RiskCity Exercise 5: Generating an elements at risk database

1. RiskCityExercise 5: Generating an elements at risk database Cees van Westen (ed) ISL 2004

2. Elements at risk / Assets • What may be impacted by a hazard event? ISL 2004

3. Two options • When you don’t have any available data: • We assume that you have at least a high resolution image from Google Earth • When you have available data: • Building footprint map • Lidar DSM • Census data Depending on your interest in the topic you may select to either do Exercise 3.1 (creating a database by starting from scratch), or Exercise 3.2 (creating a database with available footprint information). You can also decide to do both exercises, although that might perhaps take a bit too much time ISL 2004

4. If you don’t have data • You have to: • Generate mapping units • Create the attribute data for: • Urban land use • Number of buildings • Population ISL 2004

5. High res image Boundaries Mapping units Landuse Nr Buildings Population Flowchart: do it yourself option Input data Screen digitize boundaries Sample # buildings by landuse type Polygonize Interpret land use type Calculate # based on land use type & building # Calculate # based on land use type ISL 2004

6. Downloading imagery from Google Earth • Many area in the world are covered by high resolution imagery. • Better first consult than download • For detailed download you need Google Earth Pro (cost 400 US $) • You can download 4000 * 4800 resolution • Here we don’t have Google Earth Pro on all computers. Only one in room 4 – 105 • We have downloaded it already for you • At home you might like to try the trial version of the Goolge Earth Pro, which allows to download high resolution images. Go to: http://earth.google.com/intl/en/product_comparison.html ISL 2004

7. Scanning (automatic digitizing) Y Editing Improving Vectorizing X Apply attributes Raster mode Manual digitizing Improving Apply attributes Y Vector mode Digital Landscape Model X Digitizing maps Sensor ISL 2004

8. Digitizing mapping units Screen digitizing from high resolution image, on the basis of a digital road map Checking segments, and generation of polygons with unique identifiers ISL 2004

9. Digitizing mapping units High res image Digitize a new point Select lines and rename / delete them Digitize segments Create a node / remove a node Added segment Select points and move them ISL 2004

10. Before making polygons you have to make sure all lines are connected Error types: Dead end in segment (1) Intersection without node (2, 3) Double line (4) Self overlap (5) Check segments Digitize segments Check segments Added segment ISL 2004

11. Determining land use Generation of land use legend, with relevant classes for vulnerability assessment, and keeping in mind population difference Interpreting predominant landuse from the high resolution image ISL 2004

12. Landuse classification • Urban landuse mapping: ISL 2004

13. Fill in missing parts ISL 2004

14. Estimating number of buildings • Methods: • Count all buildings in the map…. • Sample buildings for landuse types Steps: • Calculate building size building_size:=iff(buildings_sampled=0,0, area/ buildings_sampled) • Average building size per land use type nr_buildings:=iff(isundef(buildings_sampled),area/avg_building_size, buildings_sampled) ISL 2004

15. Estimating population distribution • Link the number of people per building to land use type • Daytime_population:=nr_buildings * person_building * daytime ISL 2004

16. If you have available data ISL 2004

17. Number of buildings Cross: Building map with mapping units. • how much of the mapping unit is not built-up • how many individual buildings there are per mapping unit • the average building size for each urban land use. Areavacant:=iff(isundef(building_map),area,0) Area_building:=iff(isundef(building_map),?,area) Building:=iff(isundef(building_map),0,1) ISL 2004

18. Aggregate results to mapping units • Calculate per mapping unit: • Total_area= total area per mapping unit • Total_vacant_area = total vacant area per mapping unit • Avg_Size = average building size per mapping unit • Nr_buildings = number of buildings per mapping unit • Percvacant:= Total_vacant_area /Total_area ISL 2004

19. Building height & floorspace DEM from topomap DEM from Lidar minus Division by avg. building height Masking out areas without buildings Landuse map ISL 2004

20. Lidar DEM Topo DEM Altitude of objects Command Line ISL 2004

21. Calculate number of floors • Altitude_dif=LidarDEM-TopoDem • floor_nr=iff(Altitude_dif <3,0, Altitude_dif /3) • Floors:=iff(isundef(building_map),0,floor_nr) ISL 2004

22. Calculate height of buildings • First we cross the Building_map with the map Floors, which gives us all the combinations of floors per building type. • Then we calculate per building the maximum number of floors, and the total floor space for each building. • The resulting values are then read in the Cross table that links the mapping units with the building ID’s (Mapping_units_building). • And finally the total floorspace information is aggregated into the table Mapping_units_attributes ISL 2004

23. Calculate floorspace • Floorspace:=Nr_floors*Area_building • Open the cross table Mapping_units_building. And join with the table Building_map. Read in the columns: Nr_floors and Floorspace • Aggregate to Table: Mapping_units_attributes • Nr_floors_avg =average number of floors per building in mapping unit • Floorspace = floorspace per mapping unit ISL 2004

24. Population estimate • We have information on the population per ward. • We know the floorspace per mapping unit • We can therefore distribute the total population per ward over the mapping unit, also keeping in mind the land use types. • This exercise is not written out: something for the final project ISL 2004