Analyzing the Tagging Quality of the Spanish OpenStreetMap

In this paper, a framework for the assessment of the quality of OpenStreetMap is presented, comprising a batch of methods to analyze the quality of entity tagging. The approach uses Taginfo as a reference base and analyses quality measures such as completeness, compliance, consistence, granularity, richness and trust . The framework has been used to analyze the quality of OpenStreetMap in Spain, comparing the main cities of Spain. Also a comparison between Spain and some major European cities has been carried out. Additionally, a Web tool has been also developed in order to facilitate the same kind of analysis in any area of the world

Assessing the accuracy of openstreetmap data in south africa for the purpose of integrating it with authoritative data

Includes bibliographical references.The introduction and success of Volunteered Geographic Information (VGI) has gained the interest of National Mapping Agencies (NMAs) worldwide. VGI is geographic information that is freely generated by non-experts and shared using VGI initiatives available on the Internet. The NMA of South Africa i.e. the Chief Directorate: National Geo- Spatial Information (CD: NGI) is looking to this volunteer information to maintain their topographical database; however, the main concern is the quality of the data. The purpose of this work is to assess whether it is feasible to use VGI to update the CD: NGI topographical database. The data from OpenStreetMap (OSM), which is one the most successful VGI initiatives, was compared to a reference data set provided by the CD: NGI. Corresponding features between the two data sets were compared in order to assess the various quality aspects. The investigation was split into quantitative and qualitative assessments. The aim of the quantitative assessments was to determine the internal quality of the OSM data. The internal quality elements included the positional accuracy, geometric accuracy, semantic accuracy and the completeness. The _rst part of the qualitative assessment was concerned with the currency of OSM data between 2006 and 2012. The second part of the assessment was focused on the uniformity of OSM data acquisition across South Africa. The quantitative results showed that both road and building features do not meet the CD: NGI positional accuracy standards. In some areas the positional accuracy of roads are close to the required accuracy. The buildings generally compare well in shape to the CD: NGI buildings. However, there were very few OSM polygon features to assess, thus the results are limited to a small sample. The semantic accuracy of roads was low. Volunteers do not generally classify roads correctly. Instead, many volunteers prefer to class roads generically. The last part of the quantitative results, the completeness, revealed that commercial areas reach high completeness percentages and sometimes exceed the total length of the CD: NGI roads. In residential areas, the percentages are lower and in low urban density areas, the lowest. Nonetheless, the OSM repository has seen signi_cant growth since 2006. The qualitative results showed that because the OSM repository has continued to grow since 2006, the level of currency has increased. In South Africa, the most contributions were made between 2010 and 2012. The OSM data set is thus current after 2012. The amount and type of contributions are however not uniform across the country for various reasons. The number of point contributions was low. Thus, the relationship between the type of contribution and the settlement type could not be made with certainty. Because the OSM data does not meet the CD: NGI spatial accuracy requirements, the two data sets cannot be integrated at the database level. Instead, two options are proposed. The CD: NGI could use the OSM data for detecting changes to the landscape only. The other recommendation is to transform and verify the OSM data. Only those features with a high positional accuracy would then be ingested. The CD: NGI currently has a shortage of sta_ that is quali_ed to process ancillary data. Both of the options proposed thus require automated techniques because it is time consuming to perform these tasks manually

Determinação e avaliação de indicadores espaço-temporais da qualidade de dados no mapeamento colaborativo

Orientador: Prof. Dr. Marcio Augusto Reolon SchmidtCo-Orientadora: Profª. Drª. Silvana Philippi CamboimTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 28/11/2022Inclui referênciasResumo: Questões relacionadas com a desatualização do mapeamento oficial são recorrentes em diferentes localidades do Brasil e do mundo, principalmente devido ao custo-benefício para a sua produção e manutenção. Neste contexto, pesquisas têm direcionado esforços em avaliar o potencial de informações oriundas de plataformas de mapeamento colaborativo, no âmbito de estabelecer o seu potencial de integração e a determinação da qualidade. Abordagens tradicionais se baseiam em comparações em relação a bases oficiais existentes, todavia, esta nem sempre é a realidade para as cidades brasileiras, uma vez que, o mapeamento oficial pode ser inexistente ou desatualizado. Este aspecto tem impulsionado pesquisas a relacionar a qualidade dos dados colaborativos em relação a seus parâmetros intrínsecos, caracterizados por históricos de edições, quantidade de contribuições e contribuidores. Com base em tais questões, questiona-se nesta pesquisa se é possível modelar os padrões espaço-temporais de qualidade intrínseca que influenciam na completude dos dados da plataforma OpenStreetMap (OSM) para obter a sua qualidade. Além disso, questiona-se também se é possível desenvolver ferramentas para a avaliação da qualidade extrínseca na qual seja possível identificar e discutir questões acerca da heterogeneidade dos dados. Dessa forma, este trabalho objetivou o desenvolvimento de uma metodologia para modelar e avaliar padrões espaço-temporais dos indicadores de qualidade intrínseca dos dados plataforma OSM e sua relação com os indicadores de qualidade tradicionais. Foi desenvolvido um procedimento metodológico para modelar as contribuições ao longo do tempo e, a partir dos parâmetros obtidos, identificar de que maneira os padrões se comportam e quais os fatores que influenciam na sua heterogeneidade. Foram desenvolvidos complementos para avaliar e visualizar a acurácia posicional e completude no OSM, de modo que auxiliem na tomada de decisões. A modelagem matemática deu-se a partir da Regressão Logística, em células de 1x1 km. Como resultados, notou-se que o parâmetro de inclinação da curva permitiu diferenciar regiões com grandes contribuições e de crescimento gradativo ao longo do tempo, e até mesmo, a sinergia entre o OSM e os dados oficiais, a partir da importação de feições. Além disso, notou-se que existe uma relação direta da completude dos dados e a saturação da curva (estabilidade das contribuições nos últimos anos), principalmente nas análises que envolvem os eixos viários. Conclui-se que é possível utilizar os padrões espaço-temporais de contribuição como medida de qualidade intrínseca, diante das questões relacionadas com a qualidade dos dados e recomenda-se a continuidade das análises, utilizando diferentes regiões de estudo, categoriais e tamanhos de células.Abstract: Issues related to the outdated status of official mapping are recurrent in different locations in Brazil and around the world, especially due to the cost-effectiveness of its production and maintenance. In this context, research is focusing its efforts on evaluating the potential of information coming from collaborative mapping, in the context of establishing their integrating and determining quality potential. Traditional approaches are based on comparisons with existing official databases, however, this is not always the reality for Brazilian cities, since the official mapping may be non-existent or outdated. This aspect has driven research to relate the quality of collaborative data in relation to its intrinsic parameters, characterized by historical editions, number of contributions and contributors. Based on these questions, this research asks whether it is possible to model the spatiotemporal patterns of intrinsic quality that influence the completeness of the OpenStreetMap (OSM) platform data to obtain its quality. In addition, it is also questioned whether it is possible to develop tools to evaluate the extrinsic quality in which it is possible to identify and discuss questions over the heterogeneity of the data. Thus, this research aimed to develop a methodology to model and evaluate spatiotemporal patterns of the intrinsic quality indicators of data from OSM and its relationship with the traditional quality indicators. A methodological procedure was developed to model the contributions over time and based on the obtained parameters, identify how the patterns behave and which factors influence their heterogeneity. Complements were developed to evaluate and visualize the OSM positional accuracy and completeness, in order to help in decision making. The Logistic Regression based the mathematic modeling, in cells of 1x1 km. As a result, it was possible to notice that the curve slope parameter allowed to differentiate regions with large contributions and gradual growth over time, and even the synergy between OSM and official data, from the import of features. Besides that, it was noticed that there is a direct relationship between the completeness of data and the curve saturation (stability of the contributions in the last years), especially in the analysis involving road axles. It is concluded that it is possible to use spatiotemporal patterns of contributions as a measure of intrinsic quality, regarding the issues related to data quality. It is recommended to continue the analysis using different study regions, categories and cell sizes

A grounding-based ontology of data quality measures

Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic information (VGI), while also applying to other geographical data and data in general. An evaluation of the ontology in the context of data quality measures for OpenStreetMap (OSM) data, a well-known example of VGI, provides insights about which types of quality measures for OSM data have and which have not yet been considered in literature

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Open source data mining infrastructure for exploring and analysing OpenStreetMap

OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too

MethOSM: a methodology for computing composite indicators derived from OpenStreetMap data

The task of computing composite indicators to define and analyze complex social, economic, political, or environmental phenomena has traditionally been the exclusive competence of statistical offices. Nowadays, the availability of increasing volumes of data and the emergence of the open data movement have enabled individuals and businesses affordable access to all kinds of datasets that can be used as valuable input to compute indicators. OpenStreetMap (OSM) is a good example of this. It has been used as a baseline to compute indicators in areas where official data is scarce or difficult to access. Although the extraction and application of OSM data to compute indicators is an attractive proposition, this practice is by no means hassle-free. The use of OSM reveals a number of challenges that are usually addressed with ad-hoc and often overlapping solutions. In this context, this paper proposes MethOSM-a systematic methodology for computing indicators derived from OSM data. By applying MethOSM, the computation task is divided into four steps, with each step having a clear goal and a set of guidelines to apply. In this way, the methodology contributes to an effective and efficient use of OSM data for the purpose of computing indicators. To demonstrate its use, we apply MethOSM to a number of indicators used for real estate valuation of properties in Italy