Stéphane Roche

Social media platforms generate large volumes of unsolicited textual content that reflect users’ perceptions of urban transportation. This study proposes a fine-grained annotation framework that classifies social media text into three constructs: attitude, intention, and reported behavior. Each statement is linked to a specific transportation mode and sentiment polarity. Two large language models (LLMs), GPT-5.5 and DeepSeek-Flash, were evaluated using few-shot in-context learning on 200 manually annotated English–French Reddit discussions from Quebec City, Canada. GPT-5.5 was selected based on its mean joint relaxed F1-score and applied to 2475 remaining discussions. The model extracted 23,634 transportation-mode mentions and 10,760 mode-specific construct statements after deduplication. Public transportation was the most frequently mentioned category and had the highest within-mode proportion of negative statements, although its positive statements outnumbered its negative statements. Attitude was the most frequently identified construct, while intention was the least frequent. The framework distinguishes evaluations, future plans, and self-reported actions or experiences, providing a more detailed description of transportation discourse. Given the Reddit sample and remaining extraction errors, these findings are exploratory and may complement other evidence used in sustainable urban mobility planning.

Climate change is one of the most serious global challenges, and greenhouse gas emissions continue to rise despite mitigation efforts. Household consumption accounts for approximately 72% of global emissions, indicating the central role of individual behaviors. Traditional measurement instruments, such as surveys and interviews, are costly, time-consuming, and subject to response biases. User-generated textual content provides an alternative source of evidence, and natural language processing (NLP) enables its analysis at scale. Despite this potential, existing reviews have not systematically mapped its use for individual-level climate behaviors. The main goal of this research is to address this gap through a scoping review following PRISMA-ScR guidelines. Systematic searches were performed in Web of Science, Engineering Village, and Google Scholar, covering January 2015 to April 2026. A total of 2580 records were screened, and ten studies met the inclusion criteria. These studies analyzed Twitter/X, Sina Weibo, Reddit, and e-commerce reviews, covering behaviors from green transportation to waste management. The findings demonstrate that topic modeling and transformer-based models are the dominant techniques, typically combined with sentiment analysis. Recent studies extend beyond describing climate discourse toward explaining behavior. NLP-based text analysis constitutes a scalable complement to surveys and a foundation for targeted climate interventions.

This systematic review examines the usefulness of geospatial data for localization and prioritization of sustainable development goals (SDGs) and where they are applicable to the local level. This study reviewed 147 peer-reviewed articles and identified 21 of the most significant sustainable development goals that use geospatial data for monitoring and evidence-based decision-making, strengthening the alignment between local development priorities and SDGs, including the difficulties in identifying and prioritizing SDG targets that are most relevant to local contexts. This study aims to extract and analyze data from the article corpus; assess the methodological quality of studies; identify major themes and research gaps; and synthesize knowledge on localization using geospatial data for the assessment and monitoring of SDGs. The study analyzes whether the scientific literature provides solid evidence to address the difficulties related to the use of geospatial data to support the process of evaluating SDGs at basic scales of governance systems. The methodologies are applied to carry out a categorization of articles into seven categories that present a series of considerations necessary for the implementation of SDGs at the local level. Then we characterize the types of geospatial data, methodological approaches, and analytical techniques used for local SDG assessment and monitoring. Finally, we will synthesize the contributions, limitations, and challenges associated with the use of geospatial data for SDG monitoring at the local scale. The results highlight that among the reviewed studies, almost all geospatial data are used to monitor purposes, while only a few support decision-making processes. This reflects the very limited use of geospatial data in the governance of SDGs at the local level. Furthermore, SDG indicators did not provide trends, and difficulties were noted in disaggregating the data at urban scales, particularly for social and economic SDGs. Multi-strategy approaches, including GeoAI and Geo big data, produce better results than the application of single strategies of geospatial data analysis for revealing whether the performance in the targeted territories of the SDGs is going well or not. The findings of this study will contribute to the growing body of knowledge related to sustainable development goals and provide practitioners with useful information retrieved from geospatial data and practical guidance when implementing sustainable development goals in large-scale territories, like municipalities and cities.

Social media discussions about climate change offer valuable insights into how the public views climate issues and their willingness to engage in personal climate actions. Understanding individual climate actions is crucial because households are responsible for more than 70% of global carbon emissions, and lifestyle changes alone can reduce carbon emissions by approximately 15%. This research examines 17926 textual entries from two Canadian subreddits (r/ClimateCrisisCanada, r/AskACanadian) from January 2023 to December 2024 to identify individual activities and behaviours associated with greenhouse gas (GHG) emissions discussed in Canadian climate discourse, addressing this gap in understanding authentic climate behavior priorities. We employed BERTopic (Bidirectional Encoder Representations from Transformers for topic modelling) in three configurations: baseline class-based Term Frequency-Inverse Document Frequency (c-TF-IDF), Maximal Marginal Relevance (MMR) diversity optimization, and KeyBERT semantic enhancement. This multi-configuration approach was essential because individual climate behaviors are discussed using diverse terminology and overlapping contexts, requiring different analytical lenses to capture the full spectrum of behavioral discussions without missing nuanced distinctions. Thematic analysis revealed that Canadians predominantly discuss four key behavioural domains in climate discourse: energy management (representing the largest category with 3-6 topics depending on model configuration), transportation choices (consistently 2 topics across all models), dietary decisions (2-3 topics), and consumption patterns (1 topic focused primarily on waste management and recycling). Energy discussions proved most diverse, encompassing residential heating solutions, renewable energy adoption, and nuclear power preferences, whilst transportation maintained remarkable consistency across models with distinct themes of sustainable mobility options and vehicle technology transitions. The comparative modelling approach demonstrated that different BERTopic configurations capture complementary aspects of climate behaviour discourse, with KeyBERT's semantic enhancement providing the most detailed categorization of technical solutions. These findings provide empirical evidence for understanding Canadian individual climate action priorities, offering essential insights for targeted climate policy and communication strategies.

Household emissions, particularly from food consumption, represent a substantial yet underutilized component of urban greenhouse gas (GHG) emissions. While most research focuses on national or industrial scales, household behaviours remain underrepresented in local mitigation strategies. Addressing this gap requires approaches that account for both household choices and spatial context. This study applies a behavioural–geospatial framework to survey data from Québec City households to quantify and spatialize diet-related GHG emissions. Meat consumption frequencies were converted into emissions using food-specific factors, and four realistic dietary scenarios were modelled at the district level. The results reveal substantial intra-urban disparities. Districts with higher reported meat consumption exhibit significantly greater mitigation potential, independent of population size. When combined with sociodemographic indicators such as income, the analysis highlights spatial structuring of behaviours commonly perceived as private choices. By transforming survey-based behavioural data into spatially explicit GHG information, this study expands geospatial analytics beyond infrastructure-based emissions and into the domain of lifestyle-driven carbon footprints. The proposed framework enables municipalities to identify high-potential districts, design targeted interventions, and integrate eco-conscious consumption into climate resilience and socio-ecological transition strategies.

Assessing the similarity between polygonal shapes is a fundamental problem in geographic information science (GIS) with applications in spatial data quality assessment, feature matching, and cartographic generalization. This paper introduces a novel and computationally efficient shape similarity measure tailored for comparing building footprints in OpenStreetMap (OSM). Unlike traditional methods that rely on complex transformations such as Fourier descriptors or graph-based techniques, our approach is based on the average boundary distance between two polygons after applying translation and rotation corrections. This method is both easy to implement and computationally light, making it suitable for large-scale applications. The proposed measure demonstrates strong alignment with human perception of shape similarity. However, a notable limitation is that it tends to produce similarity values predominantly within the range of 70% to 100%. This behaviour arises because the measure emphasizes overall shape alignment while overlooking finer local discrepancies. As a result, subtle deviations, such as missing details or minor geometric distortions, may not significantly impact the computed similarity score. Despite this drawback, the method remains a practical and efficient alternative for evaluating shape similarity in large spatial datasets, particularly where computational simplicity and scalability are prioritized. Future works can explore potential refinements to enhance sensitivity to local shape variations while maintaining computational efficiency.

Since 2015 and the Paris Agreements, several countries have committed to sustainable development (SD) and the Sustainable Development Goals (SDGs). Higher Education Institutions (HEIs) have an important role to play in providing education and supporting research activities that integrate SD and SDG concepts. However, the context where the HEI is located has an impact on the level of development and integration of strategic guidelines, methods, and tools for measuring the performance of SDGs within the HEI. The United Nations framework remains the most developed and used tool, but it stays very global and needs to be adapted to other contexts, which leads to local initiatives by some HEIs in developing their tools. The response of HEIs to this challenge differs from one context to another, and this article aims to (i) provide a framework to analyze the different HEI contexts based on their own global, national, and local context; (ii) present and compare the context analysis of three different HEIs (ULaval, Sorbonne Univ, and UWE) in three different countries worldwide (Canada, France, and England), and (iii) discuss the limits, challenges, and research opportunities in the subject of SDG integration within HEIs. Notably, the context analysis of ULaval and UWE case studies showed that the Canadian and UK scales give global orientations with a delegation to the Quebec Province and England government for the education and research strategies. A strong leadership comes from the HEIs themselves in developing their own methods and tools for assessing and monitoring the SDGs, as is the case with ULaval and UWE. On the other hand, the Sorbonne Univ case follows the French national and European-United Nation framework but is less committed to developing its own tools and methods.

Introduction: Many tools support housing decisions for older adults but often overlook mobility patterns and social health. We explored these factors in older Canadians living at home to inform housing decisions. Methods: We conducted a mixed-methods study with 20 older adults (65+) from Quebec and Alberta living independently or in senior residences with outdoor mobility. Data collection included sociodemographic information, GPS tracking, walking interviews, daily journals, and in-depth interviews. Data from interviews, which explored physical and social assets and barriers to social health and mobility, were analyzed using deductive content analysis in NVivo 12. GPS data were subjected to spatial analysis in QGIS (Quantum Geographic Information System) to map activity spaces and mobility patterns by the number and distance of activities, activity types, and modes of transportation. Daily journals were transcribed into an Excel spreadsheet and compared with GPS data. Overall analysis was guided hierarchically by qualitative data, utilizing verbatim narratives and visualization (activity space maps) to illustrate data convergence. Results: Among 20 participants, 14 completed all activities, including GPS trackers. GPS maps showed participants mostly left home to drive for shopping or walking. Over 14 days, participants made an average of 10.4 (±5.8) trips and traveled 186.9 km (±130.4), averaging 16.8 km (±29.8) per day. Transportation modes included car (n=9), walking (n=5), and bus (n=2). Daily journals revealed that participants typically traveled alone. Interviews identified physical assets as libraries and supermarkets (n=10), while social assets were family support when desired (n=13) neighborhood familiarity (n=14), both contributing to social health. Winter weather was the most cited mobility barrier (n=13). Conclusions: These findings provide actionable insights to guide the development of user-informed decision support tools tailored to the housing decisions of Canadian older adults.

Space and place are two of the main concepts in several fields of knowledge, such as human geography, environmental psychology, urban sociology, architecture, urban planning, and others. Space is an objective and structured concept. It is mainly a physical location characterized by measured dimensions and geographical coordinates, while place is a location that holds meaning and value for an individual or a group, created through the human experience and social interactions. Sense of place is thus a set of precepted meanings and attitudinal ties toward a place (conative, affective, and cognitive bonds). From a geospatial perspective, the subjective aspect of sense of place is difficult to depict in a cartographic projection. From this angle, Place-Based Geographic Information Systems represent a set of initiatives that attempt to combine the objectivity of spaces and the subjectivity of places in digital systems, and that integrate spatial semantic characteristics as described by places’ users. In this paper, the methodological approach is mainly based on a systematic analysis and search of the scientific literature. It is a narrative review inspired and based on a scoping review strategy following the JBI methodology and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews) checklist. This bibliographic analysis is about understanding the characterization and the components of sense of place. Results take the form of a synthesis of the conceptual approaches most prominent in the literature, in addition to a conceptual model encompassing the full conceptual specificities of the sense of place concept.