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相关概念视频

Levels of Use of a GIS01:29

Levels of Use of a GIS

52
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...
52
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
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...
27
Manipulation and Analysis01:21

Manipulation and Analysis

26
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...
26
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

65
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...
65
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

47
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
47
Introduction to GIS01:28

Introduction to GIS

68
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
68

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SMapper:可视化所有类型的空间流行数据,包括稀疏和不完整的数据集.

Lynn Khellaf1, Arwin Ralf2, Khanh Toan Nguyen3

  • 1Cologne Center for Genomics, University of Cologne, 50931 Cologne, Germany.

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概括

SMapper是一种用于可视化空间流行数据的新工具,即使信息不完整. 它有助于克服流行病学,人类学和法医应用现有工具的局限性.

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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科学领域:

  • 遗传学 是一个遗传学.
  • 流行病学 流行病学
  • 人类学是人类学.
  • 法医科学 法医科学 法医科学

背景情况:

  • 空间流行数据的可视化对于理解人口层面的模式至关重要.
  • 现有的工具往往因为不完整的地理覆盖和不足的样本大小而扎.
  • 解释性挑战源于空间分析中的数据局限性.

研究的目的:

  • 介绍SMapper,一个新的网络和软件工具.
  • 实现各种空间流行数据的可视化,包括不完整的数据集.
  • 展示SMapper在解决当前可视化工具的局限性方面的实用性.

主要方法:

  • 开发基于网络的SMapper.实现的开发.
  • 创建一个独立的软件版本,兼容Singularity容器和本地Linux Python安装.
  • SMapper对人类基因型和表型数据的应用.

主要成果:

  • SMapper有效地可视化空间流行数据,适应不完整的覆盖范围和样本大小.
  • 该工具减轻了由数据限制引起的解释问题.
  • 在各种科学背景下成功应用人类遗传和表型数据.

结论:

  • SMapper为可视化空间流行数据提供了一个强大的解决方案.
  • 该工具增强了数据的解释与固有的局限性.
  • SMapper适用于流行病学,人类学和法医研究领域.