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相关概念视频

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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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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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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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Region of Convergence01:17

Region of Convergence

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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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相关实验视频

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来自果,谷歌和Meta的移动数据集的融合

Gustavo Sganzerla Martinez1,2, David J Kelvin1,2

  • 1Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.

JMIR public health and surveillance
|June 22, 2023
PubMed
概括

在COVID-19大流行和德克萨斯州的冬季风暴期间,人类流动性趋势在各大科技公司之间趋同. 这些数据对于灾难期间的公共卫生决策至关重要.

关键词:
果果果果果果果是什么意思在 COVID-19 疫情中,在COVID-19的流动性.谷歌谷歌谷歌谷歌是什么意思在这里,我们可以看到MetaMeta.资产资产是指资产的资产.数据数据的数据数据的数据.数据集数据集数据集.移动移动移动移动移动移动性是一种流动性.操作系统操作系统的操作系统.流行病是一种流行病.图案 图案 模式 模式 模式系统 系统 系统工具 工具 工具 工具验证验证的时间

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科学领域:

  • 公共卫生 公共卫生
  • 流行病学 流行病学
  • 数据科学数据科学数据科学

背景情况:

  • 人类的流动性在COVID-19大流行期间显著影响了SARS-CoV-2的传播.
  • 政府实施了行动限制,以制疾病传播.
  • 像果,谷歌和Meta这样的科技公司提供了匿名的用户移动数据.

研究的目的:

  • 为了比较果,谷歌和Meta在COVID-19大流行期间和2021年德克萨斯州冬季风暴期间的移动数据.
  • 测试不同人群在重大事件期间表现出相似的移动趋势的假设.
  • 突出聚合流动数据对公共卫生的价值,并鼓励数据共享.

主要方法:

  • 收集和分析了来自果,谷歌和Meta的2020-2022年的移动数据.
  • 在全球范围内,对58个国家进行了集中分析,这三个数据集都具有共同的特点.
  • 利用2021年德克萨斯州的冬季风暴作为基准来验证数据的稳定性.

主要成果:

  • 在大流行的第一年 (r=0.96) 观察到跨公司的流动性趋势的强烈趋同.
  • 在2021年德克萨斯州的冬季风暴期间,人们注意到了类似的趋势趋同.
  • 数据反映了留在家里的订单,流动性创纪录低,留在家里的数字很高.

结论:

  • 破坏性事件对人类移动模式产生重大影响.
  • 来自多个来源的融合数据增强了公共卫生决策的价值.
  • 在自然灾害期间,移动数据是卫生当局的宝贵资产;单一来源数据有限.