This paper explores the influence of social media in fostering resilience within an urban spatial context, specifically in Bangalore, India, during the COVID-19 lockdown, a period marked by a surge in digital communication due to movement restrictions. To control the rapid spread of the virus, over 1.38 billion people were given stay-at-home orders by the government of India during the onset of the pandemic. The restrictions in movement forced individuals to shift to online modes of connection and communication. As the field of digital epidemiology, that is, the use of digital tools and data to understand and improve health took center stage during the pandemic, the focus shifted towards the social media landscape, which is often associated with its negative aspects, such as misinformation. However, this paper delves into social media's potential to build resilience on a local scale, particularly given its increased usage during the pandemic. Through in-depth online interviews with eight urban residents, we conducted a thematic analysis to understand social media's role during the lockdown. Results indicate that social media facilitated effective information exchange and fostered a sense of community. Furthermore, it engendered an environment conducive to prosocial behavior, a known resilience amplifier. We also highlight the importance of baseline context regarding the users directly engaged in social media data generation with respect to digital epidemiology analytics tools for large-scale social media data and the need for qualitative input feeding into their design. Our study highlights the need for a balanced perspective on social media use in times of crisis, recognizing its potential to boost community resilience in an urban setting, and further enriching digital epidemiology approaches.

With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.

The COVID-19 pandemic took most communities off guard and has highlighted gaps in community preparedness and resilience in spite of the numerous technological advancements and the variety of available social media platforms that many relied on during lockdown periods. This served to emphasise the necessity for exploring the roles of social media and smart city technologies in mitigating pandemic impacts. In this systematic literature review, we examined twelve articles on social media usage and smart city technologies and their contributions to community resilience during COVID-19. The analysis focused on the use of social media platforms and smart city technologies during and after lockdown periods, examining their role in fostering community resilience. Results indicate that social media and smart city technologies were instrumental in helping communities adapt and recover from the pandemic. While past studies have examined community resilience, social media, or smart cities separately, there is limited literature collating insights on the three elements combined. We therefore argue that these technologies, employed collaboratively, enhance community resilience during crises. Nevertheless, further research is recommended, particularly on urban resilience and comparative analyses to deepen our understanding of the complex interplay between these variables.

As many urban areas undergo increasing densification, there is a growing need for methods that can extend spatial analysis and decision-making for three-dimensional (3D) environments. Traditional multicriteria evaluation (MCE) methods implemented within geographic information systems (GIS) can assist in spatial decision-making but are rarely suited for 3D environments. These methods typically use a simplified decision logic that limits the number of evaluation criteria and variability of output suitability scores. In this study, the logic scoring of preference (LSP) as a generalized MCE method is used for 3D suitability analysis to better represent human reasoning through flexible soft computing stepwise decision logic operators. This research: (1) implements the LSP–MCE method to compare the suitability of high-rise residential units in 3D, and (2) performs criteria weight sensitivity and cost–suitability analyses using datasets for the City of Vancouver, Canada. LSP aggregation structures are developed for unique priorities and requirements of three demographic profiles. The results demonstrate the method’s flexibility in representing unique preference sets comprising 2D and 3D criteria, and that cost has a significant effect on residential unit attractiveness in a dense built environment. The proposed 3D LSP–MCE method could be adapted to benefit other stakeholders, such as property tax assessors, urban planners, and developers.

Despite several high-profile data breaches and business models that commercialize user data, participation in social media networks continues to require users to trust corporations to safeguard their personal data. Since these data increasingly contain geographic references that allude to individuals' locations and movements, the need for new approaches to geoprivacy and data sovereignty has grown. We develop a geoprivacy framework that couples two emerging technologies - decentralized data storage and discrete global grid systems - to facilitate fine-grained user control over the ownership of, access to and map-based representation of their data. The framework is illustrated with a dynamic k-anonymity model that links the geographic precision of shared data to social trust within in a social network. In this framework, users' spatio-temporal data are shared through a decentralized system and are represented on a discrete global grid data model at spatial resolutions that correspond to varying degrees of trust between individuals who are exchanging information. Our framework has several advantages over centralized geoprivacy approaches, namely trust in a third-party entity is not required and geoprivacy is dynamic and context-dependent with users maintaining autonomy. As the distributed web begins to emerge, so too can the next generation of geographic information sharing tools.

The range and population of Burrowing Owls (BOs) are declining in the Canadian Prairies. Multiple anthropogenic sources of degradation (threat) are attributed to this trend. Yet, the cumulative degradation caused by these sources has not been quantified across this landscape. Using the InVEST Habitat Quality (HQ) module and a novel approach to parameterize the relative sensitivity scores in this framework, we quantified, mapped, and evaluated the relative HQ values for these species in the prairies. The results illustrate significant differences in the HQ values between the current range and areas within the historical range of these species that do not spatially intersect with their current spatial extent. However, the variations of HQ values are not statistically significant under the different spatial scenarios considered across the current breeding grounds. Nevertheless, the results within the specific land use/cover categories illustrate meaningful variations in the HQ values across the current range of these species. Our findings suggest that the existing declining trends could only be attributed to the synergistic impact of the considered threats and other factors undermining the essential ecological processes for these birds. The results also highlight the importance of expanding the spatial extent of the existing conservation measures to prevent further loss of these charismatic migratory birds from the prairie landscape. A good starting point is to consider the HQ change index, a measure defined to illustrate the relative habitat degradation under different sources of degradation. Further studies are still required to determine the spatiotemporal impacts of all sources of degradation on the habitat of these endangered species in Canada and across their global range.

Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)-a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype's interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users' understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

The increasing prevalence of user-generated, or volunteered, geographic information is changing established practices of spatial data production and use in ways that were largely unanticipated in n...

Social media has greatly expanded opportunities to study place and well-being through the availability of human expressions tagged with physical location. Such research often uses social media content to study how specific places in the offline world influence well-being without acknowledging that digital platforms (e.g., Twitter, Facebook, Youtube, Yelp) are designed in unique ways that structure certain types of interactions in online and offline worlds, which can influence place-making and well-being. To expand our understanding of the mechanisms that influence social media expressions about well-being, we describe an ecological framework of person-place interactions that asks, "at what broad levels of interaction with digital platforms and physical environments do effects on well-being manifest?" The person is at the centre of the ecological framework to recognize how people define and organize both digital and physical communities and interactions. The relevance of interactions in physical environments depends on the built and natural characteristics encountered across modes of activity (e.g., domestic, work, study). Here, social interactions are stratified into the meso-social (e.g., local social norms) and micro-social (e.g., personal conversations) levels. The relevance of interactions in digital platforms is contingent on specific hardware and software elements. Social interactions at the meso-social level include platform norms and passive use of social media, such as observing the expressions of others, whereas interactions at the micro-level include more active uses, like direct messaging. Digital platforms are accessed in a physical location, and physical locations are partly experienced through online interactions; therefore, interactions between these environments are also acknowledged. We conclude by discussing the strengths and limitations of applying the framework to studies of place and well-being.

The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed.

Volunteered geographic information (VGI) is the use of digital tools to collect, analyze, and share geographic information that was provided by individuals. Recent advances in digital communication tools and applications have led to unprecedented advances for VGI. Because official credentials are not required, VGI can include the experiences and perspectives of many people, respond rapidly to local needs, and be more spatially and temporally extensive than traditional approaches. VGI encompasses a wide range of activities, from simple and possibly unintentional actions, such as georeferencing messages or pictures shared on the Internet, to the intentional involvement of volunteers in scientific research. The motivations of volunteers and researchers for these activities are wide and ranging and relate to the products that are generated. This article focuses on intentional efforts by geographers to collect VGI. Two main barriers to adoption of VGI for decision making are concerns about data quality and incomplete representation. Strategies to address these concerns can lead to new discoveries and improved decision making in governance. In this article we introduce VGI, discuss theoretical developments, and relate them to three recent experiences.

As more data are produced by location sensors, mobile devices, and online participatory processes, the field of GIScience has grappled with issues of information quality, context, and appropriate analytical approaches for data with heterogeneous and/or unknown provenance. Data quality has often been viewed through a bifurcated lens of experts and amateurs, but consideration of what the nature of geographical expertise is reveals a much more more nuanced situation. We consider how adapting frameworks from the field of studies of experience and expertise may provide a conceptual basis and methodological framework for evaluating the quality of geographic information. For contributed geographic information, quality is typically derived from a data user’s trust in and/or perception of the reputation of the data producer. Trust and reputation of producers of geographic information has typically been derived from the presence or absence of professional qualifications and training. However this framework applies exclusively to ‘crisp’ notions of data quality, and has limited utility for more subjective contributions associated with volunteered geographic information which may provide a rich source of geographic information for many applications. We hypothesize that a conceptual framework for geographical expertise may be used as the basis for assessing information quality in both formal and informal sources of geospatial data. Two case studies are used to highlight the new concepts of geographical expertise introduced in the paper.

This e-planning visualization case study in the Toronto region investigated the use of 3D urban models as a visualization reference against which analytical models were visualized to identify micro-scale mitigation scenarios of urban heat island effects. The case studies were directed to processes of planning decision making. The Toronto region faces problems of urban heat island impacts due to the increasing frequency of extreme heat events (Bass, Krayenhoff, & Martilli, 2002). The City of Toronto and the Toronto and Region Conservation Authority (TRCA) have each implemented policies and programmes aimed at mitigating urban heat island and climate change effects (City of Toronto, 2006). This research explored ways of visualizing remote sensing heat island data to assist with the targeted application of planning policies and programs.

Social media applications have become popular methods of online communication, interaction, and social networking. Many people use social media websites and mobile applications, such as Twitter, to create and post personal expressions in public online forums. This online content presents opportunities for using social media as a data source with the potential to improve evaluation of theoretical models of emotional and stressful experiences across various topics and subfields of psychology science and practice. In this article, we discuss emerging information retrieval and analytic methods using social media for ecological momentary assessments of emotion. We describe 2 specific methods we have developed in the context of Twitter and their use in a broader study investigating relationships among people’s emotional experiences, their expressions of experiences in social media, their daily geospatial movements and locations, and their stress experiences. We conclude with a discussion of potential applications and ethical considerations for these methods in professional psychology practice and science. Les applications des médias sociaux sont devenues de populaires moyens de communication, d’interaction et de réseautage social en ligne. Un grand nombre d’utilisateurs des sites Web et des applications mobiles des médias sociaux, tel Twitter, créent des contenus d’expression personnelle qu’ils affichent ensuite sur des forums publics. Ces contenus électroniques donnent la possibilité d’utiliser les médias sociaux comme une source de données servant éventuellement à améliorer l’évaluation des modèles théoriques d’expériences affectives et stressantes dans divers domaines et sous-domaines de la science et de la pratique de la psychologie. Dans le présent article, nous examinons de nouvelles méthodes de récupération et d’analyse de l’information, basées sur l’utilisation des médias sociaux, pour effectuer des évaluations écologiques momentanées des émotions. Nous décrivons deux méthodes particulières, que nous avons élaborées dans le contexte de Twitter, et leur utilisation dans une étude plus générale sur les liens existant entre les expériences affectives des personnes, le récit de leurs expériences dans les médias sociaux, leurs déplacements et emplacements géographiques quotidiens et leurs expériences en matière de stress. Nous terminons par une discussion sur les éventuelles applications de ces méthodes dans la science et la pratique de la psychologie et les considérations éthiques inhérentes à ces applications.

Understanding how people move and interact within urban settings has been greatly facilitated by the expansion of personal computing and mobile studies. Geosocial data derived from social media applications have the potential to both document how large segments of urban populations move about and use space, as well as how they interact with their environments. In this paper we examine spatial and temporal clustering of individuals’ geosocial messages as a way to derive personal activity centres for a subset of Twitter users in the City of Toronto. We compare the two types of clustering, and for a subset of users, compare to actual self-reported activity centres. Our analysis reveals that home locations were detected within 500 m for up to 53% of users using simple spatial clustering methods based on a sample of 16 users. Work locations were detected within 500 m for 33% of users. Additionally, we find that the broader pattern of geosocial footprints indicated that 35% of users have only one activity centre, 30% have two activity centres, and 14% have three activity centres. Tweets about environment were more likely sent from locations other than work and home, and when not directed to another user. These findings indicate activity centres defined from Twitter do relate to general spatial activities, but the limited degree of spatial variability on an individual level limits the applications of geosocial footprints for more detailed analyses of movement patterns in the city.

Increasingly, social media data are linked to locations through embedded GPS coordinates. Many local governments are showing interest in the potential to repurpose these firsthand geo-data to gauge spatial and temporal dynamics of public opinions in ways that complement information collected through traditional public engagement methods. Using these geosocial data is not without challenges since they are usually unstructured, vary in quality, and often require considerable effort to extract information that is relevant to local governments’ needs from large data volumes. Understanding local relevance requires development of both data processing methods and their use in empirical studies. This paper addresses this latter need through a case study that demonstrates how spatially-referenced Twitter data can shed light on citizens’ transportation and planning concerns. A web-based toolkit that integrates text processing methods is used to model Twitter data collected for the Region of Waterloo (Ontario, Canada) between March 2014 and July 2015 and assess citizens’ concerns related to the planning and construction of a new light rail transit line. The study suggests that geosocial media can help identify geographies of public perceptions concerning public facilities and services and have potential to complement other methods of gauging public sentiment.