1. Compiling good geospatial data
Session 3 –
Making a good map –
Compiling good geospatial data
Sokna Sek – MOH/DPHI
Leng Ing – MOH/DPHI
Sat Chab – MOH/DPHI
Steeve Ebener – HGLC
Izay Pantanilla – HGLC

2. Compiling good geospatial data
The geospatial data cycle
Geospatial data life cycle
Critical steps to create good maps ! 2

3. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
The 6 dimensions of data quality:
Completeness: No data gap
Uniqueness: No duplicates
Timeliness: Up-to-date
Validity: Conform to the defined format, type, range,...
Accuracy: Correctness
Consistency: No difference across sources

4. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Validity:
Geographic coordinate system and map projection
Geographic extent of the area being covered
Language(s) included in the data
File format(s) for sharing data
Metadata standard used to document the data
Accuracy:
Scale (vector layers)
Spatial resolution (raster layers)
Positional accuracy (vector layers)
Positional accuracy (GNSS reading)
Positional precision (GNSS reading)
Timeliness:
Period for which the data is being considered as relevant
Consistency

5. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Geographic Coordinate System
System in which geospatial data is defined by a 3-D surface and measured in latitude and longitude.
Angular units: The unit of measure on the spherical reference system.
Prime meridian: The longitude origin of the spherical reference system.
Datum: Defines the relationship of the reference spheroid to the Earth's surface.
Spheroid: The reference spheroid for the coordinate transformation.
Note: When displaying data that's using a geographic coordinate system, ArcMap uses a 'pseudo-Plate Carree' projection. Basically, we just treat the coordinate values as if they're linear and just display the data. 

6. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Geographic Coordinate System
IMPORTANT: Must use the same Geographic Coordinate System on each dataset being combined on a map

7. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Projected Coordinate System
System in which geospatial data is defined by a flat 2-D surface and can be measured in units of meters and feet.
Map projection
A method by which the curved surface of the earth is portrayed on a flat surface
The systematic transformation of points on the Earth’s surface to corresponding points on a plane (flat) surface
The earth is 3D but maps need to be flat!
This requires distortion of some parts of the map.

8. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Map projection – Basic projection techniques
Cylindrical
Conical
Azimuthal

9. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Map projection – Basic projection types
Each projection preserves a particular relationship or characteristic:
Equal-Area — correctly shows the size of a feature
Conformal — correctly shows the shape of features
Equidistant — correctly shows the distance between two features
True Direction — correctly shows the compass direction between two features
A map can not be at the same time equal-area, conformal and/or equidistant – it can only be one
A map projection is to be chosen based on the needs

10. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Map projection – Examples
Projection
Technique
Type
Equirectangular
Cylindrical
Equidistant
Simplest geometry; distances along meridians are conserved. Plate carrée: special case having the equator as the standard parallel.
Lambert cylindrical equal-area
Equal-area
Universal Transverse Mercator (UTM)
Conformal
Divides the Earth into sixty zones, each being a six-degree band of longitude
Robinson
Pseudocylindrical
Compromise (neither equal-area nor conformal)
Used to create global maps

11. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Geospatial and attribute data format (most used)
Vector
Shapefiles (actually composed of the 3 to 8 files)
GeoJSON (QGIS)
Raster
Georeferenced: Geotiff
Not georeferenced: .jpeg, .png, etc.
GRID
Tabular:
Spreadsheets: .xls, .dbf (for point type data and when they contain the latitude and longitude)
Combined vector/raster/tabular
Geodatabases
Recommended

12. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Scale, accuracy, resolution and precision
Scale: The ratio or relationship between a distance or area on a map and the corresponding distance or area on the ground, commonly expressed as a fraction or ratio. A map scale of 1/100,000 or 1:100,000 means that one unit of measure on the map equals 100,000 of the same unit on the earth.
Accuracy: The degree to which a measured value conforms to true or accepted values. Accuracy is a measure of correctness.
Resolution (raster format): The dimensions represented by each cell or pixel in a raster.
Precision: The number of significant digits used to store numbers, particularly coordinate values. Precision measures exactness.

13. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Precision
At the equator: 360 º
40’075 km
1 º ͌
111’320 m
Recommended
During data collection in the field (GNSS enabled devices)
When generating or extracting vector format geospatial data (precision level of vertices)

14. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Accuracy vs Precision

15. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Scale and accuracy
United States Geological Survey mapping standards: "requirements for meeting horizontal accuracy as 90 per cent of all measurable points must be within 1/30th of an inch for maps at a scale of 1:20,000 or larger, and 1/50th of an inch for maps at scales smaller than 1:20,000."

16. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Scale and resolution
Values are very close to those for accuracy
Tobler W. (1987): Measuring Spatial Resolution, Proceedings, Land Resources Information Systems Conference, Beijing, pp

17. Compiling good geospatial data
Defining the data set specifications
Metadata – Data about the data
For users to ensure that the data is appropriate for their own purpose
Should be captured as much as possible during data collection and completed before data dissemination
Apply to both geospatial and statistical data
Different standards exists (FGDC, ISO) but they first need to be converted into a metadata profile (selection of fields) before being used. 17

18. Compiling good geospatial data
Defining the data set specifications
Metadata – Data about the data
A minimum metadata should cover:
Where is the data coming from?
When was it created/last updated?
What is the method behind the data (scale, accuracy,..)?
Which geographic coordinate/projection system is being used?
Are there any use or redistribution restrictions attached to the data?
Who can I contact if I have questions?

19. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
To summarize
• The purpose behind the use of geospatial data will guide the choice of a specific scale of work
• This scale will directly influence the positional accuracy and spatial resolution that should be used when compiling, collecting, or extracting geospatial data;
• The highest accuracy possible should be sought when using GNSS-enabled devices to allow for the largest use possible of the resulting data; and
• A precision level down to the meter (5 digits in decimal degrees) is being recommended.

20. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Those defined for MOH Cambodia (HIS geo-enabling process)

21. Defining the data set specifications
Compiling good geospatial data
Defining the data set specifications
Those defined for MOH Cambodia (HIS geo-enabling process)

22. Defining the ground reference
Compiling good geospatial data
Defining the ground reference
Two types
Remote sensing imagery (geospatial)
The image also has its own accuracy
890 m
Master lists (geospatial and attribute data)
Topic to
Be covered
tomorrow

23. Compiling existing data
Compiling good geospatial data
Compiling existing data
The data specifications and the ground reference (satellite images and master lists) are used as the reference to compile and check existing geospatial and attribute data

24. Compiling existing data
Compiling good geospatial data
Compiling existing data
Potential source of data
Government
Ministry of health: health facility master list/registry with location, health districts, disease statistics
Ministry of Interior/National Statistical Agency/National Mapping Agency: Administrative divisions master list and boundaries; village master list with location
Ministry of Meteorology/Meteorological agency: climate data
Ministry of Finance: economic surveys
National mapping agency: Administrative boundaries, Digital Elevation model
National Statistical Agency: census, survey
Ministry of Environment/Agriculture: Hydrographic network
NGOs (UN,…) and volunteer groups (i.e. OSM): administrative boundaries, road network, hydrographic network, populated places,…
Research groups/universities: Population distribution grids, land cover
Other type of institutions: satellite images
Private sector including GIS software companies (e.g. Esri): basemap layers

25. Compiling existing data
Compiling good geospatial data
Compiling existing data
Potential source of data – online free shapefiles (for download)
There are many hundreds of websites
Here are some examples:
Open Development Cambodia :
DIVA GIS:
United Nations:
Wide range of spatial data (not all freely accessible)
Global Administrative Unit Layers (need to apply for access)
Open Street Map
openstreetmap.org, openstreetmapdata.com ,
ISCGM:
Government data

26. Compiling existing data
Compiling good geospatial data
Compiling existing data
Potential source of data – Online Free population data (for download)
Worldpop:
GEOHIVE:
Gridded Population of the World
Free satellite images and other raster (for download)
Global Land Cover 30:
GLCF:
Landsat, Aster, SRTM, Forest cover,...
CGIAR:
SRTM 90m

27. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled
Does the data comply with the necessary data quality criteria (Completeness, Uniqueness, Timeliness, Validity, Accuracy, Consistency)?
Comply to the defined data specifications?
Consistent with the ground references (remote sensing images and master lists)
If not, might have to search for other sources or complete the identified gaps

28. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled – Issues to be checked
Incomplete, inaccurate or missing location information e.g. GPS, shapefiles
Administrative divisions change over time
Multiple versions of same data from different sources
Census every 10 years in most countries
Disease data incomplete/inaccurate – may be paper records for smaller admin units
No single body aggregates spatial data
Permissions required for access/use
Free online: often no metadata – may be out of date/wrong …

29. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled – Issues to be checked
Generalization level (Scale issue)
Mismatch between sources

30. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled – Issues to be checked
56 meters
Difference in data collection protocols
Inaccurate GPS readings or errors in the unit setup

31. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled – Issues to be checked
Data collected at different point in time and therefore corresponding to different geographies

32. Compiling existing data
Compiling good geospatial data
Compiling existing data
Assessing the data that has been compiled – Issues to be checked
Data ownership
Not all the data are in the public domain !!!
There might be:
Data use restrictions
Can use them but under some conditions
Can’t even use the data
Data sharing restriction
Can use the data to make a map but can’t share the data to a third party