Challenging problems of geospatial visual analytics

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From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

Detecting spatial features from data-maps: The visual intersection of data as support to decision-making

The assessment of spatial systems can be supported by the analysis of data coming from different sources and describing different aspects such as economic, social, environmental, energy, housing or mobility issues. Nevertheless, the analysis of such a large amount of data is difficult. In order to improve the readability of data also with non-technicians, new methods of communication are needed, which could facilitate the sharing of information among people with different skills and backgrounds. In this context, the paper shows the developments in geo-visualisation to support and improve the processes of planning and decision-making. First, the use of a map-based visualisation is suitable for intuitively understanding the location and distribution of specific elements. Second, the graphic interface can be used to drive users in the investigation of data. It can provide a linear method that is more comprehensive to the human mind in dealing with the complexity of spatial systems. In addition, the possibility to select and filter data by single attributes allows databases to be explored interactively and read by differently skilled users. The intersection and overlapping of information enables users to discover the relationships between data, the inefficiencies and critical areas, thus providing suggestions for further reasoning in planning and decision-making. Furthermore, collaborative and participatory sessions require quick answers and simple readability. Thus, the real time response to simple queries widens the opportunities for improving the discussion. A case study describes the methodology used for sharing the data collected during an Interreg IVB NWE Project named “CoDe24” (INTERREG IVB NWE, 2005; ERDF European Territorial Cooperation 2007-2013, 2010). By the use of a web-GIS visualisation tool, namely GISualisation, the project partnership was allowed to explore the data concerning the railways and train typologies along the Genoa-Rotterdam corridor. Despite the high factor of usability of the tool, it was not employed much by participants to the project so that further reasoning is needed to evaluate how digital tools are perceived by professionals

The Possibility of Big Data Spatio-Temporal Analytics for Understanding Human Behavior and Their Spatial Patterns in Urban Area

13301甲第4630号博士（学術）金沢大学博士論文要旨Abstrac

A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Publisher Copyright: © 2022 by the authors.Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, weargue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.Peer reviewe

Presentation of data mining applications in web based geovisual analytical environment: Example of COVID-19 vaccine tweets

Mekânsal görsel analitik, mekânsal bilgilerin etkileşimli görsel ara yüzlerle ele alındığı analitik akılyürütme bilimidir. Mekânsal görsel analitik sistemleri sayesinde, Twitter gibi sosyal medyaplatformlarındaki büyük veri setlerinden bir konu hakkında elde edilen veriler son kullanıcıya etkileşimliharitalama sistemleriyle sunulabilir. 11 Mart 2020’de Dünya Sağlık Örgütü’nün COVID-19 salgınınıduyurmasının ardından Twitter veri trafiğinde de ciddi bir artış görülmüştür. Bu çalışmada, COVID-19salgını döneminin önemli tartışmalarından biri olan COVID-19 aşıları hakkındaki tweet trafiğininzamansal ve mekânsal gelişimi veri madenciliği teknikleriyle incelenmiş ve görsel analitik ortamdasunulmuştur. Bu çalışma ile twitter gibi sosyal medya platformlarının sahip olduğu büyük veri olarakkabul edilen veri setlerinin veri madenciliği yöntemleriyle analiz edilerek afet ve kriz yönetimi açısındanönemli çıkarımlar yapılabileceği ortaya konmuştur.Spatial visual analytics is the science of analytical reasoning in which spatial information is handled with interactive visual interfaces. Thanks to spatial visual analytics systems, data obtained from large data sets on social media platforms such as Twitter can be presented to the end user with interactive mapping systems. After the World Health Organization announced the COVID-19 outbreak on March 11, 2020, there has been a significant increase in Twitter data traffic. In this study, the temporal and spatial development of tweet traffic about COVID-19 vaccines, which is one of the important discussions of the COVID-19 epidemic period, was examined with data mining techniques and presented in a visual analytical environment. With this study, it has been revealed that important inferences can be made in terms of disaster and crisis management by analyzing the data sets, which are accepted as big data, of social media platforms such as twitter with data mining methods

GEO-VISUALISATION AND VISUAL ANALYTICS FOR SMART CITIES: A SURVEY

Geo-Visualisation (GV) and Visual Analytics (VA) of geo-spatial data have become a focus of interest for research, industries, government and other organisations for improving the mobility, energy efficiency, waste management and public administration of a smart city. The geo-spatial data requirements, increasing volumes, varying formats and quality standards, present challenges in managing, storing, visualising and analysing the data. A survey covering GV and VA of the geo-spatial data collected from a smart city helps to portray the potential of such techniques, which is still required. Therefore, this survey presents GV and VA techniques for the geo-spatial urban data represented in terms of location, multi-dimensions including time, and several other attributes. Further, the current study provides a comprehensive review of the existing literature related to GV and VA from cities, highlighting the important open white spots for the cities’ geo-spatial data handling in term of visualisation and analytics. This will aid to get a better insight into the urban system and enable sustainable development of the future cities by improving human interaction with the geo-spatial data