Esta dissertação está inserida na área de configuração urbana, com foco em sistemas viários segregados por cidades. O objetivo desta pesquisa foi a exploração de dados viários urbanos disponíveis na Web e a extração de parâmetros com aplicabilidade em análises de morfologias de cidades. A intenção do trabalho foi corroborar a hipótese: A qualidade adquirida por dados de mapas colaborativos permite sua utilização em análises da morfologia urbana a nível local. Mapas colaborativos são plataformas online que agregam informações geográficas fornecidas pelos próprios usuários, de acordo com a experiência local. O Projeto OpenStreetMap foi utilizado como fonte de dados para avaliar o potencial e as limitações dos mapas colaborativos na representação das malhas viárias de municípios brasileiros. Os sistemas viários foram expressos em forma de grafos, em que as arestas representaram as ruas e os nós, as interseções entre ruas. Do ponto de vista da Engenharia de Transportes, o foco do uso da Teoria de Grafos em pesquisas de morfologias viárias está em questões como mensuração da acessibilidade, eficiência e resiliência da rede a falhas. As principais ferramentas computacionais utilizadas foram as bibliotecas OSMnx e Networkx, escritas em linguagem Python, com funções de construção e análises de redes. Para avaliar a viabilidade de utilização das ferramentas e da plataforma de dados propostos, foi realizada uma comparação com o estudo de Lima, Silva e van der Waerden (2003). Esse trabalho avaliou a compacidade e a acessibilidade global das malhas viárias de 11 cidades, com intuito de comparar exemplos brasileiros com estrangeiros. A reprodução do estudo de referência foi possível, com construção de redes com aumento médio de 47% de elementos, o que indicou expansão e maior detalhamento dos sistemas viários. Além disso, foi detectada uma diferença na comparação das cidades brasileiras com o panorama internacional. Marília e São Carlos apresentaram malhas mais próximas do padrão americano, junto com Piracicaba que já estava nesse grupo. Por outro lado, a malha viária de Presidente Prudente passou ao grupo de cidades mais próximas do padrão europeu. Em seguida, os materiais propostos neste trabalho foram utilizados para montar um banco de dados com mais de 300 cidades brasileiras de diferentes portes. Os parâmetros avaliados caracterizaram os municípios de acordo com a conectividade, acessibilidade e regularidade da malha viária. A grande variação destes aspectos ressaltou a diferença nas configurações ocasionadas pelos fatores influentes na formação de malhas viárias: processo histórico-social de crescimento urbano, limites geográficos e diferentes políticas de expansão. / This dissertation is inserted in the area of urban configuration, focusing on road systems segregated by cities. The objective of this research was the exploration of urban road data available on the Web and the extraction of parameters with applicability in analyzes of city morphologies. The intention of the work was to corroborate the hypothesis: The quality acquired by collaborative maps data allows its use in analyzes of the urban morphology at the local level. Collaborative maps are online platforms that aggregate geographic information provided by the users themselves, according to local experience. The OpenStreetMap Project was used as a data source to evaluate the potential and limitations of collaborative maps in the representation of road networks in Brazilian municipalities. The road systems were expressed in graph form, in which the edges represented the streets and nodes, the intersections between streets. From the point of view of Transport Engineering, the focus of the use of Graph Theory in research on road morphologies is on issues such as accessibility, efficiency and network resilience to failure measurement. The main computational tools used were the OSMnx and Networkx libraries, written in Python language, with built-in functions and network analysis. To evaluate the feasibility of using the proposed tools and data platform, a comparison was made with the study by Lima, Silva and van der Waerden (2003). This work evaluated the compactness and global accessibility of the road networks of 11 cities, in order to compare Brazilian examples with foreigners. The reproduction of the reference study was possible, with construction of networks with an average increase of 47% of elements, which indicated expansion and greater detailing of the road systems. In addition, a difference was detected in the comparison of Brazilian cities with the international panorama. Marília and São Carlos presented meshes closer to the American standard, along with Piracicaba who was already in that group. On the other hand, the road network of Presidente Prudente passed to the group of cities closest to the European standard. Then, the materials proposed in this work were used to build a database with more than 300 Brazilian cities of different sizes. The evaluated parameters characterized the municipalities according to the connectivity, accessibility and regularity of the road network. The great variation of these aspects highlighted the difference in the configurations caused by influencing factors in the formation of road networks: historical-social process of urban growth, geographical limits and different expansion policies

Das Projekt OpenStreetMap als freie Wiki-Weltkarte gewinnt als Quelle von Geodaten für unter-schiedlichste Bedürfnisse innerhalb der Geowissenschaften, des Geomarketings und auch im Alltag immer mehr an Bedeutung. Die kostenlosen, von Freiwilligen einer Community gesammelten geo-graphischen Daten, sogenannte nutzergenerierte Daten, dienen heute vielen Anwendern als Daten-grundlage und stehen in der Konkurrenz zu proprietären Geodaten von kommerziellen Anbietern. Neben Straßendaten sind zahlreiche zusätzliche Daten innerhalb OpenStreetMap verfügbar. Die aktuelle Technologie des Webmapping 2.0 und die dafür zahlreich verfügbaren Open Source GIS Systeme erlauben dem Anwender eine Vielzahl von Möglichkeiten zu Bearbeitung von Geodaten. Die freie Verfügbarkeit von Daten und Software machen die Nutzung und Verarbeitung von Geoda-ten somit auch für kleinere Unternehmen und Privatnutzer bezahlbar. Die vorliegende Studienarbeit stellt ein Verfahren vor, welches es ermöglicht aus OpenStreetMap-Daten Postleitzahlgebiete zu erstellen. Postleitzahlgebiete sind für viele Bereiche der Wirtschaft sehr wichtige Planungsstrukturen. Als Datengrundlage werden OSM-Adressdaten genutzt, aus denen, unter Nutzung von Open Source GIS Software, die Postleitzahlgebiete erzeugt werden. Die Generie-rung ist dabei automatisierbar und ohne die Nutzung grafischer Benutzeroberflächen möglich. Sie liefert als Ergebnis die Postleitzahlgebiete Deutschlands. Diese werden anschließend, unter Berück-sichtigung der ISO-Normen für Geoinformation, mit einem kommerziellen Datensatz verglichen und auf ihre Nutzbarkeit für Geomarketing und andere nützliche Anwendungen hin überprüft. / The OpenStreetMap project as the Free Wiki World Map as a source of gains for a wide variety of geospatial data needs within the geosciences, the geomarketing and in everyday life is becoming increasingly important. The free, a community of volunteers gathered geo-graphical data, so-called user-generated data, now serve many users as basic data and are in competition with proprietary spatial data from commercial providers. In addition to road data within OpenStreetMap numerous additional data is available. The current technology of the Web Mapping 2.0 and the many available Open Source GIS systems provide the user with a variety of options for managing spatial data. The free availability of data and software make the use and processing of geospatial data thus affordable for small businesses and home users. The current work presents a method that allows to create postcode areas from OpenStreetMap data. Postcode areas are very important planning structures for many areas of the economy. The OSM address data are used as data base, out of which the zip code areas are produced by taking advan-tage of Open Source GIS software. The creation is automated and without the use of graphical user interfaces. It provides as result the postal code areas of Germany. Taking into account the ISO stan-dards for geoinformation, the postal code areas are later compared with a commercial data set and their usability for geomarketing and other useful application is tested.

Navigation systems are one of the most commonly found electronic gadgets in modern vehicles nowadays. Alongside navigation units this technology is made readily available to individuals in everyday devices such as a mobile phone. Digital maps which come preloaded on these devices accommodate within them an extensive dataset of spatial information from around the globe which aids the driver in achieving a well guided driving experience. Apart from being essential for navigation this sensor information backs up other vehicular applications in making intelligent decisions. The quality of this information delivered is in direct relation to the underlying dataset used to produce these maps. Since we live in a highly dynamic environment with constantly changing geography, an effort is necessary to keep these maps updated with the most up to date information as frequently as possible. The digital map of interest in this study is OpenStreetMap, the underlying data of which is a combination of donated as well as crowdsourced information from the last 10 years. This extensive dataset helps in building of a detailed digital map of the world using well defined cartographic techniques. The information within OpenStreetMap is currently enhanced by a large group of volunteers who willing use donated satellite imagery, uploaded GPS tracks, field surveys etc. to correct and collect necessary data for a region of interest. Though this method helps in improving and increasing the quality and quantity of the OpenStreetMap dataset, it is very time consuming and requires a great deal of human effort. Through this thesis an effort is made to automatically enrich this dataset by preprocessing crowdsourced sensor data collected from the navigation system and driver assistance systems (Traffic Sign Recognition system and a Lane Detection System) of a driving vehicle. The kind of data that is algorithmically derived includes the calculation of the curvature of the underlying road, correction of speed limit values for individual road segments being driven and the identification of change in the geometry of existing roads due to closure of old ones or addition of new ones in the Nuremberg region of Bavaria, Germany. Except for a small percentage of speed limit information on roads segments, other information is currently not available in the OpenStreetMap database for use in safety and comfort related applications. The navigation system has the ability to deliver geographical data in form of GPS coordinates at a certain frequency. This set of GPS coordinates can grouped together to form a GPS track visualizing the actual path traversed by a driving vehicle. A large number of such GPS tracks repeatedly collected from different vehicles driving in a region of interest gives all GPS points which lie on a particular road. These points, after outlier elimination methods are used as a dataset to scientifically determine the underlying curvature of the road with the aid of curve fitting techniques. Additional information received from the lane detection system helps identify curves on a road for which the curvature must be calculated. The fusion of information from these sources helps to achieve curvature results with high accuracy. Traffic sign recognition system helps detect traffic signs while driving, the fusion of this data with geographical information from the navigation system at the instance of detection helps determine road segments for which the recognized speed limit values are valid. This thesis successfully demonstrates a method to automatically enrich OpenStreetMap data by crowdsourcing raw sensor data from multiple vehicles equipped with driver assistance systems. All OpenStreetMap attributes were 100% updated into the database and the results have proven the effectiveness our system architecture. The positive results obtained in combination with minimal errors promise a better future for assisted driving.

Das Projekt OpenStreetMap als freie Wiki-Weltkarte gewinnt als Quelle von Geodaten für unter-schiedlichste Bedürfnisse innerhalb der Geowissenschaften, des Geomarketings und auch im Alltag immer mehr an Bedeutung. Die kostenlosen, von Freiwilligen einer Community gesammelten geo-graphischen Daten, sogenannte nutzergenerierte Daten, dienen heute vielen Anwendern als Daten-grundlage und stehen in der Konkurrenz zu proprietären Geodaten von kommerziellen Anbietern. Neben Straßendaten sind zahlreiche zusätzliche Daten innerhalb OpenStreetMap verfügbar. Die aktuelle Technologie des Webmapping 2.0 und die dafür zahlreich verfügbaren Open Source GIS Systeme erlauben dem Anwender eine Vielzahl von Möglichkeiten zu Bearbeitung von Geodaten. Die freie Verfügbarkeit von Daten und Software machen die Nutzung und Verarbeitung von Geoda-ten somit auch für kleinere Unternehmen und Privatnutzer bezahlbar. Die vorliegende Studienarbeit stellt ein Verfahren vor, welches es ermöglicht aus OpenStreetMap-Daten Postleitzahlgebiete zu erstellen. Postleitzahlgebiete sind für viele Bereiche der Wirtschaft sehr wichtige Planungsstrukturen. Als Datengrundlage werden OSM-Adressdaten genutzt, aus denen, unter Nutzung von Open Source GIS Software, die Postleitzahlgebiete erzeugt werden. Die Generie-rung ist dabei automatisierbar und ohne die Nutzung grafischer Benutzeroberflächen möglich. Sie liefert als Ergebnis die Postleitzahlgebiete Deutschlands. Diese werden anschließend, unter Berück-sichtigung der ISO-Normen für Geoinformation, mit einem kommerziellen Datensatz verglichen und auf ihre Nutzbarkeit für Geomarketing und andere nützliche Anwendungen hin überprüft.:Kurzfassung ........................................................................................... I Abstract ................................................................................................. II Abbildungsverzeichnis ........................................................................... V Tabellenverzeichnis .............................................................................. VI Formeln ............................................................................................... VII Abkürzungsverzeichnis ...................................................................... VIII 1 Einleitung ............................................................................................ 1 1.1 Motivation ........................................................................................ 1 1.2 Aufbau der Arbeit ............................................................................. 2 1.3 Ziel der Arbeit .................................................................................. 3 2 Theorie ............................................................................................... 4 2.1 Postleitzahlen .................................................................................. 4 2.2 Nutzung von Postleitzahlen in der Privatwirtschaft ......................... 6 2.3 Postleitzahlen in OpenStreetMap .................................................... 8 2.4 Qualität von Geodaten .................................................................. 13 2.5 Vergleich von Daten unterschiedlicher Herkunft ............................ 18 2.5.1 Qualität von OSM-Daten ............................................................. 19 2.5.2 Vergleich von OSM-Daten mit Daten kommerzieller Anbieter ...... 20 2.5.3 Vergleichsmethoden für Polygondatensätze .............................. 22 2.5.3.1 Vergleichsmethoden ................................................................ 23 2.5.3.2 Vergleichskriterien und Qualitätsmaße .................................... 24 2.6 Gebietsgenerierung aus Punktdaten ............................................. 29 2.6.1 Allgemeines und Literatur ........................................................... 29 2.6.2 Voronoi-Verfahren ...................................................................... 32 2.7 Open Source GIS Software ............................................................ 35 3 Praxis ................................................................................................ 41 3.1 Technische Voraussetzungen ........................................................ 41 3.2 Datengrundlage ............................................................................. 41 3.3 Allgemeiner Arbeitsablauf .............................................................. 43 3.3.1 Datenvorverarbeitung ................................................................ 43 3.3.2 Erzeugung der Gebiete ............................................................... 46 3.3.3 Datennachbearbeitung ............................................................... 47 3.3.4 Export der Daten als Shapefile ................................................... 47 3.4 Praktische Umsetzung ................................................................... 48 3.4.1 Datenvorverarbeitung ................................................................ 48 3.4.2 Erzeugung der Gebiete ............................................................... 50 3.4.3 Datennachbearbeitung ............................................................... 51 3.4.4 Export der Daten als Shapefile ................................................... 51 3.5 Ergebnisse ..................................................................................... 51 3.6 Vergleich der Daten ....................................................................... 52 4 Fazit .................................................................................................. 63 5 Ausblick ............................................................................................. 65 Quellenverzeichnis ............................................................................... IX Literaturquellen .................................................................................... IX Internetquellen .................................................................................... XV Anhang ............................................................................................... XIX A Anhang Quellcodes .......................................................................... XIX A.1 Quellcode Import Deutschlandgrenze .......................................... XIX A.2 Quellcode Vorverarbeitung ........................................................... XIX A.3 Quellcode Erzeugung Polygone .................................................... XXI A.4 Quellcode Nachbearbeitung ........................................................ XXII A.5 Quellcode Export ........................................................................ XXIII B Anhang Screenshots PDF-Karten .................................................... XXV B.1 Postleitzonen (PLZ1) Deutschland OSM ....................................... XXV B.2 Postleitregionen (PLZ2) Deutschland OSM ................................. XXVI B.3 Postleitzahlen 3-stellig Deutschland OSM ................................. XXVII B.4 Postleitzahlgebiete (PLZ5) Deutschland OSM .......................... XXVIII B.5 Vergleich der Postleitzahlen ....................................................... XXIX B.6 Vergleich PLZ5 Hamburg .............................................................. XXX B.7 Punktdichte PLZ5-Centroide OpenStreetMap ............................. XXXI B.8 Punktdichte PLZ5-Centroide TeleAtlas ...................................... XXXII B.9 Euklidische Distanz PLZ3-Centroide OpenStreetMap ............... XXXIII B.10 Euklidische Distanz PLZ3-Centroide TeleAtlas ........................ XXXIV B.11 Euklidische Distanz PLZ5-Centroide OpenStreetMap ............... XXXV B.12 Euklidische Distanz PLZ5-Centroide TeleAtlas ........................ XXXVI C Anhang Tabelle .......................................................................... XXXVII C.1 Tabelle Vergleich PLZ5-Gebiete Hamburg ............................... XXXVII D Anhang ............................................................................................ XLI D.1 CD ................................................................................................ XLI / The OpenStreetMap project as the Free Wiki World Map as a source of gains for a wide variety of geospatial data needs within the geosciences, the geomarketing and in everyday life is becoming increasingly important. The free, a community of volunteers gathered geo-graphical data, so-called user-generated data, now serve many users as basic data and are in competition with proprietary spatial data from commercial providers. In addition to road data within OpenStreetMap numerous additional data is available. The current technology of the Web Mapping 2.0 and the many available Open Source GIS systems provide the user with a variety of options for managing spatial data. The free availability of data and software make the use and processing of geospatial data thus affordable for small businesses and home users. The current work presents a method that allows to create postcode areas from OpenStreetMap data. Postcode areas are very important planning structures for many areas of the economy. The OSM address data are used as data base, out of which the zip code areas are produced by taking advan-tage of Open Source GIS software. The creation is automated and without the use of graphical user interfaces. It provides as result the postal code areas of Germany. Taking into account the ISO stan-dards for geoinformation, the postal code areas are later compared with a commercial data set and their usability for geomarketing and other useful application is tested.:Kurzfassung ........................................................................................... I Abstract ................................................................................................. II Abbildungsverzeichnis ........................................................................... V Tabellenverzeichnis .............................................................................. VI Formeln ............................................................................................... VII Abkürzungsverzeichnis ...................................................................... VIII 1 Einleitung ............................................................................................ 1 1.1 Motivation ........................................................................................ 1 1.2 Aufbau der Arbeit ............................................................................. 2 1.3 Ziel der Arbeit .................................................................................. 3 2 Theorie ............................................................................................... 4 2.1 Postleitzahlen .................................................................................. 4 2.2 Nutzung von Postleitzahlen in der Privatwirtschaft ......................... 6 2.3 Postleitzahlen in OpenStreetMap .................................................... 8 2.4 Qualität von Geodaten .................................................................. 13 2.5 Vergleich von Daten unterschiedlicher Herkunft ............................ 18 2.5.1 Qualität von OSM-Daten ............................................................. 19 2.5.2 Vergleich von OSM-Daten mit Daten kommerzieller Anbieter ...... 20 2.5.3 Vergleichsmethoden für Polygondatensätze .............................. 22 2.5.3.1 Vergleichsmethoden ................................................................ 23 2.5.3.2 Vergleichskriterien und Qualitätsmaße .................................... 24 2.6 Gebietsgenerierung aus Punktdaten ............................................. 29 2.6.1 Allgemeines und Literatur ........................................................... 29 2.6.2 Voronoi-Verfahren ...................................................................... 32 2.7 Open Source GIS Software ............................................................ 35 3 Praxis ................................................................................................ 41 3.1 Technische Voraussetzungen ........................................................ 41 3.2 Datengrundlage ............................................................................. 41 3.3 Allgemeiner Arbeitsablauf .............................................................. 43 3.3.1 Datenvorverarbeitung ................................................................ 43 3.3.2 Erzeugung der Gebiete ............................................................... 46 3.3.3 Datennachbearbeitung ............................................................... 47 3.3.4 Export der Daten als Shapefile ................................................... 47 3.4 Praktische Umsetzung ................................................................... 48 3.4.1 Datenvorverarbeitung ................................................................ 48 3.4.2 Erzeugung der Gebiete ............................................................... 50 3.4.3 Datennachbearbeitung ............................................................... 51 3.4.4 Export der Daten als Shapefile ................................................... 51 3.5 Ergebnisse ..................................................................................... 51 3.6 Vergleich der Daten ....................................................................... 52 4 Fazit .................................................................................................. 63 5 Ausblick ............................................................................................. 65 Quellenverzeichnis ............................................................................... IX Literaturquellen .................................................................................... IX Internetquellen .................................................................................... XV Anhang ............................................................................................... XIX A Anhang Quellcodes .......................................................................... XIX A.1 Quellcode Import Deutschlandgrenze .......................................... XIX A.2 Quellcode Vorverarbeitung ........................................................... XIX A.3 Quellcode Erzeugung Polygone .................................................... XXI A.4 Quellcode Nachbearbeitung ........................................................ XXII A.5 Quellcode Export ........................................................................ XXIII B Anhang Screenshots PDF-Karten .................................................... XXV B.1 Postleitzonen (PLZ1) Deutschland OSM ....................................... XXV B.2 Postleitregionen (PLZ2) Deutschland OSM ................................. XXVI B.3 Postleitzahlen 3-stellig Deutschland OSM ................................. XXVII B.4 Postleitzahlgebiete (PLZ5) Deutschland OSM .......................... XXVIII B.5 Vergleich der Postleitzahlen ....................................................... XXIX B.6 Vergleich PLZ5 Hamburg .............................................................. XXX B.7 Punktdichte PLZ5-Centroide OpenStreetMap ............................. XXXI B.8 Punktdichte PLZ5-Centroide TeleAtlas ...................................... XXXII B.9 Euklidische Distanz PLZ3-Centroide OpenStreetMap ............... XXXIII B.10 Euklidische Distanz PLZ3-Centroide TeleAtlas ........................ XXXIV B.11 Euklidische Distanz PLZ5-Centroide OpenStreetMap ............... XXXV B.12 Euklidische Distanz PLZ5-Centroide TeleAtlas ........................ XXXVI C Anhang Tabelle .......................................................................... XXXVII C.1 Tabelle Vergleich PLZ5-Gebiete Hamburg ............................... XXXVII D Anhang ............................................................................................ XLI D.1 CD ................................................................................................ XLI