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Ibadan Metropolis
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
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DataCards
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Indirectly Affected + Directly affected
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Houses Destroyed + Houses Damaged
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Temporal Behaviour
Deaths
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DataCards
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Houses Destroyed , Houses Damaged
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Indirectly Affected, Directly affected >
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Spatial Distribution
Houses Destroyed + Houses Damaged
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Indirectly Affected + Directly affected
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Statistics
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Composition of Disasters
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|
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 |
Accidental Death | 4 | 4 | | | | | | | | | | | | | | | |
Air pollution | 1 | | | | | | | | | | | | | | | | |
Armed Robbery | 25 | 3 | 53 | | | | | 50 | | | | 38968300 | | | | | |
Building Collapse | 27 | 32 | 73 | | 6 | 6 | | | | 8 | | | | | | | |
Crime | 456 | 324 | 161 | 10 | | 2 | 605 | 1360 | | | | 7041846500 | | | | | |
Diahorrea | 124 | 124 | | | | | | | | | | | | | | | |
Domestic Accident | 1 | | | | | | 1 | | | | | | | | | | |
Drowning | 11 | 12 | | | | | | | | | | | | | | | |
Electrocution | 11 | 18 | 1 | | | | | 1 | | | | | | | | | |
Epidemic Cholera | 1 | 4 | | | | | | | | | | | | | | | |
Epidemics | 2 | | 17 | | | | 50 | | | | | | | | | | |
Explosion | 2 | 12 | | | | | | | | | | | | | | | |
Fell into a Black Oil Tanker | 1 | 1 | | | | | | | | | | | | | | | |
Fell into a Well | 7 | 7 | | | | | | | | | | | | | | | |
Fire | 103 | 198 | 87 | | 371 | 1598 | 3528 | 517 | | | | 3538339996 | | | | | |
Flood | 60 | 120 | 100 | 53 | 3102 | 9112 | 101 | 624 | | 100 | | 251500000 | | | | | |
Flooded stream | 2 | 2 | | | | | | | | | | | | | | | |
Flooding | 5 | 9 | | | | | | | | | | | | | | | |
Food Poisoning | 3 | 6 | | | | | | | | | | | | | | | |
Fuel Explosion | 1 | 5 | | | | | | | | | | | | | | | |
Fuel Tanke Explosion | 1 | | | | | | | | | | | 50500000 | | | | | |
Gas Explosion | 1 | | 2 | | | 2 | | 2 | | | | | | | | | |
Generator Fumes | 1 | 2 | | | | | | | | | | | | | | | |
Gun Shot Injuries | 1 | 1 | | | | | | | | | | | | | | | |
House on Fire | 3 | 3 | | | | | | | | | | | | | | | |
Industrial Accident | 2 | 1 | | | | | | | | | | | | | | | |
Meningitis | 57 | 57 | | | | | | | | | | | | | | | |
Motor Accident | 19 | 19 | | | | | | | | | | | | | | | |
Others | 1 | 1 | | | | | | | | | | | | | | | |
Pipeline Vandalism | 1 | 8 | | | | | | | | | | | | | | | |
Rainstorm | 30 | 13 | 8 | 1 | 16026 | 52 | 5 | 3010 | | | | 877919992 | | | | | |
Rape | 2 | | 1 | | | | | 1 | | | | 10000 | | | | | |
Strongwind | 1 | | 1 | | 2 | 30 | 90 | | | | | | | | | | |
Sudden Death | 6 | 6 | | | | | | | | | | | | | | | |
Suicide | 19 | 35 | | | | | | | | | | | | | | | |
Trapped in a Petrol Tanker | 1 | 1 | | | | | | | | | | | | | | | |
Tripped into a Well | 1 | 1 | | | | | | | | | | | | | | | |
Tuberclosis | 133 | 133 | | | | | | | | | | | | | | | |
Typhoid Fever | 33 | 33 | | | | | | | | | | | | | | | |
Urban Violence | 409 | 63 | 966 | 59 | 226 | 15 | 3595 | 1314 | | | | 409872100 | | | | | |
Vehicle Accident | 805 | 1080 | 2593 | | 40 | 4 | 3780 | 48 | 50 | 2 | | 103855000 | | | | | |
Violence | 49 | 123 | | | | | | | | | | | | | | | |
Wind | 2 | 3 | | | | | | | | | | | | | | | |
Windstorm | 2 | | 2 | | | | 1 | | | | | 200 | | | | | |
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Spatial Distribution
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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 |
AKINYELE | AKY | 170 | 254 | 170 | 1 | 2 | 3 | 198 | 57 | | | | 927818000 | | | | | |
EGBEDA | EGB | 152 | 265 | 209 | 3 | | 4 | 360 | 37 | | | | 10096700 | | | | | |
IBADAN | XXX | 293 | 365 | 542 | 55 | 103 | 107 | 2700 | 1209 | | 100 | | 1168532898 | | | | | |
IBADAN N.E. | INE | 177 | 160 | 246 | 5 | 1 | 144 | 180 | 310 | | | | 5752083510 | | | | | |
IBADAN NORTH | IBN | 326 | 260 | 480 | 5 | 1044 | 2356 | 759 | 718 | 50 | | | 824923596 | | | | | |
IBADAN N.W. | INW | 158 | 82 | 103 | 3 | 401 | 108 | 3545 | 306 | | 10 | | 1155336652 | | | | | |
IBADAN S.E. | ISE | 87 | 38 | 181 | 27 | 1032 | 1078 | 435 | 1046 | | | | 182572850 | | | | | |
IBADAN S.W. | ISW | 371 | 336 | 432 | 8 | 3180 | 3014 | 804 | 539 | | | | 1391135052 | | | | | |
IDO | IDO | 109 | 81 | 111 | 7 | 1002 | 1004 | 128 | 551 | | | | 122956866 | | | | | |
LAGELU | LGE | 43 | 30 | 21 | 1 | | | 128 | 6 | | | | 50523800 | | | | | |
OLUYOLE | OLY | 466 | 519 | 1482 | 3 | 12006 | 3003 | 2370 | 1132 | | | | 722677444 | | | | | |
ONA-ARA | ONA | 75 | 74 | 88 | 5 | 1002 | | 149 | 1016 | | | | 4154720 | | | | | |
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Temporal Behaviour
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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 |
1991 | 4 | 4 | | | | | | | | | | | | | | | |
1996 | 1 | 1 | | | | | | | | | | | | | | | |
1999 | 3 | 3 | 2 | | | | | | | | | 10000 | | | | | |
2000 | 90 | 109 | 64 | | 1 | 4 | 49 | 97 | | | | 154735498 | | | | | |
2001 | 88 | 112 | 11 | | 100 | 3 | | 3 | | | | 102350500 | | | | | |
2002 | 71 | 88 | 8 | 6 | 1 | 301 | | 311 | | | | 61600000 | | | | | |
2003 | 73 | 87 | 40 | 3 | | 1 | 15 | 17 | | | | 4204957 | | | | | |
2004 | 68 | 156 | 5 | | | | | 5 | | | | 9150000 | | | | | |
2005 | 137 | 99 | 300 | 7 | 35 | 7 | 104 | 79 | 50 | | | 470352700 | | | | | |
2006 | 143 | 97 | 115 | 3 | 1 | 1099 | 1100 | 121 | | | | 321355509 | | | | | |
2007 | 138 | 138 | 321 | 7 | 109 | 16 | 1 | 138 | | | | 1127434998 | | | | | |
2008 | 93 | 107 | 92 | | 1 | 1 | 16 | 77 | | | | 1008089062 | | | | | |
2009 | 152 | 145 | 306 | 8 | | 5 | 577 | 25 | | | | 92014300 | | | | | |
2010 | 181 | 247 | 461 | 24 | 1 | | 592 | 509 | | | | 80310000 | | | | | |
2011 | 162 | 215 | 709 | 54 | 101 | 106 | 585 | 372 | | 108 | | 315664000 | | | | | |
2012 | 254 | 186 | 397 | 5 | 3108 | 9011 | 318 | 102 | | | | 348213200 | | | | | |
2013 | 153 | 161 | 76 | | 16040 | | 106 | 4022 | | 2 | | 763913696 | | | | | |
2014 | 345 | 354 | 241 | 4 | 269 | 164 | 6336 | 1041 | | | | 6898072670 | | | | | |
2015 | 269 | 155 | 917 | 2 | 6 | 103 | 1957 | 3 | | | | 553193000 | | | | | |
2016 | 2 | | | | | | | 5 | | | | 2148000 | | | | | |
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Summary: DataCards: 2427 Period:1991 - 2016
Highest Mortality: Vehicle Accident: 1080 Deaths; 805 DataCards Crime: 324 Deaths; 456 DataCards Fire: 198 Deaths; 103 DataCards Highest Housing Damages: Rainstorm: 16078 Houses; 30 DataCards Flood: 12214 Houses; 60 DataCards Fire: 1969 Houses; 103 DataCards |
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