Dissertations / Theses on the topic 'Cities and towns – Data processing'

There has been little interest in eighteenth century urban history in England and particularly in the significance of patterns of urban social structure during the transition from a traditional to a modern society. One reason for this is the intractable and fragmentary nature of the sources for this precensus period. In this study three types of source, a town directory, a Parliamentary Poll Book and the city rate and national tax returns for Bristol in 1774/5, were collated using nominal record linkage techniques to give a body of information which covered 80% of the city's heads of household. With the use of this database and various computer techniques occupation, sex, wealth, place of residence and voting allegiance were analysed. The results suggest that a professional or leisured suburban group was by this date well established in distinct areas of the city. The supremacy of the traditional élite, the overseas merchants, was challenged by this group, although the merchants themselves were in part joining the suburban dwellers. Poorer Bristolians still concentrated in dockside parishes and in parts of the city which were becoming increasingly unfashionable and homogeneous as the richer men moved out, though this process was not very far advanced and there was still a degree of mixing in the older city parishes. The economic structure of the city was changing with increased emphasis on services, professions and distribution. This increased disparities in wealth within the city and between the city and its hinterland and gave the ability to the rich to further their isolation from the poor by moving to the suburbs. The 1774 election pointed to the continuing importance of traditional influences (here of religion) In society, but also confirmed suggestions that the professions and distributors were drawing away from the mass of the populace. A revision of previous interpretations of the nature of Bristol society is necessary to accommodate this growing and important group - the emergent middle class. The thesis shows that a comprehensive computer-based study can make usable dubious sources (in particular fiscal records) and use them to revise interpretations of English urban communities at this date.

Thesis submitted in fulfilment of the requirements for the degree Master of Technology INFORMATION TECHNOLOGY in the Faculty of INFORMATICS AND DESIGN at the Cape Peninsula University of Technology 2013
In this information age, Enterprise Resource Planning (ERP) system implementation simplifies complexities within organisational heterogeneous Information Systems (IS). Following the early era of computerisation, ERP systems were designed to provide a formal integration of the whole enterprise with a business focussed approach to the application of Information and Communication Technology (ICT). With the advent of ERP systems, organisations in industry have invested substantially in these systems to assist and augment their ability to form more effective management of their resources across corporate walls and business functions through an integration of information and operations. In the public sector, governments at all levels have also turned to ERP systems to address their needs. As such, ERP implementation has become a new solution that many public institutions are venturing into, based on the evidence of benefits in the commercial sector. However, in the public sector, ERP systems should not be focussed on just internal organisational benefits. Due cognisance must be given, during design and implementation, to the citizen, who is the key public sector beneficiary. The benefits from ERP systems implementation and utilisation are well researched and documented by various scholars. However research has mainly focused on the private sector. Given the growing importance of ERP systems in the public sector it has become imperative to advance understanding in the arena of government with regards to public ERP system implementation and utilisation. Thus, an important question that warrants investigation is: ―What are the benefits for the citizen when ERP systems are implemented by municipalities?‖ The empirical work reported in this research investigated, via an in-depth case study, how the citizen benefits from ERP implementations in municipalities. Fifteen respondents, who are employees the City of Cape Town Municipality, were interviewed. Their interview transcripts, together with the other supporting documents they supplied and information from the City‘s website, were analysed through hermeneutical analysis. The analysis of the data was facilitated by the use of Atlas.ti, a computer-aided qualitative analysis software tool. Four key findings emanated from the analysis.  Indirect ERP-system benefits: The benefits in this category denoted those benefits that are generically found in any ERP system regardless of it being implemented in a private or public enterprise setup.  Direct citizen benefits: These are the benefits that are noticeable and directly extended to the citizens even without the knowledge of the existence of the ERP system.  Effective ERP system benefits management: This finding elaborated how the ERP system can be sustainably managed to ensure that it delivers maximum benefits to the citizens in a long term.  Efficient and effective use of public money: This finding explained the outcomes of utilising the ERP system, specifically in public enterprises, such as municipalities. The findings of this study are important in that they would assist to articulate and improve the business cases when municipalities commence with planning for ERP system acquisition. This is especially important given the high cost related to ERP system implementation. More importantly, the findings provide a basis for the identification of citizen benefits which are related to ERP system implementation. This provides municipal management a frame within which to improve the on-going management of ERP systems. The findings thus support improved ERP system benefits management and which would in turn ensure improved service delivery to the citizenry.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

Includes bibliographical references (64 p.)
2 v. : ill., maps (chiefly col.) ; 30 cm.
Title page, contents and abstract only. The complete thesis in print form is available from the University Library.
The concept of sustainable development is explored with a focus upon water resources and urban development. Simulation of urban growth patterns and water resources management has been undertaken as part of the case study. The artificial Neural Networks technique has been employed to model regional water consumption.
Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 1999?

Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia
A rápida expansão das áreas urbanas e os rápidos avanços da tecnologia levaram ao entrelaçamento tecnológico das cidades, tornando-as dependentes em uma variedade de setores dos sistemas que as controlam. Com os diversos setores de uma cidade sendo controlados por máquinas, a necessidade de monitorá-los aumentou, dando-se a oportunidade de centralizar as informações para que os líderes da cidade pudessem tomar decisões mais coerentes e lógicas. As chamadas Cidades Inteligentes estão em constante evolução, trazendo novos dispositivos de Internet das Coisas e novas fontes de dados, gerando cada vez mais informações com o passar dos anos. O principal problema com essa evolução exponencial é que os sistemas de ingestão antigos não conseguem lidar com o aumento na quantidade de dados gerados.Este trabalho teve em vista o problema de ingestão da Plataforma Urbana da Ubiwhere e teve como objetivo propor um sistema que permite a coleta de dados das diferentes fontes que a Ubiwhere tem e pode ter no futuro, enquanto fornece processamento e entrega quase em tempo real.O seguinte documento propõe uma arquitetura para resolver o problema da Ubiwhere na coleta e processamento de dados de diferentes fontes de Smart City com ênfase na análise das diferentes opções para resolver o problema de ingestão de dados.Além disso, esta tese descreve o mecanismo de Regras desenvolvido para permitir que os usuários forneçam suas próprias regras personalizadas para que as comparações de dados em tempo real possam ser feitas em diferentes conjuntos de dados para uma tomada de decisão mais preventiva e eficiente com base no resultado dos limites definidos pelo utilizador.Por último, mas não menos importante, demonstramos nossa solução usando um caso de uso de Smart Cities do mundo real, particularmente nos setores de tráfego.
The swift expansion of urban areas and the rapid advances of technology, led to the technological entanglement of cities, making them dependent in a variety of sectors on the systems that control them. With the different sectors in a city becoming controlled by machines, the need to monitor them has increased giving rise to an opportunity to have the information centralized so that city leaders can take more coherent and logical decisions. These so-called Smart Cities have been in constant evolution bringing about new Internet of Things devices and new data sources generating more and more information as the years go by. The main issue with this exponential evolution is that legacy ingestion systems cannot cope with the rise in the amount of data that is generated.This work had in view Ubiwhere´s Urban Platform ingestion issue and aimed to propose a system that allows the gathering of data from the different sources that Ubiwhere has and may have in the future whilst providing near real-time processing and delivery.The following document proposes an architecture to solve Ubiwhere´s problem in collecting and processing data from different Smart City sources with emphasis on the analysis of the different options to solve the problem of data ingestion.Additionally, this thesis describes the Rule mechanism developed for allowing users to provide their own custom rules so that real-time data comparisons can be made given different data sets for more preemptive and efficient decision making based on the result of the user-defined thresholds.Last but not least, we demonstrate our solution using a real-world Smart Cities use case namely in the sectors of Traffic.

碩士
淡江大學
資訊工程學系碩士班
101
With the rapid development of sensor technologies, in nowadays modern cities are deployed with various types of sensors to gather the environmental data. It is without doubt that how to deal with the huge amount of data collected by various sensors in an efficient way and to transform these data to useful information for the citizens to make use of has become an important research topic in building intelligent cities. The vast amount of data produced around us such as the temperature, the road conditions and the air quality can be numerically analyzed by utilizing the cloud computing technology. In this research, we implemented a MapReduce-based sensor data processing platform for intelligent cities. Specifically, we focus on using the MapReduce framework to process the raw data uploaded from the sensors, and then using HBase, which is a distributed, scalable, big data store, to save the sensor data. Besides, we use the Hadoop Distributed File System (HDFS) to store the street images captured by event data recorders installed in vehicles. In sum, the data processing platform we developed can be an important building block for constructing various useful applications to serve the citizens in intelligent cities.

碩士
淡江大學
資訊工程學系資訊網路與通訊碩士班
102
With the rapid development of sensor technologies, along with the increasing popularity of mobile devices and wireless/mobile networks, the volume of data generated by human beings and all sorts of devices are getting larger and larger every day. It is without doubt that how to deal with the huge amount of data in an efficient way and to transform these data into useful information for people to make use of has become an important research topic. In this thesis, we focus on handling the large amount of environmental conditions data collected by various sensor devices. These data produced around us such as the temperature, the road conditions and the air quality can be numerically analyzed by utilizing the cloud computing technology. Therefore, we implemented a sensor data processing platform for intelligent cities based on Hadoop. We assume that sensor data are embedded in the image files captured by the vehicle drive recorders and the smartphones. With Android smartphones, users can upload the image files to the Hadoop cluster by the Separatags Android App we developed. After the image files are uploaded, we use the MapReduce framework to process them. Specifically, in the Map task we utilize the well-developed Hadoop Image Processing Interface (HIPI) library to extract the desired sensor data from the image files, and then in the Reduce task these sensor data are inserted into HBase. Besides, we use the Hadoop Distributed File System (HDFS) to store the street images captured by driving recorders installed in vehicles. People can then use their Android smartphones or standard web browsers to access the sensor data and the street images. In sum, the data processing platform we developed can be an important building block for constructing various useful and creative applications to serve people living in intelligent cities.

Text mining is involved in a broad scope of applications in diverse domains that mainly, but not exclusively, serve political, commercial, medical and academic needs. Along with the rapid development of the Internet technology in recent thirty years and the advent of online social media and network in a decade, text data is obliged to entail features of online social data streams, for example, the explosive growth, the constantly changing content and the huge volume. As a result, text mining is no longer merely oriented to textual content itself, but requires consideration of surroundings and combining theories and techniques of stream processing and social network analysis, which give birth to a wide range of applications used for understanding thoughts spread over the world , such as sentiment analysis, mass surveillance and market prediction. Automatically discovering sequences of words that represent appropriate themes in a collection of documents, topic detection closely associated with document clustering and classification. These two tasks play integral roles in revealing deep insight into the text content in the whole text mining framework. However, most existing detection techniques cannot adapt to the dynamic social context. This shows bottlenecks of detecting performance and deficiencies of topic models. In this thesis, we take aim at text data stream, investigating novel techniques and solutions for robust text mining to tackle arising challenges associated with the online social context by incorporating methodologies of stream processing, topic detection and document clustering and classification. In particular, we have advanced the state-of-theart by making the following contributions: 1. A Multi-Window based Ensemble Learning (MWEL) framework is proposed for imbalanced streaming data that comprehensively improves the classification performance. MWEL ensures that the ensemble classifier is maintained up to date and adaptive to the evolving data distribution by applying a multi-window monitoring mechanism and efficient updating strategy. 2. A semi-supervised learning method is proposed to detect latent topics from news streams and the corresponding social context with a constraint propagation scheme to adequately exploit the hidden geometrical structure as supervised information in given data space. A collective learning algorithm is proposed to integrate the textual content into the social context. A locally weighted scheme is afterwards proposed to seek an improvement of the algorithm stability. 3. A Robust Hierarchical Ensemble (RHE) framework is introduced to enhance the robustness of the topic model. It, on the one hand, reduces repercussions caused by outliers and noises, and on the other overcomes inherent defects of text data. RHE adapts to the changing distribution of text stream by constructing a flexible document hierarchy which can be dynamically adjusted. A discussion of how to extract the most valuable social context is conducted with experiments for the purpose of removing some noises from the surroundings and efficiency of the proposed.

Indiana University-Purdue University Indianapolis (IUPUI)
Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.

Dimensionality reduction techniques such as feature extraction and feature selection are critical tools employed in artificial intelligence, machine learning and pattern recognitions tasks. Previous studies of dimensionality reduction have three common problems: 1) The conventional techniques are disturbed by noise data. In the context of determining useful features, the noises may have adverse effects on the result. Given that noises are inevitable, it is essential for dimensionality reduction techniques to be robust from noises. 2) The conventional techniques separate the graph learning system apart from informative feature determination. These techniques used to construct a data structure graph first, and keep the graph unchanged to process the feature extraction or feature selection. Hence, the result of feature extraction or feature selection is strongly relying on the graph constructed. 3) The conventional techniques determine data intrinsic structure with less systematic and partial analyzation. They maintain either the data global structure or the data local manifold structure. As a result, it becomes difficult for one technique to achieve great performance in different datasets. We propose three learning models that overcome prementioned problems for various tasks under different learning environment. Specifically, our research outcomes are listing as followings: 1) We propose a novel learning model that joints Sparse Representation (SR) and Locality Preserving Projection (LPP), named Joint Sparse Representation and Locality Preserving Projection for Feature Extraction (JSRLPP), to extract informative features in the context of unsupervised learning environment. JSRLPP processes the feature extraction and data structure learning simultaneously, and is able to capture both the data global and local structure. The sparse matrix in the model operates directly to deal with different types of noises. We conduct comprehensive experiments and confirm that the proposed learning model performs impressive over the state-of-the-art approaches. 2) We propose a novel learning model that joints SR and Data Residual Relationships (DRR), named Unsupervised Feature Selection with Adaptive Residual Preserving (UFSARP), to select informative features in the context of unsupervised learning environment. Such model does not only reduce disturbance of different types of noise, but also effectively enforces similar samples to have similar reconstruction residuals. Besides, the model carries graph construction and feature determination simultaneously. Experimental results show that the proposed framework improves the effect of feature selection. 3) We propose a novel learning model that joints SR and Low-rank Representation (LRR), named Sparse Representation based Classifier with Low-rank Constraint (SRCLC), to extract informative features in the context of supervised learning environment. When processing the model, the Low-rank Constraint (LRC) regularizes both the within-class structure and between-class structure while the sparse matrix works to handle noises and irrelevant features. With extensive experiments, we confirm that SRLRC achieves impressive improvement over other approaches. To sum up, with the purpose of obtaining appropriate feature subset, we propose three novel learning models in the context of supervised learning and unsupervised learning to complete the tasks of feature extraction and feature selection respectively. Comprehensive experimental results on public databases demonstrate that our models are performing superior over the state-of-the-art approaches.