GIS
H1: Introductory session
1.1. Some definitions: What is GIS?
What is GIS?
diff definitions, say the same = database, tools for data-analysis
• “... a powerful set of tools for collecting, storing, retrieving at will, transforming and
displaying spatial data from the real world for a particular set of purposes.”
(Burrough, 1986)
• “... an information technology which stores, analyses, and displays both spatial and
non-spatial data.” (Parker, 1988)
• “... a computer-based system to aid in the collection, maintenance, storage, analysis,
output, and distribution of spatial data and information.” (Bolstad, 2016)
Important elements in these definitions:
• collecting, storing, managing, manipulating, querying, analyzing, and visualizing
data.
• used for problem-solving and policy support.
• digital technology: hardware, software, and procedures.
• spatial and non-spatial data.
• Through web-based and wireless technology, GIS supports easier access to and
sharing of spatial data (--> geographical data infrastructures)
• GIS is no longer a toolbox or an information system only. It is an acronym for
geographic information science, involves research on GIS and research with GIS.
Store data in a database, manage them as needed, analyze them, and visualize (maps, figures using GIS).
collecting: more recent, software that directly collects data (an app on a phone, and you go to the field to
collect data). The goal of GIS: solve problems that require special information. can be solved with GIS.
Geographic Information Science. discipline on its own. not only developing new things but also applying
methods for your own research.
1.2. Spatial and non-spatial data
Spatial and non-spatial phenomena
Spatial phenomena/objects
• linked to 1 location or to a set of locations (e.g., temperature, elevation, buildings, parcels).
• spatial phenomena/objects: spatial and non-spatial properties.
characterized by two types of data:
• Spatial (geometric) data: coordinate pair/set of coordinate pairs defines the location
of a spatial phenomenon/object. points create polygons
• Non-spatial (thematic) data = attributes: characteristics of a phenomenon/object,
useful in the context of an application of GIS (e.g. T in a location, total population in an area,
land use of a parcel...).
Non-spatial data: stored as a non-spatial attribute to spatial data, in a table added to the spatial object. point
(location, special information is the coordinates of the point), set of info for the point: this point is a tree.
,linking of spatial and non-spatial data = essence of GIS = added value of GIS compared to
other, non-spatial information systems.
Most GIS applications: spatial objects organized in different layers (maps). Each layer can be
visualized and analyzed separately, or in combination.
combine diff sets of data => to visualize how it looks when you combine all.
Spatial (geometric) data
• Represent spatial phenomena with points, lines, and polygons
• Coordinates linked to 3 types of spatial data, for each, you can have non-spatial
data stored in a table
Point: pair of coordinates (x,y)=> trees info on shape and location. line= road=> set of segments that link
two points from which you have the coordinates. The number of points gives you the resolution of the
mapping. closed loop: first point will be last point, polygons: 2D spaces
Non-spatial (thematic) data
set of attributes you can add to ID (unique for each set of coordinates).
Typical questions that may be answered by a GIS
Most essential characteristic of a GIS: linking of spatial and non- spatial data = added value
of a GIS to other non- spatial information systems.
linking spatial and non-spatial information, you can process this data based on many different parameters
Typical questions
• Identification: What are the characteristics of a location/object?
Bv: identify the owner of a given parcel
• Location: where does a certain phenomenon occur?
Bv: localize all non-built parcels within a commune
• Spatio-temporal analysis: Which spatial changes have occurred over a certain
period? Bv: visualize extent of built-up area after 1950, identify someone who has many diff parcels
• Overlay analysis: analyze spatial relationships between phenomena.
bv: How many buildings are within a buffer zone around a factory?
• Spatial modeling: compare/evaluate alternative scenarios, simulation of reality
Vb:
o Geo-marketing: determine the most suitable location for a shopping center based on
accessibility and distribution of potential clients
o Network analysis: simulate the impact of changes in traffic conditions
o 3D terrain analysis: determine the impact of large infrastructures in the landscape (e.g.
windmills)
,Layers in a spatial database
geospatial data: four different shapes => Layer types: point, line, polygon, raster (layers)
1.3. Spatial data models
Reality = complex => spatial model (simplification) of reality.
2 different methods of describing spatial reality: Field approach & Object approach
field approach = description of a terrain in the form of attributes
• Terrain characteristics: attribute values assigned to a set of locations.
• each attribute has a unique value at each location => field of attribute values.
• to describe phenomena characterized by continuous spatial variation (temperature,
elevation...).
• Attribute values: often sampled for irregular set of locations, characteristics of the
sampling (sampling scheme, sampling density) depend on the sampling technique,
the expected spatial correlation between attribute values, and the accuracy
required.
• Measured values for irregular set of
locations: often transformed into a
regular grid by an interpolation
method.
, Interpolation based on “inverse distance weighting.”
Weigh the average value based on 3 actual measurements (3 points: distances).
Weigh each value with inverse distance => further away, less weight
Resample into a grid of the same distance => For each point, with the same distances, you estimate something
to do that you can use for inverse distance weighting
object approach= objects defined on the terrain, determining geometric and thematic chars.
Each entity specified by specific boundaries=> discrete variations: building and tree: no transition between
• For each object, geometric & thematic characteristics are determined.
• For each object class, spatially represented by means of points, lines, or areas.
• method to represent phenomena occurring on earth’s surface as a collection of
objects with clear boundaries, phenomena that are characterized by a discrete
spatial variation (e.g. parcels, houses, roads...).
Model transformations
from a data model based on a field approach to a data model based on objects
from continuous in space to something discrete, vb:
• soil map (types: discrete) based on soil profile analysis (continuous data to represent the field)
• Interpretation of land use or vegetation characteristics, based on aerial photographs or satellite
imagery (continuous data => overlaid zones => discrete zones)
• Construction of a digital elevation model based on triangulation (see further)
Visual interpretation of land use from satellite imagery (urban)
several stacked layers, for each a value is associated with
a wavelength (solar reflection towards space, satellite
captures: information which the human eye can’t see!)
Composite images: combined 3 layers of raster images
with different wavelengths. (=continuous values)
If you take images in blue, green, and red ranges, you get
true color images.
Shifting layers: replace red with something else, with
higher reflection in the green spectrum for building and
urban areas, in blue (reflection of green is bleu)
diff spectrums for different. types of zones
water will appear almost black in this type of image. Red
is a vegetation false color composition, used to look at
vegetation (types, etc. to see where vegetation is).
each pixel’s given values (3 bcs 3 layers) and then define areas corresponding to overlaid zones. residential area
(the yellow lines). into discrete entities with the lines
Construction of a digital terrain model based on triangulation
You can sample topography into specific polygons (triangles here). You can
have continuous variations in space or discrete entities
H1: Introductory session
1.1. Some definitions: What is GIS?
What is GIS?
diff definitions, say the same = database, tools for data-analysis
• “... a powerful set of tools for collecting, storing, retrieving at will, transforming and
displaying spatial data from the real world for a particular set of purposes.”
(Burrough, 1986)
• “... an information technology which stores, analyses, and displays both spatial and
non-spatial data.” (Parker, 1988)
• “... a computer-based system to aid in the collection, maintenance, storage, analysis,
output, and distribution of spatial data and information.” (Bolstad, 2016)
Important elements in these definitions:
• collecting, storing, managing, manipulating, querying, analyzing, and visualizing
data.
• used for problem-solving and policy support.
• digital technology: hardware, software, and procedures.
• spatial and non-spatial data.
• Through web-based and wireless technology, GIS supports easier access to and
sharing of spatial data (--> geographical data infrastructures)
• GIS is no longer a toolbox or an information system only. It is an acronym for
geographic information science, involves research on GIS and research with GIS.
Store data in a database, manage them as needed, analyze them, and visualize (maps, figures using GIS).
collecting: more recent, software that directly collects data (an app on a phone, and you go to the field to
collect data). The goal of GIS: solve problems that require special information. can be solved with GIS.
Geographic Information Science. discipline on its own. not only developing new things but also applying
methods for your own research.
1.2. Spatial and non-spatial data
Spatial and non-spatial phenomena
Spatial phenomena/objects
• linked to 1 location or to a set of locations (e.g., temperature, elevation, buildings, parcels).
• spatial phenomena/objects: spatial and non-spatial properties.
characterized by two types of data:
• Spatial (geometric) data: coordinate pair/set of coordinate pairs defines the location
of a spatial phenomenon/object. points create polygons
• Non-spatial (thematic) data = attributes: characteristics of a phenomenon/object,
useful in the context of an application of GIS (e.g. T in a location, total population in an area,
land use of a parcel...).
Non-spatial data: stored as a non-spatial attribute to spatial data, in a table added to the spatial object. point
(location, special information is the coordinates of the point), set of info for the point: this point is a tree.
,linking of spatial and non-spatial data = essence of GIS = added value of GIS compared to
other, non-spatial information systems.
Most GIS applications: spatial objects organized in different layers (maps). Each layer can be
visualized and analyzed separately, or in combination.
combine diff sets of data => to visualize how it looks when you combine all.
Spatial (geometric) data
• Represent spatial phenomena with points, lines, and polygons
• Coordinates linked to 3 types of spatial data, for each, you can have non-spatial
data stored in a table
Point: pair of coordinates (x,y)=> trees info on shape and location. line= road=> set of segments that link
two points from which you have the coordinates. The number of points gives you the resolution of the
mapping. closed loop: first point will be last point, polygons: 2D spaces
Non-spatial (thematic) data
set of attributes you can add to ID (unique for each set of coordinates).
Typical questions that may be answered by a GIS
Most essential characteristic of a GIS: linking of spatial and non- spatial data = added value
of a GIS to other non- spatial information systems.
linking spatial and non-spatial information, you can process this data based on many different parameters
Typical questions
• Identification: What are the characteristics of a location/object?
Bv: identify the owner of a given parcel
• Location: where does a certain phenomenon occur?
Bv: localize all non-built parcels within a commune
• Spatio-temporal analysis: Which spatial changes have occurred over a certain
period? Bv: visualize extent of built-up area after 1950, identify someone who has many diff parcels
• Overlay analysis: analyze spatial relationships between phenomena.
bv: How many buildings are within a buffer zone around a factory?
• Spatial modeling: compare/evaluate alternative scenarios, simulation of reality
Vb:
o Geo-marketing: determine the most suitable location for a shopping center based on
accessibility and distribution of potential clients
o Network analysis: simulate the impact of changes in traffic conditions
o 3D terrain analysis: determine the impact of large infrastructures in the landscape (e.g.
windmills)
,Layers in a spatial database
geospatial data: four different shapes => Layer types: point, line, polygon, raster (layers)
1.3. Spatial data models
Reality = complex => spatial model (simplification) of reality.
2 different methods of describing spatial reality: Field approach & Object approach
field approach = description of a terrain in the form of attributes
• Terrain characteristics: attribute values assigned to a set of locations.
• each attribute has a unique value at each location => field of attribute values.
• to describe phenomena characterized by continuous spatial variation (temperature,
elevation...).
• Attribute values: often sampled for irregular set of locations, characteristics of the
sampling (sampling scheme, sampling density) depend on the sampling technique,
the expected spatial correlation between attribute values, and the accuracy
required.
• Measured values for irregular set of
locations: often transformed into a
regular grid by an interpolation
method.
, Interpolation based on “inverse distance weighting.”
Weigh the average value based on 3 actual measurements (3 points: distances).
Weigh each value with inverse distance => further away, less weight
Resample into a grid of the same distance => For each point, with the same distances, you estimate something
to do that you can use for inverse distance weighting
object approach= objects defined on the terrain, determining geometric and thematic chars.
Each entity specified by specific boundaries=> discrete variations: building and tree: no transition between
• For each object, geometric & thematic characteristics are determined.
• For each object class, spatially represented by means of points, lines, or areas.
• method to represent phenomena occurring on earth’s surface as a collection of
objects with clear boundaries, phenomena that are characterized by a discrete
spatial variation (e.g. parcels, houses, roads...).
Model transformations
from a data model based on a field approach to a data model based on objects
from continuous in space to something discrete, vb:
• soil map (types: discrete) based on soil profile analysis (continuous data to represent the field)
• Interpretation of land use or vegetation characteristics, based on aerial photographs or satellite
imagery (continuous data => overlaid zones => discrete zones)
• Construction of a digital elevation model based on triangulation (see further)
Visual interpretation of land use from satellite imagery (urban)
several stacked layers, for each a value is associated with
a wavelength (solar reflection towards space, satellite
captures: information which the human eye can’t see!)
Composite images: combined 3 layers of raster images
with different wavelengths. (=continuous values)
If you take images in blue, green, and red ranges, you get
true color images.
Shifting layers: replace red with something else, with
higher reflection in the green spectrum for building and
urban areas, in blue (reflection of green is bleu)
diff spectrums for different. types of zones
water will appear almost black in this type of image. Red
is a vegetation false color composition, used to look at
vegetation (types, etc. to see where vegetation is).
each pixel’s given values (3 bcs 3 layers) and then define areas corresponding to overlaid zones. residential area
(the yellow lines). into discrete entities with the lines
Construction of a digital terrain model based on triangulation
You can sample topography into specific polygons (triangles here). You can
have continuous variations in space or discrete entities
