Rapid population growth and its impact on residential land use in Ghana: The case of Madina-Adenta in the Accra metropolitan area

Rapid population growth rates coupled with low levels of economic development in developing countries have created among others immense obstacles to the provision of adequate housing to the majority of residents. Population growth rates are growing faster than the provision of new housing and housing infrastructure. This has resulted in intensive usage of the existing stock of housing and deterioration of housing environments. Some of the manifestations of housing and residential land use intensification are increasing room occupancy levels, in—situ housing adjustments involving physical changes in housing space and housing space conversions. Intensification of residential land use also has environmental impacts such as increasing problems of waste disposal. Given the predominance of existing housing stock in providing housing space in any given year, it is crucial for housing policy\u27 and programme development to identify the factors which facilitate the efficient provision of housing space from the existing stock of housing. This study undertaken in Ghana examines the phenomenon of housing and residential land use intensification under different socio-economic contexts and primarily seeks an insight into the factors of residential change. Income, housing space availability, room occupancy level, level of change in household size, length of residence and tenure are individually identified as being significant factors of residential change. Household size stress (which directly reflects the impact of population growth) is identified as providing motivation for the undertaking of housing change. Logistic regression modelling further isolates the most significant variables influencing residential change and indicates that security of tenure is an essential prerequisite for effective provision of housing from the existing stock

COVID-19, systemic crisis, and possible implications for the wild meat trade in sub-Saharan Africa

Wild animals play an integral and complex role in the economies and ecologies of many countries across the globe, including those of West and Central Africa, the focus of this policy perspective. The trade in wild meat, and its role in diets, have been brought into focus as a consequence of discussions over the origins of COVID-19. As a result, there have been calls for the closure of China’s “wet markets”; greater scrutiny of the wildlife trade in general; and a spotlight has been placed on the potential risks posed by growing human populations and shrinking natural habitats for animal to human transmission of zoonotic diseases. However, to date there has been little attention given to what the consequences of the COVID-19 economic shock may be for the wildlife trade; the people who rely on it for their livelihoods; and the wildlife that is exploited. In this policy perspective, we argue that the links between the COVID-19 pandemic, rural livelihoods and wildlife are likely to be more complex, more nuanced, and more far-reaching, than is represented in the literature to date. We develop a causal model that tracks the likely implications for the wild meat trade of the systemic crisis triggered by COVID-19. We focus on the resulting economic shockwave, as manifested in the collapse in global demand for commodities such as oil, and international tourism services, and what this may mean for local African economies and livelihoods. We trace the shockwave through to the consequences for the use of, and demand for, wild meats as households respond to these changes. We suggest that understanding and predicting the complex dynamics of wild meat use requires increased collaboration between environmental and resource economics and the ecological and conservation sciences

Critical Invasion Science: Weeds, Pests, and Aliens

The study of invasive plants and animals calls strongly for a critical approach due to the deeply social nature of invasion landscapes, the power relations affecting the science of invasions, and the differential impacts of weed or pest control on lives and landscapes. I first explore what a “critical” invasion science means. Then I investigate several aspects of invasion science ripe for critical analysis: the history of the science (to understand what the science is doing and why), the terminology and categories of analysis, and the highly contested social, political, and ethical context within which invasion management takes place. I conclude with four proposals for further work in critical invasion science and examples of the types of questions it might ask

Procurement Planning and Service Delivery in Local Government: a case study of Lira District and Municipality

A research dissertation report submitted to the faculty of business and administration, Uganda Christian University in partial fulfillment of the requirements for the degree of Master of business administration (finance).The main purpose of the study was to assess the relationship between procurement planning and effective service delivery by local government procurement system in Uganda with a case study of Lira District local government and municipal. The study was guided by three objectives: To establish whether procurement planning process contribute to effective service delivery, to examine the procurement planning practices that contributes to effective service delivery and to examine the relationship between the value of procurement planning and service delivery. Procurement planning was poorly done and executed at Lira Municipal Council (MC) and this led to a number of procurements not being executed and conducting procurements outside the procurement plan, hence hampering on service delivery in the district. The study was necessitated since procurement planning forms the basis of all procurement activities and public procurement’s main goal is to provide efficient and effective services to the citizens. The study employed both qualitative and quantitative research design. The target population comprised of 170 and 130 sample was used (with a response rate of 90.77% got) district employees. Cluster sampling technique was employed and within each cluster, random and purposive sampling techniques were used to select samples for this study. Data was collected using structured questionnaires. The quantitative data collected was analysed using SPSS. The study findings reveals that, there is positively, strong and significantly relationship between procurement planning and service delivery. Procurement planning process represented by r. value of .947**, procurement planning practice at r. value of .989** and finally value of procurement planning at r. of .971** all at 0.000 level of significance. It can also be concluded that of the three the constructs under procurement planning (procurement planning process, practices and value) the one that has more impact on improvement of service delivery is procurement planning value and will lead to improvement in service delivery by 30.9%; followed by the procurement planning process (20.9%); and finally, the procurement planning practice by 17%. Finally, a number of recommendations were suggested such as the need to do market capability analysis (market survey), effectively design a comprehensive procurement plans and create awareness to all staff on the need to prepare procurement plans

Unveiling the hidden processes under a structural adjustment program in Ghana : a study of agricultural production in Berekum, 1985 - 1995

Thesis, Queen's University, 1999The table of contents for this item can be shared with the requester. The requester may then choose one chapter, up to 10% of the item, as per the Fair Dealing provision of the Canadian Copyright Ac

Deep Neural Network-based Approach for IoTService QoS Predicti

Building innovative and complex applications on top of the Internet of Things (IoT) services provided by huge connected devices and software while satisfying quality of service (QoS) parameters has become a challenging topic. Identifying suitable services according to their QoS parameters is one of the main underlying features to enable optimal selection, composition, and self-management of IoT systems. Checking each service to get its accurate QoS is not feasible. QoS prediction has been proposed these last years to try to cope with this issue. Mainly, the existing approaches rely on collaborative filtering methods, which suffer from scalability issues, that can considerably hamper the performance of QoS prediction. To overcome this limit, in this paper, we propose a deep-learning-based QoS prediction approach for IoT services. The approach we propose relies on Long Short-Term Memory (LSTM) to capture the service representation through a service latent vector and on Residual Network (ResNet) for QoS prediction. Unlike existing deeplearning-based approaches that assume a pre-defined static set of services, our approach addresses the QoS prediction problem for dynamic environments where the services are not necessarily fixed in advance