Beyond data collection: Objectives and methods of research using VGI and geo-social media for disaster management

This paper investigates research using VGI and geo-social media in the disaster management context. Relying on the method of systematic mapping, it develops a classification schema that captures three levels of main category, focus, and intended use, and analyzes the relationships with the employed data sources and analysis methods. It focuses the scope to the pioneering field of disaster management, but the described approach and the developed classification schema are easily adaptable to different application domains or future developments. The results show that a hypothesized consolidation of research, characterized through the building of canonical bodies of knowledge and advanced application cases with refined methodology, has not yet happened. The majority of the studies investigate the challenges and potential solutions of data handling, with fewer studies focusing on socio-technological issues or advanced applications. This trend is currently showing no sign of change, highlighting that VGI research is still very much technology-driven as opposed to theory- or application-driven. From the results of the systematic mapping study, the authors formulate and discuss several research objectives for future work, which could lead to a stronger, more theory-driven treatment of the topic VGI in GIScience.Carlos Granell has been partly funded by the Ramón y Cajal Programme (grant number RYC-2014-16913

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

Objectives: Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens’ role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics. Methods: Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively. Results: Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks. Conclusions: For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring

Crowdsourced Data Mining for Urban Activity: A Review of Data Sources, Applications and Methods

The penetration of devices integrated with location-based services and internet services has generated massive data about the everyday life of citizens and tracked their activities happening in cities. Crowdsourced data, such as social media data, POIs data and collaborative websites, generated by the crowd, has become fine-grained proxy data of urban activity and widely used in research in urban studies. However, due to the heterogeneity of data types of crowdsourced data and the limitation of previous studies mainly focusing on a specific application, a systematic review of crowdsourced data mining for urban activity is still lacking. In order to fill the gap, this paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on those papers, the review firstly conducts a bibliometric analysis identifying underpinning domains, pivot scholars and papers around this topic. The review also synthesises previous research into three parts: main applications of different data sources and data fusion; application of spatial analysis in mobility patterns, functional areas and event detection; application of socio-demographic and perception analysis in city attractiveness, demographic characteristics and sentiment analysis. The challenges of this type of data are also discussed in the end. This study provides a systematic and current review for both researchers and practitioners interested in the applications of crowdsourced data mining for urban activity.This research is funded by a scholarship from the China Scholarship Counci

Quantifying the relationship between public sentiment and urban environment in Barcelona

Public sentiment provides an important social reference for urban management and planning. The relationship between public sentiment and a single type of land use has yielded stable results in previous studies. Hitherto, there has been relatively little research on the correlation of the entire urban environment with public sentiment. Based on the unit of statistical area in Barcelona city, this research uses Twitter sentiment to represent public sentiment and develops a regression model for understanding the interrelationship of four layers: sociodemographic, built-environment, human mobility and socioeconomic activities. The result shows that: 1) The long-term spatial difference in public sentiment has correlations with the urban environment, though it is not decisive. 2) Regardless of disruptive events that are directly associated with public sentiments, the wealthier areas show a more positive correlation with higher public sentiment. 3) The distribution of sentiment tweets (non-neutral) has a close relationship with places where there is a high flow of human activities. This study contributes to the systematic literature of urban applications of sentiment analysis with new empirical observations and a transferable methodology

Advancing Science with VGI: Reproducibility and Replicability of Recent Studies using VGI

In scientific research, reproducibility and replicability are requirements to ensure the advancement of our body of knowledge. T his holds true also for VGI - related research and studies. However, the characteristics of VGI suggest particular difficulties in ensuring reproducibility and replicability . In this paper, we aim to examine the current situation in VGI - related research , and identify strategies to ensure realization of its full potential. To do so, we first investigate the different aspects of reprod ucibility and replicability and their impact on VGI - related research . These impacts are different depending on the objectives of the study. Therefore , we examine the study focus of VGI - related research to assess the current body of research and structure o ur assessment . Th is work is based on a rigorous review of the elements of reproducibility and a systematic mapping and analysis of 58 papers on the use of VGI in the crisis management field. Results of our investigation show that reproducibility issues related to data are a serious concern , while reproducibility issues related to analysis methods and processes face fewer challenges. Howe ver, since most studies still focus on analyzing the source data, reproducibility and replicability are still an unsolved problem in VGI - related research. Therefore, we show initiative s tackling the problem, and finally formulate strategies to improve the situatio

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies