverse weather events in Trondheim using the CitizenSensing participatory system

Visual Exploration of Climate-Related Volunteered Geographic Information