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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

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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

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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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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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相关实验视频

Updated: Jan 7, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: February 25, 2013

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一个多源行为数据框架,用于可解释的城市旅游预测.

Zirui Nie1, Zhonghua Nie2

  • 1Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, 192-0364, Japan.

Scientific reports
|December 24, 2025
PubMed
概括

本研究介绍了一种使用长短期记忆 (LSTM) 和图形神经网络 (GNN) 预测城市旅游需求的混合预测模型. 该模型通过整合多种数据源准确预测需求,增强智能旅游管理.

关键词:
深度学习是一种深度学习.情绪分析 情绪分析图表神经网络的神经网络多源行为数据多源行为数据结构方程建模 结构方程建模旅游预测 旅游预测

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科学领域:

  • 数据科学数据科学数据科学
  • 城市规划 城市规划
  • 旅游管理 旅游管理

背景情况:

  • 由于波动性和复杂的行为模式,城市旅游需求预测是复杂的.
  • 现有的预测方法与旅游数据的多面性质作斗争.

研究的目的:

  • 为准确和强大的城市旅游需求预测开发混合预测框架.
  • 为了提高预测能力,利用多来源的行为数据.

主要方法:

  • 长短期记忆 (LSTM) 网络与图形神经网络 (GNN) 的集成.
  • 利用多个来源的数据:社交媒体情绪,在线旅行社 (OTA) 活动,气象数据和移动信号记录.
  • 从八个中国城市 (2022-2024) 收集的数据集.

主要成果:

  • 实现了平均绝对百分比误差 (MAPE) 的6.31%和83.7%的趋势准确度.
  • 混合模型的表现优于单一模型的基准.
  • 情绪指数,用户参与度和假期效应被确定为准确性的关键决定因素.

结论:

  • 拟议的框架为智能旅游提供了一个可扩展和可解释的智能预测范式.
  • 数据异质性和模型适应性对于提高城市旅游业预测性能至关重要.