Walkability is a popular term used to describe aspects of the built and social environment that have important population-level impacts on physical activity, energy balance, and health. Although the ...term is widely used by researchers, practitioners, and the general public, and multiple operational definitions and walkability measurement tools exist, there are is no agreed-upon conceptual definition of walkability.
To address this gap, researchers from Memorial University of Newfoundland hosted "The Future of Walkability Measures Workshop" in association with researchers from the Canadian Urban Environmental Health Research Consortium (CANUE) in November 2017. During the workshop, trainees, researchers, and practitioners worked together in small groups to iteratively develop and reach consensus about a conceptual definition and name for walkability. The objective of this paper was to discuss and propose a conceptual definition of walkability and related concepts.
In discussions during the workshop, it became clear that the term walkability leads to a narrow conception of the environmental features associated with health as it inherently focuses on walking. As a result, we suggest that the term Active Living Environments, as has been previously proposed in the literature, are more appropriate. We define Active Living Environments (ALEs) as the emergent natural, built, and social properties of neighbourhoods that promote physical activity and health and allow for equitable access to health-enhancing resources.
We believe that this broader conceptualization allows for a more comprehensive understanding of how built, natural, and social environments can contribute to improved health for all members of the population.
News media perform an important role in shaping how Canadian society views Indigenous peoples and issues. They are rarely passive, neutral bystanders, as media routinely employ a particular set of ...frames (e.g., criminality, economic burden, threats to unity, promotion of social justice) when covering Indigenous stories. We explore how the use of such frames is influenced by dynamics of power as they relate to majority/minority linguistic differences. Through a controlled comparison, we examine the 2008-2019 media coverage of Indigenous responses to Ontario's Far North Act and Québec's Plan Nord - both of which concerned resource development on or near Indigenous territory. We find that where media serve the linguistic majority, they are much more likely to frame Indigenous responses to development plans as a threat to national unity. In contrast, where media serve the linguistic minority, they are significantly more likely to frame Indigenous responses in terms of social justice. Our findings suggest that traditional understandings of the differences between mainstream and ethnic or minority media fail to capture the complex dynamics at work in multilingual states. The paper addresses this gap in the literature and provides a broader understanding of how media and power dynamics shape the representation of Indigenous contentious politics.
The Standards and Guidelines for Consultant Archaeologists (Ontario 2011) introduced a new requirement for archaeologists working in Ontario CRM to engage Aboriginal communities in response to ...growing criticisms from these communities over being excluded from the process. Considered vague by many involved in the industry, both archaeologists and Aboriginal community representatives have developed their own strategies for complying with these requirements and their own opinions on how what they do over the course of engagement does or does not fit into that policy. However, many Aboriginal concerns remain unaddressed in the current engagement process, leaving open the possibility that tension and conflict may arise in the field. While some archaeologists have been open to the recent changes in policy advocating for more transparency and collaboration, others have been resistant and continued to defend their position of authority over the management and interpretation of the archaeological record.
This paper explores a study completed with 1st grade students while a five-week mindfulness unit was implemented in their classroom. The paper discusses tactics for teaching mindfulness strategies to ...students and results observed in the students over the course of the five-week study. It also contains journal entries completed by students and their families both in the classroom and at home.
Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of ...weather conditions and time of day. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time global, operational SAR data have been made freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. We assessed our algorithm over three recent flood events using coincident very high- spatial resolution imagery and operational flood maps. Using very high resolution optical imagery, we estimated an area-normalized accuracy of 89.8 ± 2.8% (95% c.i.) over Houston, Texas following Hurricane Harvey in late August 2017, representing an improvement of between 1.6% and 9.8% over flood maps derived from a simple backscatter threshold. Additionally, comparison of our results with SAR-derived Copernicus Emergency Management Service (EMS) maps following devastating floods in Thessaly, Greece and Eastern Madagascar in January and March 2018, respectively, yielded overall agreement rates of 98.5% in both cases. Importantly, our algorithm was able to ingest hundreds of SAR and optical images served on the GEE to produce flood maps over affected areas within minutes, circumventing the need for time-consuming data download and pre-processing steps. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.
•A new flood detection and monitoring algorithm based on dense Sentinel-1 SAR data is presented.•Temporal backscatter anomalies correct for bias arising from difference in sensor configuration and view angles.•Temporal Z-scores provide an objective measure of change due to flooding.•Integrating Sentinel-1 and Landsat data allow for distinction between seasonal water regimes and new flooding.•Google Earth Engine allows for rapid deployment of algorithm during flood events.
Accurately quantifying surface water extent in wetlands is critical to understanding their role in ecosystem processes. However, current regional- to global-scale surface water products lack the ...spatial or temporal resolution necessary to characterize heterogeneous or variable wetlands. Here, we proposed a fully automatic classification tree approach to classify surface water extent using Sentinel-1 synthetic aperture radar (SAR) data and training datasets derived from prior class masks. Prior classes of water and non-water were generated from the Shuttle Radar Topography Mission (SRTM) water body dataset (SWBD) or composited dynamic surface water extent (cDSWE) class probabilities. Classification maps of water and non-water were derived over two distinct wetlandscapes: the Delmarva Peninsula and the Prairie Pothole Region. Overall classification accuracy ranged from 79% to 93% when compared to high-resolution images in the Prairie Pothole Region site. Using cDSWE class probabilities reduced omission errors among water bodies by 10% and commission errors among non-water class by 4% when compared with results generated by using the SWBD water mask. These findings indicate that including prior water masks that reflect the dynamics in surface water extent (i.e., cDSWE) is important for the accurate mapping of water bodies using SAR data.
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) ...over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.
The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and ...other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.
•A fully automated algorithm was developed to map wetland inundation dynamics.•Multiple wetland inundation maps (1-m) were produced for the Prairie Pothole Region.•Mapped wetlands show high accuracy when compared to existing surface water products.•The algorithm is scalable for mapping wetland inundation at large geographic scales.
To examine the salubrious role of social interaction in modulating the development of allodynia (increased sensitivity to typically innocuous physical stimuli) and depressive-like behavior post ...peripheral nerve injury in mice. The determination of potential mechanisms that mediate social influences on the behavioral and physiological response to peripheral nerve injury.
Mice were pair housed or socially isolated for 2 weeks before spared nerve injury (SNI). Animals were cannulated; socially isolated animals were centrally treated with oxytocin; and socially paired animals were centrally treated with an oxytocin receptor antagonist. Animals were subsequently monitored for the development of mechanical allodynia and depressive-like behavior, and tissue was collected for analysis of the central levels of the cytokine interleukin 1 beta (IL-1beta).
Depressive-like behavior was assessed via the Porsolt forced swim test, developed only among socially isolated mice with nerve injury. Socially isolated mice with nerve injury also were the only experimental group to exhibit increased frontal cortex IL-1beta gene expression on day 7 post injury. Moreover, central treatment of socially isolated mice with oxytocin, a neuropeptide associated with social bonding, attenuated the effects of SNI on depressive-like behavior and reduced frontal cortex IL-1beta protein levels in socially isolated animals. Conversely, pair-housed animals treated with a selective oxytocin receptor antagonist developed depressive-like behavior equivalent to that of socially isolated animals and displayed increased IL-1beta protein levels within the frontal cortex.
These data suggest that social interaction significantly alters the affective and neuroinflammatory responses to SNI through a mechanism that could involve oxytocin.
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