1. GIS as an environment for integration and analysis
of spatial data
D. Kolesov
kolesov.dm@gmail.com
NextGIS
2014

2. General information
Example of GIS-approach to problem solving
Problem formulation and exploration
Preparation of the data
Slopes of hills and ravines
Analysis of terrain type
Put all pieces together
Conclusion
Appendix

3. What is GIS
Geographic information system (GIS)
A geographic information system (GIS) is a computer system
designed to capture, store, manipulate, analyze, manage, and
present all types of geographical data.

4. Examples of applications
For example GIS can answer the next questions (in geology, forest
industry, medicine and other fields of knowledge):
What is located in . . . ?
How likely that a parameter in this location will be greater
than given threshold?
What if . . . ?

5. GIS data types
Raster data is a matrix (image) of a paramether’s values
(elevation, density, . . . ). Elements of the matrix (pixels)
should have spatial coordinates.
Vector data consist of coordinates and nonspatial attributes:
Point objects.
Line/Polyline objects.
Polygon objects.

6. Layers and spatial operations
Spatial queries and queries
by attributes.
Nearest neighbour analysis.
Geometry union,
overlapping, buffers, . . . .
Reprojecting.

7. General information
Example of GIS-approach to problem solving
Problem formulation and exploration
Preparation of the data
Slopes of hills and ravines
Analysis of terrain type
Put all pieces together
Conclusion
Appendix

8. Traveling salesman problem

9. The optimal path

10. Quality of paths
Time of the travel is the main criteria of the quality. The time
depends on:
1. Terrain type:
Road.
Pasture.
Forest.
River.
2. Steepness of hillsides.
3. Weather.
4. . . .

11. Digital elevation model (DEM)
A digital elevation model is a digital model is 3D representation of terrain elevation
data.
We need to construct (or take from somewhere) the function z = F(x, y), where z is
the elevation, x and y are coordinates of location.
We can achieve it by using:
1. Interpolation of known points (classic techniques such as polynomial
interpolation, splines, . . . or special methods of geostatistics).
2. Analysis of remote sensing data (ASTER GDEM and SRTM are examples of
global elevation data sets).
3. Solutions of third-party geodesic companies.
Fig.: ASTER GDEM data (spatial resolution is approximately 30 meters per pixel)

12. Visual analysis: elevation profiles
We have the elevation matrix. What can we do with it?

13. Morphological analysis of DEMs
We can construct the quadratic
approximation of DEMs
z = F(x, y) in running window:
z = ax2
+by2
+cxy +dx +ey +f
The differentials give many useful information:
0-order differential: elevation.
1-order differentials:
slope s:
s = arctg (| (F)|) = arctg (
∂z
∂x
)2 + (
∂z
∂y
)2 = arctg d2 + e2
aspect.
2-order differentials:
profile convexity;
plan convexity.

14. Analysis of terrain type: data sources
Topographic maps.
OpenStreetMap.
Analysis of remote sensing data (Landsat, Aster, . . . ).
Solutions of third-party geodesic companies.

15. Multispectral remote sensing data
A multispectral image is one that captures image data at specific
frequencies across the electromagnetic spectrum. Multispectral
images are the main type of images acquired by remote sensing
radiometers.
Different objects reflect the different spectrum frequencies.
Fig.: Curve of vegetation’s reflectance

16. Example: Landsat data
Landsat has 7 bands, so pixels of Landsat images have numeric 7
characteristic (7 reflectation values at different frequencies).
Fig.: 3-d band (red, 630–690 nm) Fig.: 4-th band (near infrared,
760-900 nm)
Different objects reflect the different spectrum frequencies =>
pixels of different object are mapped in different areas of
7-dimentional space.

17. Multispectral pattern recognition
Fig.: Complosite (7-5-3 bands are
used as R-G-B)
Fig.: Random points are taken
from the areas (see the left Fig.)
and then projected on the plain of
3/4 bands
A pixel can be represented as a point of N-dimensional space. So
we have the well-studied classification problem.

18. Classification result
Left: composite image (7-5-3), right: classification result.
The colors are:
Yellow: pine forest, dry area.
Blue: deciduous forest, wet land or marshes.
Red: pastures.

19. Some conclusions
Now we have calculated and received:
Map of hill-slopes (ASTER GDEM).
Roads, rivers and lakes (OSM).
Map of vegetation (Landsat).

20. General information
Example of GIS-approach to problem solving
Problem formulation and exploration
Preparation of the data
Slopes of hills and ravines
Analysis of terrain type
Put all pieces together
Conclusion
Appendix

21. Estimation of path quality
We’ll create the raster map showing the cost of moving between
different geographic locations. The cost depends on the moving
speed of a pedistrian: if the speed is high then the cost is small and
vice versa. The quality of a path is the cumulative cost along the
path.
The costs of the next areas are:
Road: 5 km/h => cost: 1/5
Pine forest, dry area: 3.5 km/h => cost: 1/3.5
Deciduous forest, wet land or marshes: 2 km/h => cost: 1/2
Pastures: 4.2 km/h => cost: 1/4.2
Rivers and lakes: 0.1 km/h => cost: 1/0.1
A steep slope (> 10 degrees) slows down the moving speed
=> 2*cost.

22. Cost raster

23. Cumulative cost of one of the points

24. Optimal path – 1

25. The graph of the paths

26. The final answer

27. Conclusion
GIS is a composition of databases, maps and methods of data
analysis.
This combination creates a powerful instrument of spatial data
processing.

28. Useful links
GIS communities:
1. Russian GIS community http://gis-lab.info/.
2. Open GIS and open GIS developer: http://www.osgeo.org/.
Open sourse GIS:
1. A Free and Open Source Geographic Information System
QGIS: http://www.qgis.org/.
2. A Free and Open Source Geographic Information System
GRASS: http://grass.osgeo.org/.
Global spatial data:
1. OpenStreetMap: http://www.openstreetmap.org/.
2. Landsat: http://landsat.gsfc.nasa.gov/.
3. MODIS: http://modis.gsfc.nasa.gov/.
4. ASTER: http://asterweb.jpl.nasa.gov/.