A PageRank-based Reputation Model for VGI Data

AbstractQuality of data is one of the key issues in the domain of Volunteered geographic information (VGI). To this purpose, in literature VGI data has been sometime compared with authoritative geospatial data. Evaluation of single contributions to VGI databases is more relevant for some applications and typically relies on evaluating reputation of contributors and using it as proxy measures for data quality. In this paper, we present a novel approach for reputation evaluation that is based on the well known PageRank algorithm for Web pages. We use a simple model for describing different versions of a geospatial entity in terms of corrections and completions. Authors, VGI contributions and their mutual relationships are modeled as nodes of a graph. In order to evaluate reputation of authors and contributions in the graph we propose an algorithm that is based on the personalized version of PageRank

Virtual Globes for UAV-based data integration: Sputnik GIS and Google Earth™ applications

“This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Digital Earth on 03 May 2018, available online: https://www.tandfonline.com/doi/abs/10.1080/17538947.2018.1470205"The integration of local measurements and monitoring via global-scale Earth observations has become a new challenge in digital Earth science. The increasing accessibility and ease of use of virtual globes (VGs) represent primary advantages of this integration, and the digital Earth scientific community has adopted this technology as one of the main methods for disseminating the results of scientific studies. In this study, the best VG software for the dissemination and analysis of high-resolution UAV (Unmanned Aerial Vehicle) data is identified for global and continuous geographic scope support. The VGs Google Earth and Sputnik Geographic Information System (GIS) are selected and compared for this purpose. Google Earth is a free platform and one of the most widely used VGs, and one of its best features its ability to provide users with quality visual results. The proprietary software Sputnik GIS more closely approximates the analytical capacity of a traditional GIS and provides outstanding advantages, such as DEM overlapping and visualization for its disseminationThis work was supported by Xunta de Galicia under the Grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” (Ref. ED431B 2016/030 and Ref. ED341D R2016/023). The authors also acknowledge support provided by “Realización de vuelos virtuales en las parcelas del proyecto Green deserts LIFE09 / ENV/ES / 000447”S

An Open Data and Citizen Science Approach to Building Resilience to Natural Hazards in a Data-Scarce Remote Mountainous Part of Nepal

The citizen science approach has gained momentum in recent years. It can enable both experts and citizen scientists to co-create new knowledge. Better understanding of local environmental, social, and geographical contexts can help in designing appropriate plans for sustainable development. However, a lack of geospatial data, especially in the context of developing countries, often precludes context-specific development planning. This study therefore tests an innovative approach of volunteer citizen science and an open mapping platform to build resilience to natural hazards in the remote mountainous parts of western Nepal. In this study, citizen scientists and mapping experts jointly mapped two districts of Nepal (Bajhang and Bajura) using the OpenStreetMap (OSM) platform. Remote mapping based on satellite imagery, capacity building, and mobilization of citizen scientists was performed to collect the data. These data were then uploaded to OSM and later retrieved in ArcGIS to produce a usable map that could be exploited as a reference resource for evidence-based decision-making. The collected data are freely accessible to community members as well as government and humanitarian actors, and can be used for development planning and risk reduction. By piloting in two communities of western Nepal, we found that using open data platforms for collecting and analyzing location-based data has a mutual benefit for researchers and communities. Such data could be vital in understanding the local landscape, environmental risk, and distribution of resources. Furthermore, they enable both researchers and local people to transfer technical knowledge, collect location-specific data, and use them for better decision-making

BRAZILIAN NSDI TEN YEARS LATER: CURRENT OVERVIEW, NEW CHALLENGES AND PROPOSITIONS FOR NATIONAL TOPOGRAPHIC MAPPING

Cartographic data represents the main and basic component of a Spatial Data Infrastructure. SDI, in turn, has the role of supporting, with strategic information, the most diverse political and economic actions, in the management and planning of public actions. Thus, this work aims, initially, to present an overview of cartography in Brazil through the analysis of the evolution of topographic mapping coverage in the country. For each of the main scales used, a coverage map was created. The analyzes reflect three different periods (until 1997, between 1998 and 2007, after 2008) in order to relate how and to what degree, the creation of Brazilian National SDI (in 2008) had an impact on the mapping production in the country. Given the current panorama, as a final objective, this paper aims at to present proposals to leverage the coverage of this reference data. One of them is the use of new data sources such as Volunteered Geographic Information, especially in areas with outdated mapping or without mapping, as has already been used in some countries. Another proposition is to share the responsibility of mapping through partnerships with other levels of government, which would result the decentralization and the optimization of cartographic production

Evaluating Reputation in VGI-enabled Applications

ABSTRACT Volunteered Geographic Information (VGI) is an approach to crowdsource information about geospatial objects around us, as implemented in Open Street Map, Google Map Maker and WikiMapia projects. The value of this content has been recognized by both researchers and organizations for acquiring free, timely and detailed spatial data versus standard spatial data warehouses where objects are created by professionals with variable updating time. However, evaluating its quality and handling its heterogeneity remain challenging concerns. For instance, VGI data sources have been compared to authoritative geospatial ones on specific regions/areas in order to determine an average overall quality level. In user-oriented VGI-based applications, it can be more relevant to assess the quality of particular contents, like specific Points of Interest. In this case, evaluation can be performed indirectly by reputation scores associated with the specific content. This paper focuses on this last aspect. Our contribution primarily provides a comprehensive model and architecture for reputation evaluation aimed to assess quality of VGI content. On the other hand, we also focus on applications by discussing two motivating scenarios for reputation-enhanced VGI data in the context of geospatial decision support systems and in recommending tourist itineraries

AVALIAÇÃO DA ACURÁCIA POSICIONAL DE DADOS COLABORATIVOS DO OPENSTREETMAP: EIXOS VIÁRIOS DE BAIRRO NO MUNICÍPIO DE UBERLÂNDIA (MG): Evaluation of the positional accuracy of OpenStreetMap colaborative data: neighborhood road axes in the municipality of Uberlândia (MG)

The growing voluntary activity in the creation and distribution of geospatial data has attracted the attention of official mapping agencies in relation to the use of data from collaborative mapping in the production of a reference cartographic base. However, the quality of voluntary geographic information is heterogeneous, and it is necessary to measure the quality of its data. To this end, several studies have been developed in recent decades. The objective of this article is to analyze the positional accuracy of the representation of road axes in OpenStreetMap, comparing it with the reference mapping, in one of the districts of the municipality of Uberlândia, MG. For the accuracy analysis, the PEC-PCD criteria established by Decree n° 89,817, ET-ADGV Standard and ET-CQDG Standard were used, applied to a set of 353 pairs of homologous points in streets and road axes in the Morumbi neighborhood. The results show that the positional accuracy of the collaborative data for the study area obtained class B of the PEC-PCD for the scale of 1:10,000, considered with precision compatible with the mapping for the region, but below the usual cadastral scales.RESUMO - A crescente atividade voluntária na criação e distribuição de dados geoespaciais tem atraído a atenção de agências oficiais de mapeamento em relação ao uso dos dados oriundos do mapeamento colaborativo na produção de base cartográfica de referência. Entretanto, a qualidade da informação geográfica voluntária é heterogênea, sendo necessário mensurar a qualidade de seus dados. Para esse fim, diversos estudos vem sendo desenvolvidos nas últimas décadas. O objetivo deste artigo é analisar a acurácia posicional da representação dos eixos viários no OpenStreetMap, comparando-o com o mapeamento de referência, em um dos bairros do município de Uberlândia, MG.  Para a análise de precisão, foram utilizados os critérios do PEC-PCD estabelecidos pelo Decreto nº 89.817, Norma ET-ADGV e Norma ET-CQDG, aplicados em um conjunto de 353 pares de pontos homólogos nos logradouros e eixos de vias do bairro Morumbi. Os resultados mostram que a acurácia posicional dos dados colaborativos para a área de estudo obteve a classe B do PEC-PCD para a escala de 1:10.000, considerada com precisão compatível com o mapeamento para a região, porém abaixo das escalas usuais cadastrais. ABSTRACT - The growing voluntary activity in the creation and distribution of geospatial data has attracted the attention of official mapping agencies in relation to the use of data from collaborative mapping in the production of a reference cartographic base. However, the quality of voluntary geographic information is heterogeneous, and it is necessary to measure the quality of its data. To this end, several studies have been developed in recent decades. The objective of this article is to analyze the positional accuracy of the representation of road axes in OpenStreetMap, comparing it with the reference mapping, in one of the districts of the municipality of Uberlândia, MG. For the accuracy analysis, the PEC-PCD criteria established by Decree n° 89,817, ET-ADGV Standard and ET-CQDG Standard were used, applied to a set of 353 pairs of homologous points in streets and road axes in the Morumbi neighborhood. The results show that the positional accuracy of the collaborative data for the study area obtained class B of the PEC-PCD for the scale of 1:10,000, considered with precision compatible with the mapping for the region, but below the usual cadastral scales

AUTHORITATIVE CARTOGRAPHY IN BRAZIL AND COLLABORATIVE MAPPING PLATFORMS: CHALLENGES AND PROPOSALS FOR DATA INTEGRATION

Brazil has a large area with missing or outdated mapping on the largest scales of its authoritative mapping. The use of data from collaborative mapping platforms appears as an alternative that may contribute to minimizing this problem, either by updating or completing the mapping coverage in Brazil, as proposed or performed by some National Mapping Agencies abroad. The present work aims to analyze a methodology to provide accurate and documented integration of volunteered geographic information and the Brazilian authoritative mapping. The proposal starts with the semantic compatibility between the conceptual models adopted in both official cartography and OpenStreetMap platform. The research continues with the identification of object classes with the most significant potential for integration. Finally, we developed some experiments to evaluate and validate the OSM data integration process in a 1:25,000 scale cartographic database. Even in regions with a recent mapping, the results of the preliminary assessment indicate the potential for an increase of about 52% and 16% of features in the ‘road system’ category, which suggests a very promising method for use in areas with missing or outdated mapping, and its applicability to other categories

A comparison of temporal and location-based sampling strategies for GPS-triggered electronic diaries

Self-reporting is a well-established approach within the medical and psychological sciences. In order to avoid recall bias, i.e. past events being remembered inaccurately, the reports can be filled out on a smartphone in real-time and in the natural environment. This is often referred to as ambulatory assessment and the reports are usually triggered at regular time intervals. With this sampling scheme, however, rare events (e.g. a visit to a park or recreation area) are likely to be missed. When addressing the correlation between mood and the environment, it may therefore be beneficial to include participant locations within the ambulatory assessment sampling scheme. Based on the geographical coordinates, the database query system then decides if a self-report should be triggered or not. We simulated four different ambulatory assessment sampling schemes based on movement data (coordinates by minute) from 143 voluntary participants tracked for seven consecutive days. Two location-based sampling schemes incorporating the environmental characteristics (land use and population density) at each participant’s location were introduced and compared to a time-based sampling scheme triggering a report on the hour as well as to a sampling scheme incorporating physical activity. We show that location-based sampling schemes trigger a report less often, but we obtain more unique trigger positions and a greater spatial spread in comparison to sampling strategies based on time and distance. Additionally, the location-based methods trigger significantly more often at rarely visited types of land use and less often outside the study region where no underlying environmental data are available