During the course of the last decade successive governments in the UK have placed young people’s aspirations at the core of their attempts to address poor outcomes within the education system and the labour market. An area-based approach to policy has come to the fore which links ‘low aspirations’ with particular community- and neighbourhood-level factors, in particular area-level deprivation. This area-based focus on the determinants of aspirations has faced intensifying critique from the academic research base. Responding to this policy and research debate, this thesis examines whether, and how, young people’s occupational aspirations are shaped by the areas they live in. The thesis is based on a mixed methods research design and has two sections: an extensive phase and an intensive phase. The extensive phase of the research consists of logistic regression analysis of data from the Understanding Society Youth Questionnaire, and considers whether the types of occupations young people aspire to vary between different types of area. The intensive phase of the research consists of phenomenographic analysis of semi-structured interviews conducted with young people in a deprived, outer-urban neighbourhood in Manchester, and considers how young people’s subjective orientations towards the area they live in produce different forms of aspiration. The thesis finds compelling evidence that young people’s occupational aspirations are shaped by the areas they live in, but does not corroborate the claim at the core of current government policy, that aspirations are lower in more deprived areas. The extensive phase of the research instead identifies area type, rather than deprivation, as the primary area-level factor shaping young people’s aspirations, with young people from particular inner city area types almost five times as likely as their peers from deprived outer-urban areas to aspire to ‘higher’ professional, managerial and technical occupations. Meanwhile, the intensive phase of the research finds evidence that experiences of neighbourhood and family life in an area of concentrated deprivation can lead young people to adopt particular forms of aspiration that require lower levels of skill and further training, but on closer examination of the motivations for these forms of aspiration, finds little evidence that these aspirations are straightforwardly ‘low’. Above all, the research demonstrates that young people produce multiple different senses of place, and myriad forms of aspiration, from within the same deprived spatial context: they do not simply reproduce what they see around them when imagining their futures. While there is compelling evidence that young people’s occupational aspirations are shaped by the areas they live in, these area effects demand more nuanced research alongside policy approaches that are more receptive to young people’s constructions of place.

Submitted by Kilvya Braga (kilvyabraga@hotmail.com) on 2018-04-19T14:06:04Z No. of bitstreams: 1 ANDREY OLIVEIRA DE SOUZA - TESE (PPGEQ) 2016.pdf: 3632853 bytes, checksum: f5a7eefa8ec61e628514023c54aa4ebb (MD5) / Made available in DSpace on 2018-04-19T14:06:04Z (GMT). No. of bitstreams: 1 ANDREY OLIVEIRA DE SOUZA - TESE (PPGEQ) 2016.pdf: 3632853 bytes, checksum: f5a7eefa8ec61e628514023c54aa4ebb (MD5) Previous issue date: 2016 / Há muitos anos o risco de explosão e consequentes requisitos legais para classificação de áreas tem sido uma preocupação mundial. A norma brasileira para classificação de área de atmosfera explosiva é uma tradução fiel da norma internacional (IEC 60079-10-1). Diversos autores defendem que os critérios aplicados por esta norma não tem base científica, podendo levar a erros por excesso, ou mesmo a uma falsa impressão de segurança. Portanto, o presente trabalho teve por objetivo desenvolver uma alternativa confiável para classificação de área. Para tanto, foi desenvolvido modelo CFD, através do software ANSYS CFX 16.1, validado e parametrizado para ter aplicação possível em ampla faixa de condições de vazamento sônico. Para definir condições de vazamento aleatórias a serem simuladas, utilizou-se a técnica estatística de amostragem multidimensional do Latin Hipercubo, variando-se pressão e temperatura do reservatório, diâmetro do orifício, propriedades dos gases e direção do vazamento em relação à gravidade e vento. Os resultados mostram que o domínio de cálculo deve ser parametrizado em 8 metros de comprimento para cada milímetro de diâmetro da fonte de liberação. A malha deve ser parametrizada com 50 elementos ao longo do diâmetro do orifício, mantendo-se a estrutura hexaédrica em todo o domínio. A gravidade mostrou-se não interferir nos resultados de extensão e volume de atmosfera explosiva em vazamentos sônicos. O desvio de idealidade nas condições do reservatório, previsto pela aplicação da equação de Soave Redlich Kwong, também não influenciou significantemente a previsão de extensão e volume de atmosfera explosiva. A análise das simulações de condições de vazamentos aleatórias permitiu o desenvolvimento de equação integral simples e prática para determinação confiável de extensão de atmosfera explosiva. A consideração da direção do vento nas simulações demonstra que o volume da atmosfera explosiva não estar diretamente relacionado à sua extensão. Por fim, a relação entre o volume hipotético da atmosfera explosiva e seu alcance é aplicada como critério para definir risco de ignição em uma proposta de classificação de área mais confiável, que leve em consideração os efeitos de dispersão. / For many years the risk of explosion and consequent legal requirements for area classification has been a global concern. The Brazilian standard for area classification by explosive atmospheres is a faithful translation of the international standard. Many authors defends that these standard has not a scientific basis, what can causes many mistakes, because of excess or a false impression of safety. Therefore, the present work has as objective to develop a reliable alternative to area classification. For that, was developed a CFD model, by software ANSYS CFX 16.1, validated and parameterized to a great interval of sonic leak. To set random leak conditions to be simulated, it was used a statistical technique of multidimensional sampling (Latin Hipercubo), varying pressure and temperature of reservoir, orifice diameter, gas properties and leak direction relative to gravity and wind. The results show that the calculation domain should be parameterized in 8 meters length for millimeter in diameter from the source of release. The mesh must be parameterized in elements 50 along the hole diameter, while maintaining the hexahedral structure throughout the domain. Gravity proved not interfere in the extension and volume results of explosive atmosphere in sonic leaks. The deviation from ideality at reservoir conditions observed by applying the equation of Soave Redlich Kwong also not significantly influenced the extension and volume of explosive atmosphere. The analysis of simulations of random leaks conditions allowed the development of simple and practical integral equation for reliable determination of explosive atmosphere extension. Consideration of wind direction in the simulations show that the volume of the explosive atmosphere could not be directly related to its length. Finally, the relationship between the hypothetical volume of explosive atmosphere and its extension is applied as a criterion to define the risk of ignition in a proposal for a more reliable area classification, which takes into account the effects of dispersion.

The most common approach to analysing satellite imagery is building or object segmentation,which expects an algorithm to find and segment objects with specific boundaries thatare present in the satellite imagery. The company Vricon takes satellite imagery analysisfurther with the goal of reproducing the entire world into a 3D mesh. This 3D reconstructionis performed by a set of complex algorithms excelling in different object reconstructionswhich need sufficient labeling in the original 2D satellite imagery to ensure validtransformations. Vricon believes that the labeling of areas can be used to improve the algorithmselection process further. Therefore, the company wants to investigate if multinomiallarge area classification can be performed successfully using the satellite image data availableat the company. To enable this type of classification, the company’s gold-standarddataset containing labeled objects such as individual buildings, single trees, roads amongothers, has been transformed into an large area gold-standard dataset in an unsupervisedmanner. This dataset was later used to evaluate large area classification using several stateof-the-art Deep Convolutional Neural Network (DCNN) semantic segmentation architectureson both RGB as well as RGB and Digital Surface Model (DSM) height data. Theresults yield close to 63% mIoU and close to 80% pixel accuracy on validation data withoutusing the DSM height data in the process. This thesis additionally contributes with a novelapproach for large area gold-standard creation from existing object labeled datasets.

Bezüglich des Waldes hat sich in den letzten Jahren seitens der Politik und Wirtschaft ein steigender Informationsbedarf entwickelt. Zur Bereitstellung dieses Bedarfes stellt die Fernerkundung ein wichtiges Hilfsmittel dar, mit dem sich flächendeckende Datengrundlagen erstellen lassen. Die k-nächsten-Nachbarn-Methode (kNN-Methode), die terrestrische Aufnahmen mit Fernerkundungsdaten kombiniert, stellt eine Möglichkeit dar, diese Datengrundlage mit Hilfe der Fernerkundung zu verwirklichen. Deshalb beschäftigt sich die vorliegende Dissertation eingehend mit der kNN-Methode. An Hand der zwei Merkmale Grundfläche (metrische Daten) und Totholz (kategoriale Daten) wurden umfangreiche Berechnungen durchgeführt, wobei verschiedenste Variationen der kNN-Methode berücksichtigt wurden. Diese Variationen umfassen verschiedenste Einstellungen der Distanzfunktion, der Wichtungsfunktion und der Anzahl k-nächsten Nachbarn. Als Fernerkundungsdatenquellen kamen Landsat- und Hyperspektraldaten zum Einsatz, die sich sowohl von ihrer spektralen wie auch ihrer räumlichen Auflösung unterscheiden. Mit Hilfe von Landsat-Szenen eines Gebietes von verschiedenen Zeitpunkten wurde außerdem der multitemporale Ansatz berücksichtigt. Die terrestrische Datengrundlage setzt sich aus Feldaufnahmen mit verschiedenen Aufnahmedesigns zusammen, wobei ein wichtiges Kriterium die gleichmäßige Verteilung von Merkmalswerten (z.B. Grundflächenwerten) über den Merkmalsraum darstellt. Für die Durchführung der Berechnungen wurde ein Programm mit Visual Basic programmiert, welches mit der Integrierung aller Funktionen auf der Programmoberfläche eine benutzerfreundliche Bedienung ermöglicht. Die pixelweise Ausgabe der Ergebnisse mündete in detaillierte Karten und die Verifizierung der Ergebnisse wurde mit Hilfe des prozentualen Root Mean Square Error und der Bootstrap-Methode durchgeführt. Die erzielten Genauigkeiten für das Merkmal Grundfläche liegen zwischen 35 % und 67 % (Landsat) bzw. zwischen 65 % und 67 % (HyMapTM). Für das Merkmal Totholz liegen die Übereinstimmungen zwischen den kNN-Schätzern und den Referenzwerten zwischen 60,0 % und 73,3 % (Landsat) und zwischen 60,0 % und 63,3 % (HyMapTM). Mit den erreichten Genauigkeiten bietet sich die kNN-Methode für die Klassifizierung von Beständen bzw. für die Integrierung in Klassifizierungsverfahren an. / Mapping forest variables and associated characteristics is fundamental for forest planning and management. The following work describes the k-nearest neighbors (kNN) method for improving estimations and to produce maps for the attributes basal area (metric data) and deadwood (categorical data). Several variations within the kNN-method were tested, including: distance metric, weighting function and number of neighbors. As sources of remote sensing Landsat TM satellite images and hyper spectral data were used, which differ both from their spectral as well as their spatial resolutions. Two Landsat scenes from the same area acquired September 1999 and 2000 regard multiple approaches. The field data for the kNN- method comprise tree field measurements which were collected from the test site Tharandter Wald (Germany). The three field data collections are characterized by three different designs. For the kNN calculation a program with integration all kNN functions were developed. The relative root mean square errors (RMSE) and the Bootstrap method were evaluated in order to find optimal parameters. The estimation accuracy for the attribute basal area is between 35 % and 67 % (Landsat) and 65 % and 67 % (HyMapTM). For the attribute deadwood is the accuracy between 60 % and 73 % (Landsat) and 60 % and 63 % (HyMapTM). Recommendations for applying the kNN method for mapping and regional estimation are provided.