The subject of this thesis is digitalization of bus transportation. The input is represented by a sequence of GNSS data which are transformed to the OpenStreetMap format. Doing so, it is enriched by the information OpenStreetMap format provides and it gains its positional advantages as well. Then this thesis deals with ways by which one can detect bus lines from this general sequence of GNSS coordinates. A bus line is recognized as a repeating trajectory, which satisfies criteria derived from its expectable or defined characteristics. A few clustering solutions are proposed and tested for their performance. On the basis of this testing, there is one solution chosen as the best performing one, to be the proposed solution of this thesis. The overall output will therefore be formed by automatic mapping of bus lines with no theoretical area limit and with minimum manual intervention needed. It lays the foundations for various intelligent real-time processes to be implemented as well as allowing for infrastructure to be processed for the statistics purposes or urban planning.

This master's thesis deals with creating OpenStreetMap terrain mapping mobile application. The introduction describes project OpenStreetMap and the methods of collecting data. After that mobile platforms specifics are described. Android and OSMTracker are introduced in the next part of this work. Analysis is followed by description of implementation of OSMTracker extension.

Inflammation is a homeostatic response to tissue injury or infection, which is normally short- lived and quickly resolves to limit tissue damage. In contrast, chronic inflammation has been linked to a variety of human diseases, including cancers such as glioblastoma multiforme (GBM). GBMs are very aggressive tumors with very low patient survival rates, which have not improved in several decades. GBM tumors are characterized by necrosis and profound inflammation; with cytokines secreted by both GBM cells and the tumor microenvironment. The mechanisms by which chronic inflammation develops and persists in GBM regardless of multiple anti-inflammatory feedback loops remain elusive. This project identifies a molecular switch which promotes chronic inflammation in GBM, but not primary human astrocytes.

Conventionally geographic data produced and disseminated by the national mapping agencies are used for studying various urban issues. These data are not commonly available or accessible, but also are criticized for being expensive. However, this trend is changing along with the rise of Volunteered Geographic Information (VGI). VGI, known as user generated content, is the geographic data collected and disseminated by individuals at a voluntary basis. So far, a huge amount of geographic data has been collected due to the increasing number of contributors and volunteers. More importantly, they are free and accessible to anyone.   There are many formats of VGI such as Wikimapia, Flickr, GeoNames and OpenStreetMap (OSM). OSM is a new mapping project contributed by volunteers via a wiki-like collaboration, which is aimed to create free, editable map of the entire world. This thesis adopts OSM as the main data source to uncover the hidden patterns around the urban systems. We investigated some fundamental issues such as city rank size law and the measurement of urban sprawl. These issues were conventionally studied using Census or satellite imagery data.   We define the concept of natural cities in order to assess city size distribution. Natural cities are generated in a bottom up manner via the agglomeration of individual street nodes. This clustering process is dependent on one parameter called clustering resolution. Different clustering resolutions could derive different levels of natural cities. In this respect, they show little bias compared to city boundaries imposed by Census bureau or extracted from satellite imagery. Based on the investigation, we made two findings about rank size distributions. The first one is that all the natural cities in US follow strictly Zipf’s law regardless of the clustering resolutions, which is different from other studies only investigating a few largest cities. The second one is that Zipf’s law is not universal at the state level, e.g., Zipf’s law for natural cities within individual states does not hold valid.   This thesis continues to detect the sprawling based on natural cities. Urban sprawl devours large amount of open space each year and subsequently leads to many environmental problems. To curb urban sprawl with proper policies, a major problem is how to objectively measure it. In this thesis, a new approach is proposed to measure urban sprawl based on street nodes. This approach is based on the fact that street nodes are significantly correlated with population in cities. Specifically, it is reported that street nodes have a linear relationship with city sizes with correlation coefficient up to 0.97. This linear regression line, known as sprawl ruler, can partition all cities into the sprawling, compact and normal cities. This study verifies this approach with some US census data and US natural cities. Based on the verification, this thesis further applies it to three European countries: France, Germany and UK, and consequently categorizes all natural cities into three classes: sprawling, compact and normal. This categorization provides a new insight into the sprawling detection and sets a uniform standard for cross comparing sprawling level across an entire country. / QC 20101206

This paper describes a partitioning algorithm for real-world transportation networks incorporating previously unaccounted parameters like signalized traffic intersection, road segment length, traffic density, number of lanes and inter-partition communication overhead due to the migration of vehicles from one partition to another. We also describe our hypothetical framework for distributed simulation of the partitioned road network on SUMO, where a master controller is currently under development using TraCI APIs and MPI library to coordinate the parallel simulation and synchronization between the sub-networks generated by our proposed algorithm.

A falta de padronização de dados pode resultar em perda de informações de suma importância nas diversas áreas do conhecimento, impossibilitando a integração de dados entre diferentes sistemas ou de diferentes bancos de dados, ou seja, os dados podem não ser interoperáveis. A solução para a integração de dados pode ser chamada de interoperabilidade, que são convenções e normas de formatos (extensões) e ontologias (padrões comuns) instituídos para que os sistemas possam dialogar. Um banco de dados de biodiversidade é um instrumento muito importante para as iniciativas de sua conservação, sendo útil para o seu conhecimento, registro histórico entre outros. Este trabalho desenvolveu uma metodologia para interoperar dados modelados no padrão Taxonomic Data Working Group (TDWG) e tags do OpenStreetMap (OSM) sobre a espécie Sotalia guianensis, conhecida como Boto Cinza. Dentro deste escopo, este trabalho se justifica pelo cenário de ameaça de extinção do Boto Cinza, pela necessidade no desenvolvimento de metodologias para a disponibilização de dados de ocorrência de Boto Cinza em bancos de dados de biodiversidade e pela necessidade de se desenvolver metodologia que permita a interoperabilidade entre bancos de dados de biodiversidade e outros Sistemas de Informação Geográfica (SIG). Este estudo propõe uma metodologia de baixo custo, com a utilização de plataformas livres, para que dados espaciais de Biodiversidade sejam modelados de maneira a evitar problemas taxonômicos, além de serem disponibilizados para conhecimento geral da população. O trabalho se mostra inovador por integrar dados do Global Diversity Information Facility (GBIF) com as Tags do OSM, possibilitando o cadastro padronizado e gratuito em uma plataforma livre e de alcance mundial através da criação de uma etiqueta interoperável de equivalência entre o padrão TDWG e as etiquetas do OSM. O resultado deste trabalho é a metodologia para a modelagem e publicação de dados de Boto Cinza no GBIF e OSM de forma interoperável, que foi implementada, testada e cujos resultados são positivos / The absence of data standardization may result in loss of information of major importance through several areas of knowledge, hindering data integration among different information systems or databases, that is, the data may not be interoperable. The solution for data integration may be called interoperability, which is comprised of conventions, data format standards (file extensions) and ontologies (standards), empowering the communication among information systems. A biodiversity database is a very important tool for biodiversity conservation initiatives, being useful for knowledge transfer, historical data storage among other activities. This work developed a methodology for interoperate data between the Taxonomic Data Working Group (TWDG) standard and OpenStreetMap (OSM) tags on Sotalia guianensis species, as known as Guiana dolphin. This work has its motivation scenario on the fact that the Guiana dolphin is under threat of extinction. This scenario demands the development of methodologies for the publication of the locations where the Guiana dolphin is being spotted over the biodiversity databases and the development of a methodology for interoperability among biodiversity databases as well as Geographic Information Systems (SIG). This study proposes a low cost methodology, which uses open-source platforms and focuses on two main goals: avoidance of taxonomical problems on biodiversity spatial data modelling and to provide the biodiversity spatial data to the population in general. This work proves itself innovative by integrating Global Diversity Information Facility (GBIF) data with OSM tags, allowing a free and standardized registry of data in an open-source global-scale platform by using an interoperable tag of equivalence data between the TDWG standard and OSM tags. The result of this study is the methodology for data modelling and publication of the Guiana dolphin on GBIF and OSM in an interoperable manner, which has been implemented, tested and gave positive results

A falta de padronização de dados pode resultar em perda de informações de suma importância nas diversas áreas do conhecimento, impossibilitando a integração de dados entre diferentes sistemas ou de diferentes bancos de dados, ou seja, os dados podem não ser interoperáveis. A solução para a integração de dados pode ser chamada de interoperabilidade, que são convenções e normas de formatos (extensões) e ontologias (padrões comuns) instituídos para que os sistemas possam dialogar. Um banco de dados de biodiversidade é um instrumento muito importante para as iniciativas de sua conservação, sendo útil para o seu conhecimento, registro histórico entre outros. Este trabalho desenvolveu uma metodologia para interoperar dados modelados no padrão Taxonomic Data Working Group (TDWG) e tags do OpenStreetMap (OSM) sobre a espécie Sotalia guianensis, conhecida como Boto Cinza. Dentro deste escopo, este trabalho se justifica pelo cenário de ameaça de extinção do Boto Cinza, pela necessidade no desenvolvimento de metodologias para a disponibilização de dados de ocorrência de Boto Cinza em bancos de dados de biodiversidade e pela necessidade de se desenvolver metodologia que permita a interoperabilidade entre bancos de dados de biodiversidade e outros Sistemas de Informação Geográfica (SIG). Este estudo propõe uma metodologia de baixo custo, com a utilização de plataformas livres, para que dados espaciais de Biodiversidade sejam modelados de maneira a evitar problemas taxonômicos, além de serem disponibilizados para conhecimento geral da população. O trabalho se mostra inovador por integrar dados do Global Diversity Information Facility (GBIF) com as Tags do OSM, possibilitando o cadastro padronizado e gratuito em uma plataforma livre e de alcance mundial através da criação de uma etiqueta interoperável de equivalência entre o padrão TDWG e as etiquetas do OSM. O resultado deste trabalho é a metodologia para a modelagem e publicação de dados de Boto Cinza no GBIF e OSM de forma interoperável, que foi implementada, testada e cujos resultados são positivos / The absence of data standardization may result in loss of information of major importance through several areas of knowledge, hindering data integration among different information systems or databases, that is, the data may not be interoperable. The solution for data integration may be called interoperability, which is comprised of conventions, data format standards (file extensions) and ontologies (standards), empowering the communication among information systems. A biodiversity database is a very important tool for biodiversity conservation initiatives, being useful for knowledge transfer, historical data storage among other activities. This work developed a methodology for interoperate data between the Taxonomic Data Working Group (TWDG) standard and OpenStreetMap (OSM) tags on Sotalia guianensis species, as known as Guiana dolphin. This work has its motivation scenario on the fact that the Guiana dolphin is under threat of extinction. This scenario demands the development of methodologies for the publication of the locations where the Guiana dolphin is being spotted over the biodiversity databases and the development of a methodology for interoperability among biodiversity databases as well as Geographic Information Systems (SIG). This study proposes a low cost methodology, which uses open-source platforms and focuses on two main goals: avoidance of taxonomical problems on biodiversity spatial data modelling and to provide the biodiversity spatial data to the population in general. This work proves itself innovative by integrating Global Diversity Information Facility (GBIF) data with OSM tags, allowing a free and standardized registry of data in an open-source global-scale platform by using an interoperable tag of equivalence data between the TDWG standard and OSM tags. The result of this study is the methodology for data modelling and publication of the Guiana dolphin on GBIF and OSM in an interoperable manner, which has been implemented, tested and gave positive results

Im Bereich der städtebasierten siedlungsstrukturellen Analysen spielen Gebäudedaten mit Informationen zur Gebäudenutzung und dem Gebäudetyp eine wichtige Rolle. Auf diesen Daten basiert die Modellierung von demografischen und sozioökonomischen Kenngrößen, welche bei Aufgaben der Siedlungsentwicklung oder in der Infrastrukturplanung zum Einsatz kommen. Vonseiten der amtlichen Vermessungsanstalten stehen kleinräumige und flächendeckende Daten zur Gebäudenutzung nur in begrenztem Umfang zur Verfügung. Eine darauf aufbauende Bevölkerungsabschätzung ist aus diesem Grund nur eingeschränkt möglich. Das Ziel dieser Arbeit war es, die Integration von nutzergenerierten Geodaten aus dem OpenStreetMap (OSM) Projekt für den Einsatz zur Abschätzung gebäudebasierter Bevölkerungs- und Wohnungszahlen zu untersuchen. Der Fokus liegt dabei besonders auf der Abgrenzung von Wohn- und Nichtwohnnutzung innerhalb von Gebäuden. Diese Informationen sind in den amtlichen Geobasisdaten nicht zu finden, können jedoch aus OSM Punkt- und Polygondaten extrahiert werden. Hauptgegenstand der Untersuchung ist die Entwicklung eines Modells zur Integration der Nichtwohnnutzungsinformationen aus OSM, welches Gewerbeinformationen anhand der OSM-Tags analysiert. Dazu war neben einer geeigneten Typologie, die Festlegung von Regeln zur Verarbeitung mehrerer Gewerbe in einem amtlichen Gebäude notwendig. Über räumliche Verschneidungen der Datensätze erfolgt die eigentliche Datenintegration. Zur Umsetzung des Modells wurden drei Python-Skripte erarbeitet, welche alle notwendigen Vorverarbeitungsschritte und anschließenden Modellberechnungen automatisiert durchführen. Zusätzlich zur Bestimmung des Nichtwohnnutzungsanteiles in den Gebäuden, fand eine Wohnung- und Bevölkerungsabschätzung mittels gebäudetypischer Kenngrößen auf Gebäudebasis statt. Mittels der Abschätzungsergebnisse ohne und mit OSM-Gewerbeinformationen, konnte eine Bewertung dieser Methode erfolgen. Eine Beurteilung der Qualität des Modells im Vergleich zur Realität benötigt geeignete Validierungsdaten. Diese wurden in Form von baublockbezogenen statistischen Einwohner- und Wohnungszahlen von der Stadt Dresden bereitgestellt und zur Ergebnisdiskussion herangezogen. Regionale Unterschiede konnten anhand von Übersichts- und Detailkarten sowie statistischen Analysen herausgearbeitet werden.:Inhaltsverzeichnis ............................................................................. I Abkürzungsverzeichnis ..................................................................... V Abbildungsverzeichnis ...................................................................... VII Tabellenverzeichnis .......................................................................... IX 1 Einleitung ...................................................................................... 11 1.1 Motivation ................................................................................... 11 1.2 Zielstellung und Aufbau der Arbeit ............................................. 12 2 Theoretische und praktische Grundlagen ...................................... 15 2.1 Räumliche Modellierung der Siedlungsstruktur ........................... 15 2.1.1 Siedlungsstrukturelle Begriffe ................................................. 15 2.1.2 Räumliche Daten ..................................................................... 18 2.2 Semantische Integration ............................................................ 23 2.2.1 Interoperabilität ...................................................................... 24 2.2.2 Datenintegration ..................................................................... 25 2.3 Semantik in Geodaten ................................................................ 28 2.3.1 Attributierung in OpenStreetMap ............................................. 28 2.3.2 Nutzungsinformationen in amtlichen Geobasisdaten ............... 31 3 Modellierung sozioökonomischer Kenngrößen ............................... 33 3.1 Bedarf an kleinräumigen Nutzungsinformationen ....................... 33 3.2 Modellierungsansätze ................................................................. 35 3.2.1 Ableitung sozioökonomischer Daten aus der Fernerkundung .. 35 3.2.2 Modellierung mit Hilfe von Geobasisdaten ............................... 36 3.3 Vorteile und Mängel der vorgestellten Ansätze .......................... 40 3.4 Möglichkeiten von OSM zur Verbesserung der Modellierung ....... 42 4 Methodik ........................................................................................ 43 4.1 Herangehensweise ..................................................................... 43 4.2 Flächen- und Gebäudenutzungen in amtlichen und OSM-Daten .. 44 4.2.1 Typologie der Flächen- und Gebäudenutzung .......................... 44 4.2.2 Typologie der Flächenbeanspruchung von Gewerben .............. 47 4.3 Modell zur Abschätzung des Nichtwohnnutzungsanteils ............. 50 4.3.1 Gebäudetypische Kenngrößen ................................................. 51 4.3.2 Vorgehensweise ...................................................................... 52 5 Daten ............................................................................................. 55 5.1 Untersuchungsgebiet .................................................................. 55 5.2 OpenStreetMap ........................................................................... 56 5.3 Amtliche Geobasisdaten .............................................................. 57 6 Praktische Umsetzung .................................................................... 59 6.1 Datenmanagement ...................................................................... 59 6.1.1 Eingesetzte Software ............................................................... 59 6.1.2 OSM-Import .............................................................................. 60 6.1.3 Datenorganisation ................................................................... 64 6.2 Praktische Umsetzung des Modell zur Abschätzung des Nichtwohnnutzungsanteils ................................................................ 65 6.2.1 Vorverarbeitung ....................................................................... 65 6.2.2 Modellberechnungen und Bevölkerungsabschätzung .............. 74 6.3 Validierung des Modells .............................................................. 79 6.3.1 Validierungsdaten .................................................................... 79 6.3.2 Durchführung ........................................................................... 80 7 Ergebnisse ..................................................................................... 83 7.1 Untersuchung zur Vollständigkeit der semantischen Informationen anhand der Gewerbe POI .......................................................................................................... 83 7.1.1 Datengrundlagen ..................................................................... 84 7.1.2 Gewerbevergleich durch zufällige Straßenwahl ........................ 84 7.1.3 Gezielter Gewerbevergleich durch Ortsbegehung .................... 86 7.2 Charakterisierung des Ergebnisdatensatzes .............................. 88 7.3 Validierung .................................................................................. 91 7.4 Ergebnisvisualisierung ................................................................ 93 8 Ergebnisdiskussion ........................................................................ 97 8.1 Kleinräumige Betrachtung ........................................................... 97 8.2 Dateninkonsistenz ...................................................................... 101 8.3 Methodische Schwächen und Stärken ........................................ 103 8.4 Weitere Analysemöglichkeiten .................................................... 104 9 Zusammenfassung ........................................................................ 105 9.1 Fazit ........................................................................................... 105 9.2 Ausblick ...................................................................................... 106 Literaturverzeichnis .......................................................................... 109 A Anhang .......................................................................................... 115 Anhang 1: IÖR Flächenschema ......................................................... 115 Anhang 2: Flächen- und Gebäudenutzungstypologie........................ 116 Anhang 3: Klassifikationsschema nach (Burckhardt, 2012) .............. 118 Anhang 4: Skript-Auszug 1 - Datenverarbeitung in der OSM-Punktdatei ................................................................................ 120 Anhang 5: Skript-Auszug 2 - Die Funktion „calculateGWTotal“ .......... 121 Anhang 6: Karten - Untersuchungsgebiet Dresden in 1:100.000 ..... 122 Anhang 7: CD-Inhalt ......................................................................... 122 / Building data with information of building uses and building types play an important role for city-based settlement structure analyses. The estimation of demographic and socio-economic parameters is based on this data. They were used in the field of settlement development or in infrastructure planning. The availability of area-wide and small-scale data of building uses from surveying authorities is limited. For this reason, the estimation of population based on this data cannot be realised sufficiently. Therefore, it was the aim of this research paper to analyse the integration of user-generated geodata from OpenStreetMap (OSM)-project for estimating building-based population and housing units. The research focuses on the separation of residential and non-residential usage within buildings. Not being detected in official geodatasets, the information can be retrieved from OSM-point and polygondata. The development of a model for the integration of non-residential information from OSM is the main subject of this research. It contains the analyses of commercial information out of the OSM Tags. Besides an appropriate typology specifications are necessary to process multiple businesses within one official building. The actual data integration occurs with the help of spatial intersections between the datasets. The implementation of the model is based on three Python-scripts, executing all pre-processing and following calculation steps automatically. In addition to the identification of non-residential building-parts, an estimation of population and housing units per building, based on typical building parameters took place. By means of the estimation results with and without OSM-information it was possible to valuate this method. Validation data is necessary to measure the quality of the model in comparison to reality. This datasets was provided by the City of Dresden, consisting of statistical population and building unit numbers based on building blocks, and used for the discussion of the results. To describe regional differences, maps with overview and detailed scales as well as statistical schemata where used.:Inhaltsverzeichnis ............................................................................. I Abkürzungsverzeichnis ..................................................................... V Abbildungsverzeichnis ...................................................................... VII Tabellenverzeichnis .......................................................................... IX 1 Einleitung ...................................................................................... 11 1.1 Motivation ................................................................................... 11 1.2 Zielstellung und Aufbau der Arbeit ............................................. 12 2 Theoretische und praktische Grundlagen ...................................... 15 2.1 Räumliche Modellierung der Siedlungsstruktur ........................... 15 2.1.1 Siedlungsstrukturelle Begriffe ................................................. 15 2.1.2 Räumliche Daten ..................................................................... 18 2.2 Semantische Integration ............................................................ 23 2.2.1 Interoperabilität ...................................................................... 24 2.2.2 Datenintegration ..................................................................... 25 2.3 Semantik in Geodaten ................................................................ 28 2.3.1 Attributierung in OpenStreetMap ............................................. 28 2.3.2 Nutzungsinformationen in amtlichen Geobasisdaten ............... 31 3 Modellierung sozioökonomischer Kenngrößen ............................... 33 3.1 Bedarf an kleinräumigen Nutzungsinformationen ....................... 33 3.2 Modellierungsansätze ................................................................. 35 3.2.1 Ableitung sozioökonomischer Daten aus der Fernerkundung .. 35 3.2.2 Modellierung mit Hilfe von Geobasisdaten ............................... 36 3.3 Vorteile und Mängel der vorgestellten Ansätze .......................... 40 3.4 Möglichkeiten von OSM zur Verbesserung der Modellierung ....... 42 4 Methodik ........................................................................................ 43 4.1 Herangehensweise ..................................................................... 43 4.2 Flächen- und Gebäudenutzungen in amtlichen und OSM-Daten .. 44 4.2.1 Typologie der Flächen- und Gebäudenutzung .......................... 44 4.2.2 Typologie der Flächenbeanspruchung von Gewerben .............. 47 4.3 Modell zur Abschätzung des Nichtwohnnutzungsanteils ............. 50 4.3.1 Gebäudetypische Kenngrößen ................................................. 51 4.3.2 Vorgehensweise ...................................................................... 52 5 Daten ............................................................................................. 55 5.1 Untersuchungsgebiet .................................................................. 55 5.2 OpenStreetMap ........................................................................... 56 5.3 Amtliche Geobasisdaten .............................................................. 57 6 Praktische Umsetzung .................................................................... 59 6.1 Datenmanagement ...................................................................... 59 6.1.1 Eingesetzte Software ............................................................... 59 6.1.2 OSM-Import .............................................................................. 60 6.1.3 Datenorganisation ................................................................... 64 6.2 Praktische Umsetzung des Modell zur Abschätzung des Nichtwohnnutzungsanteils ................................................................ 65 6.2.1 Vorverarbeitung ....................................................................... 65 6.2.2 Modellberechnungen und Bevölkerungsabschätzung .............. 74 6.3 Validierung des Modells .............................................................. 79 6.3.1 Validierungsdaten .................................................................... 79 6.3.2 Durchführung ........................................................................... 80 7 Ergebnisse ..................................................................................... 83 7.1 Untersuchung zur Vollständigkeit der semantischen Informationen anhand der Gewerbe POI .......................................................................................................... 83 7.1.1 Datengrundlagen ..................................................................... 84 7.1.2 Gewerbevergleich durch zufällige Straßenwahl ........................ 84 7.1.3 Gezielter Gewerbevergleich durch Ortsbegehung .................... 86 7.2 Charakterisierung des Ergebnisdatensatzes .............................. 88 7.3 Validierung .................................................................................. 91 7.4 Ergebnisvisualisierung ................................................................ 93 8 Ergebnisdiskussion ........................................................................ 97 8.1 Kleinräumige Betrachtung ........................................................... 97 8.2 Dateninkonsistenz ...................................................................... 101 8.3 Methodische Schwächen und Stärken ........................................ 103 8.4 Weitere Analysemöglichkeiten .................................................... 104 9 Zusammenfassung ........................................................................ 105 9.1 Fazit ........................................................................................... 105 9.2 Ausblick ...................................................................................... 106 Literaturverzeichnis .......................................................................... 109 A Anhang .......................................................................................... 115 Anhang 1: IÖR Flächenschema ......................................................... 115 Anhang 2: Flächen- und Gebäudenutzungstypologie........................ 116 Anhang 3: Klassifikationsschema nach (Burckhardt, 2012) .............. 118 Anhang 4: Skript-Auszug 1 - Datenverarbeitung in der OSM-Punktdatei ................................................................................ 120 Anhang 5: Skript-Auszug 2 - Die Funktion „calculateGWTotal“ .......... 121 Anhang 6: Karten - Untersuchungsgebiet Dresden in 1:100.000 ..... 122 Anhang 7: CD-Inhalt ......................................................................... 122

Transport models are key in predicting travel behaviour and planning transport systems. Transport models can be either aggregated or disaggregated. Disaggregation means that travel behaviour is represented on an individual level, which can be beneficial because it offers a higher detail level and reduces aggregation bias. Input data for transport models can be both expensive and inaccessible, especially comprehensive data. Thus, it is advantageous to explore the utilisation of open data, which is free and accessible. The objective of the thesis was to evaluate how OpenStreetMap and other Open Data can be utilised in disaggregated transport modelling. The scope of the study was Uppsala, Sweden. In the thesis, a disaggregate transport model was designed, which only considered commuting trips made by public transport. Destinations and a synthetic population were estimated based on OpenStreetMap map features, SCB census data, and LuTRANS land use data. A travel survey was utilised in model calibration, and UL boarding data was used for model validation. The results showed that OpenStreetMap provided sufficient data for estimating a synthetic population and destinations for a disaggregate transport model when combined with other open data sources. Population and land usecensus data were essential for calibrating the model. However, the model came with limitations caused by assumptions, generalisation, technical constraints, and the partial incompleteness of open data. The thesis concludes that Open Data, such as OpenStreetMap, can be utilised sufficiently for transport modelling, with proper assumptions and processing. The openness of the data also increases the replicability of such a model. / Transportmodeller är viktiga i att förutspå resvanemönster och för att kunna planera transportsystemet. Transportmodeller kan vara antingen aggregeradeeller disaggregerade. Disaggregering betyder att resvanor är representerade påindividuell nivå, vilket kan vara fördelaktigt då det innebär en högre detalj nivå och mindre partiskhet orsakad av aggregering (aggregation bias). Indata förtransportmodeller kan vara både dyrt och svåråtkomligt, speciellt för mer omfattande data. Därav kan det vara till stor nytta att utforska möjligheten att använda öppnadata (Open Data), som är gratis och lättåtkomligt. Syftet med examensarbetetvar att utvärdera hur OpenStreetMap och annan Open Data kan användas idisaggregerad transportmodellering. Den geografiska omfattningen av studien är Uppsala tätort. En disaggregerad transportmodell togs fram i examensarbetet, sombara tog hänsyn till jobbresor med kollektivtrafik. Destinationer och en syntetiskbefolkning uppskattades utifrån OpenStreetMap objekt, befolkningsdata från SCB, samt markanvändningsdata från LuTRANS. En resvaneundersökning utnyttjadesför modellkalibrering och påstigningsdata från UL användes för modellvalidering.Resultaten visade att OpenStreetMap erbjöd tillräckligt med data för att ta framoch uppskatta en syntetisk befolkning och destinationer för en disaggregeradtransportmodell, om den kombineras med andra öppna datakällor. Befolkning- ochmarkanvändningsdata var avgörande i att kalibrera modellen. Dock så innefattar modellen vissa begränsningar som är orsakada av antaganden, generalisering, tekniskabegränsningar, samt ofullständigheten av Open Data. Slutsatsen är att Open Data, så som OpenStreetMap, kan utnyttjas för transportmodellering, om det kombineras med välformulerade antaganden och processering av datan. Datans öppenheten medför även en ökad replikerbarhet för en sådan modell.

Im Bereich der städtebasierten siedlungsstrukturellen Analysen spielen Gebäudedaten mit Informationen zur Gebäudenutzung und dem Gebäudetyp eine wichtige Rolle. Auf diesen Daten basiert die Modellierung von demografischen und sozioökonomischen Kenngrößen, welche bei Aufgaben der Siedlungsentwicklung oder in der Infrastrukturplanung zum Einsatz kommen. Vonseiten der amtlichen Vermessungsanstalten stehen kleinräumige und flächendeckende Daten zur Gebäudenutzung nur in begrenztem Umfang zur Verfügung. Eine darauf aufbauende Bevölkerungsabschätzung ist aus diesem Grund nur eingeschränkt möglich. Das Ziel dieser Arbeit war es, die Integration von nutzergenerierten Geodaten aus dem OpenStreetMap (OSM) Projekt für den Einsatz zur Abschätzung gebäudebasierter Bevölkerungs- und Wohnungszahlen zu untersuchen. Der Fokus liegt dabei besonders auf der Abgrenzung von Wohn- und Nichtwohnnutzung innerhalb von Gebäuden. Diese Informationen sind in den amtlichen Geobasisdaten nicht zu finden, können jedoch aus OSM Punkt- und Polygondaten extrahiert werden. Hauptgegenstand der Untersuchung ist die Entwicklung eines Modells zur Integration der Nichtwohnnutzungsinformationen aus OSM, welches Gewerbeinformationen anhand der OSM-Tags analysiert. Dazu war neben einer geeigneten Typologie, die Festlegung von Regeln zur Verarbeitung mehrerer Gewerbe in einem amtlichen Gebäude notwendig. Über räumliche Verschneidungen der Datensätze erfolgt die eigentliche Datenintegration. Zur Umsetzung des Modells wurden drei Python-Skripte erarbeitet, welche alle notwendigen Vorverarbeitungsschritte und anschließenden Modellberechnungen automatisiert durchführen. Zusätzlich zur Bestimmung des Nichtwohnnutzungsanteiles in den Gebäuden, fand eine Wohnung- und Bevölkerungsabschätzung mittels gebäudetypischer Kenngrößen auf Gebäudebasis statt. Mittels der Abschätzungsergebnisse ohne und mit OSM-Gewerbeinformationen, konnte eine Bewertung dieser Methode erfolgen. Eine Beurteilung der Qualität des Modells im Vergleich zur Realität benötigt geeignete Validierungsdaten. Diese wurden in Form von baublockbezogenen statistischen Einwohner- und Wohnungszahlen von der Stadt Dresden bereitgestellt und zur Ergebnisdiskussion herangezogen. Regionale Unterschiede konnten anhand von Übersichts- und Detailkarten sowie statistischen Analysen herausgearbeitet werden. / Building data with information of building uses and building types play an important role for city-based settlement structure analyses. The estimation of demographic and socio-economic parameters is based on this data. They were used in the field of settlement development or in infrastructure planning. The availability of area-wide and small-scale data of building uses from surveying authorities is limited. For this reason, the estimation of population based on this data cannot be realised sufficiently. Therefore, it was the aim of this research paper to analyse the integration of user-generated geodata from OpenStreetMap (OSM)-project for estimating building-based population and housing units. The research focuses on the separation of residential and non-residential usage within buildings. Not being detected in official geodatasets, the information can be retrieved from OSM-point and polygondata. The development of a model for the integration of non-residential information from OSM is the main subject of this research. It contains the analyses of commercial information out of the OSM Tags. Besides an appropriate typology specifications are necessary to process multiple businesses within one official building. The actual data integration occurs with the help of spatial intersections between the datasets. The implementation of the model is based on three Python-scripts, executing all pre-processing and following calculation steps automatically. In addition to the identification of non-residential building-parts, an estimation of population and housing units per building, based on typical building parameters took place. By means of the estimation results with and without OSM-information it was possible to valuate this method. Validation data is necessary to measure the quality of the model in comparison to reality. This datasets was provided by the City of Dresden, consisting of statistical population and building unit numbers based on building blocks, and used for the discussion of the results. To describe regional differences, maps with overview and detailed scales as well as statistical schemata where used.