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Related Concept Videos

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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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Selected Data About Geographic Locations01:25

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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 the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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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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Measuring sustainable tourism with online platform data.

Felix J Hoffmann1, Fabian Braesemann2,3, Timm Teubner1

  • 1Trust in Digital Services, Technische Universität Berlin, 10623 Berlin, Germany.

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|July 25, 2022
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Summary

This study explores using online platform data for sustainable tourism statistics. Machine learning accurately predicts accommodation sustainability, offering a cost-effective global tracking method.

Keywords:
Imbalanced classificationNowcastingPlatform dataSupervised learningSustainable tourismTripAdvisor

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Area of Science:

  • Environmental Science
  • Data Science
  • Tourism Studies

Background:

  • Sustainable tourism is crucial for the UN Sustainable Development Goals.
  • Balancing tourism's economic, environmental, and social impacts requires robust data.
  • Current sustainable tourism statistics may lack sufficient detail or timeliness.

Purpose of the Study:

  • To investigate the utility of online platform data for sustainable tourism statistics.
  • To assess the feasibility of using web-scraped data to measure accommodation sustainability.
  • To explore machine learning applications in generating tourism sustainability metrics.

Main Methods:

  • Web scraping data from a major online tourism platform.
  • Development and application of machine learning models.
  • Algorithmic prediction of sustainability labels for accommodations.

Main Results:

  • Machine learning techniques can predict accommodation sustainability labels with reasonable accuracy.
  • Online platform data serves as a viable alternative data source for tourism statistics.
  • The approach enables cost-effective and accurate tracking of tourism sustainability.

Conclusions:

  • Online data and machine learning offer a promising solution for enhancing sustainable tourism statistics.
  • This method provides high spatial and temporal granularity for monitoring global tourism sustainability.
  • The findings support the integration of big data into sustainable development initiatives.