GIS as an environment for integration and analysisof spatial data

D. Kolesovkolesov.dm@gmail.com

NextGIS

2014

General information

Example of GIS-approach to problem solvingProblem formulation and explorationPreparation of the data

Slopes of hills and ravinesAnalysis of terrain type

Put all pieces together

Conclusion

Appendix

What is GIS

Geographic information system (GIS)A geographic information system (GIS) is a computer systemdesigned to capture, store, manipulate, analyze, manage, andpresent all types of geographical data.

Examples of applications

For example GIS can answer the next questions (in geology, forestindustry, medicine and other fields of knowledge):

I What is located in . . . ?I How likely that a parameter in this location will be greater

than given threshold?I What if . . . ?

GIS data typesI Raster data is a matrix (image) of a paramether’s values

(elevation, density, . . . ). Elements of the matrix (pixels)should have spatial coordinates.

I Vector data consist of coordinates and nonspatial attributes:I Point objects.I Line/Polyline objects.I Polygon objects.

Layers and spatial operations

I Spatial queries and queriesby attributes.

I Nearest neighbour analysis.

I Geometry union,overlapping, buffers, . . . .

I Reprojecting.

General information

Example of GIS-approach to problem solvingProblem formulation and explorationPreparation of the data

Slopes of hills and ravinesAnalysis of terrain type

Put all pieces together

Conclusion

Appendix

Traveling salesman problem

The optimal path

Quality of pathsTime of the travel is the main criteria of the quality. The timedepends on:

1. Terrain type:I Road.I Pasture.I Forest.I River.

2. Steepness of hillsides.

3. Weather.

4. . . .

Digital elevation model (DEM)A digital elevation model is a digital model is 3D representation of terrain elevationdata.We need to construct (or take from somewhere) the function z = F (x , y), where z isthe elevation, x and y are coordinates of location.We can achieve it by using:

1. Interpolation of known points (classic techniques such as polynomialinterpolation, splines, . . . or special methods of geostatistics).

2. Analysis of remote sensing data (ASTER GDEM and SRTM are examples ofglobal elevation data sets).

3. Solutions of third-party geodesic companies.

Fig.: ASTER GDEM data (spatial resolution is approximately 30 meters per pixel)

Visual analysis: elevation profilesWe have the elevation matrix. What can we do with it?

Morphological analysis of DEMs

We can construct the quadraticapproximation of DEMsz = F (x , y) in running window:

z = ax2+by2+cxy+dx+ey+f

The differentials give many useful information:I 0-order differential: elevation.I 1-order differentials:

I slope s:

s = arctg (|∇(F )|) = arctg

(√(∂z

∂x)2 + (

∂z

∂y)2

)= arctg

(√d2 + e2

)I aspect.

I 2-order differentials:I profile convexity;I plan convexity.

Analysis of terrain type: data sources

I Topographic maps.I OpenStreetMap.I Analysis of remote sensing data (Landsat, Aster, . . . ).I Solutions of third-party geodesic companies.

Multispectral remote sensing dataA multispectral image is one that captures image data at specificfrequencies across the electromagnetic spectrum. Multispectralimages are the main type of images acquired by remote sensingradiometers.Different objects reflect the different spectrum frequencies.

Fig.: Curve of vegetation’s reflectance

Example: Landsat dataLandsat has 7 bands, so pixels of Landsat images have numeric 7characteristic (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 of7-dimentional space.

Multispectral pattern recognition

Fig.: Complosite (7-5-3 bands areused as R-G-B) Fig.: Random points are taken

from the areas (see the left Fig.)and then projected on the plain of3/4 bands

A pixel can be represented as a point of N-dimensional space. Sowe have the well-studied classification problem.

Classification result

Left: composite image (7-5-3), right: classification result.The colors are:

I Yellow: pine forest, dry area.I Blue: deciduous forest, wet land or marshes.I Red: pastures.

Some conclusions

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

General information

Example of GIS-approach to problem solvingProblem formulation and explorationPreparation of the data

Slopes of hills and ravinesAnalysis of terrain type

Put all pieces together

Conclusion

Appendix

Estimation of path quality

We’ll create the raster map showing the cost of moving betweendifferent geographic locations. The cost depends on the movingspeed of a pedistrian: if the speed is high then the cost is small andvice versa. The quality of a path is the cumulative cost along thepath.The costs of the next areas are:

I Road: 5 km/h => cost: 1/5I Pine forest, dry area: 3.5 km/h => cost: 1/3.5I Deciduous forest, wet land or marshes: 2 km/h => cost: 1/2I Pastures: 4.2 km/h => cost: 1/4.2I Rivers and lakes: 0.1 km/h => cost: 1/0.1I A steep slope (> 10 degrees) slows down the moving speed

=> 2*cost.

Cost raster

Cumulative cost of one of the points

Optimal path – 1

The graph of the paths

The final answer

Conclusion

GIS is a composition of databases, maps and methods of dataanalysis.This combination creates a powerful instrument of spatial dataprocessing.

Useful links

I GIS communities:1. Russian GIS community http://gis-lab.info/.2. Open GIS and open GIS developer: http://www.osgeo.org/.

I 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/.I 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/.