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Data: Facts and statistics collected for reference or analysis

Session 3.2: Implementing the geospatial data management  (Part 1): Documenting the process and defining the data needs. Key terms used in this session. Data: Facts and statistics collected for reference or analysis

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Data: Facts and statistics collected for reference or analysis

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  1. Session 3.2: Implementing the geospatial data management  (Part 1): Documenting the process and defining the data needs

  2. Key terms used in this session Data: Facts and statistics collected for reference or analysis Geographic data: Information describing the location and attributes of things, including their shapes and representation. Geographic data is the composite of spatial data and attribute data. Geographic feature: Man-made or naturally-created features of the Earth. Geographic object: Whereas features are in the real word (mountain, river, church, etc.), geographic objects are computer representation of features 2

  3. Key terms used in this session Geographic Information: Spatial and/or geographic data organized and presented to create some value and to answer questions Geographic Information System (GIS): An integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes Geospatial data: Also referred to as spatial data, information about the locations and shapes of geographic features and the relationships between them, usually stored as coordinates and topology. Statistical data: Also attribute data. Nonspatial information about a geographic feature, usually stored in a tablethat can be attached to a geographic object through the use of unique identifier or ID 3

  4. Documenting the process The geospatial data management cycle comprises several steps and implementing these steps takes a long time and may involve different individuals. • Documentingeachstep from the beginning as precisely as possible ensures that the process can be replicated 4 http://www.healthgeolab.net/DOCUMENTS/Guide_HGLC_Part2.1.pdf

  5. Documenting the process • Some steps may require simple documentation such as just describing the choices made and why • Other steps may require a more lengthy description of the processes and other elements involved: • Compiling existing data • Collecting or extracting data • Cleaning and validating data • Using the data 5

  6. Documenting the process The document on the geographic accessibility analysis done by the World Health Organization (WHO) to support maternal and newborn health in Cambodia is an example (below). All the elements and processes involved (indicators, targets, assumptions, tools, analyses, data, and norms) are fully documented in the report. • A good document such as this will allow someone else to obtain the same results 6 http://www.healthgeolab.net/KNOW_REP/WHO-HIS-HGF-GIS-2016.2-eng.pdf

  7. Defining the data needs Having kept in mind to document the whole data management cycle, the process of acquiring the data for the data products can begin by defining the data needs. Start by making a list of all the data – geospatial or statistical – that are needed to address the pre-defined objectives. 7

  8. Geospatial data When making a list of data, there are different features considered in Public Health. These features need to be translated into geographic objects which are computer representation of real-world objects on a map. 8

  9. Geospatial data They can be separated into four groups when looking at how they would be captured in GIS. 3 4 1 2 9

  10. Geospatial data These features are: • Fixed and for which the geography can be simplified by a point (examples: household, health facility, village when boundaries are not available,...). The geography of these objects is obtained through their geographic coordinates. • Fixed as well but for which the geography has to be represented by polygons due to their much larger extent (Examples: administrative divisions, health districts,...) or by a line (Example: road, river,....). 10

  11. Geospatial data • Mobile (Examples: individuals, patients, vehicles,...). The geography of these objects would either be obtained by considering them attached to a fixed object or by simplifying them as a point that would be located through its geographic coordinates (latitude and longitude) taken at a given time. • Continuous: some elements of our environment are not defined objects per say and not associated with one specific location, but are rather distributed spatially. These are better represented using a continuous surface (e.g. terrain, land surface attributes, population distribution) 11

  12. Types of geospatial data • These features are represented as either vector or raster format geospatial data. Vector format (shapefile) Point Line Polygon 12

  13. Types of geospatial data Raster format (Geotiff, GRID) 13

  14. Statistical data • Meanwhile, statistical data or attribute data are non-spatial information about a geographic feature, usually stored in a table. These data can be attached to a geographic object through the use of unique identifier or ID. • Examples of statistical data are: • Number of doctors in hospitals • Population count of provinces or districts • Number of positive malaria cases reported in a health facility 14

  15. Linking geospatial and statistical data Map Objects Attributes Health program specific Stats/Info Country Province/ Municipality Stats/Info Stats/Info District/Town/City Stats/Info wards/communes/ townships Table/graph Information Health facility Information Villages Stats/Info Patient • Unique identifier Common assets • Master lists 15

  16. Developing the data model While making the list of needed data, it is useful to look at how they relate to one another in the final database. • This can be done through the development of a data model A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to properties of the real world. 16

  17. Developing the data model • A data model has three (3) main levels1. They can be differentiated as: • Conceptual data model which identifies the highest-level relationships between the different entities/objects2 • Logical data model which describes the data in as much detail as possible, without regard to how they will be physically implemented in the database • Physical data model which represents how the model will be built in the database • 1 http://www.1keydata.com/datawarehousing/data-modeling-levels.html • 2Refers to any person, place, or thing that data can represent on a map 17

  18. Developing the data model Each of these models fulfils a different function and contains different features: 18

  19. Conceptual data model • Example of conceptual data model – Malaria elimination 19

  20. Logical data model • Example of logical data model – derived from the Malaria elimination conceptual data model 20

  21. Physical data model • Example of physical data model – derived from the Malaria elimination logical data model 21

  22. Evolution of the data models The development of the data models should not stop after their initial creation. The different data models should evolve as the project implementation progresses and should also be improved, completed, and potentially updated through a consultative process among involved stakeholders. 22

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