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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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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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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Updated: May 27, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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使用人类流动性数据来量化经历的城市不平等.

Fengli Xu1, Qi Wang2, Esteban Moro3,4

  • 1Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China. fenglixu@tsinghua.edu.cn.

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此摘要是机器生成的。

城市流动数据显示,在社会混合,设施的使用和适应事件方面存在不平等现象. 这项研究提供了一个新的框架,通过人与地方网络来追踪动态的城市不平等.

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科学领域:

  • 城市研究 城市研究
  • 社会学 社会学 社会学
  • 数据科学数据科学数据科学

背景情况:

  • 城市生活是由个人流动性,资源获取和社会动态所塑造的.
  • 不平等和隔离是城市经验的重要方面.
  • 细粒度的流动性数据提供了对经历过的规模不平等的新见解.

研究的目的:

  • 审查城市移动行为数据的新兴用途.
  • 为了解经历的城市不平等提出一个分析框架.
  • 通过人与地方网络分析来追踪动态不平等.

主要方法:

  • 使用细粒度的移动数据和上下文属性.
  • 开发一个代表人和地方的时间双边网络模型.
  • 分析网络重新配置以追踪不平等的维度.

主要成果:

  • 拟议的框架允许追踪经历过的社会混合,设施访问和适应事件的不平等.
  • 流动模式揭示了城市不平等的动态,生活经验.
  • 这种方法补充了现有的关于静态不平等的研究.

结论:

  • 通过时间双边网络分析的城市流动数据,为了解经历的不平等提供了强大的透镜.
  • 该框架允许在城市环境中动态跟踪社会混合,访问和弹性.
  • 这项研究强调了数据驱动方法在揭示和解决城市差异方面的潜力.