The intention of this exercise is to investigate a specific area in the growing field of urban design, while learning in the process. As urban design is one of the practices that are directly affected by the pressures resulting from the current fast changes in our environments, it will be useful and fascinating to explore this dimension in the discourse. The theme of the discourse is therefore to reflect on the different thoughts and deal with the continual change in the Urban Environment that is resulting from technological advancements. Technology is generally discussed as the human mastery in perfecting the tools by which humans attempt to control their environments.Therefore the Urban Environment is presented to be the reflection of human level of technological success. The thesis hence revises the understanding of the Urban Environment It emphasizes the fact that the Urban Environment is a continually transforming entity, whereby urban designers are expected to continually come to terms with the emerging new lifestyles, due to changing technology and its impacts on the structures of urban environments. While sharing the view that respects the values of the past (that have continued to shape human urban environments), this study advocates the maintenance of some sort of balance in design approaches, to also allow the accommodation of new technological environments. The study inciudes experimenting on reasonably bold but plausible design ideas. In doing so *''« exercise hopes to provide a stage for discussions and provoke iri-iovative thinking by urban designers, that will be useful to the discourse of urban design.

This dissertation is a study of the social dimensions and implications of the smart city, a new kind of urbanism that augments the city’s existing infrastructures with sensors, wireless communication, and software algorithms to generate unprecedented reams of real-time data. It investigates how smartness reshapes civic ties, and transforms the ways of seeing and governing urban centers long-plagued by racial and economic divides. How do the uneven adoption of smart technologies and data-driven practices affect the relationship between citizens and local government? What mediates the understanding and experience of urban inequalities in a data-driven city? In what ways does data-driven local governance address or exacerbate pervasive divides? The dissertation addresses these questions through three years of ethnographic fieldwork in Kansas City, where residents and public officials have partnered with Google and Cisco to test a gigabit internet service and a smart city program respectively. I show that the foray of tech companies into cities not only changes how urban problems are identified, but also reproduces civic divides. Young, middle-class, white residents embrace the smart city with the goal of turning the city’s problems into an economic opportunity, while already-vulnerable residents are reluctant to adopt what they perceive as surveillance technologies. This divide widens when data-driven practices of the smart city compel public officials and entrepreneurial residents to feign deliberate ignorance against longstanding issues and familiar solutions, or explore spurious connections between different datasets due to their assumptions about how creative breakthroughs surface in the smart city. These enthusiasts hope to discover connections they did not know existed, but their practices perpetuate existing stereotypes and miss underlying patterns in urban inequalities. By teasing out the intertwined relationships among tech giants, federal/local governments, local entrepreneurial groups, civic tech organizations, and nonprofits, this research demonstrates how the interests and cultural techniques of the contemporary tech industry seep into age-old practices of classification, record keeping, and commensuration in governance. I find that while these new modes of knowledge production in local government restructure the ways public officials and various publics see the city, seeing like a city also shapes the possibilities and limits of governing by data.

Submitted in fulfillment of the requirements for the Master of Information and Communications Technology, Durban University of Technology, Durban, South Africa, 2017. / The future of modern cities largely depends on how well they can tackle intrinsic problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities and associated technology infrastructures. Smart city is an emerging phenomenon that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. Key areas that have been considered for smart city initiatives include monitoring of weather, energy consumption, environmental conditions, water usage and host of others. To align with the smart city revolution in the area of environmental cleanliness, this study involves the development of a web based smart city infrastructure for refuse disposal management using the design science research approach. The Jalali smart city reference architecture provided a template to develop the proposed architecture in this study. The proposed architecture contains four layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was designed and implemented based on the proposed architecture. The signal sensing and processing layer was implemented to produce a smart refuse bin, which is a bin that contains the Arduino microcontroller board, Wi-Fi transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non browser client application, statistical feature extraction and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin, generates multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, Multilayer Perceptron Artificial Neural Network (MLP-ANN) and support vector machine were trained and compared experimentally. The MLP-ANN gave the overall best accuracy of 98.0%, and the least mean square error of 0.0036. The ThingSpeak web application connects seamlessly at all times via the internet to receive data from the smart refuse bin. Refuse disposal management agents can therefore query ThingSpeak for refuse status data via the non browser client application. The client application, then uses the trained MLP-ANN to appositely classify such data in order to determine the status of the bin. / M