A Public Data Visualization Process for Supporting Decision Making on the Phase Of Architectural Programming

2018 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Sun Woo Chang ◽  
Jung Ki Kim ◽  
Han Jong Jun
Keyword(s):  
Decision Making ◽  
Data Visualization ◽  
Public Data ◽  
Visualization Process ◽  
Architectural Programming

scholarly journals The data epic : Visualization practices for narrating life and death at a distance

2020 ◽  
Author(s):  
Jonathan Gray
Keyword(s):  
World War Ii ◽  
Data Visualization ◽  
World War ◽  
Nuclear Threat ◽  
Life And Death ◽  
Public Data ◽  
Data Culture

This chapter proposes the notion of the ‘data epic’, which is examined through two works of ‘cinematic data visualization’: The Fallen of World War II and The Shadow Peace: The Nuclear Threat. These pieces mobilize an aesthetics of distance to narrate life and death at scale, in past and possible global conflicts. While previous studies of quantification emphasize the function of distance in relation to aspirations of objectivity, this chapter explores other narrative and affective capacities of distance in the context of ‘public data culture’. The data epic can thus enrich understanding of how data are rendered meaningful for various publics, as well as the entanglement of data aesthetics and data politics involved in visualization practices for picturing collective life.

Data Visualization

2014 ◽  
pp. 322-351 ◽  
Author(s):  
Ravishankar Palaniappan
Keyword(s):  
Data Structure ◽  
Data Visualization ◽  
Real World ◽  
Research Field ◽  
Use Case ◽  
Visualization Method ◽  
Visualization Process ◽  
Active Research ◽  
Real World Datasets ◽  
Qualitative Exploration

Data visualization has the potential to aid humanity not only in exploring and analyzing large volume datasets but also in identifying and predicting trends and anomalies/outliers in a “simple and consumable” approach. These are vital to good and timely decisions for business advantage. Data Visualization is an active research field, focusing on the different techniques and tools for qualitative exploration in conjunction with quantitative analysis of data. However, an increase in volume, multivariate, frequency, and interrelationships of data will make the data visualization process notoriously difficult. This necessitates “innovative and iterative” display techniques. Either overlooking any dimensions/relationships of data structure or choosing an unfitting visualization method will quickly lead to a humanitarian uninterpretable “junk chart,” which leads to incorrect inferences or conclusions. The purpose of this chapter is to introduce the different phases of data visualization and various techniques which help to connect and empower data to mine insights. It exemplifies on how “data visualization” helps to unravel the important, meaningful, and useful insights including trends and outliers from real world datasets, which might otherwise be unnoticed. The use case in this chapter uses both simulated and real-world datasets to illustrate the effectiveness of data visualization.

Coupling Natural Hazard Estimates with Road Network Analysis to Assess Vulnerability and Risk: Case Study of Freetown (Sierra Leone)

2019 ◽  
Vol 2673 (8) ◽  
pp. 11-24
Author(s):  
Andrew Nelson ◽  
Sarah Lindbergh ◽  
Lucy Stephenson ◽  
Jeremy Halpern ◽  
Fatima Arroyo Arroyo ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Sierra Leone ◽  
Road Network ◽  
Natural Hazard ◽  
Transportation Infrastructure ◽  
The Road ◽  
Public Data ◽  
Road Intersections ◽  
Flooding Events

Many of the world’s most disaster-prone cities are also the most difficult to model and plan. Their high vulnerability to natural hazards is often defined by low levels of economic resources, data scarcity, and limited professional expertise. As the frequency and severity of natural disasters threaten to increase with climate change, and as cities sprawl and densify in hazardous areas, better decision-making tools are needed to mitigate the effects of near- and long-term extreme events. We use mostly public data from landslide and flooding events in 2017 in Freetown, Sierra Leone to simulate the events’ impact on transportation infrastructure and continue to simulate alternative high-risk disasters. From this, we propose a replicable framework that combines natural hazard estimates with road network vulnerability analysis for data-scarce environments. Freetown’s most central road intersections and transects are identified, particularly those that are both prone to serviceability loss due to natural hazard and whose disruption would cause the most severe immediate consequences on the entire road supply in terms of connectivity. Variations in possible road use are also tested in areas with potential road improvements, pointing to opportunities to harden infrastructure or reinforce redundancy in strategic transects of the road network. This method furthers network science’s contributions to transportation resilience under hydrometeorological hazard and climate change threats with the goal of informing investments and improving decision-making on transportation infrastructure in data-scarce environments.

A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures During Visual Trade Space Exploration

2009 ◽  
Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Data Visualization ◽  
Space Exploration ◽  
Computational Thinking ◽  
Test Group ◽  
Design Decision ◽  
Ongoing Research ◽  
Trade Space Exploration ◽  
Insight Into

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.

scholarly journals Scenario-Planning Method for Cost Estimation Using Morphological Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Sae-Hyun Ji ◽  
Joseph Ahn
Keyword(s):  
Decision Making ◽  
Cost Estimation ◽  
Morphological Analysis ◽  
Scenario Planning ◽  
Planning Method ◽  
Estimation Accuracy ◽  
Decision Making Process ◽  
Limited Information ◽  
Public Data ◽  
The Impact

Early cost estimates are emphasized repeatedly in the initial decision-making process to set a direction for the success of construction projects. Therefore, alternatives need to be examined, and the consequences for the cost should be analyzed carefully. This study proposes a scenario-planning method that uses morphological analysis for the estimation of construction cost. A case study was conducted using public data on 102 apartment buildings from 10 housing complex projects. The results show estimation accuracy of 4.23 to 4.86% and an average stability enhancement of 1.39 to 1.73%. The proposed process can produce adaptable scenarios and evaluate the impact of the scenarios in a complicated decision-making process with limited information provided. Furthermore, this method can provide a contingency plan to cushion against uncertainties.

scholarly journals An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2928
Author(s):  
Jeffrey D. Walker ◽  
Benjamin H. Letcher ◽  
Kirk D. Rodgers ◽  
Clint C. Muhlfeld ◽  
Vincent S. D’Angelo
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Large Scale ◽  
Research Management ◽  
Web Based ◽  
Environmental Models ◽  
Potential Benefits ◽  
Interactive Data ◽  
Data Visualizations

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

Application of Geographical Information System and Interactive Data Visualization in Healthcare Decision Making

2017 ◽  
pp. 1244-1254
Author(s):  
Zhecheng Zhu
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Visualization ◽  
Healthcare Services ◽  
Geographical Information ◽  
Multiple Data ◽  
Interactive Data ◽  
Visual Impact ◽  
Site Planning ◽  
Visualization Techniques

This paper focuses on two techniques and their applications in healthcare systems: geographic information system (GIS) and interactive data visualization. GIS is a type of technique applied to manipulate, analyze and display spatial information. It is a useful tool tackling location related problems. GIS applications in healthcare include evaluation of accessibility to healthcare facilities, site planning of new healthcare services and analysis of risks and spreads of infectious diseases. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow user to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. One area both techniques intersect is location analysis. In this paper, real life case studies will be given to illustrate how these two techniques, when combined together, help in solving quantitative or qualitative location related problem, visualizing geographical information and accelerating decision making procedures.

Interactive Data Visualization Techniques Applied to Healthcare Decision Making

2017 ◽  
pp. 1157-1171 ◽  
Author(s):  
Zhecheng Zhu ◽  
Heng Bee Hoon ◽  
Kiok-Liang Teow
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Capacity Planning ◽  
Healthcare Systems ◽  
Healthcare Decision ◽  
Healthcare Decision Making ◽  
Patient Profiling ◽  
Interactive Data ◽  
Visualization Techniques

Data visualization techniques are widely applied in all kinds of organizations, turning tables of numbers into visualizations for discovery, information communication, and knowledge sharing. Data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. In this chapter, interactive data visualization techniques are discussed and their applications to various aspects of healthcare systems are explored. Compared to static data visualization techniques, interactive ones allow users to explore the data and find the insights themselves. Four case studies are given to illustrate how interactive data visualization techniques are applied in healthcare: summary and overview, information selection and filtering, patient flow visualization, and geographical and longitudinal analyses. These case studies show that interactive data visualization techniques expand the boundary of data visualization as a pure presentation tool and bring certain analytical capability to support better healthcare decision making.

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