Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams

Neogeography is the combination of user generated data and experiences with mapping technologies. In this article we present a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The extracted information should be integrated together to form a collective knowledge about certain domain. This structured information can be used further to help users from the same domain who want to get information using simple question answering system. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

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

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Visualizing Spatio-Temporal data

The amount of spatio-temporal data produced everyday has sky rocketed in the recent years due to the commercial GPS systems and smart devices. Together with this, the need for tools and techniques to analyze this kind of data have also increased. A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. However, most of the existing visualization techniques implement a top-down approach i.e, they require prior knowledge of existing patterns. In this dissertation, I present my novel visualization technique called Storygraph which supports bottom-up discovery of patterns. Since Storygraph presents and integrated view, analysis of events can be done with losing either of time or spatial contexts. In addition, Storygraph can handle spatio-temporal uncertainty making it ideal for data being extracted from text. In the subsequent chapters, I demonstrate the versatility and the effectiveness of the Storygraph along with case studies from my published works. Finally, I also talk about edge bundling in Storygraph to enhance the aesthetics and improve the readability of Storygraph