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.

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.

Abstract. 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.

Urban intelligence is the ability to understand and navigate the physical and digital dimensions of “connected complex urban places”. For example, new infrastructures (e.g., sensors, Internet of Things {IoT} devices like smart lamp posts) are needed to capture and represent places in software platforms and on the Internet. New spatial skills and spatial thinking are needed to navigate these new interfaces and networks of places. This paper aims at understanding urban intelligence by exploring variations in how smart cities have been conceptualized; how citizens have been placed within the smart city; and how Canada’s smart cities initiative has placed on urban (and highly spatial) problems over digital technologies. The metaphor of the Roman arch is used to describe the interdependency of the building blocks of smart cities. Components (building blocks) of the smart city, be they openness, resilience or inclusion, must all be present, and build towards what we argue is the keystone of urban intelligence. We discuss how these components lead to a new consideration of the smart city, the Intelligent City.

La ville intelligente est considérée par les territoires urbains comme la solution par excellence pour répondre aux enjeux démographiques, économiques, sociaux et environnementaux auxquels ils sont actuellement confrontés. Pourtant, entre l’optimisation technologique des infrastructures d’ingénierie urbaine, le développement d’une économie de start-up ou les sirènes de la gouvernance urbaine basée sur les données, il est bien difficile d’évaluer les effets sociaux de ces transformations numériques à moyen et long terme. Même si l’engagement des citoyens, la gouvernance ouverte ou le développement durable sont généralement associés au discours sur les villes intelligentes, les risques éthiques tels que le suivi individuel, le profilage socio-spatial, la justice spatiale ou l’inclusivité demeurent des enjeux importants. Cet article propose le modèle d’Inukshuk City, comme levier de développement d’une nouvelle forme d’intelligence urbaine.

There is growing interest in assessing local food systems to guide efforts toward sustainability and aligning these assessments with the United Nations’ 17 Sustainable Development Goals (SDGs). However, the complexity of portraying local food systems poses numerous challenges for local communities, and automated text analysis and artificial intelligence (AI) offer promising solutions. This study tested the use of an automated textual analysis to assess the alignment of the Mauricie region’s food system in Quebec, Canada, with the SDGs. The analysis examined 35 organizational documents from the region using an automated text analysis based on a list of keywords for each SDG. Initially, the analysis revealed that several initiatives in the Mauricie region covered specific SDGs quite well, such as eliminating hunger (SDG 2). Areas such as health and well-being (SDG 3) received moderate attention, while SDGs such as life below water and on land (SDGs 14 and 15) were less emphasized. When these results were presented to regional stakeholders, these stakeholders reported that the findings did not closely reflect their perceptions of the food system. This study confirms the potential of automated textual analysis and AI in assessing local food systems and underscores the parameters and challenges of accurately portraying sustainability in local food systems.

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts.

Numerous studies have attempted to assess the quality of OpenStreetMap's building data by comparing it to reference datasets. Map matching (feature matching) is a critical step in this method of quality assessment, involving the matching of polygons in the two datasets. Researchers commonly use two main polygon matching algorithms: 1) the buffer intersection method and 2) the centroid comparison method. While these methods are effective for the majority of OSM building footprints, they may not achieve high accuracy in complex situations. One possible reason is that both methods only consider the position of the OSM polygon compared to that of the reference polygon. To improve these matching algorithms and propose a more robust solution, this study proposes an algorithm that considers shape similarity (using average distance method) in addition to position similarity to better identify corresponding polygons in the two datasets. The experiment results for five cities in the Province of Quebec indicate that the proposed algorithm can reduce the matching error of previous map matching algorithms from approximately 8% to approximately 3%. Furthermore, the study found that the proposed polygon matching algorithm performs more accurately than previous methods when buildings consist of multiple polygons.

Over the past two decadesDecades, patient-centeredPatient‐centered approaches have gradually been adopted to engage patientsPatients as a partnerPartner in the decisionDecision-making process and take into account their specificitiesSpecificities, valuesValue, and experiencesExperience. The Montreal ModelMontreal model combined with the DisabilityDisabilities CreationCreation Process (DCP) ModelModels is an innovative way of building a real professional-patientProfessional‐patient learning interactionLearning interaction and partnershipPartnership for addressing disabilityDisabilities as a component of the environmentEnvironment. As regards deafnessDeafness, in particular, cochlear implantsCochlear implant have become the principal way of supporting people who are severely deafDeaf. With the aid of cochlear implantsCochlear implant, a kind of electronic processorElectronic processor, it would be possible for these people to detect soundSound better. This chapter aims to demonstrate, through a few concrete examples (e.g., the Atomic Box projectAtomic box project), to what extent the combinationCombination of art and scienceArt and science makes it possible to develop socially innovative responsesSocially innovative responses (based on the two modelsModels mentioned above), and thus to assist the adoption of implants by hearing-impaired usersHearing‐impaired users. AtomicAtomic box: ArtisticArtistic and designDesign creativeCreative process The code of this chapter is 01101110 01100100 01101110 01100101 01010101 01110010 01100001 01100100 01100111 01110011 01101110 01110100 01101001 00100000.

OpenStreetMap (OSM) is one of the most well-known volunteered geographic information (VGI) projects that aims to produce a free-world map. However, there are serious concerns about its quality. Numerous studies have assessed the quality of OSM by comparing the OSM database with a reference database. Several researchers have proposed the use of quality indicators as variables that can describe OSM quality in regions where no reference data are available. A quality indicator is a variable that has a significant monotonic relationship with quality measures. In this study, a literature review was conducted to identify and define the main quality measures proposed for assessing the quality of linear features. Owing to limited access to current data, only three quality elements—completeness, positional accuracy, and attribute accuracy—were evaluated in this study. These quality measures were then used to assess the quality of the OSM roads in the province of Quebec. Finally, Spearman’s rank correlation coefficient test was applied to determine whether there was a significant correlation between the quality measures related to the three quality elements and the five potential quality indicators: population, average income, density of OSM roads, density of OSM buildings, and number of points of interest (POI). The main contribution of this study is testing the following hypothesis: “There is a significant correlation between the five mentioned variables and the measures related to the three quality elements”. Statistical analysis showed that in terms of completeness, the density of OSM roads and population were the best indicators; in terms of positional accuracy, population and income were the best indicators; and in terms of attribute accuracy, completeness was the best indicator. All five variables have significant correlations with the measures of the three elements of quality, except for the following two pairs (attribute accuracy, density of OSM roads) and (attribute accuracy, density of OSM buildings). This study proposes the density of OSM roads and number of POI as two new quality indicators that have not been found in the literature review.

The wayfinding feature highly contributed to the rise of cartographic platforms. However, no study really focused on the way these tools are both used and perceived in a wayfinding context. The qualitative investigation we conducted on 30 users of such platforms suggests that the related uses depend on - at least - two mental models: the first one assumes that the cartographic platform is a digital extension of paper maps, while the other one likens it to a GPS system.

Résumé. L’apparition des Technologies de Géolocalisation (TdG) a profondément modifié notre rapport à l’espace. Ces dernières influencent singulièrement notre manière de communiquer et de raisonner spatialement. Après avoir brièvement retracé l’avènement des TdG, ce chapitre dresse une typologie inédite des mobilités soutenues par l’usage combiné de ces médias situés et des données géographiques qu’ils mobilisent. Celle-ci est complétée par un exemple concret d’exploitation de données de mobilités contributives à des fins d’identification de points de repère sémantiques. Mots-clés

In response to the current Coronavirus SARS-CoV-2 pandemic, many countries are developing digital strategies for the identification of individuals who have been in contact with infected persons. Those strategies are mainly based on Contact Tracing applications. While the effectiveness of these technologies has not yet been demonstrated, and that their deployment conditions remain socially complex, they raise serious ethical questions. This paper presents an overview of the development of Contact Tracing and, suggests a reflection and possible solutions for their ethical and sustainable deployment through a more active and transparent citizen engagement.

For validating the feasibility and effectiveness of the WikiGIS concept that has been proposed as a solution to better meet the main dimensions and requirements of Geodesign process, we have conducted a qualitative study based on a mixed methodology: questionnaire and interview with experts in various fields such as geomatics, architecture, urban planning, Geodesign, etc. This qualitative study aims initially at ensuring the relevance of developed features, which are managing traceability contributions, navigation in the history of contributions via a time browser and cartographic interface GeoWeb 2.0; and proposed WikiGIS features, which are the parameters of the data quality, the deltification, geoprocessing and sketching tools and the multimedia hyperlinks supporting the argument. Second, it is an opportunity to better understand Geodesign. In fact, thirty (30) experts have been involved. The results show that WikiGIS features are very relevant to support the collaborative dimension of the Geodesign process.

Smart cities are especially suited for improving urban inclusion by combining digital transition and social innovation. To be smart, a city has to provide every citizen with urban spaces, public services, and common goods that are effectively affordable, whatever the citizen’s gender, culture, origin, race, or impairment. Based on two design workshops, the “Vibropod” and the “Pointe-aux-Lièvres”, this paper aims at highlighting the contributions of design fiction to the improvement of the spatial capability of hearing impaired people. This research draws its originality from both its conceptual framework, built on an interdisciplinary and intersectoral composition of arts and sciences, and its operational approach, based on the use of the DeafSpace markers and the TRIZ theory (Russian acronym for Inventive Problem Solving Theory) principles. The two design fiction workshops demonstrate that considering the singularity of the human being as an actual acoustic material constitutes an innovative opportunity to improve the role of universal design in a smart city project. By reversing the classic posture, and defining disability by looking at characteristics of the environment rather than as limits of the people themselves (their bodies or their senses), this research proposes an innovative way of addressing smart city inclusivity issues. This paper shows how increasing spatial enablement and having better control of spatial skills can offer deaf people new skills to improve the use of technology in support of urban mobility, as well as give them tools for feeling safer in urban environments.

The wayfinding feature highly contributed to the rise of cartographic platforms. However, no study really focused on the way these tools are both used and perceived in a wayfinding context. The qualitative investigation we conducted on 30 users of such platforms suggests that the related uses depend on – at least – two mental models: the first one assumes that the cartographic platform is a digital extension of paper maps, while the other one likens it to a GPS system.

Smart cities frequently rely on vast sensor networks, such as traffic cameras and ventilation controllers. This requires that we rethink methods of spatial tessellation. As tessellation is becoming more dynamic, we often combine multiple tessellation methods and switch tessellation shapes frequently for different data collection and analytics. In this article, we review how tessellation works with the object and field geographic spatial models. To achieve the “smartness” within cities, this article introduces the dynamic tessellation approach as the initial solution. Key Words: big data, sensor network, smart city, spatial tessellation.