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相关概念视频

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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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Updated: Jun 5, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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语义上可互操作的人口普查数据:使用本体学和链接数据解锁人口普查数据的语义.

Anderson Wong1, Mark Fox1, Megan Katsumi1

  • 1Urban Data Centre, School of Cities, University of Toronto, Canada.

International journal of population data science
|December 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个本体学,将加拿大人口普查数据转化为链接数据. 这种方法解决了数据纠纷和参考等价性挑战,改善了数据集成和可视化.

关键词:
人口普查的人口普查.人口普查数据 人口普查数据知识图是知识图.链接数据链接数据链接

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科学领域:

  • 社会科学 社会科学 社会科学
  • 数据科学数据科学数据科学
  • 信息科学 信息科学 信息科学

背景情况:

  • 加拿大人口普查为公共和私营部门提供了有价值的社会经济数据.
  • 现有的人口普查数据带来了挑战,包括耗时的数据争,跨数据集的参考等价性问题,以及自然语言描述的机器解释困难.
  • 这些局限性阻碍了有效的数据整合和利用用于规划和决策.

研究的目的:

  • 开发和提出一种实体学来表示加拿大人口普查数据作为链接数据.
  • 为了应对数据纠纷,参考等价性和机器解释性方面的挑战.
  • 评估拟议的本体学的有效性,并讨论其对数据集成和可视化的优势.

主要方法:

  • 为加拿大人口普查数据量身定制的新 Ontology 的开发.
  • 将人口普查数据作为链接数据的表示,使用开发的本体学.
  • 通过从现实世界使用案例中得出的能力问题来评估本体论.

主要成果:

  • 拟议的本体学有效地解决了人口普查数据中固有的数据纠纷和参考等价性问题.
  • 链接数据表示有助于提高机器解释性和对人口普查信息的解构.
  • 实证了本体论对增强数据集成和可视化能力的优势.

结论:

  • 本体论为将加拿大人口普查数据转换为机器可读的链接数据格式提供了一个强大的框架.
  • 链接人口普查数据显著提高了数据的可访问性,整合性和分析潜力.
  • 这种方法为管理和利用复杂的社会经济数据集提供了一个可扩展的解决方案.