Palmira
Martha Bohorquez
2023-04-04
Descriptivo Palmira
Palmira=read.table("Palmira_Urbano_planas.txt",head=T,sep = ",")
Palmira$INTERVALOS_HORA_t=as.POSIXct(as.character(Palmira$INTERVALOS_HORA), format="%R")####Mapas por uso de suelo, recortado dejando solo la zona urbana, con todos los puntos de los robos
Palmira1_covar=filter(Palmira,GENERO %in% c("FEMENINO","MASCULINO"))
Palmira1_covar$EDAD_Cat=cut(Palmira1_covar$EDAD, breaks=c(0,18,45,65,100), labels = c("Menor de 18","18-45","45-65","Mayor de 65"))
Palmira1_covar$Armas_indicator=ifelse(Palmira1_covar$ARMAS=="SINEMPLEODEARMAS","Sin","Con")
Palmira1_covar$SEMESTRE=dplyr::recode(Palmira1_covar$MES, "ene"="SEM_1", "feb"="SEM_1", "mar"="SEM_1", "abr"="SEM_1", "may"="SEM_1","jun"="SEM_1","jul"="SEM_2","ago"="SEM_2","sep"="SEM_2","oct"="SEM_2","nov"="SEM_2","dic"="SEM_2")
Palmira1_covar <- subset(Palmira1_covar, select = c("X_plain","Y_plain","YEAR","BARRIOS","GENERO","DIA_SEMANA","INTERVALOS_HORA","MODALIDAD","EDAD_Cat",
"Armas_indicator","MES","SEMESTRE","LONGITUD","LATITUD"))
table(Palmira1_covar$YEAR,Palmira1_covar$SEMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$GENERO)## , , = 00:00-05:59, = FEMENINO
##
##
## SEM_1 SEM_2
## 2012 60 28
## 2013 26 37
## 2014 19 21
## 2015 16 26
## 2016 32 22
## 2017 25 31
## 2018 60 98
## 2019 80 73
## 2020 47 45
## 2021 63 111
## 2022 80 0
##
## , , = 06:00-11:59, = FEMENINO
##
##
## SEM_1 SEM_2
## 2012 47 42
## 2013 50 26
## 2014 50 40
## 2015 52 67
## 2016 65 78
## 2017 76 91
## 2018 139 115
## 2019 124 118
## 2020 82 97
## 2021 100 136
## 2022 121 0
##
## , , = 12:00-17:59, = FEMENINO
##
##
## SEM_1 SEM_2
## 2012 63 56
## 2013 62 56
## 2014 56 56
## 2015 69 85
## 2016 72 79
## 2017 103 84
## 2018 108 129
## 2019 120 120
## 2020 73 83
## 2021 74 131
## 2022 131 0
##
## , , = 18:00-23:59, = FEMENINO
##
##
## SEM_1 SEM_2
## 2012 32 31
## 2013 41 23
## 2014 36 46
## 2015 39 57
## 2016 41 66
## 2017 58 75
## 2018 83 77
## 2019 123 84
## 2020 48 68
## 2021 66 88
## 2022 89 0
##
## , , = 00:00-05:59, = MASCULINO
##
##
## SEM_1 SEM_2
## 2012 67 43
## 2013 53 40
## 2014 54 33
## 2015 40 34
## 2016 45 50
## 2017 48 70
## 2018 66 90
## 2019 97 82
## 2020 63 74
## 2021 76 86
## 2022 95 0
##
## , , = 06:00-11:59, = MASCULINO
##
##
## SEM_1 SEM_2
## 2012 85 106
## 2013 97 68
## 2014 72 75
## 2015 63 72
## 2016 91 108
## 2017 102 100
## 2018 125 87
## 2019 110 90
## 2020 65 88
## 2021 98 119
## 2022 132 0
##
## , , = 12:00-17:59, = MASCULINO
##
##
## SEM_1 SEM_2
## 2012 88 104
## 2013 106 82
## 2014 90 80
## 2015 91 97
## 2016 64 90
## 2017 95 95
## 2018 92 103
## 2019 112 91
## 2020 83 97
## 2021 78 131
## 2022 133 0
##
## , , = 18:00-23:59, = MASCULINO
##
##
## SEM_1 SEM_2
## 2012 50 62
## 2013 66 49
## 2014 66 61
## 2015 72 83
## 2016 55 71
## 2017 63 104
## 2018 105 109
## 2019 113 109
## 2020 60 84
## 2021 69 88
## 2022 116 0table(Palmira1_covar$YEAR,Palmira1_covar$SEMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$EDAD_Cat)## , , = 00:00-05:59, = Menor de 18
##
##
## SEM_1 SEM_2
## 2012 1 1
## 2013 1 0
## 2014 1 0
## 2015 1 1
## 2016 7 4
## 2017 4 8
## 2018 4 3
## 2019 5 5
## 2020 3 3
## 2021 3 6
## 2022 9 0
##
## , , = 06:00-11:59, = Menor de 18
##
##
## SEM_1 SEM_2
## 2012 4 2
## 2013 3 4
## 2014 4 6
## 2015 3 5
## 2016 17 15
## 2017 14 9
## 2018 12 9
## 2019 8 2
## 2020 4 3
## 2021 1 5
## 2022 4 0
##
## , , = 12:00-17:59, = Menor de 18
##
##
## SEM_1 SEM_2
## 2012 8 6
## 2013 12 6
## 2014 10 12
## 2015 15 9
## 2016 8 6
## 2017 19 18
## 2018 8 11
## 2019 8 7
## 2020 4 2
## 2021 1 9
## 2022 15 0
##
## , , = 18:00-23:59, = Menor de 18
##
##
## SEM_1 SEM_2
## 2012 1 8
## 2013 5 7
## 2014 4 7
## 2015 5 7
## 2016 4 6
## 2017 12 12
## 2018 9 7
## 2019 6 8
## 2020 8 2
## 2021 2 5
## 2022 16 0
##
## , , = 00:00-05:59, = 18-45
##
##
## SEM_1 SEM_2
## 2012 85 53
## 2013 51 50
## 2014 55 35
## 2015 41 43
## 2016 48 46
## 2017 41 70
## 2018 100 157
## 2019 119 100
## 2020 65 90
## 2021 94 145
## 2022 123 0
##
## , , = 06:00-11:59, = 18-45
##
##
## SEM_1 SEM_2
## 2012 88 108
## 2013 103 69
## 2014 95 83
## 2015 82 95
## 2016 98 119
## 2017 111 131
## 2018 189 141
## 2019 156 153
## 2020 97 110
## 2021 136 184
## 2022 175 0
##
## , , = 12:00-17:59, = 18-45
##
##
## SEM_1 SEM_2
## 2012 103 116
## 2013 121 110
## 2014 103 91
## 2015 107 120
## 2016 100 115
## 2017 115 110
## 2018 145 180
## 2019 160 156
## 2020 96 119
## 2021 101 186
## 2022 169 0
##
## , , = 18:00-23:59, = 18-45
##
##
## SEM_1 SEM_2
## 2012 57 61
## 2013 77 52
## 2014 83 72
## 2015 85 102
## 2016 72 100
## 2017 75 123
## 2018 142 140
## 2019 182 139
## 2020 78 110
## 2021 114 138
## 2022 150 0
##
## , , = 00:00-05:59, = 45-65
##
##
## SEM_1 SEM_2
## 2012 34 12
## 2013 27 24
## 2014 13 17
## 2015 12 10
## 2016 16 19
## 2017 23 21
## 2018 18 24
## 2019 46 36
## 2020 34 23
## 2021 36 39
## 2022 33 0
##
## , , = 06:00-11:59, = 45-65
##
##
## SEM_1 SEM_2
## 2012 27 31
## 2013 36 15
## 2014 22 21
## 2015 24 28
## 2016 32 39
## 2017 41 37
## 2018 49 42
## 2019 54 40
## 2020 40 59
## 2021 54 51
## 2022 56 0
##
## , , = 12:00-17:59, = 45-65
##
##
## SEM_1 SEM_2
## 2012 35 35
## 2013 31 20
## 2014 26 27
## 2015 31 42
## 2016 21 41
## 2017 50 44
## 2018 40 38
## 2019 57 38
## 2020 40 45
## 2021 42 61
## 2022 64 0
##
## , , = 18:00-23:59, = 45-65
##
##
## SEM_1 SEM_2
## 2012 22 23
## 2013 22 13
## 2014 15 26
## 2015 19 24
## 2016 19 27
## 2017 27 41
## 2018 30 36
## 2019 41 42
## 2020 20 33
## 2021 16 29
## 2022 31 0
##
## , , = 00:00-05:59, = Mayor de 65
##
##
## SEM_1 SEM_2
## 2012 7 5
## 2013 0 3
## 2014 4 2
## 2015 2 6
## 2016 6 3
## 2017 3 1
## 2018 4 3
## 2019 6 13
## 2020 8 3
## 2021 6 6
## 2022 8 0
##
## , , = 06:00-11:59, = Mayor de 65
##
##
## SEM_1 SEM_2
## 2012 13 7
## 2013 5 6
## 2014 1 5
## 2015 6 11
## 2016 9 13
## 2017 12 11
## 2018 14 10
## 2019 16 13
## 2020 6 13
## 2021 4 12
## 2022 18 0
##
## , , = 12:00-17:59, = Mayor de 65
##
##
## SEM_1 SEM_2
## 2012 5 3
## 2013 4 2
## 2014 7 6
## 2015 7 11
## 2016 7 7
## 2017 12 5
## 2018 7 3
## 2019 4 10
## 2020 16 14
## 2021 8 5
## 2022 14 0
##
## , , = 18:00-23:59, = Mayor de 65
##
##
## SEM_1 SEM_2
## 2012 2 1
## 2013 3 0
## 2014 0 2
## 2015 2 7
## 2016 1 4
## 2017 5 3
## 2018 7 3
## 2019 7 4
## 2020 2 7
## 2021 2 2
## 2022 8 0table(Palmira1_covar$YEAR,Palmira1_covar$SEMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$Armas_indicator)## , , = 00:00-05:59, = Con
##
##
## SEM_1 SEM_2
## 2012 74 32
## 2013 27 21
## 2014 26 19
## 2015 36 21
## 2016 27 47
## 2017 43 65
## 2018 89 131
## 2019 107 87
## 2020 51 74
## 2021 80 80
## 2022 73 0
##
## , , = 06:00-11:59, = Con
##
##
## SEM_1 SEM_2
## 2012 80 82
## 2013 77 48
## 2014 62 49
## 2015 54 63
## 2016 72 99
## 2017 97 110
## 2018 157 109
## 2019 142 99
## 2020 74 104
## 2021 128 115
## 2022 100 0
##
## , , = 12:00-17:59, = Con
##
##
## SEM_1 SEM_2
## 2012 101 110
## 2013 99 66
## 2014 94 81
## 2015 88 104
## 2016 64 106
## 2017 122 116
## 2018 125 137
## 2019 136 118
## 2020 78 107
## 2021 72 114
## 2022 93 0
##
## , , = 18:00-23:59, = Con
##
##
## SEM_1 SEM_2
## 2012 65 64
## 2013 69 44
## 2014 65 65
## 2015 77 84
## 2016 65 103
## 2017 90 113
## 2018 137 126
## 2019 160 125
## 2020 68 95
## 2021 84 86
## 2022 109 0
##
## , , = 00:00-05:59, = Sin
##
##
## SEM_1 SEM_2
## 2012 53 39
## 2013 52 56
## 2014 47 35
## 2015 20 39
## 2016 50 25
## 2017 30 36
## 2018 37 57
## 2019 70 68
## 2020 59 45
## 2021 59 117
## 2022 102 0
##
## , , = 06:00-11:59, = Sin
##
##
## SEM_1 SEM_2
## 2012 52 66
## 2013 70 46
## 2014 60 66
## 2015 61 76
## 2016 84 87
## 2017 81 81
## 2018 107 93
## 2019 92 109
## 2020 73 81
## 2021 70 140
## 2022 153 0
##
## , , = 12:00-17:59, = Sin
##
##
## SEM_1 SEM_2
## 2012 50 50
## 2013 69 72
## 2014 52 55
## 2015 72 78
## 2016 72 63
## 2017 76 63
## 2018 75 95
## 2019 96 93
## 2020 78 73
## 2021 80 148
## 2022 171 0
##
## , , = 18:00-23:59, = Sin
##
##
## SEM_1 SEM_2
## 2012 17 29
## 2013 38 28
## 2014 37 42
## 2015 34 56
## 2016 31 34
## 2017 31 66
## 2018 51 60
## 2019 76 68
## 2020 40 57
## 2021 51 90
## 2022 96 0Palmira1_covar$TRIMESTRE=dplyr::recode(Palmira1_covar$MES, "ene"="Trim_1", "feb"="Trim_1", "mar"="Trim_1", "abr"="Trim_2", "may"="Trim_2","jun"="Trim_2","jul"="Trim_3","ago"="Trim_3","sep"="Trim_3","oct"="Trim_4","nov"="Trim_4","dic"="Trim_4")
Palmira1_covar <- subset(Palmira1_covar, select = c("X_plain","Y_plain","YEAR","BARRIOS","GENERO","DIA_SEMANA","INTERVALOS_HORA","MODALIDAD","EDAD_Cat",
"Armas_indicator","MES","TRIMESTRE","LONGITUD","LATITUD"))
table(Palmira1_covar$YEAR,Palmira1_covar$TRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$GENERO)## , , = 00:00-05:59, = FEMENINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 31 29 20 8
## 2013 15 11 20 17
## 2014 8 11 15 6
## 2015 2 14 13 13
## 2016 19 13 10 12
## 2017 17 8 10 21
## 2018 31 29 31 67
## 2019 47 33 35 38
## 2020 33 14 21 24
## 2021 31 32 47 64
## 2022 37 43 0 0
##
## , , = 06:00-11:59, = FEMENINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 20 27 19 23
## 2013 27 23 19 7
## 2014 24 26 16 24
## 2015 25 27 38 29
## 2016 33 32 39 39
## 2017 34 42 46 45
## 2018 61 78 56 59
## 2019 64 60 61 57
## 2020 55 27 52 45
## 2021 44 56 72 64
## 2022 68 53 0 0
##
## , , = 12:00-17:59, = FEMENINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 28 35 27 29
## 2013 31 31 27 29
## 2014 33 23 21 35
## 2015 26 43 37 48
## 2016 27 45 39 40
## 2017 57 46 32 52
## 2018 55 53 55 74
## 2019 59 61 54 66
## 2020 52 21 41 42
## 2021 39 35 77 54
## 2022 64 67 0 0
##
## , , = 18:00-23:59, = FEMENINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 15 17 14 17
## 2013 24 17 10 13
## 2014 15 21 21 25
## 2015 14 25 32 25
## 2016 22 19 36 30
## 2017 32 26 37 38
## 2018 36 47 32 45
## 2019 61 62 34 50
## 2020 34 14 31 37
## 2021 22 44 29 59
## 2022 34 55 0 0
##
## , , = 00:00-05:59, = MASCULINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 28 39 35 8
## 2013 33 20 22 18
## 2014 26 28 16 17
## 2015 13 27 18 16
## 2016 24 21 23 27
## 2017 37 11 30 40
## 2018 29 37 39 51
## 2019 59 38 36 46
## 2020 42 21 32 42
## 2021 40 36 41 45
## 2022 41 54 0 0
##
## , , = 06:00-11:59, = MASCULINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 44 41 50 56
## 2013 61 36 38 30
## 2014 36 36 39 36
## 2015 24 39 42 30
## 2016 31 60 53 55
## 2017 42 60 51 49
## 2018 67 58 54 33
## 2019 53 57 50 40
## 2020 42 23 47 41
## 2021 49 49 53 66
## 2022 50 82 0 0
##
## , , = 12:00-17:59, = MASCULINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 42 46 53 51
## 2013 52 54 41 41
## 2014 37 53 47 33
## 2015 38 53 39 58
## 2016 32 32 48 42
## 2017 52 43 46 49
## 2018 59 33 50 53
## 2019 49 63 47 44
## 2020 56 27 54 43
## 2021 37 41 61 70
## 2022 71 62 0 0
##
## , , = 18:00-23:59, = MASCULINO
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 27 23 34 28
## 2013 31 35 23 26
## 2014 32 34 29 32
## 2015 28 44 38 45
## 2016 28 27 18 53
## 2017 37 26 45 59
## 2018 37 68 62 47
## 2019 59 54 58 51
## 2020 38 22 47 37
## 2021 25 44 40 48
## 2022 50 66 0 0table(Palmira1_covar$YEAR,Palmira1_covar$TRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$EDAD_Cat)## , , = 00:00-05:59, = Menor de 18
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 0 1 0 1
## 2013 0 1 0 0
## 2014 0 1 0 0
## 2015 0 1 0 1
## 2016 4 3 3 1
## 2017 3 1 3 5
## 2018 2 2 1 2
## 2019 5 0 4 1
## 2020 3 0 0 3
## 2021 2 1 1 5
## 2022 4 5 0 0
##
## , , = 06:00-11:59, = Menor de 18
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 2 2 0 2
## 2013 2 1 3 1
## 2014 2 2 3 3
## 2015 2 1 2 3
## 2016 5 12 8 7
## 2017 6 8 7 2
## 2018 5 7 6 3
## 2019 5 3 1 1
## 2020 4 0 2 1
## 2021 0 1 2 3
## 2022 2 2 0 0
##
## , , = 12:00-17:59, = Menor de 18
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 5 3 2 4
## 2013 6 6 5 1
## 2014 4 6 5 7
## 2015 5 10 3 6
## 2016 4 4 5 1
## 2017 7 12 9 9
## 2018 3 5 9 2
## 2019 5 3 3 4
## 2020 3 1 2 0
## 2021 1 0 5 4
## 2022 11 4 0 0
##
## , , = 18:00-23:59, = Menor de 18
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 1 0 5 3
## 2013 2 3 3 4
## 2014 0 4 3 4
## 2015 2 3 3 4
## 2016 2 2 3 3
## 2017 8 4 8 4
## 2018 1 8 4 3
## 2019 3 3 4 4
## 2020 5 3 1 1
## 2021 1 1 2 3
## 2022 4 12 0 0
##
## , , = 00:00-05:59, = 18-45
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 40 45 40 13
## 2013 31 20 27 23
## 2014 24 31 18 17
## 2015 11 30 24 19
## 2016 30 18 21 25
## 2017 29 12 27 43
## 2018 48 52 62 95
## 2019 74 45 40 60
## 2020 45 20 38 52
## 2021 44 50 66 79
## 2022 52 71 0 0
##
## , , = 06:00-11:59, = 18-45
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 38 50 52 56
## 2013 62 41 40 29
## 2014 49 46 39 44
## 2015 33 49 57 38
## 2016 45 53 57 62
## 2017 48 63 65 66
## 2018 91 98 77 64
## 2019 87 69 81 72
## 2020 67 30 58 52
## 2021 64 72 91 93
## 2022 82 93 0 0
##
## , , = 12:00-17:59, = 18-45
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 45 58 58 58
## 2013 57 64 49 61
## 2014 49 54 45 46
## 2015 45 62 45 75
## 2016 42 58 57 58
## 2017 64 51 48 62
## 2018 82 63 84 96
## 2019 75 85 79 77
## 2020 61 35 64 55
## 2021 52 49 102 84
## 2022 91 78 0 0
##
## , , = 18:00-23:59, = 18-45
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 27 30 33 28
## 2013 39 38 24 28
## 2014 42 41 35 37
## 2015 35 50 52 50
## 2016 37 35 40 60
## 2017 40 35 57 66
## 2018 56 86 74 66
## 2019 97 85 67 72
## 2020 52 26 57 53
## 2021 42 72 52 86
## 2022 62 88 0 0
##
## , , = 00:00-05:59, = 45-65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 17 17 10 2
## 2013 17 10 12 12
## 2014 7 6 11 6
## 2015 3 9 5 5
## 2016 6 10 8 11
## 2017 17 6 9 12
## 2018 9 9 5 19
## 2019 26 20 21 15
## 2020 19 15 15 8
## 2021 21 15 19 20
## 2022 19 14 0 0
##
## , , = 06:00-11:59, = 45-65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 14 13 14 17
## 2013 23 13 8 7
## 2014 8 14 12 9
## 2015 12 12 15 13
## 2016 11 21 22 17
## 2017 16 25 18 19
## 2018 29 20 20 22
## 2019 20 34 22 18
## 2020 21 19 29 30
## 2021 26 28 23 28
## 2022 28 28 0 0
##
## , , = 12:00-17:59, = 45-65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 19 16 18 17
## 2013 17 14 12 8
## 2014 16 10 17 10
## 2015 11 20 21 21
## 2016 10 11 22 19
## 2017 29 21 18 26
## 2018 25 15 11 27
## 2019 26 31 16 22
## 2020 31 9 22 23
## 2021 19 23 30 31
## 2022 24 40 0 0
##
## , , = 18:00-23:59, = 45-65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 12 10 9 14
## 2013 11 11 6 7
## 2014 5 10 10 16
## 2015 4 15 11 13
## 2016 11 8 10 17
## 2017 15 12 16 25
## 2018 13 17 14 22
## 2019 15 26 20 22
## 2020 15 5 17 16
## 2021 3 13 14 15
## 2022 13 18 0 0
##
## , , = 00:00-05:59, = Mayor de 65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 2 5 5 0
## 2013 0 0 3 0
## 2014 3 1 2 0
## 2015 1 1 2 4
## 2016 3 3 1 2
## 2017 3 0 0 1
## 2018 1 3 2 1
## 2019 1 5 5 8
## 2020 8 0 0 3
## 2021 4 2 2 4
## 2022 2 6 0 0
##
## , , = 06:00-11:59, = Mayor de 65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 10 3 3 4
## 2013 1 4 6 0
## 2014 1 0 1 4
## 2015 2 4 6 5
## 2016 3 6 5 8
## 2017 6 6 7 4
## 2018 3 11 7 3
## 2019 5 11 7 6
## 2020 5 1 10 3
## 2021 3 1 7 5
## 2022 6 12 0 0
##
## , , = 12:00-17:59, = Mayor de 65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 1 4 2 1
## 2013 3 1 2 0
## 2014 1 6 1 5
## 2015 3 4 7 4
## 2016 3 4 3 4
## 2017 9 3 3 2
## 2018 4 3 1 2
## 2019 1 3 3 7
## 2020 13 3 7 7
## 2021 4 4 1 4
## 2022 8 6 0 0
##
## , , = 18:00-23:59, = Mayor de 65
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 2 0 1 0
## 2013 3 0 0 0
## 2014 0 0 2 0
## 2015 1 1 4 3
## 2016 0 1 1 3
## 2017 4 1 1 2
## 2018 3 4 2 1
## 2019 5 2 1 3
## 2020 0 2 3 4
## 2021 0 2 1 1
## 2022 5 3 0 0table(Palmira1_covar$YEAR,Palmira1_covar$TRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$Armas_indicator)## , , = 00:00-05:59, = Con
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 32 42 26 6
## 2013 18 9 9 12
## 2014 7 19 8 11
## 2015 9 27 12 9
## 2016 14 13 20 27
## 2017 32 11 28 37
## 2018 41 48 46 85
## 2019 71 36 36 51
## 2020 31 20 35 39
## 2021 38 42 41 39
## 2022 34 39 0 0
##
## , , = 06:00-11:59, = Con
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 38 42 40 42
## 2013 45 32 21 27
## 2014 28 34 23 26
## 2015 23 31 39 24
## 2016 37 35 45 54
## 2017 42 55 57 53
## 2018 84 73 58 51
## 2019 76 66 59 40
## 2020 48 26 54 50
## 2021 57 71 61 54
## 2022 43 57 0 0
##
## , , = 12:00-17:59, = Con
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 46 55 53 57
## 2013 46 53 27 39
## 2014 41 53 47 34
## 2015 33 55 45 59
## 2016 25 39 54 52
## 2017 67 55 53 63
## 2018 73 52 59 78
## 2019 69 67 56 62
## 2020 58 20 59 48
## 2021 36 36 64 50
## 2022 44 49 0 0
##
## , , = 18:00-23:59, = Con
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 30 35 33 31
## 2013 35 34 16 28
## 2014 26 39 27 38
## 2015 31 46 43 41
## 2016 38 27 38 65
## 2017 53 37 51 62
## 2018 54 83 59 67
## 2019 87 73 55 70
## 2020 43 25 51 44
## 2021 25 59 39 47
## 2022 45 64 0 0
##
## , , = 00:00-05:59, = Sin
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 27 26 29 10
## 2013 30 22 33 23
## 2014 27 20 23 12
## 2015 6 14 19 20
## 2016 29 21 13 12
## 2017 22 8 12 24
## 2018 19 18 24 33
## 2019 35 35 35 33
## 2020 44 15 18 27
## 2021 33 26 47 70
## 2022 44 58 0 0
##
## , , = 06:00-11:59, = Sin
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 26 26 29 37
## 2013 43 27 36 10
## 2014 32 28 32 34
## 2015 26 35 41 35
## 2016 27 57 47 40
## 2017 34 47 40 41
## 2018 44 63 52 41
## 2019 41 51 52 57
## 2020 49 24 45 36
## 2021 36 34 64 76
## 2022 75 78 0 0
##
## , , = 12:00-17:59, = Sin
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 24 26 27 23
## 2013 37 32 41 31
## 2014 29 23 21 34
## 2015 31 41 31 47
## 2016 34 38 33 30
## 2017 42 34 25 38
## 2018 41 34 46 49
## 2019 39 57 45 48
## 2020 50 28 36 37
## 2021 40 40 74 74
## 2022 91 80 0 0
##
## , , = 18:00-23:59, = Sin
##
##
## Trim_1 Trim_2 Trim_3 Trim_4
## 2012 12 5 15 14
## 2013 20 18 17 11
## 2014 21 16 23 19
## 2015 11 23 27 29
## 2016 12 19 16 18
## 2017 16 15 31 35
## 2018 19 32 35 25
## 2019 33 43 37 31
## 2020 29 11 27 30
## 2021 22 29 30 60
## 2022 39 57 0 0Palmira1_covar$BIMESTRE=dplyr::recode(Palmira1_covar$MES, "ene"="Bim_1", "feb"="Bim_1", "mar"="Bim_2", "abr"="Bim_2", "may"="Bim_3","jun"="Bim_3","jul"="Bim_4","ago"="Bim_4","sep"="Bim_5","oct"="Bim_5","nov"="Bim_6","dic"="Bim_6")
Palmira1_covar <- subset(Palmira1_covar, select = c("X_plain","Y_plain","YEAR","BARRIOS","GENERO","DIA_SEMANA","INTERVALOS_HORA","MODALIDAD","EDAD_Cat",
"Armas_indicator","MES","BIMESTRE","LONGITUD","LATITUD"))
table(Palmira1_covar$YEAR,Palmira1_covar$BIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$GENERO)## , , = 00:00-05:59, = FEMENINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 14 25 21 15 7 6
## 2013 10 8 8 14 17 6
## 2014 4 6 9 14 2 5
## 2015 1 1 14 10 11 5
## 2016 14 10 8 7 5 10
## 2017 13 7 5 5 13 13
## 2018 16 20 24 17 35 46
## 2019 41 21 18 25 23 25
## 2020 25 12 10 15 12 18
## 2021 19 22 22 22 49 40
## 2022 17 37 26 0 0 0
##
## , , = 06:00-11:59, = FEMENINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 12 9 26 11 14 17
## 2013 20 19 11 13 8 5
## 2014 13 22 15 8 18 14
## 2015 14 19 19 20 27 20
## 2016 18 25 22 26 29 23
## 2017 23 27 26 29 32 30
## 2018 36 50 53 30 47 38
## 2019 39 42 43 41 39 38
## 2020 36 29 17 26 48 23
## 2021 28 33 39 35 61 40
## 2022 34 49 38 0 0 0
##
## , , = 12:00-17:59, = FEMENINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 18 18 27 18 16 22
## 2013 20 25 17 19 19 18
## 2014 19 23 14 15 15 26
## 2015 17 18 34 25 29 31
## 2016 17 26 29 21 31 27
## 2017 42 28 33 20 22 42
## 2018 33 45 30 37 47 45
## 2019 36 39 45 37 40 43
## 2020 39 20 14 29 24 30
## 2021 21 28 25 47 48 36
## 2022 39 47 45 0 0 0
##
## , , = 18:00-23:59, = FEMENINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 9 8 15 11 8 12
## 2013 22 6 13 6 11 6
## 2014 12 12 12 14 18 14
## 2015 10 14 15 17 23 17
## 2016 16 14 11 23 22 21
## 2017 21 18 19 28 17 30
## 2018 24 29 30 20 26 31
## 2019 35 51 37 23 36 25
## 2020 21 14 13 25 20 23
## 2021 13 23 30 21 23 44
## 2022 19 42 28 0 0 0
##
## , , = 00:00-05:59, = MASCULINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 20 22 25 24 13 6
## 2013 21 21 11 16 13 11
## 2014 19 10 25 15 7 11
## 2015 6 9 25 12 11 11
## 2016 19 13 13 19 9 22
## 2017 28 13 7 18 28 24
## 2018 20 21 25 29 21 40
## 2019 46 25 26 22 27 33
## 2020 37 9 17 24 23 27
## 2021 21 22 33 27 32 27
## 2022 23 35 37 0 0 0
##
## , , = 06:00-11:59, = MASCULINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 30 27 28 32 35 39
## 2013 41 30 26 28 21 19
## 2014 26 21 25 23 32 20
## 2015 17 19 27 27 24 21
## 2016 19 29 43 30 41 37
## 2017 30 34 38 41 19 40
## 2018 49 33 43 37 28 22
## 2019 35 41 34 28 33 29
## 2020 31 19 15 29 36 23
## 2021 27 36 35 31 45 43
## 2022 32 47 53 0 0 0
##
## , , = 12:00-17:59, = MASCULINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 32 26 30 40 25 39
## 2013 35 33 38 34 23 25
## 2014 26 30 34 33 28 19
## 2015 23 31 37 25 42 30
## 2016 24 19 21 22 39 29
## 2017 39 25 31 29 36 30
## 2018 45 23 24 30 38 35
## 2019 31 42 39 32 31 28
## 2020 42 18 23 28 41 28
## 2021 24 28 26 39 49 43
## 2022 33 62 38 0 0 0
##
## , , = 18:00-23:59, = MASCULINO
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 20 15 15 28 11 23
## 2013 26 16 24 15 17 17
## 2014 27 11 28 19 17 25
## 2015 17 26 29 24 38 21
## 2016 11 26 18 9 25 37
## 2017 27 21 15 31 26 47
## 2018 26 41 38 41 32 36
## 2019 34 43 36 44 32 33
## 2020 30 11 19 28 31 25
## 2021 17 16 36 23 37 28
## 2022 32 49 35 0 0 0table(Palmira1_covar$YEAR,Palmira1_covar$BIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$EDAD_Cat)## , , = 00:00-05:59, = Menor de 18
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 0 1 0 0 1 0
## 2013 0 1 0 0 0 0
## 2014 0 0 1 0 0 0
## 2015 0 0 1 0 0 1
## 2016 3 2 2 3 0 1
## 2017 2 2 0 1 5 2
## 2018 2 0 2 1 0 2
## 2019 4 1 0 3 2 0
## 2020 2 1 0 0 0 3
## 2021 0 2 1 0 3 3
## 2022 4 2 3 0 0 0
##
## , , = 06:00-11:59, = Menor de 18
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 1 1 2 0 0 2
## 2013 0 2 1 3 0 1
## 2014 1 2 1 2 3 1
## 2015 2 0 1 1 3 1
## 2016 3 7 7 4 6 5
## 2017 2 7 5 6 1 2
## 2018 2 5 5 4 2 3
## 2019 2 4 2 1 0 1
## 2020 4 0 0 1 2 0
## 2021 0 1 0 1 1 3
## 2022 1 2 1 0 0 0
##
## , , = 12:00-17:59, = Menor de 18
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 4 2 2 1 2 3
## 2013 4 4 4 4 1 1
## 2014 3 1 6 5 1 6
## 2015 1 5 9 2 6 1
## 2016 2 5 1 2 4 0
## 2017 6 8 5 3 11 4
## 2018 0 4 4 5 5 1
## 2019 4 3 1 1 3 3
## 2020 2 1 1 2 0 0
## 2021 1 0 0 3 5 1
## 2022 5 7 3 0 0 0
##
## , , = 18:00-23:59, = Menor de 18
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 1 0 0 5 0 3
## 2013 0 2 3 2 2 3
## 2014 0 0 4 2 1 4
## 2015 1 2 2 3 2 2
## 2016 1 1 2 2 2 2
## 2017 7 2 3 4 5 3
## 2018 1 3 5 2 3 2
## 2019 1 3 2 2 4 2
## 2020 3 2 3 1 1 0
## 2021 1 0 1 1 2 2
## 2022 3 6 7 0 0 0
##
## , , = 00:00-05:59, = 18-45
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 18 36 31 29 14 10
## 2013 23 16 12 19 20 11
## 2014 16 12 27 17 4 14
## 2015 6 7 28 18 17 8
## 2016 25 11 12 17 11 18
## 2017 23 10 8 16 26 28
## 2018 28 33 39 40 50 67
## 2019 61 28 30 25 36 39
## 2020 41 9 15 27 29 34
## 2021 29 23 42 39 57 49
## 2022 25 53 45 0 0 0
##
## , , = 06:00-11:59, = 18-45
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 23 27 38 33 36 39
## 2013 46 32 25 29 19 21
## 2014 31 35 29 21 37 25
## 2015 22 25 35 32 32 31
## 2016 25 33 40 38 41 40
## 2017 35 39 37 47 37 47
## 2018 63 55 71 44 57 40
## 2019 53 55 48 51 49 53
## 2020 42 34 21 34 46 30
## 2021 39 46 51 45 78 61
## 2022 45 68 62 0 0 0
##
## , , = 12:00-17:59, = 18-45
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 29 32 42 45 26 45
## 2013 38 41 42 39 32 39
## 2014 30 41 32 30 32 29
## 2015 29 36 42 29 48 43
## 2016 30 31 39 28 47 40
## 2017 48 27 40 31 34 45
## 2018 54 53 38 52 68 60
## 2019 46 55 59 57 52 47
## 2020 48 21 27 38 43 38
## 2021 33 35 33 59 74 53
## 2022 46 70 53 0 0 0
##
## , , = 18:00-23:59, = 18-45
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 20 14 23 26 14 21
## 2013 34 18 25 16 20 16
## 2014 36 20 27 24 24 24
## 2015 23 30 32 28 47 27
## 2016 21 31 20 22 36 42
## 2017 27 26 22 42 28 53
## 2018 40 55 47 50 42 48
## 2019 56 73 53 47 48 44
## 2020 38 18 22 42 36 32
## 2021 27 32 55 33 50 55
## 2022 38 65 47 0 0 0
##
## , , = 00:00-05:59, = 45-65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 14 8 12 6 4 2
## 2013 8 12 7 9 9 6
## 2014 5 3 5 10 5 2
## 2015 1 2 9 2 4 4
## 2016 3 8 5 6 2 11
## 2017 11 8 4 5 9 7
## 2018 6 6 6 3 6 15
## 2019 21 14 11 14 10 12
## 2020 14 8 12 12 6 5
## 2021 10 15 11 9 15 15
## 2022 9 14 10 0 0 0
##
## , , = 06:00-11:59, = 45-65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 9 6 12 8 12 11
## 2013 15 14 7 5 8 2
## 2014 6 6 10 7 8 6
## 2015 6 9 9 11 12 5
## 2016 7 12 13 11 17 11
## 2017 12 11 18 11 11 15
## 2018 18 21 10 15 13 14
## 2019 15 19 20 14 15 11
## 2020 17 12 11 13 32 14
## 2021 15 18 21 16 17 18
## 2022 17 21 18 0 0 0
##
## , , = 12:00-17:59, = 45-65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 17 8 10 11 11 13
## 2013 11 12 8 8 9 3
## 2014 11 7 8 12 10 5
## 2015 8 7 16 13 15 14
## 2016 7 6 8 11 17 13
## 2017 21 15 14 14 11 19
## 2018 22 7 11 9 11 18
## 2019 16 20 21 11 11 16
## 2020 23 11 6 13 17 15
## 2021 9 17 16 23 18 20
## 2022 15 26 23 0 0 0
##
## , , = 18:00-23:59, = 45-65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 6 9 7 7 5 11
## 2013 11 2 9 3 6 4
## 2014 3 3 9 5 10 11
## 2015 3 7 9 8 9 7
## 2016 5 8 6 8 7 12
## 2017 11 7 9 13 8 20
## 2018 9 8 13 7 13 16
## 2019 10 14 17 17 14 11
## 2020 10 5 5 7 13 13
## 2021 2 6 8 10 7 12
## 2022 7 16 8 0 0 0
##
## , , = 00:00-05:59, = Mayor de 65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 2 2 3 4 1 0
## 2013 0 0 0 2 1 0
## 2014 2 1 1 2 0 0
## 2015 0 1 1 2 1 3
## 2016 2 2 2 0 1 2
## 2017 3 0 0 0 1 0
## 2018 0 2 2 2 0 1
## 2019 1 3 2 4 2 7
## 2020 5 3 0 0 0 3
## 2021 1 4 1 1 5 0
## 2022 1 3 4 0 0 0
##
## , , = 06:00-11:59, = Mayor de 65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 9 2 2 2 1 4
## 2013 0 1 4 4 2 0
## 2014 1 0 0 1 2 2
## 2015 1 4 1 3 4 4
## 2016 2 2 5 3 6 4
## 2017 4 4 4 6 2 3
## 2018 2 2 10 4 3 3
## 2019 4 5 7 3 8 2
## 2020 4 2 0 7 4 2
## 2021 1 3 0 4 7 1
## 2022 3 5 10 0 0 0
##
## , , = 12:00-17:59, = Mayor de 65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 0 2 3 1 2 0
## 2013 2 1 1 2 0 0
## 2014 1 4 2 1 0 5
## 2015 2 1 4 6 2 3
## 2016 2 3 2 2 2 3
## 2017 6 3 3 1 2 2
## 2018 2 4 1 1 1 1
## 2019 0 3 1 0 5 5
## 2020 8 5 3 4 5 5
## 2021 2 4 2 1 0 4
## 2022 5 5 4 0 0 0
##
## , , = 18:00-23:59, = Mayor de 65
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 2 0 0 1 0 0
## 2013 3 0 0 0 0 0
## 2014 0 0 0 2 0 0
## 2015 0 1 1 2 3 2
## 2016 0 0 1 0 2 2
## 2017 2 3 0 0 2 1
## 2018 0 4 3 2 0 1
## 2019 2 4 1 1 2 1
## 2020 0 0 2 3 1 3
## 2021 0 0 2 0 1 1
## 2022 3 4 1 0 0 0table(Palmira1_covar$YEAR,Palmira1_covar$BIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$Armas_indicator)## , , = 00:00-05:59, = Con
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 16 32 26 18 10 4
## 2013 13 8 6 8 7 6
## 2014 6 3 17 7 5 7
## 2015 6 5 25 8 6 7
## 2016 11 10 6 15 10 22
## 2017 24 10 9 17 23 25
## 2018 26 28 35 29 38 64
## 2019 59 28 20 22 35 30
## 2020 27 10 14 25 22 27
## 2021 19 25 36 34 22 24
## 2022 22 25 26 0 0 0
##
## , , = 06:00-11:59, = Con
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 22 22 36 28 22 32
## 2013 34 23 20 13 17 18
## 2014 20 18 24 15 16 18
## 2015 14 18 22 25 19 19
## 2016 18 28 26 29 28 42
## 2017 29 29 39 44 24 42
## 2018 56 51 50 36 39 34
## 2019 46 54 42 35 39 25
## 2020 33 25 16 29 48 27
## 2021 32 45 51 36 42 37
## 2022 31 34 35 0 0 0
##
## , , = 12:00-17:59, = Con
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 34 28 39 37 27 46
## 2013 33 28 38 22 19 25
## 2014 26 36 32 30 27 24
## 2015 23 27 38 29 42 33
## 2016 18 22 24 26 48 32
## 2017 52 32 38 33 39 44
## 2018 49 45 31 38 46 53
## 2019 42 48 46 34 46 38
## 2020 42 21 15 36 36 35
## 2021 20 25 27 46 38 30
## 2022 21 39 33 0 0 0
##
## , , = 18:00-23:59, = Con
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 18 21 26 27 13 24
## 2013 31 13 25 9 17 18
## 2014 21 18 26 19 19 27
## 2015 20 24 33 24 39 21
## 2016 18 30 17 22 36 45
## 2017 36 32 22 37 28 48
## 2018 37 51 49 41 36 49
## 2019 56 58 46 38 47 40
## 2020 33 13 22 35 35 25
## 2021 15 19 50 28 27 31
## 2022 31 43 35 0 0 0
##
## , , = 00:00-05:59, = Sin
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 18 15 20 21 10 8
## 2013 18 21 13 22 23 11
## 2014 17 13 17 22 4 9
## 2015 1 5 14 14 16 9
## 2016 22 13 15 11 4 10
## 2017 17 10 3 6 18 12
## 2018 10 13 14 17 18 22
## 2019 28 18 24 25 15 28
## 2020 35 11 13 14 13 18
## 2021 21 19 19 15 59 43
## 2022 18 47 37 0 0 0
##
## , , = 06:00-11:59, = Sin
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 20 14 18 15 27 24
## 2013 27 26 17 28 12 6
## 2014 19 25 16 16 34 16
## 2015 17 20 24 22 32 22
## 2016 19 26 39 27 42 18
## 2017 24 32 25 26 27 28
## 2018 29 32 46 31 36 26
## 2019 28 29 35 34 33 42
## 2020 34 23 16 26 36 19
## 2021 23 24 23 30 64 46
## 2022 35 62 56 0 0 0
##
## , , = 12:00-17:59, = Sin
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 16 16 18 21 14 15
## 2013 22 30 17 31 23 18
## 2014 19 17 16 18 16 21
## 2015 17 22 33 21 29 28
## 2016 23 23 26 17 22 24
## 2017 29 21 26 16 19 28
## 2018 29 23 23 29 39 27
## 2019 25 33 38 35 25 33
## 2020 39 17 22 21 29 23
## 2021 25 31 24 40 59 49
## 2022 51 70 50 0 0 0
##
## , , = 18:00-23:59, = Sin
##
##
## Bim_1 Bim_2 Bim_3 Bim_4 Bim_5 Bim_6
## 2012 11 2 4 12 6 11
## 2013 17 9 12 12 11 5
## 2014 18 5 14 14 16 12
## 2015 7 16 11 17 22 17
## 2016 9 10 12 10 11 13
## 2017 12 7 12 22 15 29
## 2018 13 19 19 20 22 18
## 2019 13 36 27 29 21 18
## 2020 18 12 10 18 16 23
## 2021 15 20 16 16 33 41
## 2022 20 48 28 0 0 0Palmira1_covar$CUATRIMESTRE=dplyr::recode(Palmira1_covar$MES, "ene"="CUATrim_1", "feb"="CUATrim_1", "mar"="CUATrim_1", "abr"="CUATrim_1", "may"="CUATrim_2","jun"="CUATrim_2","jul"="CUATrim_2","ago"="CUATrim_2","sep"="CUATrim_3","oct"="CUATrim_3","nov"="CUATrim_3","dic"="CUATrim_3")
Palmira1_covar <- subset(Palmira1_covar, select = c("X_plain","Y_plain","YEAR","BARRIOS","GENERO","DIA_SEMANA","INTERVALOS_HORA","MODALIDAD","EDAD_Cat","Armas_indicator","MES","CUATRIMESTRE","LONGITUD","LATITUD"))
table(Palmira1_covar$YEAR,Palmira1_covar$CUATRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$GENERO)## , , = 00:00-05:59, = FEMENINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 39 36 13
## 2013 18 22 23
## 2014 10 23 7
## 2015 2 24 16
## 2016 24 15 15
## 2017 20 10 26
## 2018 36 41 81
## 2019 62 43 48
## 2020 37 25 30
## 2021 41 44 89
## 2022 54 26 0
##
## , , = 06:00-11:59, = FEMENINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 21 37 31
## 2013 39 24 13
## 2014 35 23 32
## 2015 33 39 47
## 2016 43 48 52
## 2017 50 55 62
## 2018 86 83 85
## 2019 81 84 77
## 2020 65 43 71
## 2021 61 74 101
## 2022 83 38 0
##
## , , = 12:00-17:59, = FEMENINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 36 45 38
## 2013 45 36 37
## 2014 42 29 41
## 2015 35 59 60
## 2016 43 50 58
## 2017 70 53 64
## 2018 78 67 92
## 2019 75 82 83
## 2020 59 43 54
## 2021 49 72 84
## 2022 86 45 0
##
## , , = 18:00-23:59, = FEMENINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 17 26 20
## 2013 28 19 17
## 2014 24 26 32
## 2015 24 32 40
## 2016 30 34 43
## 2017 39 47 47
## 2018 53 50 57
## 2019 86 60 61
## 2020 35 38 43
## 2021 36 51 67
## 2022 61 28 0
##
## , , = 00:00-05:59, = MASCULINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 42 49 19
## 2013 42 27 24
## 2014 29 40 18
## 2015 15 37 22
## 2016 32 32 31
## 2017 41 25 52
## 2018 41 54 61
## 2019 71 48 60
## 2020 46 41 50
## 2021 43 60 59
## 2022 58 37 0
##
## , , = 06:00-11:59, = MASCULINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 57 60 74
## 2013 71 54 40
## 2014 47 48 52
## 2015 36 54 45
## 2016 48 73 78
## 2017 64 79 59
## 2018 82 80 50
## 2019 76 62 62
## 2020 50 44 59
## 2021 63 66 88
## 2022 79 53 0
##
## , , = 12:00-17:59, = MASCULINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 58 70 64
## 2013 68 72 48
## 2014 56 67 47
## 2015 54 62 72
## 2016 43 43 68
## 2017 64 60 66
## 2018 68 54 73
## 2019 73 71 59
## 2020 60 51 69
## 2021 52 65 92
## 2022 95 38 0
##
## , , = 18:00-23:59, = MASCULINO
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 35 43 34
## 2013 42 39 34
## 2014 38 47 42
## 2015 43 53 59
## 2016 37 27 62
## 2017 48 46 73
## 2018 67 79 68
## 2019 77 80 65
## 2020 41 47 56
## 2021 33 59 65
## 2022 81 35 0table(Palmira1_covar$YEAR,Palmira1_covar$CUATRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$EDAD_Cat)## , , = 00:00-05:59, = Menor de 18
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 1 0 1
## 2013 1 0 0
## 2014 0 1 0
## 2015 0 1 1
## 2016 5 5 1
## 2017 4 1 7
## 2018 2 3 2
## 2019 5 3 2
## 2020 3 0 3
## 2021 2 1 6
## 2022 6 3 0
##
## , , = 06:00-11:59, = Menor de 18
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 2 2 2
## 2013 2 4 1
## 2014 3 3 4
## 2015 2 2 4
## 2016 10 11 11
## 2017 9 11 3
## 2018 7 9 5
## 2019 6 3 1
## 2020 4 1 2
## 2021 1 1 4
## 2022 3 1 0
##
## , , = 12:00-17:59, = Menor de 18
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 6 3 5
## 2013 8 8 2
## 2014 4 11 7
## 2015 6 11 7
## 2016 7 3 4
## 2017 14 8 15
## 2018 4 9 6
## 2019 7 2 6
## 2020 3 3 0
## 2021 1 3 6
## 2022 12 3 0
##
## , , = 18:00-23:59, = Menor de 18
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 1 5 3
## 2013 2 5 5
## 2014 0 6 5
## 2015 3 5 4
## 2016 2 4 4
## 2017 9 7 8
## 2018 4 7 5
## 2019 4 4 6
## 2020 5 4 1
## 2021 1 2 4
## 2022 9 7 0
##
## , , = 00:00-05:59, = 18-45
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 54 60 24
## 2013 39 31 31
## 2014 28 44 18
## 2015 13 46 25
## 2016 36 29 29
## 2017 33 24 54
## 2018 61 79 117
## 2019 89 55 75
## 2020 50 42 63
## 2021 52 81 106
## 2022 78 45 0
##
## , , = 06:00-11:59, = 18-45
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 50 71 75
## 2013 78 54 40
## 2014 66 50 62
## 2015 47 67 63
## 2016 58 78 81
## 2017 74 84 84
## 2018 118 115 97
## 2019 108 99 102
## 2020 76 55 76
## 2021 85 96 139
## 2022 113 62 0
##
## , , = 12:00-17:59, = 18-45
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 61 87 71
## 2013 79 81 71
## 2014 71 62 61
## 2015 65 71 91
## 2016 61 67 87
## 2017 75 71 79
## 2018 107 90 128
## 2019 101 116 99
## 2020 69 65 81
## 2021 68 92 127
## 2022 116 53 0
##
## , , = 18:00-23:59, = 18-45
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 34 49 35
## 2013 52 41 36
## 2014 56 51 48
## 2015 53 60 74
## 2016 52 42 78
## 2017 53 64 81
## 2018 95 97 90
## 2019 129 100 92
## 2020 56 64 68
## 2021 59 88 105
## 2022 103 47 0
##
## , , = 00:00-05:59, = 45-65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 22 18 6
## 2013 20 16 15
## 2014 8 15 7
## 2015 3 11 8
## 2016 11 11 13
## 2017 19 9 16
## 2018 12 9 21
## 2019 35 25 22
## 2020 22 24 11
## 2021 25 20 30
## 2022 23 10 0
##
## , , = 06:00-11:59, = 45-65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 15 20 23
## 2013 29 12 10
## 2014 12 17 14
## 2015 15 20 17
## 2016 19 24 28
## 2017 23 29 26
## 2018 39 25 27
## 2019 34 34 26
## 2020 29 24 46
## 2021 33 37 35
## 2022 38 18 0
##
## , , = 12:00-17:59, = 45-65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 25 21 24
## 2013 23 16 12
## 2014 18 20 15
## 2015 15 29 29
## 2016 13 19 30
## 2017 36 28 30
## 2018 29 20 29
## 2019 36 32 27
## 2020 34 19 32
## 2021 26 39 38
## 2022 41 23 0
##
## , , = 18:00-23:59, = 45-65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 15 14 16
## 2013 13 12 10
## 2014 6 14 21
## 2015 10 17 16
## 2016 13 14 19
## 2017 18 22 28
## 2018 17 20 29
## 2019 24 34 25
## 2020 15 12 26
## 2021 8 18 19
## 2022 23 8 0
##
## , , = 00:00-05:59, = Mayor de 65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 4 7 1
## 2013 0 2 1
## 2014 3 3 0
## 2015 1 3 4
## 2016 4 2 3
## 2017 3 0 1
## 2018 2 4 1
## 2019 4 6 9
## 2020 8 0 3
## 2021 5 2 5
## 2022 4 4 0
##
## , , = 06:00-11:59, = Mayor de 65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 11 4 5
## 2013 1 8 2
## 2014 1 1 4
## 2015 5 4 8
## 2016 4 8 10
## 2017 8 10 5
## 2018 4 14 6
## 2019 9 10 10
## 2020 6 7 6
## 2021 4 4 8
## 2022 8 10 0
##
## , , = 12:00-17:59, = Mayor de 65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 2 4 2
## 2013 3 3 0
## 2014 5 3 5
## 2015 3 10 5
## 2016 5 4 5
## 2017 9 4 4
## 2018 6 2 2
## 2019 3 1 10
## 2020 13 7 10
## 2021 6 3 4
## 2022 10 4 0
##
## , , = 18:00-23:59, = Mayor de 65
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 2 1 0
## 2013 3 0 0
## 2014 0 2 0
## 2015 1 3 5
## 2016 0 1 4
## 2017 5 0 3
## 2018 4 5 1
## 2019 6 2 3
## 2020 0 5 4
## 2021 0 2 2
## 2022 7 1 0table(Palmira1_covar$YEAR,Palmira1_covar$CUATRIMESTRE,Palmira1_covar$INTERVALOS_HORA,Palmira1_covar$Armas_indicator)## , , = 00:00-05:59, = Con
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 48 44 14
## 2013 21 14 13
## 2014 9 24 12
## 2015 11 33 13
## 2016 21 21 32
## 2017 34 26 48
## 2018 54 64 102
## 2019 87 42 65
## 2020 37 39 49
## 2021 44 70 46
## 2022 47 26 0
##
## , , = 06:00-11:59, = Con
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 44 64 54
## 2013 57 33 35
## 2014 38 39 34
## 2015 32 47 38
## 2016 46 55 70
## 2017 58 83 66
## 2018 107 86 73
## 2019 100 77 64
## 2020 58 45 75
## 2021 77 87 79
## 2022 65 35 0
##
## , , = 12:00-17:59, = Con
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 62 76 73
## 2013 61 60 44
## 2014 62 62 51
## 2015 50 67 75
## 2016 40 50 80
## 2017 84 71 83
## 2018 94 69 99
## 2019 90 80 84
## 2020 63 51 71
## 2021 45 73 68
## 2022 60 33 0
##
## , , = 18:00-23:59, = Con
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 39 53 37
## 2013 44 34 35
## 2014 39 45 46
## 2015 44 57 60
## 2016 48 39 81
## 2017 68 59 76
## 2018 88 90 85
## 2019 114 84 87
## 2020 46 57 60
## 2021 34 78 58
## 2022 74 35 0
##
## , , = 00:00-05:59, = Sin
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 33 41 18
## 2013 39 35 34
## 2014 30 39 13
## 2015 6 28 25
## 2016 35 26 14
## 2017 27 9 30
## 2018 23 31 40
## 2019 46 49 43
## 2020 46 27 31
## 2021 40 34 102
## 2022 65 37 0
##
## , , = 06:00-11:59, = Sin
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 34 33 51
## 2013 53 45 18
## 2014 44 32 50
## 2015 37 46 54
## 2016 45 66 60
## 2017 56 51 55
## 2018 61 77 62
## 2019 57 69 75
## 2020 57 42 55
## 2021 47 53 110
## 2022 97 56 0
##
## , , = 12:00-17:59, = Sin
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 32 39 29
## 2013 52 48 41
## 2014 36 34 37
## 2015 39 54 57
## 2016 46 43 46
## 2017 50 42 47
## 2018 52 52 66
## 2019 58 73 58
## 2020 56 43 52
## 2021 56 64 108
## 2022 121 50 0
##
## , , = 18:00-23:59, = Sin
##
##
## CUATrim_1 CUATrim_2 CUATrim_3
## 2012 13 16 17
## 2013 26 24 16
## 2014 23 28 28
## 2015 23 28 39
## 2016 19 22 24
## 2017 19 34 44
## 2018 32 39 40
## 2019 49 56 39
## 2020 30 28 39
## 2021 35 32 74
## 2022 68 28 0## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## Warning: OGR support is provided by the sf and terra packages among others## OGR data source with driver: ESRI Shapefile
## Source: "/home/martha/Documentos/Alba/geo_export_99073ea9-9c06-40d6-b418-06ed3357b33b.shp", layer: "geo_export_99073ea9-9c06-40d6-b418-06ed3357b33b"
## with 99 features
## It has 7 fields