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\"This collection of fourteen essays enriches digital humanities research by examining various Canadian cultural works and the advances in technologies that facilitate these interdisciplinary collaborations. Fourteen essays in English and French survey the helix of place and space: While contributors to Part 1 chart new archival and storytelling methodologies, those in Part 2 venture forth to explore specific cultural and literary texts. Cultural Mapping and the Digital Sphere will serve as an indispensable road map for researchers and those interested in the digital humanities, women's writing, and Canadian culture and literature.\"-- Provided by publisher.

Fourteen essays map Canadian literary and cultural products via advances in digital humanities research methodologies.

New ‘big data’ streams such as street-level imagery are offering unprecedented possibilities for developing health-relevant data on the urban environment. Urban environmental features derived from street-level imagery have been used to assess pedestrian-friendly neighbourhood design and to predict active commuting, but few such studies have been conducted in Canada. Using 1.15 million Google Street View (GSV) images in seven Canadian cities, we applied image segmentation and object detection computer vision methods to extract data on persons, bicycles, buildings, sidewalks, open sky (without trees or buildings), and vegetation at postal codes. The associations between urban features and walk-to-work rates obtained from the Canadian Census were assessed. We also assessed how GSV-derived urban features perform in predicting walk-to-work rates relative to more widely used walkability measures. Results showed that features derived from street-level images are better able to predict the percent of people walking to work as their primary mode of transportation compared to data derived from traditional walkability metrics. Given the increasing coverage of street-level imagery around the world, there is considerable potential for machine learning and computer vision to help researchers study patterns of active transportation and other health-related behaviours and exposures.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national and regional wetland surveys, (ii) identifying inundated areas via satellite imagery and (iii) delineating wetlands as shallow water table areas based on groundwater modeling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modeling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFWs) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDWs) derived from groundwater modeling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater, disregarding most human alterations (potential wetlands). Seven CW maps were generated at 15 arcsec resolution (ca. 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess compared to the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km2, i.e., 21.1 % and 21.6 % of the global land area, which are among the highest values in the literature and are in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy and/or cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modeling (hydrological, ecological and biogeochemical modeling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.org/10.1594/PANGAEA.892657 (Tootchi et al., 2018).

The purpose of this study is to define the geographic boundaries of commercial areas by creating a consistent definition, combining various commercial area types, including downtowns, retail centres, financial districts, and other employment subcentres. Our research involved the collection of office, retail and job density data from 69 metropolitan regions across USA and Canada. Using this data, we conducted an unsupervised image segmentation model and clustering methods to identify distinctive commercial geographic boundaries. As a result, we identified 23,751 commercial areas, providing a detailed perspective on the commercial landscape of metropolitan areas in the USA and Canada. In addition, the generated boundaries were successfully validated through comparison with previously established commerce-related boundaries. The output of this study has implications for urban and regional planning and economic development, delivering valuable insights into the overall commercial geography in the region. The commercial boundary and used codes are freely available on the School of Cities Github, and users can reuse, reproduce and modify the boundaries.

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

While a growing body of scholarship identifies urban agriculture’s broad suite of benefits and drivers, it remains unclear how motivations to engage in urban agriculture (UA) interrelate or how they differ across cities and types of organizations. In this paper, we draw on survey responses collected from more than 250 UA organizations and businesses from 84 cities across the United States and Canada. Synthesizing the results of our quantitative analysis of responses (including principal components analysis), qualitative analysis of textual data excerpted from open-ended responses, and a review of existing literature, we describe six motivational frames that appear to guide organizations and businesses in their UA practice: Entrepreneurial, Sustainable Development, Educational, Eco-Centric, DIY Secessionist, and Radical. Identifying how practitioners stack functions and frame their work is a first step in helping to differentiate the diverse and often contradictory efforts transforming urban food environments. We demonstrate that a wide range of objectives drive UA and that political orientations and discourses differ by geography, organizational type and size, and funding regime. These six paradigms provide a basic framework for understanding UA that can guide more in-depth studies of the gap between intentions and outcomes, while helping link historically and geographically specific insights to wider social and political economic processes.

Arctic environmental changes already impact regional ecosystems, economies and northern communities, and are having increasing influence on many aspects of the global system. Interest in the Arctic has increased in concert with our improved awareness of potential changes; however, research funding has not necessarily kept pace with the need to improve our understanding of Arctic system change to inform evidence-based decision making. Analyses of data on research funding trends (2003-14) in Canada, the USA and the EU indicate that less than 3% of the total budget the funding agencies considered is allocated in any given year to Arctic-related research. Furthermore, alignment is uneven among established scientific research priorities, existing societal needs and projects awarded funding. New support mechanisms and improved alignment among resources, expertise and priorities, including Indigenous research priorities, are vital to planning and adaptation in the face of ongoing Arctic change.

In this article, I analyze discourse and identity relations within so-called ‘conventional’ agri-food networks as well as how the conventional sphere perceives, constructs and responds to alternative food movements in Canada. The paper is structured around three primary research questions: (1) How are conventional actors understanding conditions, changes, and challenges within conventional networks? (2) How do conventional actors apply this understanding in advancing conventional interests and discourses, and defending conventional networks? (3) How do conventional actors and discourse construct AFMs? For this research, I draw from survey, focus group, and in-depth interview data alongside text analysis from online sources. I elucidate the interests and motivations behind the identities, stories and messages emerging from the conventional sphere. I conclude that relationship building and communication between diverse agri-food actors may help to expand the range of agricultural knowledge, philosophies and solutions available to farmers, especially those whom are currently quite divided.