Geospatial crowdsourced data fitness analysis for spatial data infrastructure based disaster management actions

The reporting of disasters has changed from official media reports to citizen reporters who are at the disaster scene. This kind of crowd based reporting, related to disasters or any other events, is often identified as 'Crowdsourced Data' (CSD). CSD are freely and widely available thanks to the current technological advancements. The quality of CSD is often problematic as it is often created by the citizens of varying skills and backgrounds. CSD is considered unstructured in general, and its quality remains poorly defined. Moreover, the CSD's location availability and the quality of any available locations may be incomplete. The traditional data quality assessment methods and parameters are also often incompatible with the unstructured nature of CSD due to its undocumented nature and missing metadata. Although other research has identified credibility and relevance as possible CSD quality assessment indicators, the available assessment methods for these indicators are still immature. In the 2011 Australian floods, the citizens and disaster management administrators used the Ushahidi Crowd-mapping platform and the Twitter social media platform to extensively communicate flood related information including hazards, evacuations, help services, road closures and property damage. This research designed a CSD quality assessment framework and tested the quality of the 2011 Australian floods' Ushahidi Crowdmap and Twitter data. In particular, it explored a number of aspects namely, location availability and location quality assessment, semantic extraction of hidden location toponyms and the analysis of the credibility and relevance of reports. This research was conducted based on a Design Science (DS) research method which is often utilised in Information Science (IS) based research. Location availability of the Ushahidi Crowdmap and the Twitter data assessed the quality of available locations by comparing three different datasets i.e. Google Maps, OpenStreetMap (OSM) and Queensland Department of Natural Resources and Mines' (QDNRM) road data. Missing locations were semantically extracted using Natural Language Processing (NLP) and gazetteer lookup techniques. The Credibility of Ushahidi Crowdmap dataset was assessed using a naive Bayesian Network (BN) model commonly utilised in spam email detection. CSD relevance was assessed by adapting Geographic Information Retrieval (GIR) relevance assessment techniques which are also utilised in the IT sector. Thematic and geographic relevance were assessed using Term Frequency – Inverse Document Frequency Vector Space Model (TF-IDF VSM) and NLP based on semantic gazetteers. Results of the CSD location comparison showed that the combined use of non-authoritative and authoritative data improved location determination. The semantic location analysis results indicated some improvements of the location availability of the tweets and Crowdmap data; however, the quality of new locations was still uncertain. The results of the credibility analysis revealed that the spam email detection approaches are feasible for CSD credibility detection. However, it was critical to train the model in a controlled environment using structured training including modified training samples. The use of GIR techniques for CSD relevance analysis provided promising results. A separate relevance ranked list of the same CSD data was prepared through manual analysis. The results revealed that the two lists generally agreed which indicated the system's potential to analyse relevance in a similar way to humans. This research showed that the CSD fitness analysis can potentially improve the accuracy, reliability and currency of CSD and may be utilised to fill information gaps available in authoritative sources. The integrated and autonomous CSD qualification framework presented provides a guide for flood disaster first responders and could be adapted to support other forms of emergencies

Economic resilience and crowdsourcing platforms

The increased interdependence and complexity of modern societies have increased the need to involve all members of a community into solving problems. In times of great uncertainty, when communities face threats of different kinds and magnitudes, the traditional top-down approach where government provides solely for community wellbeing is no longer plausible. Crowdsourcing has emerged as an effective means of empowering communities with the potential to engage individuals in innovation, self-organization activities, informal learning, mutual support, and political action that can all lead to resilience. However, there remains limited resource on the topic. In this paper, we outline the various forms of crowdsourcing, economic and community resilience, crowdsourcing and economic resilience, and a case study of the Nepal earthquake. his article presents an exploratory perspective on the link can be found between crowdsourcing and economic resilience. It introduces and describes a framework that can be used to study the impact of crowdsourcing initiatives for economic resilience by future research. An initial a set of indicators to be used to measure the change in the level of resilience is presented.info:eu-repo/semantics/publishedVersio

Citizen Science for Citizen Access to Law

This papers sits at the intersection of citizen access to law, legal informatics and plain language. The paper reports the results of a joint project of the Cornell University Legal Information Institute and the Australian National University which collected thousands of crowdsourced assessments of the readability of law through the Cornell LII site. The aim of the project is to enhance accuracy in the prediction of the readability of legal sentences. The study requested readers on legislative pages of the LII site to rate passages from the United States Code and the Code of Federal Regulations and other texts for readability and other characteristics. The research provides insight into who uses legal rules and how they do so. The study enables conclusions to be drawn as to the current readability of law and spread of readability among legal rules. The research is intended to enable the creation of a dataset of legal rules labelled by human judges as to readability. Such a dataset, in combination with machine learning, will assist in identifying factors in legal language which impede readability and access for citizens. As far as we are aware, this research is the largest ever study of readability and usability of legal language and the first research which has applied crowdsourcing to such an investigation. The research is an example of the possibilities open for enhancing access to law through engagement of end users in the online legal publishing environment for enhancement of legal accessibility and through collaboration between legal publishers and researchers

StarBorn : Towards making in-situ land cover data generation fun with a location-based game

University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD

Mobile devices compatibility testing strategy via crowdsourcing

Purpose - This paper aims to support small mobile application development teams or companies performing testing on a large variety of operating systems versions and mobile devices to ensure their seamless working. Design/methodology/approach - This paper proposes a “hybrid crowdsourcing” method that leverages the power of public crowd testers. This leads to generating a novel crowdtesting workflow Developer/Tester- Crowdtesting (DT-CT) that focuses on developers and crowd testers as key elements in the testing process without the need for intermediate as managers or leaders. This workflow has been used in a novel crowdtesting platform (AskCrowd2Test). This platform enables testing the compatibility of mobile devices and applications at two different levels, high-level (device characteristics) or low-level (code). Additionally, a “crowd-powered knowledge base” has been developed that stores testing results, relevant issues and their solutions. Findings - The comparison of the presented DT-CT workflow with the common and most recent crowdtesting workflows showed that DT-CT may positively impact the testing process by reducing time-consuming and budget spend because of the direct interaction of developers and crowd testers. Originality/value - To authors’ knowledge, this paper is the first to propose crowdtesting workflow based on developers and public crowd testers without crowd managers or leaders, which light the beacon for the future research in this field. Additionally, this work is the first that authorizes crowd testers with a limited level of experience to participate in the testing process, which helps in studying the behaviors and interaction of end-users with apps and obtains more concrete results

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