Exploring the Possibility of Semi-Automated Quality Evaluation of Spatial Datasets in Spatial Data Infrastructure

Over the past decades, World Wide Web technology has developed greatly. One of the most important outcomes of this technology is to share data in a worldwide domain. A considerable amount of available data have spatial components and are hence called spatial data. The level of quality that spatial datasets conform plays an important role in their reliability for use in projects. This research aims to overview spatial data quality elements and select appropriate elements suitable for means of semi-automated quality evaluation. In this paper, the ISO 19100 series of standards for geographic information is used as basis for quality evaluation. The possibilities of use of different spatial data quality elements for semi-automated quality evaluation are explored and discussed. Finally, based on argumentation and reference to other research studies, a list of spatial data quality elements and sub-elements suitable for semi-automated quality evaluation of spatial datasets are presented

Towards spatio‐temporal data modeling of geo‐tagged shipping information

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Spatio-temporal data models deal with capturing information characterized by both spatial and temporal semantics. In this paper we review current approaches for spatio-temporal data modelling and present out initial results for selecting the most relevant approach: Object-Oriented modeling for means of modeling geo-tagged shipping information. The shipping information is provided by the well-known LLLOYD’s lists dataset. We have introduced the case study and dataset characteristics used in the research project and presented our data model in Unified Modeling Language (UML). The model focuses on spatio-temporal events where characteristics are categorized as thematic, spatial and temporal attributes. The paper follows up with discussion on the selected approach and results, and finally ends with presenting the future outlook

Are crowdsourced datasets suitable for specialized routing services? Case study of Openstreetmap for routing of people with limited mobility

Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most famous crowdsourced mapping platforms, we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. We assess the completeness of OSM data regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods, we present the results of fitness for use of OSM data for routing services of disabled persons. Based on empirical results, it is concluded that OSM data of relatively large spatial extents inside all studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed. Finally, the article ends with an outlook for future research study in this area

Wheelmap: the wheelchair accessibility crowdsourcing platform

Crowdsourcing (geo-) information and participatory GIS are among the current hot topics in research and industry. Various projects are implementing participatory sensing concepts within their workflow in order to benefit from the power of volunteers, and improve their product quality and efficiency. Wheelmap is a crowdsourcing platform where volunteers contribute information about wheelchair-accessible places. This article presents information about the technical framework of Wheelmap, and information on how it could be used in projects dealing with accessibility and/or multimodal transportation

Open-source geospatial tools and technologies for urban and environmental studies

AbstractOpen geospatial data and tools are an increasingly important paradigm offering the opportunity to promote the democratization of geographical information, the transparency of governments and institutions, as well as social, economic and environmental opportunities. During the past decade, developments in the area of open geospatial data and open-source geospatial software have greatly improved. Many parts of the research community believe that combining free and open software, open data, as well as open standards, leads to the creation of a sustainable ecosystem to accelerate new discoveries to help solve global cross-disciplinary societal challenges, from climate change mitigation to sustainable cities. The consistent prevalence of open source GIS studies motivated this thematic collection. The contributions are divided into two main categories. In the first category, seven concrete studies on open-source tools and technologies for urban and environmental studies are briefly presented. Each one has been implemented for and applied to a certain use case, and at the same time it may be applied to other use cases due to the reproducibility nature of open source software. The second category presents and discusses the usability of open source geospatial solutions for laser scanning technology and its applications

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

Recovery Strategies to Sustain Religious Tourism Post-COVID-19: Evidence from Iran

Religious tourism, as one of the most significant types of tourism, is among the most seriously affected economic sectors worldwide as a result of the coronavirus (COVID-19) pandemic. This study sought to identify and prioritise the strategies for sustaining post-COVID-19 religious tourism. The study was an applied survey that drew on a mixed research design. The results of the qualitative data analysis obtained through a systematic literature review revealed that the recovery strategies to boost businesses in the field of religious tourism in the post-COVID-19 future could be divided into four main strategies (dimensions) and 24 sub-strategies (criteria). Furthermore, the quantitative analysis, conducted through the R-SWARA technique, showed that ‘making it mandatory to maintain a distance from sacred monuments (e.g., tombs) and prohibiting touching or having any physical contact with such monuments’, ‘Understanding religious tourists’ health-related expectations’, ‘making it mandatory to use masks and other health-related equipment in sacred/religious locations’, and ‘reducing the capacity for welcoming tourists in sacred and tourism sites while holding religious ceremonies with a small number of attendants’ were the most significant strategies that could boost religious tourism post-COVID-19. These factors were prioritised by experts and activists in the industry who shared their opinions in this study

Enrichment of OpenStreetMap data completeness with sidewalk geometries using data mining techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions

Geospatial openness: from software to standards & data

Abstract This paper is the editorial of the Special Issue "Open Source Geospatial Software", which features 10 published papers. The editorial introduces the concept of openness and, within the geospatial context, declines it into the three main components of software, data and standards. According to this classification, the papers published in the Special Issue are briefly summarized and a future research agenda in the open geospatial domain is finally outlined

Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data

With the development of information and communications technology, user-generated content and crowdsourced data are playing a large role in studies of transport and public health. Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected. In this paper, to explore the potential of Strava Metro data in research of active travel and health, we investigate spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we attempt to estimate the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We use the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study. Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating of the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown