Geography, Department of

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A Challenging Entanglement: Health Care Providers’ Perspectives on Caring for Ill and Injured Tourists on Cozumel Island, Mexico

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-06-05
Abstract: 

Purpose: Despite established knowledge that tourists often fall ill or are injured abroad, little is known about their treatment. The intent of this study was to explore health care professionals’ treatment provision experiences on Cozumel Island, Mexico. Methods: 13 semi-structured interviews were undertaken with professionals across a number of health care vocations on Cozumel Island. Interviews were transcribed and thematically analysed to determine common challenges faced in the provision of treatment for transnational tourists. Results: Three thematic challenges emerged from the data: human and physical resource deficiencies, medical (mis)perceptions held by patients and complexities surrounding remuneration of care. Health care providers employ unique strategies to mitigate these challenges. Conclusion: Although many of these challenges exist within other touristic and peripheral spaces, we suggest that the challenges experienced by Cozumel Island’s health care professionals, and their mitigation strategies, exist as part of a complex entanglement between the island’s health care sector and its dominant tourism landscape. We call on tangential tourism services to take a larger role in ensuring the ease of access to, and provision of quality health care services for tourists on Cozumel Island.

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Article
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Palaeogeographical reconstruction and hydrology of glacial Lake Purcell during MIS 2 and its potential impact on the Channeled Scabland, USA

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-01-13
Abstract: 

Large, ice-marginal lakes that were impounded by the maximally-extended Cordilleran Ice Sheet (CIS) provided source waters for the extraordinarily large floods that formed the Channeled Scabland of Washington and Idaho, USA.  However, flood flows that drained CIS meltwater and contributed to landscape evolution during later stages of deglaciation have hitherto been poorly investigated.  This paper provides the first evidence for such a late deglacial floodwater source: glacial Lake Purcell (gLP). Sedimentary evidence records the northward extension of gLP from Idaho, USA into British Columbia, Canada and establishes its minimum palaeogeographical extent.  Sedimentary evidence suggests that the deglacial Purcell Lobe was a capable ice dam that impounded large volumes of gLP water.  A review of glacioisostatically affected lakes during CIS deglaciation suggests that gLP could have been subjected to tilts ranging from 0 – >1.25 m km-1. Sedimentary evidence suggests high lake plane tilts (⪆1.25 m km-1) are the most likely to have affected gLP.  Using this, the palaeogeography and volume of gLP are modelled, revealing that ~116 km3 of water was susceptible to sudden drainage into the Channeled Scabland via the Columbia River system. This calculation is supported by sedimentary and geomorphic evidence compatible with energetic flood flows along the gLP drainage route and suggests gLP drained suddenly, causing significant landscape change.

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Article
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Facilitating Equitable Community-Level Access to Maternal Health Services: Exploring the Experiences of Rwanda’s Community Health Workers

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-11-26
Abstract: 

Background

In Rwanda, community health workers (CHWs) are an integral part of the health system. For maternal health, CHWs are involved in linking members of the communities in which they live to the formal health care system to address preventative, routine, and acute maternal care needs. Drawing on the findings from in-depth interviews with maternal health CHWs and observational insights in ten Rwandan districts, we identify specific strategies CHWs employ to provide equitable maternal care while operating in a low resource setting.

Methods

Using case study methodology approach, we conducted interviews with 22 maternal health CHWs to understand the nature of their roles in facilitating equitable access to maternal care in Rwanda at the community level. Interviews were conducted in five Rwandan districts. Participants shared their experiences of and perceptions on promoting equitable access to maternal health service in their communities.

Results

Four key themes emerged during the analytic process that characterize the contexts and strategic ways in which maternal health CHWs facilitate equitable access to maternal care in an environment of resource scarcity. They are: 1) community building; 2) physical landscapes, which serve as barriers or facilitators both to women’s care access and CHWs’ equitable service provision; 3) the post-crisis socio-political environment in Rwanda, which highlights resilience and the need to promote maternal health subsequent to the genocide of 1994; and, 4) the strategies used by CHWs to circumvent the constraints of a resource-poor setting and provide equitable maternal health services at the community level.

Conclusion

Rwanda’s maternal CHWs are heavily responsible for promoting equitable access to maternal health services. Consequently, they may be required to use their own resources for their practice, which could jeopardize their own socio-economic welfare and capacity to meet the demands of their families. Considering the unpaid and untrained nature of this position, we highlight the factors that threaten the sustainability of CHWs’ role to facilitate equitable access to maternal care. These threats introduce turbulence into what is a relatively successful community-level health care initiative.

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Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-11-26
Abstract: 

Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC.

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Article
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Measuring (Subglacial) Bedform Orientation, Length, and Longitudinal Asymmetry – Method Assessment

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-03-20
Abstract: 

Geospatial analysis software provides a range of tools that can be used to measure landform morphometry. Often, a metric can be computed with different techniques that may give different results. This study is an assessment of 5 different methods for measuring longitudinal, or streamlined, subglacial bedform morphometry: orientation, length and longitudinal asymmetry, all of which require defining a longitudinal axis. The methods use the standard deviational ellipse (not previously applied in this context), the longest straight line fitting inside the bedform footprint (2 approaches), the minimum-size footprint-bounding rectangle, and Euler’s approximation. We assess how well these methods replicate morphometric data derived from a manually mapped (visually interpreted) longitudinal axis, which, though subjective, is the most typically used reference. A dataset of 100 subglacial bedforms covering the size and shape range of those in the Puget Lowland, Washington, USA is used. For bedforms with elongation > 5, deviations from the reference values are negligible for all methods but Euler’s approximation (length). For bedforms with elongation < 5, most methods had small mean absolute error (MAE) and median absolute deviation (MAD) for all morphometrics and thus can be confidently used to characterize the central tendencies of their distributions. However, some methods are better than others. The least precise methods are the ones based on the longest straight line and Euler’s approximation; using these for statistical dispersion analysis is discouraged. Because the standard deviational ellipse method is relatively shape invariant and closely replicates the reference values, it is the recommended method. Speculatively, this study may also apply to negative-relief, and fluvial and aeolian bedforms.

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Article
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Between A Rock and A Soft Place: Using Optical Ages to Date Ancient Clam Gardens on the Pacific Northwest

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-02-09
Abstract: 

Rock-walled archaeological features are notoriously hard to date, largely because of the absence of suitable organic material for radiocarbon dating. This study demonstrates the efficacy of dating clam garden wall construction using optical dating, and uses optical ages to determine how sedimentation rates in the intertidal zone are affected by clam garden construction. Clam gardens are rock-walled, intertidal terraces that were constructed and maintained by coastal First Nation peoples to increase bivalve habitat and productivity. These features are evidence of ancient shellfish mariculture on the Pacific Northwest and, based on radiocarbon dating, date to at least the late Holocene. Optical dating exploits the luminescence signals of quartz or feldspar minerals to determine the last time the minerals were exposed to sunlight (i.e., their burial age), and thus does not require the presence of organic material. Optical ages were obtained from three clam garden sites on northern Quadra Island, British Columbia, and their reliability was assessed by comparing them to radiocarbon ages derived from shells underneath the clam garden walls, as well as below the terrace sediments. Our optical and radiocarbon ages suggest that construction of these clam garden walls commenced between ~1000 and ~1700 years ago, and our optical ages suggest that construction of the walls was likely incremental and increased sedimentation rates in the intertidal zone by up to fourfold. Results of this study show that when site characteristics are not amenable to radiocarbon dating, optical dating may be the only viable geochronometer. Furthermore, dating rock-walled marine management features and their geomorphic impact can lead to significant advances in our understanding of the intimate relationships that Indigenous peoples worldwide developed with their seascapes.

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Article
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Spatial Relationships between Small-Holder Farms Coupled With Livestock Management Practices Are Correlated With the Distribution of Antibiotic Resistant Bacteria in Northern Tanzania

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-06-12
Abstract: 

We examined the spatial distribution of antibiotic-resistant coliform bacteria amongst livestock from three distinct cultural groups, where group-level differences in practices (e.g., antibiotic use) may influence the magnitude of antibiotic resistance, while livestock interactions (e.g., mixing herds, shared markets) between these locations may reduce heterogeneity in the distribution of antibiotic resistant bacteria. Data was collected as part of a larger study of antibiotic-resistance in northern Tanzania. Simple regression and generalized linear regression were used to assess livestock management and care practices in relation to the prevalence of multidrug-resistant (MDR) coliform bacteria. Simple and multivariable logistic regression were then used to identify how different management practices affected the odds of households being found within MDR “hotspots.” Households that had a higher median neighbourhood value within a 3000 m radius showed a significant positive correlation with livestock MDR prevalence (β = 4.33, 95% CI: 2.41–6.32). Households were more likely to be found within hotspots if they had taken measures to avoid disease (Adjusted Odds Ratio (AOR) 1.53, CI: 1.08—2.18), and if they reported traveling less than a day to reach the market (AOR 2.66, CI: 1.18—6.01). Hotspot membership was less likely when a greater number of livestock were kept at home (AOR 0.81, CI: 0.69–0.95), if livestock were vaccinated (AOR 0.32, CI: 0.21—0.51), or if distance to nearest village was greater (AOR 0.46, CI: 0.36–0.59). The probability of MDR increases when herds are mixed, consistent with evidence for passive transmission of resistant bacteria between animals. Reduced MDR with vaccination is consistent with many studies showing reduced antibiotic use with less disease burden. The neighbourhood effect has implications for design of intervention studies.

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Article
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Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-09-28
Abstract: 

Land use change (LUC) is a dynamic process that significantly affects the environment, and various approaches have been proposed to analyze and model LUC for sustainable land use management and decision making. Recurrent neural network (RNN) models are part of deep learning (DL) approaches, which have the capability to capture spatial and temporal features from time-series data and sequential data. The main objective of this study was to examine variants of the RNN models by applying and comparing them when forecasting LUC in short time periods. Historical land use data for the City of Surrey, British Columbia, Canada were used to implement the several variants of the RNN models. The land use (LU) data for years 1996, 2001, 2006, and 2011 were used to train the DL models to enable the short-term forecast for the year 2016. For the 2011 to 2016 period, only 4.5% of the land use in the study area had changed. The results indicate that an overall accuracy of 86.9% was achieved, while actual changes in each LU type were forecasted with a relatively lower accuracy. However, only 25% of changed raster cells correctly forecasted the land use change. This research study demonstrates that RNN models provide a suite of valuable tools for short-term LUC forecast that can inform and complement the traditional long-term planning process; however, further additional geospatial data layers and considerations of driving factors of LUC need to be incorporated for model improvements.

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Article
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Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-02-25
Abstract: 

Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr−1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions.

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Article
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Epidemiological and Spatial Characteristics of Interpersonal Physical Violence in a Brazilian City: A Comparative Study of Violent Injury Hotspots in Familial Versus Non-Familial Settings, 2012-2014

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-01-07
Abstract: 

This study explores both epidemiological and spatial characteristics of domestic and communityinterpersonal violence. We evaluated three years of violent trauma data in themedium-sized city of Campina Grande in North-Eastern Brazil. 3559 medical and police recordswere analysed and 2563 cases were included to identify socioeconomic and geographicpatterns. The associations between sociodemographic, temporal, and incidentcharacteristics and domestic violence were evaluated using logistic regression. Using GeographicalInformation Systems (GIS), we mapped victims’ household addresses to identifyspatial patterns. We observed a higher incidence of domestic violence among female,divorced, or co-habitant persons when the violent event was perpetrated by males. Therewas only a minor chance of occurrence of domestic violence involving firearms. 8 out of 10victims of domestic violence were women and the female/male ratio was 3.3 times greaterthan that of community violence (violence not occurring in the home). Unmarried coupleswere twice as likely to have a victim in the family unit (OR = 2.03), compared to married couples.Seven geographical hotspots were identified. The greatest density of hotspots wasfound in the East side of the study area and was spatially coincident with the lowest averagefamily income. Aggressor sex, marital status, and mechanism of injury were most associatedwith domestic violence, and low-income neighbourhoods were coincident with bothdomestic and non-domestic violence hotspots. These results provide further evidence thateconomic poverty may play a significant role in interpersonal, and particularly domesticviolence.

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