DS7002 Spatial Data Analysis
Practical Exercises
1
Session 6 – Preparation / Integration of Spatial Data
(Spatial Operation and Transformation with QGIS)
Objectives: To gain familiarity in a range of spatial operations through QGIS functionalities.
Task 1: Basic Practice to edit Spatial Features
Practice following editing functionalities:
Layer -> Toggling Editing-> Add Feature, Move Feature, Delete Selected
-> Rotate Feature, Simplify Feature
-> Reshape Feature, Split Feature
-> Add Ring, Fill Ring, Delete Ring
-> Add Part, Delete Part, Split Part
-> Node Tool
Task 2: Segregating and Aggregating Spatial Study Areas
Problem solving: Segregate / aggregate spatial study areas so that data analysis
could be carried out on different geographic scales, which allows local characteristics
to be take into account.
Functionality: Boolean Operations on Vector Data – Overlay
Data set: Map of Postcode Areas around London, Map of Inner-Outer London
Add “postarea.shp” and “london_io.shp”, practice the following functionality:
Vector -> Geoprocessing Tools -> Intersect (AND)
(input layer as “postarea.shp”,
intersect layer as “london_io.shp” )
Postcode Area is large area geography. For detail analysis, it is necessary to
segregate it into relative smaller area geography. With Intersect, Postcode
Areas are segregated by Inner London region, Outer London region and
region outside of London. Please look at the example of Postcode Area of “E”.
Vector -> Geoprocessing Tools -> Dissolve (OR)
(input layer as “postarea.shp”,
Dissolve field as ‘S_N’ )
In contrast to segregation, for overarch analysis, Postcode Area can be
aggregated by common characteristics. In this case of Dissolve, Postcode
Areas are aggregated by South of Thames and North of Thames.
Please also see Vector -> Data Management Tools -> Merge vector layers (OR)
DS7002 Spatial Data Analysis
Practical Exercises
2
Vector -> Geoprocessing Tools -> Difference (NOT)
(input layer as “postarea.shp”,
difference layer as “london_io.shp” )
If only Postcode Areas outside of London need to be analysed, simply cut the
map of Postcode Areas by London boundary with Difference.
Task 3: Creating Spatial Study Areas
Problem solving: Create new spatial study areas (or zone systems) in order to carry
out analysis, simulation or impact assessment on such study areas.
Functionality: Geoprocessing on Vector Data
Data set: Map of credit unions, Map of motorways, Map of London boroughs
Add “credit_union.shp” and “motorway.shp”, practice the folloewing functionalities:
Vector -> Geoprocessing Tools -> Convex Hall
-> Fixed distance buffer
With Convex Hall and Buffer (distance=3000m), a study area are can be
created, which covers all locations of credit unions.
With Buffer (distance=1000m), a buffer zone along motorways can be created
where environmental impact or economic impact can be assessed.
On “postarea.shp”, practice the following functionality:
Vector -> Geometry Tools -> Polygon Centroid
With Polygon Centroid, create central points of Postcode Areas. These
centroids are useful to represent each Postcode Areas or create a new study
area (for example, with Convex Hall and Buffer) for specified data analysis.
These centroids could also help to reduce the computation load, enhance the
spatial query and increase the compatibility.
On “credit_union.shp”, practice the following functionalities:
Vector -> Geometry Tools -> Delaunay triangulation
With Delaunay Triangulation, social / economic landscapes could be
presented, and impact areas could be created for further studies.
Delaunay Triangulation could also be used to present natural / physical
landscape, and for further interpolation.
Create a catchment area using the following functionality
Vector -> Geometry Tools -> Voronoi Polygons (10% buffer region)
With Voronoi Polygons, catchment areas can be created around each credit
union. In the same way, GP catchment areas or school catchment areas can
created. On such catchment areas, demand and supply could be analysed.
DS7002 Spatial Data Analysis
Practical Exercises
3
On “credit_union.shp” and “london_boroughs.shp”, practice following functionality:
Vector -> Data Management Tools -> Join Attributes by Location
(Target layer – credit_union,
Joint layer: london_boroughs)
With Join Attributes by Location, local characteristics can be extracted and
assigned on point events. In this case, information of local borough can be
assigned on each Credit Union for further analysis (e.g. marketing analysis).
Task 4: Preparing Remote Sensing Data
Problem solving: Extract height information of all features from remote sensing data
Functionality: Calculation on Raster Data
Data set: LiDAR data of DTM and DSM around Ravenscourt Park, Hammersmith,
London
Add “TQ2278_DTM_2M.asc” and “TQ2278_DSM_2M.asc”, practice following
functionality:
Raster -> Raster Calculator
In this case, heights of all features are extracted from (DSM – DTM). These
heights could be further processed / calculated to derive more information,
such as roof slope.
Task 5: Working with Data on Different Formats
Problem solving: Extract building height using vector data and raster data
Functionality: Conversion of vector data and raster data
Data set: Map of Building polygon and LiDAR data of DSM around Ravensourt Park,
Hammersmith, London
Add “tq2278_2m.shp”, practice following functionality:
Raster -> Conversion -> Rasterise (Vector to raster)
Convert Building polygons to building raster data so that Building data can be
used with DSM. In this case, if DSM data are multiplied with Building raster
data, height values of Building can be extracted from all geographic features.
On “TQ2278_DSM_2M.asc”, practice following functionality:
Raster -> Conversion -> Polygonise (Raster to vector)
DSM data can also be converted to polygons which have heights as attributes
and can therefore work with other vector data
DS7002 Spatial Data Analysis
Practical Exercises
4
Task 6: Handling Non-Spatial Attributes
Problem solving: Create two new attribute variables – population density and
Household size
Functionality: Attribute Table
Data set: Map of London boroughs
On “london_boroughs.shp”, practice following functionality:
Layer -> Open Attribute Table -> Toggle editing mode
-> Select features using expression
With Attribute Table, create new variables, calculate / update new variables of
population density and Household size
Please export the map of Intersect (AND) from Task 2, Voronoi Polygon from Task 3, height
image from Task 4 (Project -> Save as Images), a CSV table from Task 6 (Save the vector
layer of London_boroughs as CSV), and put them into your portfolio.
Because QGIS is an open source software, to which anyone can make contribution. Do you
have any suggestion to improve these functionalities you practiced in this session?