===== Checkpoint 1 (max 10 pts) =====

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Deadline **10. 10. 2021**

==== Goal ====
The goal of this checkpoint is to find the research question to be answered, build a custom model to answer the question and find relevant data sources that could help answering the question.

==== Topics ====

Questions are sorted out by the topics.

Topic **Protected sites** offers following questions:
  *  In which areas can I move outside of the marked paths (e.g. mountain biking, ski touring etc...)?
  *  What are the protected areas and their levels (monuments, nature) for specific municipalities? E.g. in a given radius or directly in the area of a municipality?
  *  What is the overlap among protected sites on various levels of administrative units (Natura 2000, birds habitation areas, national arks and other protected areas)?
  * How are the conditions for development regulated by the various levels of protected areas (cultural or natural)?
  * What is the relation between the level of protection and tourism?

The topic **Animal species** is based on the natural taxonomy of animal kingdom. We offer following questions:
  * What is the nearest animal rescue station taking care of specific species?
  * Which animals living in the area are not taken care of by any animal rescue station?
  * Which species population had biggest growth/loss in protected areas aggregated across municipalities/regions?
  * Which animal species are more endangered in municipal areas and which in wildlife (e.g. by combination of population and number of animals taken care of in animal shelters)?

==== Deliverable ====
A PDF consisting of 1-2 pages describing
  - research question to answer with detailed specification, your motivation for the topic,
  - a model of classes and properties describing how to find a solution to answer the question. Model could be in a form of E-R diagram or similar (not in a formal language),
  - list of data sources which you have found relevant for answering the research question (at least three and from various sources). 

==== Details ====

== Research question ==

Choose a research question from the list provided or come up with your own question. Consult the question with the lecturer -- all proposed questions have some solution and require integration of data sets from various sources in order to answer it. Question created by students shall be equally complicated to answer and the solution shall exist. Specify the questions for specific space and time.

== Conceptual model ==

Conceptual model represents visualization of knowledge needed to answer the question. Example question from the first tutorial -- in which areas it is possible to legally sleep overnight in nature -- is modeled in a following way:

  * what is sleeping overnight in nature,
  * what kind of areas do we know,
  * what are the restrictions in those areas.

{{ :courses:b4m36osw:celkovy_pohled.png?nolink |}}

Look into the legislation (national, European), if it handles the problem somehow. Find definitions of related terms.


The outcome model shows the relations between the inputs and outputs and shows the way how to answer the question. Feel free to use various colours and frame types to express abstract classes, different sources etc. Use any conceptual language you know to represent the model, e.g. UML or E-R model.

Tutorial models were created in [[https://www.yworks.com/products/yed|yED]].

== Data sources ==

Find the specific data sets needed to answer the data. It is possible that it does not perfectly fit to the model. If so, edit or extend the model, eventually describe the conflicts in the output PDF. 

While selecting the datasets, think about the value you get from them to solve your problem. Super rich data are not useful for you, if they does not contain one piece of information you need. 

It is also recommended not to check only the schema and structure, but also the data content. Attributes may be part of the schema, but with no data.

Some data are published only regionally. Try to look for other data providing same or similar knowledge. It may be needed to combine more datasets to complete the knowledge over larger area.