The matrix to describe, memorize, analyze, investigate

How to observe and describe a place?

The Observable.fr matrix can be used for describe a place by organizing the different aspects of this place into a hierarchical structure. There are many aspects that can be considered when describing a place, such as geography, history, culture, physical characteristics and so on. Let me give you an example of how to describe a place.

  • Location
    • Geography
      • Geographic location (city, region, country, etc.)
      • Physical characteristics (mountain, beach, river, etc.)
      • Climate (temperature, precipitation, etc.)
    • History
      • History of the region (colonization period, wars, etc.)
      • Historical monuments (buildings, statues, etc.)
    • Culture
      • Language(s) spoken
      • Customs and traditions
      • Festivals and cultural events
    • Tourism
      • Accommodations (hotels, hostels, etc.)
      • Restaurants
      • Tourist attractions (museums, parks, etc.)

Each of these child nodes could have sub-nodes for a more detailed description of the place. For example, for history, you could add sub-nodes for each historical period, for culture, you could add sub-nodes for the different traditions, for tourism, you could add sub-nodes for the different tourist attractions, and so on. This allows you to show the different aspects that make up the place and the relationships between them.

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Observation model (available in 2023)

A template is a predefined pattern of observations created by another user that you can immediately use to save time and discover different approaches. Any user can create a template and choose whether or not to share it with the public. You can add a model to your library, modify it and adapt it for new uses. You can also add a model directly to a current study or to a new study.

A template is composed of the following information:

  • The objective of the observational study

  • Possible additional explanations

  • All named analysis objectives (tabs)

  • All first-level descriptors in each analysis (N:0)

  • The category of the model according to the type of observation

  • The pseudo of the creator of the study

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