Dissertations / Theses on the topic 'Geospatial information systems and geospatial data modelling'

Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.

Thesis (Ph. D.)--University of Oregon, 2005.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 109-115). Also available for download via the World Wide Web; free to University of Oregon users.

Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent's homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly. Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision support systems (DSS) to analyze and allocate resources for active and potential hazards. We designed and implemented a geospatial data service, REST API for Pipeline Integrity Data (RAPID) to improve the amount and quality of data available to DSS. More specifically, RAPID -- built with a spatial database and the Django web framework -- allows third-party software to manage and query an arbitrary number of geographic data sources through one centralized REST API. Here, we focus on the process and peculiarities of creating RAPID's model and query interface for pipeline integrity management; this contribution describes the design, implementation, and validation of that model, which builds on existing geospatial standards.

The goal of this thesis is to assess and characterize the representativeness of sampled data that is voluntarily submitted through social media. The case study vehicle used is Twitter data associated with the 2012 Presidential election, which were in turn compared to similarly collected 2008 Presidential election Twitter data in order to ascertain the representative statewide changes in the pro-Democrat bias of sentiment-derived Twitter data mentioning either of the Republican or Democrat Presidential candidates. The results of the comparative analysis show that the MAE lessened by nearly half - from 13.1% in 2008 to 7.23% in 2012 - which would initially suggest a less biased sample. However, the increase in the strength of the positive correlation between tweets per county and population density actually suggests a much more geographically biased sample.

Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly. This thesis compares three paradigms for systems that manage vector data. Over the past few decades these methodologies have fallen in and out of favor. Today, some are considered new and experimental (distributed), others nearly forgotten (flat file), and others are the workhorse of present-day GIS (relational database). Each is well-suited to some use cases, and poorly-suited to others. This thesis investigates exemplars of each paradigm.

Orientador: Claudia Maria Bauzer Medeiros
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: Trabalhar em questões relativas a dados geoespaciais presentes em múltiplas escalas apresenta inúmeros desafios que têm sido atacados pelos pesquisadores da área de GIS (Sistemas de Informação Geográfica). De fato, um dado problema do mundo real deve frequentemente ser estudado em escalas distintas para ser resolvido. Outro fator a ser considerado é a possibilidade de manter o histórico de mudanças em cada escala. Além disso, uma das principais metas de ambientes multi-escala _e garantir a manipulação de informações sem qualquer contradição entre suas diferentes representações. A noção de escala extrapola inclusive a questão espacial, pois se aplica também, por exemplo, _a escala temporal. Estes problemas serão analisados nesta dissertação, resultando nas seguintes contribuições: (a) proposta do modelo DBV (Database Version) multi-escala para gerenciar de forma transparente dados de múltiplas escalas sob a perspectiva de bancos de dados; (b) especificação de restrições de integridade multi-escala; (c) implementação de uma plataforma que suporte o modelo e as restrições, testados com dados reais multi-escala
Abstract: Work on multi-scale issues concerning geospatial data presents countless challenges that have been long attacked by GIScience (Geographic Information Science) researchers. Indeed, a given real world problem must often be studied at distinct scales in order to be solved. Another factor to be considered is the possibility of maintaining the history of changes at each scale. Moreover, one of the main goals of multi-scale environments is to guarantee the manipulation of information without any contradiction among the different representations. The concept of scale goes beyond issues of space, since it also applies, for instance, to time. These problems will be analyzed in this thesis, resulting in the following contributions: (a) the proposal of the DBV (Database Version) multi-scale model to handle data at multiple scales from a database perspective; (b) the specification of multi-scale integrity constraints; (c) the implementation of a platform to support model and constraints, tested with real multi-scale data
Mestrado
Ciência da Computação
Mestre em Ciência da Computação

Thesis (MScAgric) -- Stellenbosch University, 2011.
ENGLISH ABSTRACT: In South Africa more than 80 percent of the total land area is used for agriculture and subsistence livelihoods. A land transaction is generally not a recurring action for most buyers and sellers, their experience and knowledge are limited, for this reason the services of property agents and valuers are sometimes used, just to get more information available. The condition of insufficient information and the inability to observe differences in land productivity gives rise to the undervaluation of good land and overvaluation of poor land. The value of a property plays an important role in the acquisition of a bond, in this context farm valuations are essential and therefore commercial banks make more use of specialist businesses that have professional valuers available. The advent of the Internet made access to comprehensive information sources easier for property agents and valuers whose critical time and resources can now be effectively managed through Geographic Information System (GIS) integrated workflow processes. This study aims to develop the blueprint for a farm valuation support system (FVSS) that assists valuers in their application of the comparable sales method by enabling them to do the following: (1) Rapid identification of the location of the subject property and transaction properties on an electronic map. (2) Comparison of the subject property with the transaction properties in terms of value contributing attributes that can be expressed in a spatial format, mainly a) location and b) land resource quality factors not considered in existing valuation systems that primarily focus on residential property. Interpretation of soil characteristics to determine the suitability of a soil for annual or perennial crops requires specialized knowledge of soil scientists, knowledge not normally found among property valuers or estate agents. For this reason an algorithm, that generates an index value, was developed to allow easy comparison of the land of a subject property and that of transaction properties. Whether this index value reflects the soil suitability of different areas sufficiently accurate was confirmed by soil suitability data of the Breede and Berg River areas, which were obtained by soil scientists by means of a reconnaissance soil survey. This index value distinguishes the proposed FVSS from other existing property valuation systems and can therefore be used by valuers as a first approximation of a property’s soil suitability, before doing further field work. A nationwide survey was done among valuers and estate agents that provided information for the design of the proposed FVSS and proved that the need for such a system does exist and that it will be used by valuers.
AFRIKAANSE OPSOMMING: Meer as 80 persent van die totale grondoppervlakte in Suid-Afrika word gebruik vir landbou en bestaansboerdery. 'n Grondtransaksie is oor die algemeen nie 'n herhalende aksie vir die meeste kopers en verkopers nie, hul ervaring en kennis is beperk, om hierdie rede word die dienste van eiendomsagente en waardeerders soms gebruik om meer inligting beskikbaar te kry. Die toestand van onvoldoende inligting en die onvermoë om verskille in grondproduktiwiteit te identifiseer gee aanleiding tot die onderwaardering van goeie grond en oorwaardering van swak grond. Die waarde van 'n eiendom speel 'n belangrike rol in die verkryging van 'n verband. In hierdie konteks is plaaswaardasies noodsaaklik en daarom maak kommersiële banke meer gebruik van gespesialiseerde maatskappye wat oor professionele waardeerders beskik. Die koms van die Internet het toegang tot omvattende inligtingsbronne makliker gemaak vir eiendomsagente en waardeerders wie se kritiese tyd en hulpbronne nou effektief bestuur kan word deur middel van Geografiese Inligtingstelsel (GIS) geïntegreerde werksprosesse. Hierdie studie poog om die bloudruk vir 'n plaaswaardasie ondersteuningstelsel te ontwikkel wat waardeerders sal help in hul toepassing van die vergelykbare verkope metode deur hul in staat te stel om die volgende te doen: (1) Vinnige identifisering van die ligging van die betrokke onderwerp eiendom en transaksie eiendomme op 'n elektroniese kaart. (2) Vergelyking van die onderwerp eiendom met transaksie eiendomme in terme van waardedraende eienskappe wat in 'n ruimtelike formaat uitgedruk word, hoofsaaklik a) ligging en b) bodem gehaltefaktore wat nie oorweeg word in bestaande residensieel georiënteerde waardasiestelsels nie. Interpretasie van grondeienskappe om die geskiktheid van grond vir eenjarige of meerjarige gewasse te bepaal vereis gespesialiseerde kennis van grondkundiges, kennis wat nie normaalweg gevind word onder eiendomswaardeerders of eiendomsagente nie. Om hierdie rede is 'n algoritme ontwikkel sodat die grond van ‘n onderwerp eiendom d.m.v. ‘n indekswaarde met transaksie eiendomme vergelyk kan word. Die indekswaarde is akkuraat genoeg bevestig toe dit vergelyk is met grond geskiktheidsdata wat deur grondkundiges in die Breede- en Bergrivier gebiede ingesamel is. Hierdie indekswaarde onderskei die voorgestelde plaaswaardasie ondersteuningstelsel van ander bestaande eiendom waardasiestelsels en kan dus deur waardeerders gebruik word as 'n eerste bepaling van 'n eiendom se grond geskiktheid, voordat verdere veldwerk gedoen word. 'n Landwye opname is gedoen onder waardeerders en eiendomsagente wat inligting voorsien het vir die ontwerp van die voorgestelde plaaswaardasie ondersteuningstelsel, asook bewys gelewer het dat daar ‘n behoefte aan so 'n stelsel bestaan en dat dit deur waardeerders gebruik sal word.

Evaluation of aerial photographs, LiDAR imagery and GPS survey points was conducted to characterise water vole habitat at Grains in the Water and Swains Greave, in the Peak District. Justification for the study is to explore an affective way to monitor water vole habitat in relation to water vole signs utilising GIS. The water vole is a rapidly declining native species (Strachan and Strachan, 2003). The geospatial data was evaluated in terms of integration and extraction. The aerial photography provided a basis for vegetation mapping after visual interpretation. The aerial photograph required geometric correction and an average control point RMSE of 4.17m for the Grains in the Water site, using a 2nd order polynomial model was achieved. Extraction of slope, aspect, stream proximity and elevation were achieved using LiDAR imagery. Pearson’s product moment correlation coefficient highlighted a significant relationship between water vole latrine density with slope at the 0.01 significance level for 4m and 6m resolution data (Grains in the Water). The Swains Greave site supported this result with a 0.01 significance level for 6m resolution slope data. Elevation and aspect did not show a significant correlation with latrine density at Grains in the Water. The main conclusion is that water vole habitat cannot be solely characterised by aerial photography and LiDAR data, as other habitat variables could affect water vole distributions, which cannot be extracted from these geospatial data e.g. pH, bank exposure and stream depth.

Context: Accurate decision-making requires updated and precise information to establish the reality of an overall situation. New data sources (e.g., wearable technologies) have been increasing the amount of available and useful data, which is now called big data. This has a great potential for transforming the entire business process and improving the accuracy of decisions. In this context, disaster management represents an interesting scenario that relies on big data to enhance decision-making. This is because it must cope with data provided not only by traditional sources (e.g., stationary sensors) but also by emerging sources - for instance, information shared by local volunteers, i.e., volunteered geographic information (VGI). When combined, these data sources can be regarded as large in volume, with different velocities, and a variety of formats. Furthermore, an analysis is required to confirm their veracity is required since these data sources are disconnected and prone to various errors. These are the 4Vs that characterize big data. Gap: However, although all these data open up further opportunities, their huge volume, together with an inappropriate data integration and unsuitable visualization, can result in information being overlooked by decision-makers. This problem arises because the integration of the available data is hampered by the intrinsic heterogeneity of their features (e.g., their occurrence in different formats). When integrated, this information also often fails to reach the decision-makers in a suitable way (e.g., in appropriate visualization formats). Moreover, there is not a clear understanding of the decision-makers needs or how the available data can meet these needs. Objective: In light of this, this thesis presents an approach for improving decision-making with heterogeneous geospatial big data based on spatial decision support systems and volunteered geographic information in disaster management. Methods: Systematic mapping studies were conducted to identify gaps in research studies with regard to the use of volunteered information and spatial decision support systems in disaster management. On the basis of these studies, two design science projects were carried out. The first of these aimed at defining the elements that are essential for ensuring the integration of heterogeneous data, whereas the second project aimed at obtaining a better understanding of decision-makers needs. A cross-organizational action research project was also conducted to define the design principles that should be observed for a spatial decision support system to effectively support decision-making with heterogeneous geospatial big data. A series of empirical case studies was undertaken to evaluate the outcomes of these projects. Results: The overall approach thus consists of the three significant outcomes that were derived from these projects. The first outcome was the conceptual architecture that defines the integration of heterogeneous data sources. The second outcome was a model-based framework that describes the connection of decision-making with appropriate data sources. The third outcome is based on the framework and comprises a set of design principles for guiding the development of spatial decision support systems for decision-making with heterogeneous geospatial big data. Conclusion: This thesis has made a useful contribution to both practice and research. In short, it defines ways of integrating heterogeneous data sources, provides a better understanding of decision-makers needs, and supports the development of a spatial decision support system to effectively assist decision-making with heterogeneous geospatial big data.
Contexto: Uma tomada de decisão precisa exige informações mais precisas e atualizadas para estabelecer a realidade da situação geral. Novas fontes de dados (e.g, tecnologias vestíveis) tem aumentado a quantidade de dados úteis disponíveis, que agora é chamado de big data. Isso tem grande potencial para transformar todo o processo de negócio e melhorar a precisão na tomada de decisão. Neste contexto, a gestão de desastres representa um interessante cenário que depende de big data para aprimorar a tomada de decisão. Isso porque, ela tem que lidar com dados fornecidos não apenas por fontes tradicionais (e.g., sensores estáticos), mas também por fontes emergentes por exemplo, informações compartilhadas por voluntários locais, i.e., as informações geográficas de voluntários (VGI). Quando combinadas, estas fontes de dados podem ser consideradas grandes em volume, com diferentes velocidades e uma variedade de formatos. Além disso, uma análise com relação à sua veracidade é necessaria uma vez que estas fontes de dados são desconectadas e propensas à erros. Estes são os 4Vs que caracterizam big data. Problema: No entanto, embora todos estes dados abrem novas oportunidades, seu grande volume em conjunto com uma integração inapropriada e uma visualização inadequada, podem tornar as informações ignoradas por tomadores de decisão. Isso ocorre, pois, a integração dos dados disponíveis torna-se complicada devido a heterogeneidade intrínseca nas suas características (e.g., dados em formatos diferentes). Quando integradas, estas informações frequentemente também não chegam aos tomadores de decisão em uma condição apropriada (por exemplo, no formato de visualização adequado). Além disso, não existe uma clara compreensão sobre as necessidades dos tomadores de decisão ou sobre como os dados disponíveis podem ser usados para atender essas necessidades. Objetivo: Dessa forma, esta tese de doutorado apresenta uma abordagem para melhorar a tomada de decisões com grande volume de dados espaciais heterogêneos baseada em sistemas de suporte à decisão espacial e informações geográficas de voluntários na gestão de desastres. Métodos: Mapeamentos sistemáticos foram conduzidos para identificar lacunas de pesquisa no uso de dados voluntários e sistemas de suporte à decisão na gestão de desastres. Com base nestes estudos, dois projetos de design science foram conduzidos. O primeiro deles buscou definir elementos essências para entender a integração de dados heterogêneos, enquanto o segundo projeto buscou fornecer um melhor entendimento das necessidades dos tomadores de decisão. Também foi conduzido um projeto de pesquisa-ação interinstitucional para definir princípios de projeto que deveriam ser observados para um sistema de suporte à decisão espacial ser efetivo no apoio a tomada de decisão com grande volume de dados espaciais heterogêneos. Uma série de estudos de caso empíricos foram conduzidos para avaliar os resultados destes projetos. Resultados: A abordagem geral então é composta pelos três resultados significantes que foram derivados destes projetos. Em primeiro lugar, uma arquitetura conceitual que especifica a integração de fontes de dados heterogêneas. O segundo elemento é uma estrutura baseada em modelo que descreve a conexão entre a tomada de decisão com as fontes de dados mais adequadas. Com base nesta estrutura, o terceiro elemento consiste em um conjunto de princípios de design que guiam o desenvolvimento de um sistema de suporte à decisão espacial para tomada de decisão com grande volume de dados espaciais heterogêneos. Conclusão: Esta tese de doutorado realizou importantes contribuições para a prática e pesquisa. Em resumo, ela define formas para integrar fontes de dados heterogêneos, fornece uma melhor compreensão sobre as necessidades dos tomadores de decisão e ajuda no desenvolvimento de sistemas de suporte à decisão espacial para tomada de decisão com grande volume de dados espaciais heterogêneos.

SANDIS II is a land warfare simulation and analysis tool developed by the Finnish Defence Research Agency. The Swedish Defence Research Agency has evaluated SANDIS II to have potential as a war gaming aid within education, at the Swedish Defence University. However, operating the tool is considered too difficult to avail that potential. In this report we propose a way of using geographical data for path-finding in terrain and automatically adjusting units’ speeds. We construct a cost raster from various types of geographic data, with each grid in the cost raster storing a value, representing a degree of mobility. Models using cost rasters are then created for adjusting unit speed and finding least-cost paths. We implement the models in Python as a stand-alone module, and describe the module’s internal methods, interface and how it can be used by SANDIS II.
SANDIS II är ett simuleringsoch analysverktyg utvecklat av Finska Försvarsmaktens Forskningsanstalt. Svenska Totalförsvarets forskningsinstitut har utvärderat SANDIS II och funnit ett potentiellt användningsområde för verktyget som stöd vid krigsspel, inom utbildning vid Försvarshögskolan. Verktyget anses dock vara för svårhanterligt för att uppfylla detta syfte. I denna rapport föreslås metoder för att beräkna de snabbaste förflyttningsvägarna i terräng och att automatiskt justera enheters hastighet i simulatorn, baserat på geografisk data. Vi konstruerar ett kostnadsraster av olika typer av terrängdata, där varje ruta i rastret tilldelas ett värde som representerar framkomlighet. Med kostnadsraster som grund skapar vi sedan modeller för att kunna justera enheters hastigheter och beräkna framryckningsrutter med så låg kostnad som möjligt. Vi implementerar modellerna i en separat Python-modul och beskriver modulens interna metoder, gränssnitt och hur det kan användas av SANDIS II.

U radu su prikazani Open Geospatial Consortium (OGC) web servisi, iz aspekta serverskih i klijentskih aplikacija. Analizirani su problemi razmene prostornih podataka u složenim sistemima sa naglaskom na komunalne službe gradova. Na osnovu analize razmene podataka, predložen je model koji unapređuje komunikaciju i pospešuje napredak celokupnog sistema implementacijom distribuiranih OGC web servisa. Predloženi model distribucije prostornih podataka može se primenjivati na sve složene sisteme, ali i unutar manjih sistema kao što su kompanije koje se sastoje iz više sektora ili podsistema
The short review of the Open Geospatial Consortium (OGC) web services have been given in this work from the perspective of server and client applications. The problems of the exchange of spatial data in the complex systems as municipal service have been analysed. Based on analysis of data exchange, the model has been proposed to improve communication and progress of the whole system by implementing OGC web services. Described model of spatial data distribution can be applied to all complex systems, but also within smaller systems such as companies which consist of more sectors or subsystems.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on March 19, 2010). Thesis advisor: Dr. Dale R. Musser. Includes bibliographical references.

Stora mängder miljödata samlas hela tiden in och för att använda all data behöver den förstås av användarna så de kan applicera kunskapen inom deras område. Visualisering skapar förståelse om data. Heatmaps kan användas för att visualisera geospatial data och interaktivitet är ett hjälpmedel för att skapa ytterligare grafiska representationer. I detta arbete evalueras JavaScript-teknikerna D3.js, Heatmap.js och Vue.js angående vad som är mest lämpligt för att visualisera geospatial data utifrån effektiviteten vid utritning av heatmaps. Ett experiment genomförs där biblioteken D3.js, Heatmap.js testas i ramverket Vue.js. Detta för att ta reda på vilket bibliotek som föredras vid utritning av heatmaps och om ett ramverk påverkar resultatet. En miljö sätts upp för att genomföra undersökningen och tester för att påvisa detta. Resultatet indikerar att Heatmap.js och mindre datamängder ger en lägre utritningstid i den tillämpning som undersökts. I framtiden är det intressant att undersöka flera bibliotek och flera datamängder.

Due to the advances in location-acquisition techniques, citizen geospatial data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study society and citizens through exploring patterns from geospatial data. In this thesis, we investigate patterns of population and human sentiments using GPS trajectory data and geo-tagged tweets. Kernel density estimation and emerging hot spot analysis are first used to demonstrate population distribution across space and time. Then a flow extraction model is proposed based on density difference for human movement detection and visualization. Case studies with volleyball game in West Lafayette and traffics in Puerto Rico verify the effectiveness of this method. Flow maps are capable of tracking clustering behaviors and direction maps drawn upon the orientation of vectors can precisely identify location of events. This thesis also analyzes patterns of human sentiments. Polarity of tweets is represented by a numeric value based on linguistics rules. Sentiments of four US college cities are analyzed according to its distribution on citizen, time, and space. The research result suggests that social media can be used to understand patterns of public sentiment and well-being.

Public health is a major factor that in reducing of disease round the world. Today, most governments recognise the importance of public health surveillance in monitoring and clarifying the epidemiology of health problems. As part of public health surveillance, public health professionals utilise the results of epidemiological analysis to reform health care policy and health service plans. There are many health reports on epidemiological analysis within government departments, but the public are not authorised to access these reports because of commercial software restrictions. Although governments publish many reports of epidemiological analysis, the reports are coded in epidemiology terminology and are almost impossible for the public to fully understand. In order to improve public awareness, there is an urgent need for government to produce a more easily understandable epidemiological analysis and to provide an open access reporting system with minimum cost. Inevitably, it poses challenges to IT professionals to develop a simple, easily understandable and freely accessible system for public use. It is not only required to identify a data analysis algorithm which can make epidemiological analysis reports easily understood but also to choose a platform which can facilitate the visualisation of epidemiological analysis reports with minimum cost. In this thesis, there were two major research objectives: the clustering analysis of epidemiological data and the geospatial visualisation of the results of the clustering analysis. SOM, FCM and k-means, the three commonly used clustering algorithms for health data analysis, were investigated. After a number of experiments, k-means has been identified, based on Davies-Bouldin index validation, as the best clustering algorithm for epidemiological data. The geospatial visualisation requires a Geo-Mashups engine and geospatial layer customisation. Because of the capacity and many successful applications of free geospatial web services, Google Maps has been chosen as the geospatial visualisation platform for epidemiological reporting.

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica
The rapidly increasing volume of digital geographic data is overwhelming for conventional analysis techniques and methods. Therefore new approaches are needed to transform data into information, and ultimately, into knowledge. Exploratory data analysis is a foundation stone in this process. It is concerned with the formation of a simplified overview of data sets. Clustering and projection are among the examples of useful methods to achieve this task. The Self-Organizing Map (SOM) algorithm performs both, in a non-linear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the topological relations in the data. The aim of this thesis is to demonstrate the effectiveness of SOM application in visual exploration of physical geography data to support the delineation of Portuguese mainland regions. The main justifications for the application of SOM in this issue are its features of stressing local factors and topological ordering. For experimental assessment, the public domain thematic maps from Instituto do Ambiente are used. Several authors’ maps of Portuguese regions are used for evaluation of empirical results.(...)

Thesis (Ph.D.) -- University of Adelaide, School of Social Sciences, Discipline of Geographical and Environmental Studies, 2008.
"December 2007" Bibliography: leaves 247-253. Also available in print form.

While it is generally accepted that residential submarkets exist, this is not the case for either their definition or delineation. This study has developed and assessed a methodology for deriving the geospatial dimensions of residential real estate submarkets based on the behaviour of the marketplace with respect to the underlying dimensions of the residential real estate living structure (RLS). Importantly, the methodology makes no prior assumptions about where the spatial boundaries might be. They were empirically derived from the data alone. It is achieved by building on previous work in the field and seeking to identify the fundamental issues in residential real estate market behaviour. The first basic premise of the thesis is that when a dwelling is sold, the commodity traded is a piece of real estate geography comprising a complex bundle of both spatial and structural attributes. The second basic premise is the recognition in the methodology of the importance of ‘location’. The price of the real estate geography varies across geographical space in a continuous fashion and it is this price variability that is defined, in this study, to be the geospatial submarket identifier. The study adopts a two-stage methodology reflecting these two basic premises. Firstly, a complex bundle of attributes is collected for every property in the study area and distilled into its underlying dimensions using principal component analysis. The resulting factors are used in the second stage as independent variables in a hedonic geographically weighted regression model to determine the price variability across geographical space of the underlying residential real estate structure. User-defined breaks in the continuous price surface delineate the geospatial submarket boundaries. The study represents a new approach to the delineation of geospatial submarket boundaries and is yet to be fully assessed by the two major identified users (the planning profession and the valuation profession). However, initial feedback indicates that the ability of the methodology to describe the geospatial submarket boundaries in terms of ‘how’ and ‘where’ location affects the market price of the underlying real estate geography, gives the land professional a better understanding of the submarket structure in which they are working.
http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1330874
Thesis (Ph.D.) -- University of Adelaide, School of Social Sciences, 2008

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems
The Agenda 2030 challenges the countries to use and produce new spatial data to support the path to Sustainable Development (SD). This requires development and adoption of Spatial Data Infrastructure (SDI), and the production of new relevant spatial data to support implementation, monitoring and reporting the progress on the targets on Sustainable Development Goals (SDGs). The importance of access to spatial data for development and resource management is widely acknowledged worldwide. Unrestricted, reliable and efficient access to accurate, timely, and upto- date spatial data may be achieved through a Spatial Data Infrastructure (SDI). Thus, most developed countries implemented and continue to develop their SDI. The Ecosystem Service (ES) is also crucially for SD and the concept needs to be expressed and communicated effectively to be successfully integrated into decision making. This study assessed the challenges and opportunities on SDI development and analyzed the documents relevant to LUP process and implementation. On the SDI, we identified and characterized through a survey the government institutions producing, sharing, and using spatial data in the country to estimate their potential contribution to the development of the Mozambican SDI. On the integration of ES into LUP, we conducted a review of relevant documents to Mozambique’s spatial planning by performing a content analysis based on ES categories. Based on the possible contribution of the institutions producing and using spatial data, we proposed an SDI for Mozambique based on four pillars: i) organizational framework; ii) legal framework; iii) technical framework; and iv) accessibility. The periodical revision of tools and participatory approaches in LUP opens opportunities for integrating ES into LUP processes. This integration could be achieved by establishing a SEA legal framework based on LUP and Environment legal frameworks assisted by a set of common planning tools that consider ES as an additional indicator applied to spatial planning in Mozambique.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
The surveying field has been impacted over many decades by new inventions and improvements in technology. This has ensured that the profession remains one of high precision with the employment of sophisticated technologies by Cadastral Experts. The use of Unmanned Aerial Systems (UAS) within surveying is not new. However, the standards, technologies, tools and licenses developed by the open source community of developers, have opened new possibilities of utilising UAS within surveying. UASs are being constantly improved to obtain high quality imagery, so efforts were made to find novel ways to add value to the data. This thesis defines a workflow aimed at deriving Cadastral Geospatial Information (Cadastral GI), as three-dimensional (3D) building volumes from the original inputted UAS imagery. To achieve this, an investigation was done to see how crowd-sourced UAS data can be uploaded to open online repositories, downloaded by Cadastral Experts, and then manipulated using open source applications. The Cadastral Experts had to utilise multiple applications and manipulate the data through many data formats, to obtain the (3D) building volumes as final results. Such a product can potentially improve the management of cadastral data by Cadastral Experts, City Managers and National Mapping Agencies. Additionally, an ideal suite of tools is presented, that can be used store, manipulate and share the 3D building volume data while facilitating the contribution of attribute data from the crowd.

Research Report submitted in partial fulfilment for the degree of Master of Science (Geographical Information Systems and Remote Sensing) School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg. August 2016.
The thematic classification of land use and land cover (LULC) from remotely sensed imagery data is one of the most common research branches of applied remote sensing sciences. The performances of the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) Support Vector Machine (SVM) learning algorithms were subjected to comparative assessment using WorldView-2 and SPOT-6 multispectral images of the Mokhotlong District in Lesotho covering approximately an area of 100 km2. For this purpose, four LULC classification models were developed using the combination of SVM –based image analysis approach (i.e. OBIA and/or PBIA) on high resolution images (WorldView-2 and/or SPOT-6) and the results were subjected to comparisons with one another. Of the four LULC models, the OBIA and WorldView-2 model (overall accuracy 93.2%) was found to be more appropriate and reliable for remote sensing application purposes in this environment. The OBIA-WorldView-2 LULC model was subjected to spatial overlay analysis with DEM derived topographic variables in order to evaluate the relationship between the spatial distribution of LULC types and topography, particularly for topographically-controlled patterns. It was discovered that although that there are traces of the relationship between the LULC types distributions and topography, it was significantly convoluted due to both natural and anthropogenic forces such that the topographic-induced patterns for most of the LULC types had been substantial disrupted.
LG2017

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Climate change is a main concern for humanity from the ending of 20th century. To improve and take care of our environment, a set of measures has been developed to monitor, manage, reduce consumption and raise efficiency of buildings, including the integration of renewable energies and the implementation of passive measures like the improvement of the building envelope. Complex methodologies are used in order to achieve these objectives. Using different tools and data translating is needed, and the loss of accuracy from the detailed input information is most of the times unavoidable. Moreover, including these measures in the development of a project have become a try and error process involving building characteristics, location data and energy efficiency measures. The raising of new technologies, capable of dealing with location-based data and semantics to relate and structure information in a machine readable way, may allow us to provide a set of technical measures to improve energy efficiency in an accessible, open, understandable and easy way from a few data about location and building characteristics. This work tries to define a model and its necessary and sufficient set of data. Its application will provide customized strategies acting as pre-feasibility constraints to help buildings achieve their energy efficiency objectives from its very conception. The model intends to be useful for non-expert users who want to know about their energy savings possibilities, and for professionals willing to get a sustainable starting point for their projects.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
It is becoming more present in agriculture’s practices the use of Unmanned Aerial Systems with sensors capable of capturing light, in the visible and in longer wavelengths of the electromagnetic spectrum once reflected on the field. These sensors have been used to perform Remote Sensing also in other knowledge fields, describing phenomenon without the risk, cost and the time consuming processes associated with in site samples collection and analysis by a technician or satellite imagery acquisition. The Vegetation Indexes developed can explain the vigor of the cultivation and its data collection processes are more cost and time efficient, allowing farmers to monitor plant grow in every critical stage. These Vegetation Indexes started by being calculated from satellite and airborne imagery, one of the main source for crop management tools, however UAS is becoming more present in Precision Agriculture, achieving better spatial and temporal resolution. This gap in spatial resolution when studying low density cultivations like olive groves and vineyards, creates Vegetation Index’s maps polluted with noise caused by the soil and therefore difficult to interpret and analyse. Hence, when the agriculture has spaced and low density vegetation becomes challenging to understand and extract information from these vegetation index’s maps regarding different spatial variability patterns of the tree canopy vigor. In these cases, where vegetation is spaced it is important to filter this noise. A Classification Model was developed with the objective of extracting just the vegetation’s canopy data. The soil was filtered and the canopy data interpolated using spatial analysis tools. The final interpolated maps produced can provide meaningful information regarding the spatial variability and be used to support decision making, identifying critical areas to be intervened and managed, or be used as an input for Variable Rate Technology applications.

Indiana University-Purdue University Indianapolis (IUPUI)
Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.