A unified ecological framework for studying effects of digital places on well-being

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

Personal activity centres and geosocial data analysis: Combining big data with small data

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

Stresscapes: validating linkages between place and stress expression on social media

Understanding how individuals and groups perceive their surroundings and how different physical and social environments may influence their state-of-mind has intrigued re-searchers for some time. Much of this research has focused on investigating why certain natural and human-built places can engender specific emotive responses (e.g. fear, disgust, joy, etc.) and, by extension, how these responses can be considered in placemaking activities such as urban planning and design. Developing a better understanding of the linkages between place and emotional state is challenging in part because both cognitive processes and the concept of place are complex, dynamic and multi-faceted and are mediated by a confluence of contextual, individual and social processes. There is evidence to suggest that social media data produced by individuals in situ and in near real-time may provide novel insights into the nature and dynamics of individuals’ responses to their surroundings. The explosion of user-generated digital data and the sensorization of environments, especially in urban settings, provide opportunities to build knowledge of place and state-of-mind linkages that will inform the design and promotion of vibrant placemaking by individuals and communities. In this paper we present a novel study, to be undertaken this summer within the Greater Toronto area in Canada, with 140 recruited participants who are frequent, geo-tagging, Twitter users. The goal of the study will be to assess emotional, acute and chronic stress experienced in urban built-environments and as expressed during daily activities. An existing automated semantic natural language processing tool will be validated through this study, and it is hoped that the methodology developed can be extrapolated to other urban environments as well, with a second validation study already planned to take place next year in London, United Kingdom

Understanding Public Opinions from Geosocial Media

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

Spatial Decision-Making for Dense Built Environments: The Logic Scoring of Preference Method for 3D Suitability Analysis

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

The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

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

Electrical and Optical Properties of Carbon-Doped GaN Grown by MBE on MOCVD GaN Templates Using a CCl 4 Dopant Source Electrical and Optical Properties of Carbon-Doped GaN Grown by MBE on MOCVD GaN Templates Using a CCl 4 Dopant Source

ABSTRACT Carbon-doped GaN was grown by plasma-assisted molecular-beam epitaxy using carbon tetrachloride vapor as the dopant source. For moderate doping mainly acceptors were formed, yielding semi-insulating GaN. However at higher concentrations p-type conductivity was not observed, and heavily doped films (>5µ10 20 cm -3 ) were actually ntype rather than semi-insulating. Photoluminescence measurements showed two broad luminescence bands centered at 2.2 and 2.9 eV. The intensity of both bands increased with carbon content, but the 2.2 eV band dominated in n-type samples. Intense, narrow (~6 meV) donor-bound exciton peaks were observed in the semi-insulating samples

Electrical and optical properties of carbon-doped GaN grown by MBE on MOCVD GaN templates using a CCl4 dopant source

Carbon-doped GaN was grown by plasma-assisted molecular-beam epitaxy using carbon tetrachloride vapor as the dopant source. For moderate doping mainly acceptors were formed, yielding semi-insulating GaN. However at higher concentrations p-type conductivity was not observed, and heavily doped films (>5 x 10{sup 20} cm{sup -3}) were actually n-type rather than semi-insulating. Photoluminescence measurements showed two broad luminescence bands centered at 2.2 and 2.9 eV. The intensity of both bands increased with carbon content, but the 2.2 eV band dominated in n-type samples. Intense, narrow ({approx}6 meV) donor-bound exciton peaks were observed in the semi-insulating samples

Electrical and optical properties of carbon-doped GaN grown by MBE on MOCVD GaN templates using a CCl4 dopant source

Carbon-doped GaN was grown by plasma-assisted molecular-beam epitaxy using carbon tetrachloride vapor as the dopant source. For moderate doping mainly acceptors were formed, yielding semi-insulating GaN. However at higher concentrations p-type conductivity was not observed, and heavily doped films (>5x10^20 cm-3) were actually n-type rather than semi-insulating. Photoluminescence measurements showed two broad luminescence bands centered at 2.2 and 2.9 eV. The intensity of both bands increased with carbon content, but the 2.2 eV band dominated in n-type samples. Intense, narrow (~;6 meV) donor-bound exciton peaks were observed in the semi-insulating samples