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In situational analysis (SA), various procedures are available, including coding and mapping, each serving distinct purposes at different levels of the research process. In this article, I present a case regarding the uses of coding based on a research project focused on the political participation of Indigenous peoples in Chile's constitutional process. The motivation for this article stems from the challenges I encountered as a newcomer to SA. Within the research process, coding-used alongside other techniques like mapping-played a crucial role in enabling engagement with the data and tackling analytical challenges. In the initial stage, coding helped identify various elements and discourses in the constitutional process that were essential to the project's findings. In a later stage, it assisted in redeveloping a positional map that initially did not reflect the full spectrum of discursive positions. In vivo codes also fostered interdisciplinary connections among bodies of literature in political science and education. These applications of coding can benefit other researchers, particularly those who are new to the methodology.

The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging machine learning and clustering algorithms, sensing and computing technologies, and image processing tools, and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual perception and environment mapping algorithms. A Shiny app was harnessed for Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to configure the flow diagram integrating evidence-based gathered and processed data (the search outcomes and screening procedures). A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out throughout June and October 2022, with search terms including “Internet of Robotic Things” + “remote big data management tools”, “sensing and computing technologies”, and “visual perception and environment mapping algorithms”. Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have been deployed as data extraction tools for literature collection, screening, and evaluation, for document flow monitoring, for inspecting qualitative and mixed methods research, and for establishing robust outcomes and correlations. For bibliometric mapping by use of data visualization, Dimensions AI was leveraged and with regards to layout algorithms, VOSviewer was harnessed.

In this conference report we provide insights into some of the key topics presented at the first international conference on situational analysis in Germany, held in November 2024 in Magdeburg. Central issues discussed include collaborations and feminist perspectives in practice for power-sensitive knowledge production, nonhumans and how they can be included in situational analysis through all types of mappings as well as methodical and methodological questions regarding mapping processes, interdisciplinary mapping, and the conceptualization of space in and through situational analysis. Adele CLARKE and her work were honored and celebrated at the conference.

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet’s increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis-related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy.

The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

In this paper, I propose a reconceptualization of human and non-human elements in qualitative research by treating them not as pre-given entities but as nested assemblages. I draw on the work of DELEUZE and GUATTARI (2004 [1980]) to integrate the metaconcept of the assemblage into situational analysis (CLARKE, FRIESE & WASHBURN, 2018), thereby addressing its limitations in handling nonhuman elements and fully aligning it with the nonhuman turn. Two heuristics-capacities and internal limits-are introduced to operationalize this approach, enabling the deconstruction of elements' perceived unity and the mapping of the modules that constitute them. I start out by exploring the nonhuman turn and the concepts of assemblages and rhizomes. I then trace the evolution of situational analysis from grounded theory methodology, highlighting inconsistencies in the treatment of nonhuman elements. By reconfiguring situational analysis's analytical space, I locate situations among other situations and treat their elements as products of heterogeneous assemblages. With this framework, I demonstrate how situational analysis can effectively analyze how nonhuman elements emerge and how they participate in situations via specific capacities.

A reduction in the demand for meat and particularly red meat has the potential to significantly enhance the sustainability and health of many people's diets. In the current work, I examine situational predictors of meat consumption in nationally representative nutrition surveys from three Western European countries: Switzerland, France and the Netherlands. More specifically, I examine whether the situational factors – the meal type, the day of the week and the location of the food consumption occasion – are predictive of whether meat and red meat are consumed. The results indicate that all three factors are linked to meat and red meat consumption with the patterns varying substantially across the different case study countries and in some cases also the gender of the consumer. The results emphasise the value of mapping situational correlates to inform situated interventions aimed at influencing meat consumption, while also highlighting important differences across both cultures and people.

Flooding remains one of the most destructive natural hazards globally, resulting in severe social, economic, and infrastructural impacts. This study investigates the role of open-source geospatial tools and community-contributed data in enhancing disaster preparedness and response, using the Jakande Housing Estate in Lagos, Nigeria as a case study. The methodology integrates multi-temporal analysis of high-resolution satellite imagery over a six-year period to detect land cover changes and assess flood risk dynamics. Initial analysis revealed significant data gaps within OpenStreetMap (OSM), prompting the initiation of a targeted mapping campaign via the Humanitarian OpenStreetMap Team (HOT) Tasking Manager. This initiative mobilized volunteers to update critical geographic features. To complement remote mapping efforts, in-situ data collection was carried out using the Open Data Kit (ODK), capturing real-time information on infrastructure conditions and displacement patterns. Spatial analyses, including change detection and overlay techniques in QGIS, revealed that over 40% of residential zones within the study area are located in high-risk flood-prone zones, with essential facilities, such as police stations and commercial clusters, also affected. The findings underscore the value of integrating remote sensing, open-source geospatial data, and participatory mapping approaches to enhance situational awareness and support evidence-based emergency response planning. Nonetheless, the study faced limitations due to variability in spatial resolution and restricted access to high-quality spectral data. Future research should explore the integration of higher-resolution imagery and predictive flood modeling to further improve impact assessments and inform long-term resilience strategies.

In this paper, we explore urban cultural politics and policy-making in Austria through the conceptual lens of the arena. In relation to this, we apply the methodological toolbox of Adele CLARKE's situational analysis. With a focus on the dynamics of cultural political conflicts and negotiation, we analyze urban cultural policies and programming. A particular focus is placed on a city-wide cultural program in the city of Graz. Via interpretive analysis of interviews and situational mappings, we aim to analytically unpack the continuous and contingent processes of cultural political negotiation with conflictual consensus as a sensitizing concept. With this objective, our analytical engagement is situated at the intersection between radical democratic theory, referring mainly to Chantal MOUFFE and Oliver MARCHART on the one hand, and social worlds and arenas theory by Adele CLARKE on the other. We hope to contribute to a theoretically sensitized and empirically informed cultural policy research effort by operationalizing the notion of conflictuality in constellations of cultural political actors and negotiation processes in cultural policy-making.