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Related Concept Videos

Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Bar Graph01:07

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Multiple Bar Graph01:07

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Review and Preview01:13

Review and Preview

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Manipulation and Analysis01:21

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities.

Harry Li, Gabriel Appleby, Camelia Daniela Brumar

    IEEE Transactions on Visualization and Computer Graphics
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    Summary
    This summary is machine-generated.

    This study identifies key challenges faced by Knowledge Graph (KG) practitioners. Improved visualization design and tailored tools are crucial for enhancing KG creation, analysis, and adoption across enterprise and academic domains.

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    Area of Science:

    • Computer Science
    • Information Science
    • Human-Computer Interaction

    Background:

    • Knowledge Graphs (KGs) are increasingly used in enterprise and academic settings.
    • Effective utilization of KGs faces challenges in creation, exploration, and analysis.
    • Current tools and workflows inadequately address practical KG implementation needs.

    Purpose of the Study:

    • To identify critical challenges faced by Knowledge Graph practitioners.
    • To explore how visualization design can alleviate these challenges.
    • To propose research directions for improving KG usability.

    Main Methods:

    • Conducted interviews with nineteen Knowledge Graph practitioners.
    • Analyzed practitioner insights from enterprise and academic use cases.
    • Identified distinct practitioner personas and their specific needs.

    Main Results:

    • Identified three key practitioner personas: KG Builders, Analysts, and Consumers.
    • KG Builders need schema enforcers; KG Analysts require customizable query builders.
    • KG Consumers benefit from tailored visualizations beyond node-link diagrams, promoting adoption and comprehension.

    Conclusions:

    • Effective KG implementation requires both technical and social solutions.
    • Visualization research directions include knowledge cards, timeline views, organic discovery interfaces, and semantic explanations.
    • Addressing practitioner needs through improved tools and workflows is essential for advancing KG adoption and efficacy.