Enhancing Data Classification Quality of Volunteered Geographic Information

Geographic data is one of the fundamental components of any Geographic Information System (GIS). Nowadays, the utility of GIS becomes part of everyday life activities, such as searching for a destination, planning a trip, looking for weather information, etc. Without a reliable data source, systems will not provide guaranteed services. In the past, geographic data was collected and processed exclusively by experts and professionals. However, the ubiquity of advanced technology results in the evolution of Volunteered Geographic Information (VGI), when the geographic data is collected and produced by the general public. These changes influence the availability of geographic data, when common people can work together to collect geographic data and produce maps. This particular trend is known as collaborative mapping. In collaborative mapping, the general public shares an online platform to collect, manipulate, and update information about geographic features. OpenStreetMap (OSM) is a prominent example of a collaborative mapping project, which aims to produce a free world map editable and accessible by anyone. During the last decade, VGI has expanded based on the power of crowdsourcing. The involvement of the public in data collection raises great concern about the resulting data quality. There exist various perspectives of geographic data quality this dissertation focuses particularly on the quality of data classification (i.e., thematic accuracy). In professional data collection, data is classified based on quantitative and/or qualitative ob- servations. According to a pre-defined classification model, which is usually constructed by experts, data is assigned to appropriate classes. In contrast, in most collaborative mapping projects data classification is mainly based on individualsa cognition. Through online platforms, contributors collect information about geographic features and trans- form their perceptions into classified entities. In VGI projects, the contributors mostly have limited experience in geography and cartography. Therefore, the acquired data may have a questionable classification quality. This dissertation investigates the challenges of data classification in VGI-based mapping projects (i.e., collaborative mapping projects). In particular, it lists the challenges relevant to the evolution of VGI as well as to the characteristics of geographic data. Furthermore, this work proposes a guiding approach to enhance the data classification quality in such projects. The proposed approach is based on the following premises (i) the availability of large amounts of data, which fosters applying machine learning techniques to extract useful knowledge, (ii) utilization of the extracted knowledge to guide contributors to appropriate data classification, (iii) the humanitarian spirit of contributors to provide precise data, when they are supported by a guidance system, and (iv) the power of crowdsourcing in data collection as well as in ensuring the data quality. This cumulative dissertation consists of five peer-reviewed publications in international conference proceedings and international journals. The publications divide the disser- tation into three parts the first part presents a comprehensive literature review about the relevant previous work of VGI quality assurance procedures (Chapter 2), the second part studies the foundations of the approach (Chapters 3-4), and the third part discusses the proposed approach and provides a validation example for implementing the approach (Chapters 5-6). Furthermore, Chapter 1 presents an overview about the research ques- tions and the adapted research methodology, while Chapter 7 concludes the findings and summarizes the contributions. The proposed approach is validated through empirical studies and an implemented web application. The findings reveal the feasibility of the proposed approach. The output shows that applying the proposed approach results in enhanced data classification quality. Furthermore, the research highlights the demands for intuitive data collection and data interpretation approaches adequate to VGI-based mapping projects. An interaction data collection approach is required to guide the contributors toward enhanced data quality, while an intuitive data interpretation approach is needed to derive more precise information from rich VGI resources

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

Knowledge and attitudes toward COVID-19 vaccination in Sudan: A cross-sectional study

Background: Vaccines are an essential part of public health interventions to mitigate the devastating health and non-health impacts of COVID-19 pandemic. Despite the fact that Sudan launched the COVID-19 vaccination program in March 2021, only 10% of the population received their two primary doses of vaccines by the end of May 2022. This delayed uptake of vaccines obviously warrants investigation. Therefore, we have conducted this study to evaluate the knowledge, attitude and acceptance of the general population in Sudan toward COVID-19 vaccines. Methodology: A descriptive cross-sectional community-based study. The data were collected using an electronic questionnaire from 403 individuals living in Khartoum, Sudan. The data were processed using the Statistical Package for Social Sciences (SPSS), and data analysis was performed using appropriate tests. Results: 51% of the participants were found to have sufficient knowledge about the COVID-19 vaccine, and the knowledge level is higher among those educated beyond the secondary school and those who were employed. Among those unvaccinated, only 47% of the participants expressed their intention to take the vaccine when offered to them. The major reason for not trusting the vaccine is safety concerns expressed by 65.5% of the unvaccinated. Conclusion: Higher education levels and employment were associated with an increase in sufficient knowledge about the vaccine in around half of the participants. However, most of participants had not taken the vaccine at the time of the study, and the trust in vaccines is not high. Effective interventions by the health authorities are needed to address these issues in order to accelerate the COVID-19 vaccination program in Sudan

Verbesserung der Datenklassifizierungsqualität von Volunteered Geographic Information

Geographic data is one of the fundamental components of any Geographic Information System (GIS). Nowadays, the utility of GIS becomes part of everyday life activities, such as searching for a destination, planning a trip, looking for weather information, etc. Without a reliable data source, systems will not provide guaranteed services. In the past, geographic data was collected and processed exclusively by experts and professionals. However, the ubiquity of advanced technology results in the evolution of Volunteered Geographic Information (VGI), when the geographic data is collected and produced by the general public. These changes influence the availability of geographic data, when common people can work together to collect geographic data and produce maps. This particular trend is known as collaborative mapping. In collaborative mapping, the general public shares an online platform to collect, manipulate, and update information about geographic features. OpenStreetMap (OSM) is a prominent example of a collaborative mapping project, which aims to produce a free world map editable and accessible by anyone. During the last decade, VGI has expanded based on the power of crowdsourcing. The involvement of the public in data collection raises great concern about the resulting data quality. There exist various perspectives of geographic data quality this dissertation focuses particularly on the quality of data classification (i.e., thematic accuracy). In professional data collection, data is classified based on quantitative and/or qualitative ob- servations. According to a pre-defined classification model, which is usually constructed by experts, data is assigned to appropriate classes. In contrast, in most collaborative mapping projects data classification is mainly based on individualsa cognition. Through online platforms, contributors collect information about geographic features and trans- form their perceptions into classified entities. In VGI projects, the contributors mostly have limited experience in geography and cartography. Therefore, the acquired data may have a questionable classification quality. This dissertation investigates the challenges of data classification in VGI-based mapping projects (i.e., collaborative mapping projects). In particular, it lists the challenges relevant to the evolution of VGI as well as to the characteristics of geographic data. Furthermore, this work proposes a guiding approach to enhance the data classification quality in such projects. The proposed approach is based on the following premises (i) the availability of large amounts of data, which fosters applying machine learning techniques to extract useful knowledge, (ii) utilization of the extracted knowledge to guide contributors to appropriate data classification, (iii) the humanitarian spirit of contributors to provide precise data, when they are supported by a guidance system, and (iv) the power of crowdsourcing in data collection as well as in ensuring the data quality. This cumulative dissertation consists of five peer-reviewed publications in international conference proceedings and international journals. The publications divide the disser- tation into three parts the first part presents a comprehensive literature review about the relevant previous work of VGI quality assurance procedures (Chapter 2), the second part studies the foundations of the approach (Chapters 3-4), and the third part discusses the proposed approach and provides a validation example for implementing the approach (Chapters 5-6). Furthermore, Chapter 1 presents an overview about the research ques- tions and the adapted research methodology, while Chapter 7 concludes the findings and summarizes the contributions. The proposed approach is validated through empirical studies and an implemented web application. The findings reveal the feasibility of the proposed approach. The output shows that applying the proposed approach results in enhanced data classification quality. Furthermore, the research highlights the demands for intuitive data collection and data interpretation approaches adequate to VGI-based mapping projects. An interaction data collection approach is required to guide the contributors toward enhanced data quality, while an intuitive data interpretation approach is needed to derive more precise information from rich VGI resources

Strain Behavior of Short Concrete Columns Reinforced with GFRP Spirals

This paper presents a comprehensive study focused on evaluating the strain generated within short concrete columns reinforced with glass-fiber-reinforced polymer (GFRP) bars and spirals under concentric compressive axial loads. This research was motivated by the lack of sufficient data in the literature regarding strain in such columns. Five full-scale RC columns were cast and tested, comprising four strengthened with GFRP reinforcement and one reference column reinforced with steel bars and spirals. This study thoroughly examined the influence of various test parameters, such as the reinforcement type, longitudinal reinforcement ratio, and spacing of spiral reinforcement, on the strain in concrete, GFRP bars, and spirals. The experimental results showed that GFRP–RC columns exhibited similar strain behavior to steel–RC columns up to 85% of their peak loads. The study also highlighted that the bearing capacity of the columns increased by up to 25% with optimized reinforcement ratios and spiral spacing, while the failure mode transitioned from a ductile to a more brittle nature as the reinforcement ratio increased. Additionally, it is preferable to limit the compressive strain in GFRP bars to less than 20% of their ultimate tensile strain and the strain in GFRP spirals to less than 12% of their ultimate strain to ensure the safe and reliable use of these materials in RC columns. This research also considers the prediction of the axial load capacities using established design standards permitting the use of FRP bars in compressive members, namely ACI 440.11-22, CSA-S806-12, and JSCE-97, and underscores their limitations in accurately predicting GFRP–RC columns’ failure capacities. This study proposes an equation to enhance the prediction accuracy for GFRP–RC columns, considering the contributions of concrete, spiral confinement, and the axial stiffness of longitudinal GFRP bars. This equation addresses the shortcomings of existing design standards and provides a more accurate assessment of the axial load capacities for GFRP–RC columns. The proposed equation outperformed numerous other equations suggested by various researchers when employed to estimate the strength of 42 columns gathered from the literature

Guided Classification System for Conceptual Overlapping Classes in OpenStreetMap

The increased development of Volunteered Geographic Information (VGI) and its potential role in GIScience studies raises questions about the resulting data quality. Several studies address VGI quality from various perspectives like completeness, positional accuracy, consistency, etc. They mostly have consensus on the heterogeneity of data quality. The problem may be due to the lack of standard procedures for data collection and absence of quality control feedback for voluntary participants. In our research, we are concerned with data quality from the classification perspective. Particularly in VGI-mapping projects, the limited expertise of participants and the non-strict definition of geographic features lead to conceptual overlapping classes, where an entity could plausibly belong to multiple classes, e.g., lake or pond, park or garden, marsh or swamp, etc. Usually, quantitative and/or qualitative characteristics exist that distinguish between classes. Nevertheless, these characteristics might not be recognizable for non-expert participants. In previous work, we developed the rule-guided classification approach that guides participants to the most appropriate classes. As exemplification, we tackle the conceptual overlapping of some grass-related classes. For a given data set, our approach presents the most highly recommended classes for each entity. In this paper, we present the validation of our approach. We implement a web-based application called Grass&Green that presents recommendations for crowdsourcing validation. The findings show the applicability of the proposed approach. In four months, the application attracted 212 participants from more than 35 countries who checked 2,865 entities. The results indicate that 89% of the contributions fully/partially agree with our recommendations. We then carried out a detailed analysis that demonstrates the potential of this enhanced data classification. This research encourages the development of customized applications that target a particular geographic feature

A review of volunteered geographic information quality assessment methods.

With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information (VGI). During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI

Fast and accurate method for sub‐synchronous oscillation detection

Abstract Detection of sub‐synchronous oscillation (SSO) accurately within a short time poses a big challenge to avoiding the potential risks of the SSO phenomenon. This paper proposes a novel technique for detecting the SSO phenomenon based on identifying the SSO frequency and magnitude rather than applying fast Fourier transform. The voltage magnitude signal is used as an input signal. The SSO magnitude is identified based on estimating the max value of the voltage magnitude signal, whereas the time corresponding to the zero crossings is defined for calculating the SSO frequency. In order to confirm its effectiveness, the proposed technique is tested with different SSO types (induction generator effect, torsional interaction, and sub‐synchronous control interaction). Moreover, the impact of the series compensation level change on the proposed method performance is investigated. The results prove the effectiveness and the speed of the proposed method in all studied cases. Compared to the traditional methods within a wide range of compensation levels, the proposed technique is fast and accurate. The IEEE first benchmark model is adapted with a doubly‐fed induction generator in the MATLAB program to analyze the performance of the proposed method for detecting the SSO phenomenon