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Region: Angola - [ago]          Profile:

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Disaster type
Province
Angola        

Profile:
This Country Profile shows a set of typical results known as "Preliminary Analysis" comming from the disaster database. Charts, Maps and tables below will provide you with a basic understanding of the effects of many types of disasters occurred in the region.   Click here for more info
Composition of Disasters

Deaths
DataCards
Indirectly Affected + Directly affected
Houses Destroyed + Houses Damaged


Temporal Behaviour

Deaths
DataCards
Houses Destroyed , Houses Damaged
Indirectly Affected, Directly affected
>


Spatial Distribution

Deaths
DataCards
Houses Destroyed + Houses Damaged
Indirectly Affected + Directly affected



Statistics



Composition of Disasters       get it as Excel
Event DataCards Deaths Injured Missing Houses
Destroyed
Houses
Damaged
Indirectly Affected Directly affected Relocated Evacuated Losses $USD Losses $Local Education centers Hospitals Damages in crops Ha. Lost Cattle Damages in roads Mts
AVIATION ACCIDENT11655015010415041
CHOLERA44942707768271218
COASTAL EROSION1100300100060001800
CONFLICT11759
DROUGHT1735643105472324767556655401253057600000000106675
EARTHQUAKE286563924392458
EPIDEMIC94675157764794
EROSION33412532219325303
EXPLOSION1151
EXTREME TEMPERATURE12711355135532520000
FIRE1810433004153514001357200320000000
FLOOD14897930033098751143923149431663415068163287778241165876045533030009958536009738150000007122000000040
Heavy Rain With Lightnings5944218611095325327646217175432208260041074494000163247
LANDSLIDE31884501153108665466476564627640000
LIGHTNING514047514903207725
NAVIGATION ACCIDENT110
PANDEMIC11313
PLANE CRASH4331501515150173136
POLLUTION177
RIVERINE FLOOD382871553906371757894106326460171
ROAD ACCIDENT232042442329617749250000001
STRUCTURAL COLLAPSE2353479948188242447500006213
TORNADO2109109654


Spatial Distribution       get it as Excel
Geography Code DataCards Deaths Injured Missing Houses
Destroyed
Houses
Damaged
Indirectly Affected Directly affected Relocated Evacuated Losses $USD Losses $Local Education centers Hospitals Damages in crops Ha. Lost Cattle Damages in roads Mts
Bengo18315337273175
Benguela09636259538363176791804743714170419754562260000033030000058536000050000000022000000000
Bié1132969224285034139847313
Cabinda0121025174122173868
Cuando Cubango131150301057193741105888109781011070333
Cuanza Norte0526241274263127298442318273622740001210
Cuanza Sul06310338045243372
Cunene161515810498592023105482976232663600000000
Huambo1019751321144885117907810142
Huíla15221636810345114976131812308100000001
Luanda04471720523424243699592254164221187717778242014282904559999247132
Lunda Norte082117399829413666320000000
Lunda Sul17191259743211270
Malanje073917816601020221114737176311
Moxico121
Namibe14380522296620615733501901
Uíge03633373116787774300
Zaire02234892544544357666443


Temporal Behaviour       get it as Excel
Year DataCards Deaths Injured Missing Houses
Destroyed
Houses
Damaged
Indirectly Affected Directly affected Relocated Evacuated Losses $USD Losses $Local Education centers Hospitals Damages in crops Ha. Lost Cattle Damages in roads Mts
19171162963963540540
19771160956823
19782204385432432446
1979186543924392458
19801141498414
19811100030001701000200000000000
19831
19841
198611414
1991122
199216563434
1993212424
19951101501515150150136
19991663626
200111418
200221350301863602110000001
20032281787878
2004115015003001800180001500
20055462439214061328771912541880762069696640000244
2007746815691211719182553945543210533785639640005188524
200891287923054060619332253650151
20094157610042015615021501413581
201035213207225325978174349861628152500000063
20112411239947384217386160732216932
2012202146577741194072555506923985481011177
201347207201320691338565691613686223080898136133448
201427192115317986306036192565639590420337211639247500001964534766
2015422011319960186683592802509456619801575045478594450330300014585360007450000000622000000011
201681531965934333803879973784411112826332815400003652154
20173278149154740434557522655411529860000001
Angola

Summary:
DataCards: 355
Period:1917 - 2017

Highest Mortality:
FIRE: 1043 Deaths; 18 DataCards
FLOOD: 979 Deaths; 148 DataCards
EPIDEMIC: 467 Deaths; 9 DataCards
Highest Housing Damages:
DROUGHT: 6431054 Houses; 17 DataCards
RIVERINE FLOOD: 375663 Houses; 38 DataCards
FLOOD: 124267 Houses; 148 DataCards


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