riskcity exercise 5: generating an elements at risk database

ISL 2004

International Institute for Geo-Information Science and Earth Observation (ITC)

RiskCityRiskCity

Exercise 5: Generating an Exercise 5: Generating an elements at risk database elements at risk database

Cees van Westen (ed)

ISL 2004


Elements at risk / AssetsElements at risk / Assets• What may be impacted by a hazard event?

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Two optionsTwo 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

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If you don’t have dataIf you don’t have data

• You have to:• Generate mapping units • Create the attribute data for:

• Urban land use • Number of buildings• Population

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Flowchart: do it yourself optionFlowchart: do it yourself option

High res image

Boundaries

Landuse Nr Buildings

Input data

Population

Polygonize

Screen digitize

boundariesMapping units

Interpret land use type

Sample # buildings by landuse type

Calculate # based on land use

type

Calculate # based on land use type &

building #

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Downloading imagery from Downloading imagery from Google EarthGoogle 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

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International Institute for Geo-Information Science and Earth Observation (ITC)Digitizing mapsDigitizing maps

X

Y

Scanning (automatic digitizing)

EditingImproving

Vectorizing

Apply attributes

X

Y

Manual digitizing

Raster mode

Vector mode

Sensor

ImprovingApply attributes

Digital LandscapeModel

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Digitizing mapping unitsDigitizing 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

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Digitizing mapping unitsDigitizing mapping units

High res image

Digitize segments

Added segment

Digitize a new point

Create a node /

remove a nodeSelect points

and move them

Select lines and rename / delete them

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Check segmentsCheck segments

Digitize segments Check segments

Added segment

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)

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Determining land useDetermining 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

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Landuse classificationLanduse classification

• Urban landuse mapping:

Name Code Description

Com _bus iness Com _b Bus iness offices

Com _hotel com _h Hotels

Com _m arket com _m Com m ercial area: m arket area

Com _shop com _s Com m ercial: shops and shopping m alls

Ind_hazardous ind_h Hazadous m aterial s torage or m anufacture

Ind_indus tries ind_i Indus tries

Ind_warehouse ind_w Warehouses and workshops

Ins_fire ins_f Fire brigade

Ins_hospital ins_h Hospitals

Ins_office ins_o Office buildings

Ins_police ins_p Police s tation

Ins_school ins_s Ins titutional : schools

Pub_cem etery Pub_g Cem etery

Pub_cultural pub_c Ins titutional: cultural buildings such as m usea, theaters

Pub_electricity pub_e Electricity ins tallations

Pub_religious pub_r Religious buildings such as churches , m osques or tem ples

Rec_flat_area rec_f Recreational: flat area or foorball field

Rec_park rec_p Recreational: park area

Rec_s tadium rec_s Recreational : s tadium

Res_large res_5 Res idential: large free s tading houses

Res_m od_s ingle res_4 Res idential, m oderately s ized s ingle fam ily houses

Res_m ulti res_3 Res idential: m ulti s torey buildings

Res_sm all_s ingle res_2 Res idential, sm all s ingle fam ily houses , m os tly in rows

Res_squatter res_1 Res idencial, low class houses : squatter areas

River riv River

unknown u

Vac_car vac_c Vacant : car parking and buss tation

Vac_cons truction vac_u Vacant area which is prepared for building cons truction

vac_dam aged vac_d Area recently dam aged by hazard events

Vac_shrubs vac_s Vacant land with shrubs , trees and gress

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Fill in missing partsFill in missing parts

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Estimating number of buildingsEstimating number of buildings

• Methods:1. Count all buildings in the map….

2. 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 typenr_buildings:=iff(isundef(buildings_sampled),area/avg_building_size,

buildings_sampled)

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Estimating population distributionEstimating population distribution• Link the number of people per building to

land use type

• Daytime_population:=nr_buildings * person_building * daytime

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If you have available dataIf you have available data

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Number of buildingsNumber of buildings

Areavacant:=iff(isundef(building_map),area,0)

Area_building:=iff(isundef(building_map),?,area)

Building:=iff(isundef(building_map),0,1)

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.

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Aggregate results to mapping unitsAggregate 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

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Building height & floorspaceBuilding height & floorspaceDEM from Lidar

Division by avg. building height

DEM from topomap

minus

Masking out areas without buildings

Landuse map

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Altitude of objectsAltitude of objects

Command Line

Lidar DEM

Topo DEM

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Calculate number of floorsCalculate number of floors

• Altitude_dif=LidarDEM-TopoDem• floor_nr=iff(Altitude_dif <3,0,

Altitude_dif /3)• Floors:=iff(isundef(building_map),0,fl

oor_nr)

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Calculate height of buildingsCalculate 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

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Calculate floorspaceCalculate 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

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Population estimatePopulation 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

riskcity exercise 5: generating an elements at risk database

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