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published_or_final_version / Geography and Geology / Doctoral / Doctor of Philosophy

Most large contemporary mines already have considerable amounts of data, much of which goes largely unused. The key challenge in big data is increasing data utilization. Much of the data in the mine (not plant) come from a variety of systems, each with different databases and reporting environments. Standard technology deployments create a "silo-ification" of data leading to poor system usage. Through modern server monitoring, data utilization can quantifiably be measured. A host of other quantifiable, often automated approaches, to measuring data use and value can also be incorporated as a means of monitoring value generation. A data valuation tool is presented to measure the data assets at an operation. The Data Value Index (DVI) quantifies business intelligence best practices and user interaction considering managerial flexibility and data utilization rates. The DVI is built considering many case studies of data warehousing at various mining companies, some of which will be presented.

In the United States, nonprofit organizations (NPOs) face a precarious future because of their heavy reliance on the financial support of government funding, other donor agencies, and philanthropic foundation resources. The purpose of this study is to understand how and to what extent leadership of NPOs can benefit from using funder-required performance data to improve organizational sustainability. Using Pfeffer and Salincik's resource dependency theory to explain organizational sustainability, this qualitative multiple case study of 10 NPOs in a northeastern U.S. state includes 14 interviews with NPO leadership, a document review of NPO 990 tax filings, annual performance reports, and board meeting minutes. All data were inductively coded and then subjected to a thematic analysis procedure. Key findings indicated 6 overarching themes associated with NPO sustainability and funder-required performance measures that impact program sustainability but are mainly used for compliance: (a) NPO adoption and use of performance measures; (b) data collection and evaluation for external compliance; (c) information pertaining to financial, operational, and administrative decision making; (d) NPO leadership decisions regarding internal constructs, operations, and management; (e) resource dependency; and (f) sustainability practices. The implications for positive social change include strategies for NPO leaders to use to ensure survival, continuous community impact, and awareness for policymakers regarding legislative and regulatory developments that may inadvertently harm NPOs.

Protection of the visual environment begins with a comprehensive documentation and evaluation of existing conditions followed by the development of guidelines pertaining to future alterations. This thesis examines existing methods of visual assessment and the needs of the land planner for the purpose of understanding the necessary components of evaluating the visual environment effectively. The objective has been to develop a new method of visual documentation and evaluation that can be utilized by land planners for the visual assessment of road corridors. In order to achieve this objective, a visual assessment of a Significant road corridor in Blacksburg, Virginia has been conducted. Various necessary components have been included in the assessment and a relational database management program has been used in the storage of all collected data. As a result of this process, it was found that a new method, which borrows from past processes, addresses the needs of the land planner, and utilizes an interactive medium for storage of data, is successful in addressing the objective. The new method has been successful in including the necessary components such as qualitative evaluation with adaptive descriptive nomenclature and photographic documentation of the existing corridor. The database has many qualities which are meaningful to land planners. Relational database management programs have the capability of storing text as well as photographs. For land planners to view the various aspects of the corridor, a simple pressing of their computer mouse button moves the them from one aspect to another. / Master of Landscape Architecture

Yes / The reuse of existing datasets to identify mechanisms for improving healthcare quality has been widely encouraged. There has been limited application within dementia care. Dementia Care Mapping (DCM) is an observational tool in widespread use, predominantly to assess and improve quality of care in single organisations. DCM data has the potential to be used for secondary purposes to improve quality of care. However, its suitability for such use requires careful evaluation. This study conducted in-depth interviews with 29 DCM users to identify issues, concerns and challenges regarding the secondary use of DCM data. Data was analysed using modified Grounded Theory. Major themes identified included the need to collect complimentary contextual data in addition to DCM data, to reassure users regarding ethical issues associated with storage and reuse of care related data and the need to assess and specify data quality for any data that might be available for secondary analysis. / This study was funded by the Faculty of Health Studies, University of Bradford.

No / Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. There is growing emphasis on sharing and reusing dementia care-related datasets to improve the quality of dementia care. Consequently, there is a need to develop data management solutions for collecting, integrating and storing these data in formats that enhance opportunities for reuse. Dementia Care Mapping (DCM) is an observational tool that is in widespread use internationally. It produces rich, evidence-based data on dementia care quality. Currently, that data is primarily used locally, within dementia care services, to assess and improve quality of care. Information-rich DCM data provides opportunities for secondary use including research into improving the quality of dementia care. But an effective data management solution is required to facilitate this. A rationale for the warehousing of DCM data as a technical data management solution is suggested. The authors also propose a data model for a DCM data warehouse and present user-identified challenges for reusing DCM data within a warehouse.

Call number: LD2668 .T4 1985 S642 / Master of Landscape Architecture

For local authorities to manage land policies effectively data bases of land use information that are current and mirror development on the ground are required. At present local authorities have no mechanisms in place to acquire maintain and spatially link land use information. Detailed land use information is not generally available at the local level. Generally little attention is paid to maintaining the expensive data which is assembled when planning schemes, development plans or projects are prepared. Land use planning has traditionally focussed on the control rather than the facilitation of development. Details of the actual land use on the ground are generally ignored as tariffs for tax purposes are set on the zoning of the land or a flat rate rather than the actual land use. This lack of land use information, which is exacerbated by informal settlement, causes delays in approving new land uses. There is generally no data available for informal areas and land use and tenure is subject to the informal rules that have evolved with such settlements. If these areas are to be included in the formal land management systems, ways of including and maintaining land use information about these settlements must be developed. By reviewing land information theory, the South African legal land development framework and using a small town as a case study, I have shown that provided certain conditions are met a Spatial Decision Support System (SDSS), designed to record and maintain the land use data necessary to support land use planning in both formal and informal contexts, could be a valuable land management tool. Such a system should be implemented in partnership with local communities and should; • support local level land use decision making and regulation • serve as a land management tool to integrate formal and informal communities • have mechanisms to keep land use information current • be transparent about the type of land use information • develop linkages with regional government to provide detailed land information over time. / Thesis (M.Sc.Sur.)-University of Natal, Durban, 2002.

Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further. / AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.