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

Automating Global Geospatial Data Set Analysis : Visualizing flood disasters in the cities of the Global South

Flooding is the most devastating natural hazard affecting tens of millions of people yearly and causing billions of USD dollars in damages globally. The people most affected by flooding globally are those with a high level of everyday vulnerability and limited resources for flood protection and recovery. Geospatial data from the Global South is severely lacking, and geospatial proficiency needs to be improved at a local level so that geospatial data and data analysis can be efficiently utilized in disaster risk reduction schemes and urban planning in the Global South. This thesis focuses on the use of automated global geospatial dataset analysis in disaster risk reduction in the Global South by using the Python programming language to produce an automated flood analysis and visualization model. In this study, the automated model was developed and tested in two, highly relevant cases: in the city of Bangkok, Thailand, and in the urban area of Tula de Allende, Mexico. The results of the thesis show that with minimal user interaction, the automated flood model ingests flood extent and depth data produced by ICEYE, a global population estimation raster produced by the German Aerospace Agency (DLR) and OpenStreetMap (OSM) data, performs multiple relevant analyses of these data, and produces an interactive map highlighting the severity and effects of a flooding event. The automated flood model performs consistently and accurately while producing key statistics and standardized visualizations of flooding events which offers first responders a very fast first estimation of the scale of a flooding event and helps plan an appropriate response anywhere around the globe. Global geospatial data sets are often created to examine large scale geographical phenomena; however, the results of this thesis show that they can also be used to analyze detailed local-level phenomena when paired together with supporting data. The advantage of using global geospatial data sets is that when sufficiently accurate and precise, they remove the most time-consuming part of geospatial analysis: finding suitable data. Fast reaction is of utmost importance in the first hours of a natural hazard like flooding, thus, automated analysis produced on a global scale could significantly help international humanitarian aid and first responders. Using an automated model also standardizes the results removing human errors and interpretation from the results enabling the accurate comparison of historical flood data in due time.Tulvat ovat luonnonilmiöihin liittyvistä riskeistä tuhoisimpia, ja ne vaikuttavat kymmeniin miljooniin ihmisiin vuosittain sekä aiheuttavat miljardien dollarien vahingot maailmanlaajuisesti. Tulvista kärsivät usein maailmanlaajuisesti ne ihmiset, jotka ovat jo ennestään haavoittuvia ja joilla on suhteellisesti heikoimmat keinot suojautua tulvilta ja selviytyä tulvan aiheuttamista tuhoista. Monissa globaalin etelän maissa on niukasti paikkatietoaineistoa ja paikkatieto-osaamista on syytä lisätä erityisesti paikallisella tasolla, jotta paikkatietoaineistoa ja analyysin hyödynnettävyyttä voidaan parantaa katastrofiriskien vähentämissuunnitelmissa sekä kaupunkisuunnittelussa globaalissa etelässä. Tämä opinnäytetyö keskittyy automatisoidun globaalin paikkatietoaineiston analyysin hyödyntämiseen katastrofiriskien vähentämisessä globaalissa etelässä käyttämällä Python-ohjelmointikieltä automatisoidun tulva-analyysi- ja visualisointimallin tuottamiseen. Tässä tutkimuksessa automatisoitua mallia kehitettiin ja testattiin kahdessa tulvariskien kannalta erittäin relevantissa tapauksessa: Bangkokissa, Thaimaassa ja Tula de Allende:n kaupunkialueella, Meksikossa. Tämän tutkielman tulokset osoittavat, että automatisoitu tulvamalli osaa lukea ICEYE:n tuottaman tulvan laajuus- ja syvyysaineiston, Saksan ilmailu- ja avaruuskeskuksen (DLR) tuottaman maailmanlaajuisen väestönarviorasterin, sekä OpenStreetMap (OSM) -aineiston, suorittaa aineistolle tulvan tuhojen tulkinnan kannalta olennaisia analyyseja, ja tuottaa lopputuloksena interaktiivisen kartan, joka korostaa tulvatapahtuman laajuutta ja vaikutuksia. Automatisoitu tulvamalli toimii johdonmukaisesti ja tuottaa tilastoja sekä standardoituja visualisointeja tulvatapahtumista, mikä tarjoaa ensivastehenkilöille erittäin nopean ensimmäisen arvion tulvatapahtuman laajuudesta. Tämä auttaa kohdentamaan pelastustoimenpiteitä riskitilanteessa vaihtelevissa ympäristöissä eri puolilla maailmaa. Globaalit paikkatietoaineistot luodaan usein laajojen maantieteellisten ilmiöiden tutkimiseen, mutta tämän tutkielman tulokset osoittavat kuitenkin, että niillä voidaan analysoida myös hyvin paikallistason ilmiöitä, kun ne yhdistetään muihin relevantteihin tietolähteisiin. Globaalien paikkatietoaineistojen käytön etuna on, että ollessaan riittävän tarkkoja ne poistavat paikkatietoanalyysin aikaa vievimmän osan: sopivan tiedon löytämisen. Nopea reagointi on äärimmäisen tärkeää luonnonuhkien, kuten tulvien, ensimmäisinä tunteina ja kansainvälisen humanitaarisen avun ja ensivastetoimijoiden tulisi hyödyntää maailmanlaajuisia automatisoituja analyysejä. Automaattinen malli myös standardoi tulokset poistaen tuloksista inhimilliset virheet ja tulkinnat, mikä mahdollistaa historiallisten tulvatietojen tarkan vertailun

Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Developing a Framework for Stigmergic Human Collaboration with Technology Tools: Cases in Emergency Response

Information and Communications Technologies (ICTs), particularly social media and geographic information systems (GIS), have become a transformational force in emergency response. Social media enables ad hoc collaboration, providing timely, useful information dissemination and sharing, and helping to overcome limitations of time and place. Geographic information systems increase the level of situation awareness, serving geospatial data using interactive maps, animations, and computer generated imagery derived from sophisticated global remote sensing systems. Digital workspaces bring these technologies together and contribute to meeting ad hoc and formal emergency response challenges through their affordances of situation awareness and mass collaboration. Distributed ICTs that enable ad hoc emergency response via digital workspaces have arguably made traditional top-down system deployments less relevant in certain situations, including emergency response (Merrill, 2009; Heylighen, 2007a, b). Heylighen (2014, 2007a, b) theorizes that human cognitive stigmergy explains some self-organizing characteristics of ad hoc systems. Elliott (2007) identifies cognitive stigmergy as a factor in mass collaborations supported by digital workspaces. Stigmergy, a term from biology, refers to the phenomenon of self-organizing systems with agents that coordinate via perceived changes in the environment rather than direct communication. In the present research, ad hoc emergency response is examined through the lens of human cognitive stigmergy. The basic assertion is that ICTs and stigmergy together make possible highly effective ad hoc collaborations in circumstances where more typical collaborative methods break down. The research is organized into three essays: an in-depth analysis of the development and deployment of the Ushahidi emergency response software platform, a comparison of the emergency response ICTs used for emergency response during Hurricanes Katrina and Sandy, and a process model developed from the case studies and relevant academic literature is described

Real-time social media sentiment analysis for rapid impact assessment of floods

Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin