Effect of Geospatial Uncertainty Borderization on Users’ Heuristic Reasoning

A crucial step in measuring the resilience of railway infrastructure is to quantify the extent of its vulnerability to natural hazards. In this paper, we analyze the vulnerability of the German railway network to four types of natural hazards that regularly cause disruptions in German rail operations: floods, mass movements, slope fires, and tree falls. Using daily train traffic data matched with various data on disruptive events, we quantify the extent to which these four types of natural hazard reduce daily train traffic volumes. With a negative binomial count data regression, we find evidence that the track segments of the German railway network are most vulnerable to floods, followed by mass movements and tree-fall events. On average, floods reduce traffic on track segments by 19% of the average daily train traffic, mass movements by 16%, and tree fall by 4%. Moreover, when more than one type of natural hazard affects the track segment on the same day, train traffic on that segment falls by 34% of the average train traffic. Slope fires have an ambiguous and nonrobust effect on train traffic due to the reverse causality due to its triggering factors. This is the first study that attempts to rank different natural hazards according to their impact on railway traffic. The results have implications for the selection of resilience strategy and can help prioritize policy measures.

Digital geoscientific and geospatial datasets are rapidly growing in both number and size. These data present powerful new resources for understanding the evolution of the earth, but working with them requires computational skills are not part of typical geoscience curricula at universities. To leverage the power of these growing geoscientific and geospatial data, students need targeted educational resources that provide basic computational skills.The new textbook Introduction to Python for Geographic Data Analysis provides a framework for learning to work with (geospatial) datasets of varying size from loading the data to producing interactive visualizations of processed data. Part 1 of the book covers the basics of programming using the Python language, introducing both programming concepts and their Python syntax. It also covers the analysis of tabular data using the pandas Python library and the basics of data visualization. Part 2 introduces working with geospatial data, including fundamental geospatial concepts, working with vector and raster data, geospatial data visualization, and loading data from online sources. Part 3 includes several case studies that build on things presented in the first two parts to demonstrate what can be done with the readers’ new skills. Finally, the appendices provide information about best practices in programing, version control with git and GitHub, and other practical coding tips that promote open, reproducible science.The book materials are freely available online at https://pythongis.org, and we anticipate that hard copies of the book will be available later in 2026. We hope the book will appeal to a broad range of “geo” scientists, including teachers who provide courses on introductory programming or data analysis for geology and geography students, those interested in learning to interact with and batch process large datasets, and those interested in finding open-source alternatives to commercial GIS software packages.

Prototyping the visualization of geographic and sensor data for agriculture

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets.In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts.By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.

Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open databases containing a lot of facts and numbers. However, most of them do not offer a concise and insightful data interpretation or visualization tool, which can help users to process all of the information in a consistently comparable way. Canadian Federal and Provincial Elections is an example of these databases. This information exists in numerous websites, as separate tables so that the user needs to traverse through a tree structure of scattered information on the site, and the user is left with the comparison, without providing proper tools, data-interpretation or visualizations. In this paper, we provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges. We hope that the methodology used here can help in developing similar tools to achieve some of the goals of publicly available datasets. The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the data through various techniques, including Geospatial visualization. To reproduce the results, we have open-sourced the tool.

The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.

Geomatics techniques can support management and recovery activities in the aftermath of man-made and natural disasters. However, disaster management also poses big challenges related to every stage of the geo-information cycle, i.e., data acquisition, processing, management, and distribution. The sixth edition of the International Symposium on Geo-information for Disaster Management (Gi4DM) did bring together—for the sixth time—researchers, developers, data providers, and users from all over the world to discuss these challenges. Gi4DM 2010 was jointly organized by ASITA (Federazione Italiana delle Associazioni Scientifiche per le Informazioni Territoriali e Ambientali) and ISPRS (International Society for Photogrammetry and Remote Sensing). ASITA is the Italian Federation of the Scientific Associations devoted to environmental information. The federation is composed of four different associations that are involved in acquisition, processing, management, and visualization of geospatial data. Specifically, the aforementioned associations are: SIFET, Societa Italiana di Fotogrammetria e Topografia (photogrammetry and topography), AIC, Associazione Italiana di Cartografia (mapping), AIT, Associazione Italiana di Telerilevamento (remote sensing), AM/FM/GIS Italia, Automated Mapping/ Facilities Management/Geographic Information Systems/Italia (GIS). ISPRS is an international NGO devoted to the development of international cooperation for the advancement of knowledge, research, development, and education in the photogrammetry, remote sensing, and spatial information sciences. It aims to contribute to the well-being of humanity and sustainability of the environment especially by means of integration and application of geomatics techniques. As far as the Gi4DM conference is concerned, the program was arranged as follows:

The objective of this study is implementation of system architecture for collecting and analysing data as well as visualizing results for hydrodynamic modelling of flood flows in river valleys using remote sensing methods, tree-dimensional geometry of spatial objects and GPU multithread processing. The proposed solution includes: spatial data acquisition segment, data processing and transformation, mathematical modelling of flow phenomena and results visualization. Data acquisition segment was based on aerial laser scanning supplemented by images in visible range. Vector data creation was based on automatic and semiautomatic algorithms of DTM and 3D spatial features modelling. Algorithms for buildings and vegetation geometry modelling were proposed or adopted from literature. The implementation of the framework was designed as modular software using open specifications and partially reusing open source projects. The database structure for gathering and sharing vector data, including flood modelling results, was created using PostgreSQL. For the internal structure of feature classes of spatial objects in a database, the CityGML standard was used. For the hydrodynamic modelling the solutions of Navier-Stokes equations in two-dimensional version was implemented. Visualization of geospatial data and flow model results was transferred to the client side application. This gave the independence from server hardware platform. A real-world case in Poland, which is a part of Widawa River valley near Wroclaw city, was selected to demonstrate the applicability of proposed system.

Natural resources are in a constant state of depletion. The main reason being over exploitation of these resources and not contributing to their replenishment. To deal with these challenges, ecologists, leaders and experts have recommended the adoption of an agroforestry agriculture system. Agroforestry is more sustainable agricultural method that provides a long term vision to combat food insecurity. In spite of agroforestry being very useful, still individuals can not foresee it’s advantages. Serious games can motivate people in agroforestry scenarios and involve users in high interaction. GIS is a tool that can help in decision of proper location for agroforestry depending upon past data or information available. This paper defines a conceptual study for serious game to dynamically create 3D real-world agro-forestry environments. The serious games aim to provide visualization, intuitive management, and analysis of geospatial, hydrological and economic data to help participants for the decision support in agroforestry systems.

Abstract. As 3D visualisation of geospatial data has become more prominent over the last decade within the Mapping and Geospatial communities, they have begun to use various techniques and processes that have previously been used by the Simulation community. In the reverse the Simulation community has progressively been making wider use of rich sources of geospatial data as the basis of terrain models. This has partly been facilitated by a continued development of processing ability of computers enabling the 3D display of geospatial data on a broader range of devices including mobile platforms.In the past the Geospatial and Simulation communities have worked relatively independent from each other. This has subsequently led the Simulation community to manage geospatial data so that it would support their applications in the generation of terrains in both 2D and 3D.The study will look at the two fields and how they have come together over the years. It will also consider how the evolution of standards are having an impact on the two communities and the trends that are happening within 3D mapping and simulation that are influencing and driving these changes.Lessons learned from the Simulation community and how its work can help in a range of topics including generalisation in visualisation of data, will be given. The study will be able to offer views and perspectives of 3D visualisation in mapping presented by someone who has a Geospatial background and spent a decade using geospatial knowledge to support simulation research and has worked on generating 3D virtual representations and exploited the output for a range of purposes. Details of how the two communities can work together in the future to benefit from a mutual understanding of each other’s needs and requirements will also be given.

We present a data visualization approach that allows users to interactively analyse sequential movement patterns and automatically extract rules for marine animal behaviour. This enhances the ability of oceanic experts to understand marine ecosystems. Traditional time-series analysis, basic statistical methods, and single-view visualizations often fail to capture the intricate spatial and temporal dynamics of marine ecosystems. Our visual analytics approach, that leverages sequential pattern mining and rule mining techniques, integrates SPMF's Vertical Mining of Maximal Sequential Patterns (VMSP) algorithm and T-Rule Growth algorithm to efficiently extract and visualize movement patterns and behavioural rules of marine animals. The system also incorporates automated pre-processing to clean and prepare large datasets, reducing the time and effort required for manual handling. The visualization presents oceanic data from detection stations, allowing researchers to discover patterns in the movements of marine species. Our interface also provides geographical visualization of selected patterns or rules and helps users to navigate and find the relevant information. Key principles of our model include multi-view visualizations that provide comprehensive insights by displaying data from various perspectives simultaneously, and geo-spatial time-varying data visualization that captures and represents the dynamic movements of marine species. This approach allows domain experts to interactively explore and analyse sequential patterns and rules, enhancing their ability to make informed decisions. Additionally, feedback received, and insights gained from specialized professionals was used to determine if this approach would be a beneficial complement to existing practice.

Understanding the location of risk to natural hazards, namely the areas of high exposure and vulnerability is a major priority that was identified by the Sendai framework for Disaster Reduction 2015–2030 in order to reach substantial reduction of disaster risk. It is also a necessary decision-making tool for disaster mitigation policy-makers in Japan and around the world. This paper successfully develops a simple methodology using only open data to build the first large-scale (whole country), fine resolution (sub-municipal level) social vulnerability analysis in the context of five different types of natural hazards (earthquake, tsunami, storm surge, flooding and landslide). The result is then compared to an indicator of exposure of population to these hazards in Japan in order to propose a representation of disaster risk. Results show that vulnerability in Japan is highly heterogeneous with urban/rural and north/south fractures. Combining the social vulnerability index with exposure analysis, results show a wide variety of spatial patterns of risk areas in Japan.

Department of Energy (DOE) nuclear facilities must comply with DOE Order 420.1C Facility Safety, which requires that all such facilities review their natural phenomena hazards (NPH) assessments no less frequently than every ten years. The Order points the reader to Standard DOE-STD-1020-2012. In addition to providing a discussion of the applicable evaluation criteria, the Standard references other documents, including ANSI/ANS-2.29-2008 and NUREG-2117. These documents provide supporting criteria and approaches for evaluating the need to update an existing probabilistic seismic hazard analysis (PSHA). All of the documents are consistent at a high level regarding the general conceptual criteria that should be considered. However, none of the documents provides step-by-step detailed guidance on the required or recommended approach for evaluating the significance of new information and determining whether or not an existing PSHA should be updated. Further, all of the conceptual approaches and criteria given in these documents deal with changes that may have occurred in the knowledge base that might impact the inputs to the PSHA, the calculated hazard itself, or the technical basis for the hazard inputs. Given that the DOE Order is aimed at achieving and assuring the safety of nuclear facilities—which is a function not only of the level of the seismic hazard but also the capacity of the facility to withstand vibratory ground motions—the inclusion of risk information in the evaluation process would appear to be both prudent and in line with the objectives of the Order. The purpose of this white paper is to describe a risk-informed methodology for evaluating the need for an update of an existing PSHA consistent with the DOE Order. While the development of the proposed methodology was undertaken as a result of assessments for specific SDC-3 facilities at Idaho National Laboratory (INL), and it is expected that the application at INL will provide a demonstration of the methodology, there is potential for general applicability to other facilities across the DOE complex. As such, both a general methodology and a specific approach intended for INL are described in this document. The general methodology proposed in this white paper is referred to as the “seismic hazard periodic review methodology,” or SHPRM. It presents a graded approach for SDC-3, SDC-4 and SDC-5 facilities that can be applied in any risk-informed regulatory environment once risk-objectives appropriate for the framework are developed. While the methodology was developed for seismic hazard considerations, it can also be directly applied to other types of natural hazards.

Canada’s vast regions are reacting to climate change in uncertain ways. Understanding of local disaster risks and knowledge of underlying causes for negative impacts of disasters are critical factors to working toward a resilient environment across the social, economic, and the built sectors. Historically, floods have caused more economical and social damage around the world than other types of natural hazards. Since the 1900s, the most frequent hazards in Canada have been floods, wildfire, drought, and extreme cold, in terms of economic damage. The recent flood events in the Canadian provinces of Ontario, New Brunswick, Quebec, Alberta, and Manitoba have raised compelling concerns. These include should communities be educated with useful knowledge on hazard risk and resilience so they would be interested in the discussion on the vital role they can play in building resilience in their communities. Increasing awareness that perceived risk can be very different from the real threat is the motivation behind this study. The main objectives of this study include identifying and quantifying the gap between people’s perception of exposure and susceptibility to the risk and a lack of coping capacity and objective assessment of risk and resilience, as well as estimating an integrated measure of disaster resilience in a community. The proposed method has been applied to floods as an example, using actual data on the geomorphology of the study area, including terrain and low lying regions. It is hoped that the study will encourage a broader debate if a unified strategy for disaster resilience would be feasible and beneficial in Canada.

LBNL and APPA (the team) jointly examined the extent to which differences in distribution feeder characteristics are correlated with differences in their reliability performance when exposed to three different types of natural hazards (wildlife, weather, and vegetation). The team employed data-driven approaches to quantify the relationships between various measures of feeder reliability and a suite of feeder characteristics individually and jointly via a statistically-based clustering method.