Dissertations / Theses on the topic 'Cities and towns – Data processing'
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Chittineni, Ruparani. "Discrete representation of urban areas through simplification of digital elevation data." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-12172002-212246.
Full textBaigent, Elizabeth. "Bristol society in the later eighteenth century with special reference to the handling by computer of fragmentary historical sources." Thesis, University of Oxford, 1985. http://ora.ox.ac.uk/objects/uuid:1c29c607-abe8-486b-9694-e11682413a3a.
Full textYeung, Kwok-wai Albert, and 楊國偉. "A photogrammetric land information system for urban analysis: a study of the development of Kowloon from1964 to 1979." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31231445.
Full textGreenberg, Joshua David. "Analysis of urban-rural gradients using satellite data /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/5498.
Full textRamesh, Sathya Dong Pinliang. "High resolution satellite images and LiDAR data for small-area building extraction and population estimation." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/ark:/67531/metadc12188.
Full textChandiwana, Takauya. "An assessment of citizen benefits of enterprise resource planning systems in municipalities." Thesis, Cape Peninsula University of Technology, 2013. http://hdl.handle.net/20.500.11838/1364.
Full textIn 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.
Ramesh, Sathya. "High Resolution Satellite Images and LiDAR Data for Small-Area Building Extraction and Population Estimation." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc12188/.
Full textFleming, Nicholas S. "Sustainability and water resources management for the northern Adelaide Plains, South Australia / Nicholas S. Fleming." 1999. http://hdl.handle.net/2440/19525.
Full text2 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?
Manyanga, Siyabonga Siyanda Sabelo. "Understanding the implications of information and communication technology (ICT) for local governance : the example of eThekwini Municipality, Durban." Thesis, 2002. http://hdl.handle.net/10413/2255.
Full textSantos, Francisco Gonçalves Palma Barão dos. "Data ingestion in Smart Cities." Master's thesis, 2021. http://hdl.handle.net/10316/95527.
Full textA 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.
Li, Chia-Chen, and 李佳蓁. "Implementation of a MapReduce-based Sensor Data Processing Platform for Intelligent Cities." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/29283526104220744399.
Full text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.
Liao, Wei-Che, and 廖韋哲. "SeparaTags: A Sensor Data Processing Platform based on Android and Hadoop for Building Intelligent Cities." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/2zw49p.
Full text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.
Wang, Ye. "Robust Text Mining in Online Social Network Context." Thesis, 2018. https://vuir.vu.edu.au/38645/.
Full textYe, Nan. "Comparison between high-resolution aerial imagery and lidar data classification of canopy and grass in the NESCO neighborhood, Indianapolis, Indiana." Thesis, 2014. http://hdl.handle.net/1805/5276.
Full textUrban 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.
Teng, Luyao. "Research on Joint Sparse Representation Learning Approaches." Thesis, 2019. https://vuir.vu.edu.au/40024/.
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