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As the largest carbon reservoir in ecosystems, soil accounts for more than twice as much carbon storage as that of vegetation biomass or the atmosphere. The goal of this study is to examine spatial patterns of soil organic carbon (SOC) in Canadian forest area at an eco-region scale and to explore its relationship with different ecological variables. In this study, the first Canadian forest soil database published in 1997 by the Canada Forest Service was analyzed along with other long-term eco-climatic data (1961 to 1991) including precipitation, air temperature, Normalized Difference Vegetation Index (NDVI), slope, aspect, and elevation. Additionally, an eco-region framework established by the Environment Canada was adopted in this study for SOC distribution assessment. Exploratory spatial data analysis techniques, with an emphasis on spatial autocorrelation analysis, were employed to explore how forest SOC was spatially distributed in Canada. Correlation analysis and spatial regression analysis were applied to determine the most dominant ecological factors influencing SOC distribution in different eco-regions. At the national scale, a spatial error model was built up to adjust for spatial effects and to estimate SOC patterns based on ecological and ecosystem property factors. Using the significant variables derived in the spatial error model, a predictive SOC map in Canadian forest area was generated. Findings from this study suggest that high SOC clusters tend to occur in coastal areas, while low SOC clusters occur in western boreal eco-region. In Canadian forest area, SOC patterns are strongly related to precipitation regimes. Although overall SOC distribution is influenced by both climatic and topographic variables, distribution patterns are shown to differ significantly among eco-regions, thus verifying the eco-region classification framework for SOC zonation mapping in Canada.

Introduction Choice and access to health care are important determinants of health outcomes. Various issues influence choice and determine the degree of, and differences in access to health care. Choice of health care facilities by individuals is often determined by the interplay between patient and provider characteristics. The influence of factors that determine choice of a health care facility or a provider varies depending on individual patient's socio-ecological factors, type and severity of illness (including the presence or absence of co-morbidities), cost of healthcare (including travel costs), and the presence or absence of a third party such as a health insurance plan. On the other hand, provider or facility factors, which include spatial and non-spatial factors such as technical and functional dimensions of quality of care, are the supply–side factors that influence choice of provider and facility. In order to achieve universal health coverage and attain the Sustainable Development Goals, Nigeria adopted a prepayment health care financing method through the National Health Insurance Scheme (NHIS) in 2005. However, population coverage of the scheme remains very low, while it also has a reputation of less than optimal performance. Evidence showed that while some accredited NHIS facilities were burdened with a high volume of enrolees, others had registered low volume (of enrolees). This study explored the influence and magnitude of the various factors responsible for the poor performance of the scheme as well as the lopsided/uneven distribution of enrolees across these health care facilities. Findings will assist in repositioning the scheme for better performance as well as serve as a guide for other countries planning to design and implement similar schemes. This will enable such schemes to learn from and avoid mistakes made under the present scheme. Methods This study was cross-sectional in design, with descriptive and analytical components. Data were collected using a mixed-method approach (geo-spatial, quantitative and qualitative). The geo-spatial component was achieved using three data layers of x and y coordinates: the enrolees' locations, locations of NHIS facilities and locations of health care facilities typically used by enrolees, were used in the spatial analysis to identify the closest NHIS accredited health care facility to each enrolee's residence and also estimate the distance between enrolee's location and NHIS facility being utilised. The Distance to the Nearest Hub (points) function in Quantum GIS 3.10 was used to automatically assign enrolees to the nearest NHIS facility while the Join by lines (Hub Lines) function was used to assign enrolees to the NHIS facility they used. Spider web diagrams that depict geo-spatial relationship between enrolees' residence, patronised health care facilities and health care facilities closest to the residences were constructed. Quantitative data were collected from 432 NHIS enrolees using an adapted questionnaire. A checklist was also used to collect data on structural components of health facilities such as the number and cadre of the health workforce, availability and functionality of medical equipment and facility infrastructure. Quantitative data were analysed using STATA and frequency tables were generated. Qualitative data were collected through in-depth interviews conducted among 29 participants of the NHIS, HMOs, enrolees, head of facilities and an academic. Qualitative data analysis was done using an inductive thematic approach. Audio-taped interviews were transcribed and codes were generated. Themes were thereafter searched for and generated from the codes. Emerging themes were named, documented and analysed accordingly. A conceptual framework that illustrated the Nigeria contextual environment, the health system and the current governance of the NHIS with a highlight on the relationships, factors and patterns of interaction among stakeholders was designed. Results The majority of the enrolees received care across a small proportion of the accredited facilities and bypassed nearby health facilities to receive care. Almost all the study respondents, 405 (93.9%) bypassed, however, only 147 (34.0%) reported to have done so. In this study, predictors of bypass of healthcare facilities were younger age (OR 0.67, CI 0.46 – 0.99, p = 0.046) and employment in the civil service (OR 0.49, CI 0.31-0.79, p = 0.003). Older age (1.66, CI 1.07-2.58, p = 0.024), attainment of tertiary level of education (OR 1.57, CI 1.02-2.44, p = 0.043), high socioeconomic status (OR 1.94, CI 1.24 -3.02, p = 0.003) and presence of multiple morbidities (OR 1.66, CI 0.99-2.78, p = 0.053) were predictors of personal choice of health facility. Physical infrastructure was poor in all the facilities; most of the facilities depended on more than one source of power supply and water supply was mainly from other sources apart from pipe-borne. Identified predictors of satisfaction with care were age, occupation and seeking information about quality of care. Knowledge of the NHIS and patronage of faith-based health facilities were also predictors of satisfaction with care. Respondents who were younger than 35 years of age were more likely to be satisfied with care than those who were older (OR 1.85, CI = 1.05 – 3.25, p< 0.05). Private sector workers under the scheme (OR 1.84, CI 1.03 – 3.28, p< 0.05) were more likely to be satisfied with care than those employed in the civil service. Likewise, compared with those who did not seek information, those who did (OR 1.63, CI = 1.04 – 2. 53, p< 0.05) were more likely to report satisfaction with care. Respondents who claimed not to have a knowledge of the NHIS were more likely to be satisfied with care (OR 1.65, CI = 1.06 – 2.55, p< 0.05). Likewise, patronage of faith–based facilities was identified to be a predictor of satisfaction with care (OR 1.84, CI = 1.09 – 3.08, p< 0.05). Qualitative data revealed that there was a very low level of trust among the stakeholders. The design and operations of the scheme indicated that the NHIS managers lacked the technical and managerial skills required to manage the scheme and other stakeholders. Both the NHIS officials and the health care providers were of the opinion that the HMOs had more political influence than other stakeholders in the scheme, and were using this to take advantage of others. Enrolees and health care providers were reluctant to collaborate with the scheme at inception, because of the low level of trust in government policies generally. In addition, at inception of the scheme, the majority of the enrolees were arbitrarily allocated to the few available health care providers. For some of the enrolees, choice of health care facilities was based on perceived quality of care and occasionally, as a result of proximity to places of residence. Instances of corrupt and unethical practices were reported across the board among the scheme stakeholders. Discussion There was a high level of facility bypassing among study respondents, though only a few of them claimed to be aware of this. This finding is because of the allocation or assignment of majority of the enrolees to the few facilities that were available to participants in the scheme at its inception. The study also revealed that younger age enrolees and civil servants bypassed more than their respective counterparts did. Studies have shown that younger people are more likely to explore and become more adventurous than older individuals. The apparent bypassing among civil servants was largely because of the arbitrary allocation of reluctant enrolees to the available few health care providers at the inception of the scheme. This also explained the skewed distribution of the enrolees in these few facilities under the scheme. Findings also support the observation that most of the facilities with fewer enrolees were those that stayed away from the scheme at inception. However, the observed lopsided/uneven pattern was difficult to reverse despite the complaints of the facilities with fewer enrolees and the efforts of the scheme to address the skewness. It should also be noted that high social economic class is a strong factor of personal choice of healthcare facilities. The only plausible explanation was the fact that this group of enrolees were not civil servants and who had the financial capacity to pay the premiums, which enabled them buy into the scheme voluntarily and personally chose facilities where to receive care. The state of physical infrastructure in all the facilities that were involved in the study is illustrative of the weak health system in Nigeria. Poor facility infrastructure is a known recipe for the failure of social health insurance. Ability to search for healthcare facilities and in the process, the phenomenon of bypass as seen in this study appeared to play a major role in satisfaction with care amongst younger people, and among those from the private sector, the economic ability to search for and receive care in healthcare facilities of choice, and that meets their expectations. Similarly, enrolees who had the opportunity and sought information about the quality of care in the facilities before enrolment were more likely to be satisfied with care than those who did not seek information. Enrolees who claimed they had no knowledge of the scheme were more likely to be satisfied than those who had knowledge of it and may have had a higher expectation of the quality of care than they received. Satisfaction with care that was attributed to patronage of faith-based facilities in this study has similarities with findings in previous studies. Compared with other types of facilities, it has been reported that the likelihood of higher levels of satisfaction with care among those who patronise faith-based facilities, may have been as a result of higher levels of functional quality, (including spiritual care, that is more valued in this setting) in addition to the technical quality of care. The fundamental finding from the qualitative component of the study was a high level of mistrust of government by almost all the stakeholders involved in the scheme. This manifested itself in the reluctance of the majority of the private health facilities to collaborate with the government in providing health care services to enrolees on the scheme at inception. The same explanation goes for the then potential enrolees' outright refusal to take up the opportunity to access health care services through the scheme. Previous failed government policies both in the health and in the non-health sectors were cited as reasons for the low interest in the scheme. Because of this, except for the government health facilities that were instructed to do so, majority of the private facilities stayed away from providing care to enrolees on the scheme until some years later. Thus, the majority of these enrolees at inception were assigned to the few health facilities that were available. This is what was primarily responsible for the lopsided/uneven distribution of enrolees across the NHIS accredited facilities, whereby some had a high volume of enrolees, while the majority, especially those that showed interest in the scheme much later had very low volumes. Unfortunately, this pattern of enrolees' distribution may be irreversible. In addition, mistrust also exists between the NHIS and the HMOs, between the HMOs and providers, and to some extent between the enrolees and providers. It is important to note that the design of the scheme put the HMOs in a powerful position, which they used to influence the political class to their advantage. To compound the situation, NHIS officials had poor technical and managerial skills to administer the scheme. These are indications of an inefficiently managed health intervention. Under these circumstances, it is highly unlikely that universal health coverage could be achieved unless the observed challenges are appropriately addressed. In addressing these issues, a reform should be considered in the design of the scheme and appropriate training given to the NHIS officials saddled with its day-to-day operation. Conclusion This study has elucidated the reasons for the poor uptake and skewed distribution of enrolees across accredited NHIS facilities in the study area. In addition to poor structure and inefficient management, the high level of mistrust among the stakeholders has played a major role in the lopsided/uneven geo-spatial pattern of enrolees' distribution across the NHIS accredited health facilities. As it is presently structured and managed, the NHIS is highly unlikely to achieve its set objectives. It is advocated that a reform that addresses the observed anomalies be instituted to enable the scheme achieve its goals. This is a lesson for other countries planning to design and implement similar schemes.

Heterogeneous geospatial big data, from multi-modal Earth Observation (EO) data to geo-social media data, has become more and more accessible in recent years. This provides a potential data source for automatically extracting and mapping key geographical characteristics, hence mitigating the global mapping problem using current data mining methods. These automated geographic feature mapping techniques, especially for man-made infrastructure, are crucial to a lot of our socio-economic existence. Machine learning techniques, among many other data mining methodologies, have demonstrated better performance across a wide range of academic domains, most notably natural language processing and computer vision. In recent times, there has been a growing interest in research on ML-based Geospatial Artificial Intelligence (GeoAI), particularly in its ability to support autonomous mapping with heterogeneous geographical data. Though the potential is high and obvious, it remains a major challenge to handle inherent heterogeneity and empower data synergy when building robust and scalable GeoAI models for large-scale automated mapping purposes. For geospatial analysis, citizen science initiative is seen to be the most effective. This is a result of the fast development of Web 2.0 and crowdsourcing/Volunteered Geographic Information (VGI) technologies. These technologies enable even regular users or volunteer mappers to develop, gather, and distribute geospatial data using a variety of digital devices (such as desktop computers, mobile tablets, and smartphones). The technological obstacles to digital mapping have been significantly reduced by ongoing crowdsourcing and VGI efforts. In the real world, though, problems with global mapping have persisted for a considerable amount of time even in higher-income nations. Intelligent automated mapping techniques for geospatial analysis are desperately needed in this situation since they may effectively and efficiently close significant data gaps across nations. The research effort reported in this dissertation explores the possibilities of using citizen science or VGI to conduct geospatial analysis of different man-made infrastructures using ML from diverse geospatial data sources (e.g., multi-modal EO data, OSM, and GIS data). Three main research questions (RQs), derived from data-driven, method-driven, and application-driven research perspectives, are established to better address the issue of geospatial analysis with remote sensing and citizen science. The thesis especially goes in this direction by i) investigating the data-driven issue that combines ML for segmentation tasks; ii) creating strategies to deal with VGI data noises; and iii) using the created strategies in various mapping tasks. This creates even more intriguing possibilities for related works in the future.

The main focus of this thesis was to gain a better understanding about the dynamics of risk perception and its influence on people’s evacuation behavior. Another major focus was to improve our knowledge regarding geo-spatial and temporal variations of risk perception and hurricane evacuation behavior. A longitudinal dataset of more than eight hundred households were collected following two major hurricane events, Ivan and Katrina. The longitudinal survey data was geocoded and a geo-spatial database was integrated to it. The geospatial database was composed of distance, elevation and hazard parameters with respect to the respondent’s household location. A set of Bivariate Probit (BP) model suggests that geospatial variables have had significant influences in explaining hurricane risk perception and evacuation behavior during both hurricanes. The findings also indicated that people made their evacuation decision in coherence with their risk perception. In addition, people updated their hurricane evacuation decision in a subsequent similar event.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Town and Regional Planning in the Faculty of Informatics and Design at the Cape Peninsula University of Technology / Political independence for Zimbabwe in 1980 brought about fundamental socio-conomic changes which impacted on urbanisation trends in the country. For instance the removal of colonial influx control laws and regulations which had previously served to curtail ruralurban migration marked the beginning of a new developmental era characterised by rapid urbanisation. Political and economic downturns later experienced in the country in the last decade of the twentieth century and after resulted in massive de-industrialisation, company closures and high unemployment. These changes impacted significantly on the spatial structure of cities which had to firstly adapt to socio-political integration, and had to later focus on the basic challenges of providing shelter and alternative means of employment in a depressed economy. The physical and spatial manifestations of such changes included the rapid growth of informality, the collapse of urban infrastructure and the apparent disregard for the colonially styled urban development management frameworks. This research consequently evaluates the performance of current urban planning frameworks and practices in the face of such changing circumstances. This is against a backdrop of the apparent failure by urban planning to transform in line such development trends. The study explores the theoretical framework of rapid urbanisation, urban planning, and technological innovation in urban development management systems. The main focus of the study is on the transformation of planning practices and frameworks. Geo-spatial technology (GST) is mainly used as a demonstration and methodological tool for analysis and evaluation. The methodology is made up of two contrasting case studies based in the CBD of Harare (Zimbabwe‟s capital city) and Epworth (an informal settlement on the outskirts of Harare). The CBD case study measures the performance of planning frameworks within a formally planned set up whilst the Epworth case study demonstrates the (in)effectiveness of current planning practices to contemporary urban development challenges. The main findings of the research support the need to have mobile planning frameworks and tools that have the capacity to promptly respond to fast changing developmental trends. The other main finding highlights the positive relationship between impartial participation in planning and high level of success in achieving planning objectives.

<p dir="ltr">Open-access bioinformatics data enables accessible public health research for a variety of stakeholders, including teachers and low-resourced researchers. This project outlines two case studies utilizing open-access bioinformatics data sets and analysis software as proofs of concept for the types of research projects that can be adapted for workforce development purposes. The first case study is a spatial temporal analysis of Lyme disease rates in the United States from 2008 to 2020 using freely available data from the United States Department of Agriculture and Centers for Disease Control and Prevention to determine how urbanization and other changes in land use have impacted Lyme disease rates over time. The second case study conducts a pangenome analysis using bacteriophage data from the Actinobacteriophage Database to determine conserved gene regions related to host specificity.</p>