Communicating spatial planning decisions at the landscape and farm level with landscape visualization

Landscape visualizations have the potential to support participatory environmental planning at different spatial scales and decision levels from international to farm level. However, it is yet unclear what specific demands are relevant for visualization on the different decision levels. In this context more knowledge is needed about visualization objectives and the respective tasks, intended effects and suitable techniques for the specific levels. Especially the farm level has been neglected in research, although farmers make many decisions that affects public interests in the visual landscape. Farmers need to communicate these decisions to the public in an understandable way. The question of how visualization can support participation in the planning process at the municipal level is examined by drawing on the findings of the Interactive Landscape Plan Koenigslutter, Germany (IALP) about the preferences and reactions of citizens to visualizations used in the landscape planning process at the local decision level. On this basis, we examined the applicability and differences of the findings for the farm level. Furthermore, in order to explore visualization opportunities at the farm scale, the farm management system MANUELA was used as an example of an information platform that could serve as a basis for farm scale visualizations. By transferring landscape planning results to the farm level, we developed recommentations about the application of visualization, intended effects and appropriate techniques at the farm scale. The general findings for the municipal level show that visualization can improve participation by providing participants with a common image of the planning proposals for discussion and collaborative decisions. Different visualization methods offer different capabilities for supporting participation in the different planning phases. At the farm scale, 2D visualizations and diagrams are often sufficient to communicate information to customers about farm performance for providing ecosystem services. They may consist of maps and supporting information that is easily generated from GIS data. However, for a higher (more interactive) level of communication and participation activities, such as discussions with affected neighbors about land use changes or the integration of citizens' proposals, more sophisticated visualization techniques would be required. Visualization techniques are needed that farmers can use to easily simulate visual impacts of land use changes at the landscape scale

Linking Science and Management in a Geospatial, Multi- Criteria Decision Support Tool

Land managers are often faced with balancing management activities to accomplish a diversity of objectives in complex, dynamic ecosystems. In this chapter, we present a multi-criteria decision support tool (the Future Forests Geo-Visualization Decision Support (FForGeoVDS)) designed to inform management decisions by capturing information on how climate change may impact the structure and function of forested ecosystems and how that impact varies across the landscape. This interactive tool integrates spatial outputs from various empirical models in a structured decision framework that allows users to customize weights for multiple management objectives and visualize suitability outcomes across the landscape. As a proof of concept, we demonstrate customized objective weightings designed to: (1) identify key parcels for sugarbush (Acer saccharum) conservation, (2) target state lands that may serve as hemlock (Tsuga canadensis) refugia, and (3) examine how climate change may impact forests under current and future climate scenarios. These case studies exemplify the value of considering multiple objectives in a flexible structure to best match stakeholder needs and demonstrate an important step toward using science to inform management and policy decisions

Data fusion and visualization towards city disaster management: Lisbon case study

INTRODUCTION: Due to the high level of unpredictability and the complexity of the information requirements, disaster management operations are information demanding. Emergency response planners should organize response operations efficiently and assign rescue teams to particular catastrophe areas with a high possibility of surviving. Making decisions becomes more difficult when the information provided is heterogeneous, out of date, and often fragmented. OBJECTIVES: In this research work a data fusion of different information sources and a data visualization process was applied to provide a big picture about the disruptive events in a city. This high-level knowledge is important for emergency management authorities. This holistic process for managing, processing, and analysing the seven Vs (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value) in order to generate actionable insights for disaster management. METHODS: A CRISP-DM methodology over smart city-data was applied. The fusion approach was introduced to merge different data sources. RESULTS: A set of visual tools in dashboards were produced to support the city municipality management process. Visualization of big picture based on different data available is the proposed work. CONCLUSION: Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the most affected area.info:eu-repo/semantics/publishedVersio

The GRaPPa Lab: Supporting Team Decision Making in Complex Environments

poster abstractThe GRaPPa (Group Psychology and Performance) Lab operates within the School of Informatics at Indiana University Purdue University Indianapolis (IUPUI), in cooperation with the User Simulation and Experience Research Lab. The focus of our research is on interdependent teams in technologically complex work environments characterized by uncertainty, stress, high risk, changing moods, and varying levels of expertise. The GRaPPa Lab employs a mixed-methodological approach. Field studies provide rich and nuanced knowledge about individuals and teams at work in complex environments. Likewise, controlled laboratory experiments have provided the foundation for countless contributions to our understanding of the human characteristics that impact the development and use of systems, devices, and environments. Yet such experiments are limited in what they can tell us about work situated in real-world settings, just as field studies are limited in their support for precision and replicability. The GRaPPa Lab leverages the strengths of both through the use of simulated task environments and scaled worlds in the search for holistic assessments of group behavior and task performance. This poster will showcase aspects of an ongoing research program, Bridging the Situation Space to Decision Space Gap. This project is examining the modeling and visualization of decision space information to supplement situation space information in the contexts of disease contagion and emergency management. To enhance the decision support of emergency responders, we are examining the ability of decision space visualization tools to enhance option awareness and support more robust decision making. This work is focused on detailing the impact of the decision space information provided to users, relating the correctness of decisions to the levels of complexity represented in the events, and the affordances for understanding alternative actions. This ongoing project is focused on prototyping multiple visualization methods and testing them in human-in-the-loop experiments based on the domain of emergency crisis management. In addition, the computer models underlying the decision space are being expanded to support increasingly complex situations. This research provides further insight into the value of decision space information and option awareness for users working in complex environments

Development of a web based GIS for health facilities mapping, monitoring and reporting: A case study of the Zambian Ministry of health

Around the world health professionals and authorities, in many cases, do not have the ability to visualize health related spatial information to make timely decisions. The high cost of deploying a desktop Geographical Information System (GIS) for Public Health management coupled with the need for specialised training in order to use geospatial tools have contributed to the low uptake of GIS as a decision support tool in public health management. Lack of a real time data collection and visualization tool for health facilities has in many cases led to late responses in situations where time critical decision had to be made. This research reviewed recent literature on GIS in health care with particular emphasis on web GIS technologies and how they can aid in analysing health care needs, access, and utilization to support in the planning and evaluation of new service locations as well as use of GIS in disease surveillance. This research is aimed at producing a web based GIS that can be used to collect data from health facilities and in turn provide this data to public health administrators to support decision making, it also focuses on creating a portal for public interaction with health facilities spatial information.Key Words: Spatial Decision Support System, Web GIS, Mapping, Health geograph

Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data

In this paper, we propose a semantic approach for monitoring information publishedon social networks about a specific event. In the era of Big Data, when an emergencyoccurs information posted on social networks becomes more and more helpful foremergency operators. As direct witnesses of the situation, people share photos, videosor text messages about events that call their attention. In the emergency operationcenter, these data can be collected and integrated within the management processto improve the overall understanding of the situation and in particular of the citizenreactions. To support the tracking and analyzing of social network activities, there arealready monitoring tools that combine visualization techniques with geographicalmaps. However, tweets are written from the perspective of citizens and the informationthey provide might be inaccurate, irrelevant or false. Our approach tries to dealwith data relevance proposing an innovative ontology-based method for filteringtweets and extracting meaningful topics depending on their semantic content. In thisway data become relevant for the operators to make decisions. Two real cases used totest its applicability showed that different visualization techniques might be neededto support situation awareness. This ontology-based approach can be generalizedfor analyzing the information flow about other domains of application changing theunderlying knowledge base.This work is supported by the project emerCien grant funded by the Spanish Ministry of Economy and Competitivity (TIN2012-09687)

Increasing Understanding During Collaboration Through Advanced Representations

this paper is to present an environment which has been specifically designed for multiple ways to represent and manipulate information. Several representations, when coupled with appropriate visualization techniques, lead to opportunities for increasing understanding of AEC project characteristics. More specifically, when a numerical constraint solver (SpaceSolver) is integrated within a document-centric collaboration environment (ICC), synergies between information exchange and solution space exploration contribute very positively to the quality of projects. In particular, the ICC environment provides a framework for representing and visualizing information structures that are created during collaboration. Conceptually, an information architecture and visualization techniques to support the virtual AEC enterprise are emphasized. A plug-in architecture allows for the addition of processspecific functionality. The constraint solver SpaceSolver presents a complementary collaborative approach, with strict semantics to support decision making and conflict management. The use of solution spaces during collaborative negotiation avoids premature decisions in the design process, allows detection of conflicting project requirements at early stages of the project, and increases the designers' understanding of hidden relations between design parameters. Together, the ICC environment supports the management of an information space that, when linked to a constraint satisfaction problem, can explain important restrictions and decisions for an effective negotiation. The combination of a flexible framework with more rigid modules, such as constraint solvers, provides a useful compromise and, thus, comprehensive support for a range of AEC projects. Two recently completed construction pr.

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Toward cognitive digital twins using a BIM-GIS asset management system for a diffused university

The integrated use of building information modeling (BIM) and geographic information system (GIS) is promising for the development of asset management systems (AMSs) for operation and maintenance (O & M) in smart university campuses. The combination of BIM-GIS with cognitive digital twins (CDTs) can further facilitate the management of complex systems such as university building stock. CDTs enable buildings to behave as autonomous entities, dynamically reacting to environmental changes. Timely decisions based on the actual conditions of buildings and surroundings can be provided, both in emergency scenarios or when optimized and adaptive performances are required. The research aims to develop a BIM-GIS-based AMS for improving user experience and enabling the optimal use of resources in the O & M phase of an Italian university. Campuses are complex assets, mainly diffused with buildings spread across the territory, managed with still document-based and fragmented databases handled by several subjects. This results in incomplete and asymmetrical information, often leading to ineffective and untimely decisions. The paper presents a methodology for the development of a BIM-GIS web-based platform (i.e., AMS-app) providing the real-time visualization of the asset in an interactive 3D map connected to analytical dashboards for management support. Two buildings of the University of Turin are adopted as demonstrators, illustrating the development of an easily accessible, centralized database by integrating spatial and functional data, useful also to develop future CDTs. As a first attempt to show the AMS app potential, crowd simulations have been conducted to understand the buildings' actual level of safety in case of fire emergency and demonstrate how CDTs could improve it. The identification of data needed, also gathered through the future implementation of suitable sensors and Internet of Things networks, is the core issue together with the definition of effective asset visualization and monitoring methods. Future developments will explore the integration of artificial intelligence and immersive technologies to enable space use optimization and real-time wayfinding during evacuation, exploiting digital tools to alert and drive users or authorities for safety improvement. The ability to easily optimize the paths with respect to the actual occupancy and conditions of both the asset and surroundings will be enabled

The decision exploration lab:supporting the business analyst in understanding automated decisions

A Decision Management System (DMS) provides means to model and automate enterprise decisions and they are applied in a wide range of industries, among which health care, commerce, insurance, finance and transportation. These systems make millions of decisions each day without direct human supervision, impacting the life of millions of people and impacting economies at a large scale. The multiplicative effect of decision automation provides the opportunity tofine-tune the decision system. By analyzing its global and emerging properties rather than focusing on the details of each decision, the system as a whole can be better adapted to the reality it models.Like expert systems, DMSs provide a clear separation of decision logic, information related to individual decisions and decision execution. These data spaces contain a wealth of information related to the structure and functioning of a DMS. In this thesis various ways are explored to visualize and analyze this data in order to help a business user to gain a deeper understanding of automated decisions.To address the problem of understanding the global and emerging properties of automated decision making systems, we combine interactive analysis of the decision data with analysis of the decision logic. We present a visual analytics system, the Decision Exploration Lab (DEL), which provides a verbal analysis mode and a visual decision exploration mode. In verbal mode the user can make selections on past decisions using controlled natural language. In visual de-cision exploration mode, the decision data is analyzed using Multiple Correspondence Analysis (MCA). The analysis results are visualized using interactive techniques to show the important structure of the decision data to the user. Correlated concepts can be clustered at a level of granularity that suits the needs of the business analyst. Clustered concepts can next be linked to therules of the decision logic that are relevant for the subset of decisions which match these concepts. We evaluated our approach with two use case scenarios from the car insurance industry. Apart from the above, we propose a number of technical contributions, enhancements and extensions to information visualization methods, for multivariate categorical data. Firstly, wepresent a generic algorithm to generate all well-known treemap layouts as well as other rectangular space-filling layouts. Secondly, we present explanatory and interactive visualization techniques to support interpretation and usage of MCA. Thirdly, we present labeling and scale adjustment techniques in order to improve the usability of 2D-plots