UMKC Center for Health Insights
Earl F Glynn
2013-12-12. Last modified 2015-01-04.
https://data.kcmo.org/311/KCMOPS311-Data/7at3-sxhp
2015-01-03
The current file has 819903 rows.
health <- d[d$DEPARTMENT == "Health", ]
dim(health) ## [1] 14014 34There are 14014 Health Department records in the file of 819903 records.
table(health$SOURCE, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## EDC 0 0 0 0 0 0 1 0 0
## EIP 1 0 0 0 0 1 0 0 0
## EMAIL 13 38 29 35 21 32 44 40 1
## INSPE 0 13 1 1 25 4 4 1 0
## MAIL 0 0 0 3 1 1 0 0 0
## PHONE 864 1335 1364 1592 1634 1713 1701 1722 4
## SYS 0 0 0 0 1 0 0 0 0
## TWIR 0 0 0 0 0 0 5 5 0
## VOICE 0 10 14 18 9 0 0 0 0
## WALK 1 2 6 9 6 10 8 5 0
## WEB 3 33 42 246 310 395 409 228 0Reports by Status:
table(health$STATUS,health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## CANC 0 0 1 0 0 1 0 0 0
## DUP 0 0 9 4 0 0 0 1 0
## OPEN 0 0 0 0 0 0 0 110 4
## RESOL 882 1431 1446 1900 2007 2155 2172 1889 1
## RHOLD 0 0 0 0 0 0 0 1 0Reports by Creation Year and Month:
table(health$CreationYear, health$CreationMonth)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 2007 18 35 60 62 58 79 70 82 100 137 97 84
## 2008 101 63 77 105 102 125 152 162 163 178 109 94
## 2009 73 70 94 94 119 122 148 158 166 195 132 85
## 2010 88 95 121 150 172 174 183 212 200 228 165 116
## 2011 105 93 154 142 190 190 182 224 227 219 168 113
## 2012 122 147 173 171 180 177 202 228 240 199 178 139
## 2013 135 103 140 229 201 202 237 213 246 213 138 115
## 2014 101 112 148 153 165 154 244 198 197 232 147 150
## 2015 5 0 0 0 0 0 0 0 0 0 0 0table(health$WORK.GROUP, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## Health-- 299 376 498 22 0 4 8 17 0
## Health-Air Quality- 0 0 0 161 166 151 115 96 1
## Health-Communicable Disease Control- 0 0 0 0 1 2 0 0 0
## Health-Community Environmental Health- 0 0 0 65 74 76 94 82 0
## Health-Food Protection- 0 0 0 382 411 426 605 472 0
## Health-Healthy Homes- 0 0 0 34 27 36 18 37 0
## Health-Healthy Homes-Bedbugs 0 0 0 9 57 82 59 47 0
## Health-Lead Poisoning Prevention- 0 0 0 4 7 2 1 2 0
## Health-Noise Control- 0 0 0 175 175 291 260 212 0
## Health-Rat- 583 1055 958 996 1046 1063 979 1013 4
## Health-Tobacco Control- 0 0 0 56 43 23 33 23 0Reports by Creation Year and Close Year
table(health$CreationYear, health$CloseYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 2007 868 13 1 0 0 0 0 0 0
## 2008 0 1405 26 0 0 0 0 0 0
## 2009 0 0 1431 24 0 0 0 0 0
## 2010 0 0 0 1886 18 0 0 0 0
## 2011 0 0 0 0 1993 14 0 0 0
## 2012 0 0 0 0 0 2126 29 0 0
## 2013 0 0 0 0 0 0 2138 34 0
## 2014 0 0 0 0 0 0 0 1890 0
## 2015 0 0 0 0 0 0 0 0 1counts <- as.data.frame.matrix(table(health$REQUEST.TYPE, health$CREATION.YEAR))
counts <- cbind(counts, TOTAL=apply(counts,1,sum))
counts <- rbind(counts, TOTAL=apply(counts,2,sum))
counts ## 2007 2008 2009 2010 2011 2012 2013 2014 2015 TOTAL
## Barking Dog 0 0 0 0 0 0 1 0 0 1
## Bedbugs Problem 0 0 0 9 57 82 59 45 0 252
## Childhood Lead Poisoning Prevention 0 0 0 4 7 2 1 2 0 16
## Communicable Disease Control 0 0 0 0 1 1 0 0 0 2
## Food Establishment Complaints 0 0 0 382 411 425 605 469 0 2292
## Health-All 299 376 498 22 0 0 0 0 0 1195
## Healthy Homes Program 0 0 0 34 27 36 18 37 0 152
## Hotel and Motel Inspections 0 0 0 39 46 41 59 57 0 242
## Integrated Pest Management Class 0 0 0 0 0 0 0 2 0 2
## Noise Complaints 0 0 0 175 175 291 259 213 0 1113
## Outdoor Air Quality 0 0 0 161 166 151 115 96 1 690
## Public Pool Inspections 0 0 0 14 15 15 20 11 0 75
## Public Restroom Sanitation 0 0 0 12 13 20 14 14 0 73
## Questions or Comments 0 0 0 0 0 5 9 20 0 34
## Rat Control Education 583 66 0 0 0 0 0 0 0 649
## Rat Control Treatment 0 989 958 996 1046 1063 979 1012 4 7047
## Smoking Complaints 0 0 0 56 43 23 33 23 0 178
## Zoning Inspections and Violations 0 0 0 0 0 1 0 0 0 1
## TOTAL 882 1431 1456 1904 2007 2156 2172 2001 5 14014table(health$ZIP.CODE, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 0 0 1 3 3 2 1 3 0
## 64055 1 3 0 0 0 0 0 0 0
## 64101 1 1 1 3 0 6 3 5 0
## 64102 0 0 2 4 2 4 2 2 0
## 64105 8 8 21 21 18 24 25 16 0
## 64106 21 31 35 65 78 69 78 48 0
## 64108 21 28 41 97 85 99 114 73 0
## 64109 40 74 68 65 97 97 69 109 3
## 64110 60 92 80 89 95 118 96 118 0
## 64111 28 56 73 120 122 174 134 108 0
## 64112 14 18 23 42 38 52 61 52 0
## 64113 11 37 25 43 46 40 47 50 0
## 64114 24 41 39 75 81 91 101 78 0
## 64116 3 11 11 21 20 15 37 27 0
## 64117 8 8 18 24 18 18 38 29 0
## 64118 7 10 15 37 32 21 40 27 0
## 64119 7 14 7 11 24 18 23 24 0
## 64120 3 4 8 16 23 18 13 3 0
## 64123 21 22 25 26 48 57 54 41 0
## 64124 18 29 47 38 62 47 51 43 0
## 64125 4 7 9 12 10 7 14 19 0
## 64126 14 27 20 14 22 32 27 20 0
## 64127 107 161 143 184 199 264 206 165 0
## 64128 90 163 165 193 163 180 178 167 0
## 64129 9 26 25 22 23 36 50 31 0
## 64130 133 247 265 257 277 253 229 261 0
## 64131 54 85 75 83 75 84 93 98 0
## 64132 66 96 71 100 105 110 124 123 2
## 64133 11 13 20 26 17 34 26 23 0
## 64134 24 49 32 53 51 37 59 59 0
## 64136 0 1 2 8 5 4 12 9 0
## 64137 10 7 4 14 9 8 15 12 0
## 64138 22 25 21 36 40 33 43 40 0
## 64139 1 1 3 1 1 0 0 2 0
## 64145 4 1 5 19 5 8 5 8 0
## 64146 2 1 1 1 0 0 1 2 0
## 64147 0 0 1 0 0 2 2 1 0
## 64149 0 0 0 0 0 0 1 0 0
## 64151 6 5 12 8 26 11 16 21 0
## 64152 2 3 4 9 7 4 7 10 0
## 64153 5 6 6 15 32 14 18 19 0
## 64154 5 4 8 16 12 16 12 12 0
## 64155 7 6 9 10 11 27 20 16 0
## 64156 1 0 2 1 0 1 0 0 0
## 64157 3 4 8 4 9 10 11 13 0
## 64158 3 2 4 7 8 5 10 7 0
## 64160 0 0 0 0 0 1 0 0 0
## 64161 3 3 1 9 2 4 3 7 0
## 64163 0 1 0 2 6 0 3 0 0
## 64165 0 0 0 0 0 1 0 0 0table(health$COUNCIL.DISTRICT, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 105 145 125 44 27 24 15 2 0
## 1 56 72 102 94 82 75 124 108 0
## 2 48 64 81 86 117 80 86 91 0
## 3 294 522 513 708 730 792 720 690 1
## 4 119 212 235 419 484 574 558 441 2
## 5 181 325 319 359 384 396 432 467 2
## 6 79 91 81 194 183 215 237 202 0table(health$QUALITY.OF.SERVICE, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 882 1429 1452 1795 1866 1978 2073 1998 5
## 1 0 0 0 17 9 16 6 0 0
## 2 0 0 0 4 3 5 6 2 0
## 3 0 0 0 15 19 8 9 0 0
## 4 0 1 1 27 24 51 19 0 0
## 5 0 1 3 46 86 98 59 1 0table(health$TIMELINESS.OF..SERVICE, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 882 1429 1452 1795 1866 1983 2079 1999 5
## 1 0 0 0 12 5 11 5 0 0
## 2 0 0 0 7 2 4 3 0 0
## 3 0 0 0 17 20 5 7 0 0
## 4 0 1 1 25 25 45 14 2 0
## 5 0 1 3 48 89 108 64 0 0table(health$CUSTOMER.SERVICE, health$CreationYear)##
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 882 1429 1452 1795 1867 1990 2076 1998 5
## 1 0 0 0 5 2 6 5 0 0
## 2 0 0 0 7 2 2 3 1 0
## 3 0 0 0 9 11 8 8 0 0
## 4 0 1 2 29 29 38 13 1 0
## 5 0 1 2 59 96 112 67 1 0counts <- sort(table(health$ADDRESS.WITH.GEOCODE), decreasing=TRUE)
counts <- data.frame(Address=names(counts), Counts=counts)
row.names(counts) <- 1:nrow(counts)
head(counts, 50)## Address Counts
## 1 2400 TROOST AVE\nKansas City, Missouri 64108\n(39.08309077100046, -94.5706809889997) 90
## 2 414 12TH ST\nKansas City, Missouri 64106\n(39.099740099000485, -94.5776004439997) 81
## 3 4508 MONROE AVE\nKansas City, Missouri 64130\n(39.043731110000465, -94.54149021999967) 27
## 4 1603 39TH ST\nKansas City, Missouri 64111\n(39.05724046800049, -94.6040902639997) 25
## 5 417 18TH ST\nKansas City, Missouri 64108\n(39.091630335000445, -94.57811090599967) 24
## 6 6006 31ST ST\nKansas City, Missouri 64129\n(39.0687102870005, -94.51382046299966) 23
## 7 5620 BANNISTER RD\nKansas City, Missouri 64132\n(38.98687337400048, -94.55216991399965) 22
## 8 6101 87TH ST\nKansas City, Missouri 64138\n(38.96726104200047, -94.51712059999966) 21
## 9 3325 MONTGALL AVE\nKansas City, Missouri 64128\n(39.06518084600049, -94.55191030899965) 20
## 10 8600 WARD PKWY\nKansas City, Missouri 64114\n(38.97211338700049, -94.60567334699965) 19
## 11 2450 GRAND BLVD\nKansas City, Missouri 64108\n(39.08256072100045, -94.58180051399967) 18
## 12 3051 VAN BRUNT BLVD\nKansas City, Missouri 64128\n(39.069660920000445, -94.5205509439997) 18
## 13 5150 OAK TRFY\nKansas City, Missouri 64118\n(39.21427358200049, -94.57336919499966) 16
## 14 9236 MCGEE ST\nKansas City, Missouri 64114\n(38.96009038800048, -94.58761032099966) 16
## 15 928 WYANDOTTE ST\nKansas City, Missouri 64105\n(39.102730272000485, -94.58545094499965) 16
## 16 4019 31ST ST\nKansas City, Missouri 64128\n(39.06930539400048, -94.53644641799968) 15
## 17 5101 TROOST AVE\nKansas City, Missouri 64110\n(39.0340104550005, -94.57342111699967) 15
## 18 5714 LOCUST ST\nKansas City, Missouri 64110\n(39.022741134000455, -94.5821208489997) 15
## 19 918 9TH ST\nKansas City, Missouri 64106\n(39.103180814000496, -94.57135046499968) 15
## 20 2456 OLIVE ST\nKansas City, Missouri 64127\n(39.08094061100047, -94.55457092899968) 14
## 21 3045 60TH ST\nKansas City, Missouri 64130\n(39.017380716000446, -94.54922088199964) 14
## 22 1223 COLORADO AVE\nKansas City, Missouri 64127\n(39.09688013400046, -94.52013036199969) 13
## 23 1600 PARVIN RD\nKansas City, Missouri 64116\n(39.166740710000454, -94.56055012199965) 13
## 24 2400 Troost Ave\nKansas City, Missouri 64108\n(39.08309077100046, -94.5706809889997) 13
## 25 2546 PARK AVE\nKansas City, Missouri 64127\n(39.07915111700049, -94.55584074399968) 13
## 26 5500 PROSPECT AVE\nKansas City, Missouri 64130\n(39.0264007830005, -94.55520007499968) 13
## 27 7100 PARVIN RD\nKansas City, Missouri 64117\n(39.1689404170005, -94.49696026699968) 13
## 28 9103 39TH ST\nKansas City, Missouri 64133\n(39.052450430000476, -94.47237056099965) 13
## 29 1421 PROSPECT AVE\nKansas City, Missouri 64127\n(39.09526002500047, -94.55158075599968) 12
## 30 3132 POPLAR AVE\nKansas City, Missouri 64128\n(39.06777002500047, -94.52679055399966) 12
## 31 4001 MILL ST\nKansas City, Missouri 64111\n(39.05353011900047, -94.59381075299967) 12
## 32 4163 BROADWAY BLVD\nKansas City, Missouri 64111\n(39.051340782000466, -94.58901034499968) 12
## 33 2454 PARK AVE\nKansas City, Missouri 64127\n(39.08118028700045, -94.55574049599966) 11
## 34 4800 BANNISTER RD\nKansas City, Missouri 64132\n(38.98687337400048, -94.55216991399965) 11
## 35 5706 WABASH AVE\nKansas City, Missouri 64130\n(39.0226408850005, -94.55657013899969) 11
## 36 6330 BROOKSIDE PLZ\nKansas City, Missouri 64113\n(39.013407320000454, -94.59073066799965) 11
## 37 8304 HICKMAN MILLS DR\nKansas City, Missouri 64132\n(38.97554019100045, -94.5541100159997) 11
## 38 11300 HOLMES RD\nKansas City, Missouri 64131\n(38.92043113400047, -94.58379050599967) 10
## 39 11801 BLUE RIDGE BLVD\nKansas City, Missouri 64134\n(38.91005022200045, -94.52113054299969) 10
## 40 1524 CHELSEA AVE\nKansas City, Missouri 64127\n(39.093200896000496, -94.52567053499968) 10
## 41 1603 ELMWOOD AVE\nKansas City, Missouri 64127\n(39.09234014200047, -94.52893034199968) 10
## 42 2000 GRAND BLVD\nKansas City, Missouri 64108\n(39.088851030000455, -94.58150092099964) 10
## 43 2532 POPLAR AVE\nKansas City, Missouri 64127\n(39.07865102700049, -94.5257004939997) 10
## 44 2620 43RD ST\nKansas City, Missouri 64117\n(39.17148004700044, -94.5464300539997) 10
## 45 3801 BROADWAY BLVD\nKansas City, Missouri 64111\n(39.05847213600049, -94.59058843699967) 10
## 46 3803 TRUMAN RD\nKansas City, Missouri 64127\n(39.09414871700045, -94.53767172899967) 10
## 47 4002 OAK TRFY\nKansas City, Missouri 64116\n(39.157762440000454, -94.57280868899966) 10
## 48 4416 26TH ST\nKansas City, Missouri 64127\n(39.07820048400049, -94.53106091199965) 10
## 49 8023 WAYNE AVE\nKansas City, Missouri 64131\n(38.982070175000445, -94.56923026499965) 10
## 50 8320 CHURCH RD\nKansas City, Missouri 64158\n(39.24274064600047, -94.46184100499966) 10