Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Identifying success factors in crowdsourced geographic information use in government

Crowdsourcing geographic information in government is focusing on projects that are engaging people who are not government officials and employees in collecting, editing and sharing information with governmental bodies. This type of projects emerged in the past decade, due to technological and societal changes - such as the increased use of smartphones, combined with growing levels of education and technical abilities to use them by citizens. They also flourished due to the need for updated data in relatively quick time when financial resources are low. They range from recording the experience of feeling an earthquake to recording the location of businesses during the summer time. 50 cases of projects in which crowdsourced geographic information was used by governmental bodies across the world are analysed. About 60% of the cases were examined in 2014 and in 2017, to allow for comparison and identification of success and failure. The analysis looked at different aspects and their relationship to success: the drivers to start a project; scope and aims; stakeholders and relationships; inputs into the project; technical and organisational aspect; and problems encountered. The main key factors of the case studies were analysed with the use of Qualitative Comparative Analysis (QCA) which is an analytical method that combines quantitative and qualitative tools in sociological research. From the analysis, we can conclude that there is no “magic bullet” or a perfect methodology for a successful crowdsourcing in government project. Unless the organisation has reached maturity in the area of crowdsourcing, identifying a champion and starting a project that will not address authoritative datasets directly is a good way to ensure early success and start the process of organisational learning on how to run such projects. Governmental support and trust is undisputed. If the choice is to use new technologies, this should be accompanied by an investment of appropriate resources within the organisation to ensure that the investment bear fruits. Alternatively, using an existing technology that was successful elsewhere and investing in training and capacity building is another path for success. We also identified the importance of intermediary Non-Governmental Organizations (NGOs) with the experience and knowledge in working with crowdsourcing within a partnership. These organizations have the knowledge and skills to implement projects at the boundary between government and the crowd, and therefore can offer the experience to ensure better implementation. Changes and improvement of public services, or a focus on environmental monitoring can be a good basis for a project. Capturing base mapping is a good point to start, too. The recommendation of the report address organisational issues, resources, and legal aspects

Sequential assimilation of crowdsourced social media data into a simplified flood inundation model

Flooding is the most common natural hazard worldwide. Severe floods can cause significant damage and sometimes loss of life. During a flood event, hydraulic models play an important role in forecasting and identifying potential inundated areas, where emergency responses should be deployed. Nevertheless, hydraulic models are not able to capture all of the processes in flood propagation because flood behaviour is highly dynamic and complex. Thus, there are always uncertainties associated with model simulations. As a result, near-real time observations are required to incorporate with hydraulic models to improve model forecasting skills. Crowdsourced (CS) social media data presents an opportunity for supporting urban flood management as it can provide insightful information collected by individuals in near real-time. In this thesis, approachesto maximise the impact of CS social media data (Twitter) to reduce uncertainty in flood inundation modelling (LISFLOOD-FP) through data assimilation were investigated. The developed methodologies were tested and evaluated using a real flooding case study of Phetchaburi city, Thailand. Firstly, two approaches (binary logistic regression and fuzzy logic) were developed based on Twitter metadata and spatiotemporal analysis to assess the quality of CS social media data. Both methods produced good results, but the binary logistic model was preferred as it involved less subjectivity. Next, the generalized likelihood uncertainty estimation methodology was applied to estimate model uncertainty and identify behavioural parameter ranges. Particle swarm optimisation was also carried out to calibrate for an optimum model parameter set. Following this, an ensemble Kalman filter was applied to assimilate the flood depth information extracted from the CS data into the LISFLOOD-FP simulations using various updating strategies. The findings show that the global state update suffers from inconsistency of predicted water levels due to overestimating the impact of the CS data, whereas a topography based local state update provides encouraging results as the uncertainty in model forecasts narrows, albeit for a short time period. To extend the improvement time span, a combination of state and boundary updating was further investigated to correct both water levels and model inputs, and was found to produce longer lasting improvements in terms of uncertainty reduction. Overall, the results indicate the feasibility of applying CS social media data to reduce model uncertainty in flood forecasting

Demonstrating the potential of Picture Pile as a citizen science tool for SDG monitoring

The SDGs are a universal agenda to address the world’s most pressing societal, environmental and economic challenges. The supply of timely, relevant and reliable data is essential in guiding policies and decisions for successful implementation of the SDGs. Yet official statistics cannot provide all of the data needed to populate the SDG indicator framework. Citizen science offers a novel solution and an untapped opportunity to complement traditional sources of data, such as household surveys, for monitoring progress towards the SDGs, while at the same time mobilizing action and raising awareness for their achievement. This paper presents the potential offered by one specific citizen science tool, Picture Pile, to complement and enhance official statistics to monitor several SDGs and targets. Designed to be a generic and flexible tool, Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. The results show that Picture Pile could contribute to the monitoring of fifteen SDG indicators under goals 1, 2, 11, 13, 14 and 15 based on the Picture Pile campaigns undertaken to date. Picture Pile could also be modified to support other SDGs and indicators in the areas of ecosystem health, eutrophication and built-up areas, among others. In order to leverage this particular tool for SDG monitoring, its potential must be showcased through the development of use cases in collaboration with governments, NSOs and relevant custodian agencies. Additionally, mutual trust needs to be built among key stakeholders to agree on common goals that would facilitate the use of Picture Pile or other citizen science tools and data for SDG monitoring and impact

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

Empowering Citizen Science: A Generic Data Collection Framework

Citizen Science (CS) is collaboration between scientists and citizens to expand opportunities for scientific data collection and problem solving. Recent advancements such as the Internet, social networks and smart devices have created a technological platform for CS to engage more citizens to work on a wide range of scientific problems. Due to technical, financial and management resource constraints many organisations struggle to develop effective tools to collect scientific data in CS projects. A robust web and mobile interface for scientific data collection will ensure collection of higher quality scientific data. While web and mobile applications have been developed for some CS projects many CS projects are hindered by the complexity and intrinsic costs of implementing these applications. This thesis describes a web-based model for CS data collection suitable for both small CS communities and larger scientific organisations. Offering features commonly used in CS projects, this model reduces costs associated with software implementation and management in CS. A CS campaign is undertaken as a case study that validates our model in a real world scenario. Overall the generic data collection framework presented will empower communities and organisations to engage and use CS in more ways and on large scales

Empowering Citizen Science: A Generic Data Collection Framework

Citizen Science (CS) is collaboration between scientists and citizens to expand opportunities for scientific data collection and problem solving. Recent advancements such as the Internet, social networks and smart devices have created a technological platform for CS to engage more citizens to work on a wide range of scientific problems. Due to technical, financial and management resource constraints many organisations struggle to develop effective tools to collect scientific data in CS projects. A robust web and mobile interface for scientific data collection will ensure collection of higher quality scientific data. While web and mobile applications have been developed for some CS projects many CS projects are hindered by the complexity and intrinsic costs of implementing these applications. This thesis describes a web-based model for CS data collection suitable for both small CS communities and larger scientific organisations. Offering features commonly used in CS projects, this model reduces costs associated with software implementation and management in CS. A CS campaign is undertaken as a case study that validates our model in a real world scenario. Overall the generic data collection framework presented will empower communities and organisations to engage and use CS in more ways and on large scales

Crowdsourced Geographic Information Use in Government

This report is based on a six-month study of the use of volunteered geographic information (VGI) by government. It focuses on government use of information relating to a location, which was produced through what is known as “crowdsourcing”, the process of obtaining information from many contributors amongst the general public, regardless of their background and skill level. The aim of this report is to provide a guide for the successful implementation of VGI in government