Este documento hace públicos los procedimientos y los datos utilizados para analizar la incidencia delictiva en Querétaro, en arreglo con los datos del SESNSP.
Te indicamos dónde descargar los datos, y cómo procesarlos en R. La base de datos es demasiado grande para abrirse en excel, así que necesitarás un software estadístico.
Resumen
- Entre Junio y Julio, el número de carpetas de investigación en Querétaro creció en 17.68%, en tanto que a nivel nacional lo hizo en 8.64%. Querétaro es en este periodo el octavo estado con mayor crecimiento.
- En lo que va de 2020, Querétaro ocupa el sexto lugar en carpetas de investigación por cada 100 mil habitantes, posición que ocupa desde 2018.
- En el mes de julio, también ocupa el sexto lugar nacional, pero no debemos olvidar que en mayo fue el cuarto, y el abril el segundo.
- El Estado de Querétaro es el primer lugar nacional en Acoso sexual, el tercero en Lesiones dolosas y en aborto, y el último en feminicidios, considerados todos los delitos en tasas por 100 mil habitantes. También es el tercer lugar nacional en tasa por cada 100 mil habitantes de Robo en transporte público individual, Robo en transporte público colectivo, y Robo en transporte individual.
- A nivel nacional, en lo que va de 2020 el municipio de Querétaro ocupa la posición 18 en tasa de carpetas de investigación por cada 100 mil habitantes; El Marqués ocupa la posición 42 y San Juan del Río la posición 76.
- Los delitos de Aborto,Despojo,Robo en transporte individual alcanzaron en julio sus niveles máximos registrados en QUerétaro.
- Otros delitos cuya tasa es superior a la registrada en julio del año pasado son Abuso sexual, Acoso Sexual, Electorales,Falsedad, Homicidio culposo, Otros delitos contra la familia, Otros delitos contra la sociedad, Otros delitos que atentan contra la libertad y la seguridad sexual, Robo a transeúnte en vía pública,Robo de maquinaria, Robo en transporte público colectivo y Violencia de género en todas sus modalidades distinta a la violencia familiar.
- A nivel estatal, los delitos más frecuentes en julio fueron Otros robos, Lesiones dolosas, Violencia familiar, Robo de vehículo automotor, Amenazas, Otros delitos del Fuero Común,Robo a negocio, Fraude y Robo a casa habitación.
- Entre los delitos con mayor aumento en Querétaro entre junio y julio están el Robo de vehículo automotor, que creció 42% al pasar de 238 a 338 carpetas; la violencia familiar, que al aumentar de 261 a 344 carpetas creció 31.80%, y el robo a casa habitación y las lesiones dolosas, con aumentos de 19.4% cada una, al pasar de 190 a 227 carpetas y de 397 a 474 carpétas, respectivamente.
- A nivel municipal, Huimilpan registró en Julio 67 delitos, la cantidad máxima jamás registrada para un solo mes en ese municipio.
- En tanto, Cadereyta de Montes, El Marqués y San Juan del Río también registraron sus máximos niveles en lo que va del año.
- Jalpan de Serra, Landa de Matamoros, y Peñamiller registraron aumentos en su tasa por cada 100 mil habitantes en comparación con julio de 2019.
- Durante julio, en San Juan del Río, los delitos más frecuentes fueron Otros robos, Violencia familiar, Amenazas y Lesiones dolosas. En el municipio de Querétaro,Otros robos, Robo de vehículo automotor, Lesiones dolosas y Robo a negocio fueron los delitos más comunes.
- En tres de los ltimos 5 aos, el homicidio doloso alcanz sus niveles mximos en septiembre.
#plotly nos ayudará con los gráficos
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
#Cargo kintr para ver las tablas
library(knitr)
#instalo una reshape para transformar la estructura de las bases de datos
library(reshape2)
#indico el directorio de trabajo; si quieres trabajar en C:/, descargas o alguna otra carpeta, deberás modificar esta línea.
setwd("D:/")
#la base de datos de población de CONAPO viene en dos partes, las descomprimimos
elzip<-unzip("Municipal-Delitos-2015-2020_jul2020.zip", list = TRUE)
elzip<-unzip("Municipal-Delitos-2015-2020_jul2020.zip", elzip$Name[9])
#las leemos
pop1<-read.table("base_municipios_final_datos_01.csv",TRUE,",")
pop2<-read.table(file = "base_municipios_final_datos_02.csv",header = TRUE,sep = ",")
#Las fusionamos
pop<-rbind(pop1,pop2)
#cambiamos los nombres para evitar problemas de codificacipon
names(pop)<-names(pop)<-c(names(pop[1:6]),"ANO",names(pop[8:9]))
#Creo una tabla con la población por cada entidad federativa
names(pop)[7]<-"ANO"
years=unique(pop$ANO)
ent=unique(pop$CLAVE_ENT)
ent<-as.data.frame(ent)
for(i in 1:length(years)){
a<-subset(pop,pop$ANO==years[i])
tpob<-aggregate(a$POB~a$CLAVE_ENT,a,sum)
tpobDF<-as.data.frame(tpob)
tpobDF<-tpobDF[,2]
ent<-cbind(ent,tpobDF)
}
names(ent)<- c("Entidad", paste0("year",years))
### AHORA LOS DATOS DE SESNSP
#DELITOS
esteMes<-"Julio"
anterior= "Junio"
ruta<-"D:/Municipal-Delitos-2015-2020_jul2020/Municipal-Delitos-2015-2020_jul2020.csv"
#+Municipal-Delitos-2015-2020_mar2020/
#+Municipal-Delitos-2015-2020_mar2020.csv"
delitos<-read.csv(file = ruta,header = TRUE,sep = ",")
names(delitos)<-c("Ano",names(delitos[2:21]))
delitos2<-melt(
data = delitos,
id.vars = names(delitos[1:9]),
measure.vars = names(delitos[10:21]),
variable.name = "meses")
delitos2$value[is.na(delitos2$value)]<-0
Delitos por estado (Serie Anual)
losAnos<-unique(delitos2$Ano)
porEstadoAnual<-as.data.frame(order(unique(delitos2$Clave_Ent)))
for (i in 1:length(losAnos)) {
misub=subset(delitos2,delitos2$Ano==losAnos[i])
mitab<-as.data.frame(aggregate(misub$value~misub$Clave_Ent,misub,sum))[2]
porEstadoAnual<-cbind(porEstadoAnual,mitab)
}
names(porEstadoAnual)<-c("clave de la entidad",paste0("year",losAnos))
tasaPorEstadoAnual<-porEstadoAnual
tasaPorEstadoAnual[,2:7]<-round(porEstadoAnual[,2:7]/ent[,2:7]*100000,2)
nomEnt<-c()
for (i in 1:32) {
nomEnt<-c(nomEnt,delitos2$Entidad[delitos2$Clave_Ent==i][1])
}
for (i in 1:length(nomEnt)) {
porEstadoAnual[i,1]<-nomEnt[i]
tasaPorEstadoAnual[i,1]<-nomEnt[i]
}
Serie Anual (Absolutos)
kable(porEstadoAnual)
| Aguascalientes |
23212 |
23729 |
33548 |
38834 |
38429 |
20132 |
| Baja California |
119944 |
109109 |
111722 |
103028 |
104011 |
52411 |
| Baja California Sur |
21415 |
24606 |
24174 |
23438 |
22644 |
10634 |
| Campeche |
1886 |
2237 |
2056 |
2157 |
2312 |
1123 |
| Coahuila de Zaragoza |
46569 |
51242 |
56311 |
56307 |
52936 |
28127 |
| Colima |
6561 |
10877 |
24425 |
24494 |
26554 |
14363 |
| Chiapas |
21618 |
22189 |
25364 |
28892 |
23294 |
10219 |
| Chihuahua |
61280 |
57904 |
68819 |
68898 |
71837 |
39043 |
| Ciudad de México |
169701 |
179720 |
204078 |
241030 |
242849 |
111266 |
| Durango |
29088 |
32183 |
34851 |
31903 |
30338 |
15369 |
| Guanajuato |
95782 |
106265 |
117857 |
133749 |
137658 |
70818 |
| Guerrero |
36783 |
36561 |
32799 |
27695 |
27343 |
13350 |
| Hidalgo |
27504 |
33754 |
43963 |
51222 |
49750 |
23586 |
| Jalisco |
95331 |
136820 |
166599 |
162756 |
156654 |
73818 |
| México |
202205 |
325038 |
345693 |
341028 |
354602 |
191248 |
| Michoacán de Ocampo |
30899 |
32558 |
41836 |
45190 |
45377 |
26212 |
| Morelos |
49245 |
45448 |
44329 |
44936 |
43191 |
22672 |
| Nayarit |
6651 |
3668 |
3220 |
4545 |
4642 |
2326 |
| Nuevo León |
72350 |
84746 |
83974 |
81125 |
75871 |
41896 |
| Oaxaca |
6127 |
31607 |
31938 |
41989 |
43788 |
22408 |
| Puebla |
64399 |
51061 |
53800 |
61172 |
76557 |
35535 |
| Querétaro |
32817 |
42900 |
53379 |
57809 |
60515 |
29858 |
| Quintana Roo |
32496 |
18958 |
26518 |
34043 |
45896 |
22807 |
| San Luis Potosí |
21419 |
28613 |
35179 |
38362 |
52288 |
26628 |
| Sinaloa |
25812 |
22141 |
22931 |
23486 |
23443 |
12503 |
| Sonora |
28659 |
39423 |
25969 |
18197 |
23438 |
16442 |
| Tabasco |
57452 |
59434 |
60395 |
58271 |
56561 |
24447 |
| Tamaulipas |
44527 |
48528 |
47163 |
44048 |
42413 |
17969 |
| Tlaxcala |
8317 |
6775 |
6964 |
6369 |
4411 |
2300 |
| Veracruz de Ignacio de la Llave |
45539 |
42312 |
66379 |
60758 |
89822 |
44495 |
| Yucatán |
34716 |
34288 |
24390 |
13129 |
16419 |
4681 |
| Zacatecas |
16179 |
17136 |
18874 |
21070 |
23952 |
13402 |
Serie Anual (Tasa por 100 mil habitantes)
kable(tasaPorEstadoAnual)
| Aguascalientes |
1742.87 |
1750.80 |
2438.47 |
2782.22 |
2715.02 |
1403.28 |
| Baja California |
3572.11 |
3205.94 |
3226.28 |
2925.90 |
2906.50 |
1441.90 |
| Baja California Sur |
2974.94 |
3338.69 |
3204.95 |
3038.79 |
2873.17 |
1321.47 |
| Campeche |
205.71 |
239.65 |
216.32 |
222.99 |
234.95 |
112.23 |
| Coahuila de Zaragoza |
1552.01 |
1683.90 |
1823.63 |
1797.79 |
1666.94 |
873.86 |
| Colima |
909.11 |
1480.54 |
3267.11 |
3221.48 |
3435.89 |
1829.32 |
| Chiapas |
407.29 |
411.37 |
462.90 |
519.28 |
412.46 |
178.33 |
| Chihuahua |
1694.46 |
1586.66 |
1865.32 |
1848.13 |
1907.86 |
1027.05 |
| Ciudad de México |
1873.34 |
1984.98 |
2255.23 |
2665.85 |
2689.00 |
1233.73 |
| Durango |
1632.71 |
1786.00 |
1915.42 |
1737.20 |
1637.28 |
822.31 |
| Guanajuato |
1615.04 |
1771.83 |
1945.29 |
2186.44 |
2229.74 |
1137.06 |
| Guerrero |
1028.44 |
1016.34 |
907.49 |
763.00 |
750.36 |
365.05 |
| Hidalgo |
948.58 |
1148.58 |
1476.77 |
1699.32 |
1630.76 |
764.19 |
| Jalisco |
1197.13 |
1698.37 |
2044.37 |
1975.44 |
1881.55 |
877.77 |
| México |
1228.94 |
1951.18 |
2050.24 |
1999.38 |
2056.19 |
1097.37 |
| Michoacán de Ocampo |
665.25 |
694.97 |
886.01 |
949.87 |
946.94 |
543.21 |
| Morelos |
2550.89 |
2325.04 |
2241.16 |
2246.21 |
2135.45 |
1109.17 |
| Nayarit |
556.34 |
301.99 |
261.00 |
362.91 |
365.33 |
180.51 |
| Nuevo León |
1389.90 |
1600.73 |
1562.24 |
1487.21 |
1371.21 |
746.79 |
| Oaxaca |
152.44 |
781.10 |
784.27 |
1024.87 |
1062.62 |
540.79 |
| Puebla |
1026.36 |
804.62 |
838.87 |
944.18 |
1170.15 |
538.05 |
| Querétaro |
1585.70 |
2029.59 |
2475.64 |
2630.15 |
2702.63 |
1309.77 |
| Quintana Roo |
2131.06 |
1211.44 |
1651.84 |
2069.19 |
2724.54 |
1323.48 |
| San Luis Potosí |
776.55 |
1028.71 |
1254.74 |
1357.87 |
1837.27 |
929.05 |
| Sinaloa |
855.90 |
726.08 |
745.13 |
756.49 |
748.74 |
396.08 |
| Sonora |
993.46 |
1348.87 |
876.79 |
606.54 |
771.56 |
534.74 |
| Tabasco |
2367.92 |
2418.60 |
2428.49 |
2316.09 |
2222.98 |
950.40 |
| Tamaulipas |
1274.12 |
1375.86 |
1325.08 |
1226.80 |
1171.34 |
492.22 |
| Tlaxcala |
642.14 |
515.44 |
523.07 |
472.50 |
323.35 |
166.67 |
| Veracruz de Ignacio de la Llave |
552.57 |
508.77 |
792.40 |
720.38 |
1058.17 |
521.03 |
| Yucatán |
1630.78 |
1590.44 |
1117.65 |
594.55 |
735.00 |
207.21 |
| Zacatecas |
1010.11 |
1059.95 |
1158.06 |
1282.89 |
1447.61 |
804.24 |
Posicion de Querétaro por año (de acuerdo con tasa por cada 100k habitantes)
posicionAnual<-c()
for (i in 1:length(losAnos)) {
a<-tasaPorEstadoAnual[22,i+1]
if(a==0){b=0}else{b<-1+length(tasaPorEstadoAnual[tasaPorEstadoAnual[i+1]>a,i+1])}
posicionAnual<-c(posicionAnual,b)
}
posicionesAnual<-data.frame(losAnos, posicionAnual)
names(posicionesAnual)<-c("Año","Posición del estado de Querétaro a nivel nacional")
kable(posicionesAnual, caption="Posición de Querétaro en la incidencia delictiva anual")
Posición de Querétaro en la incidencia delictiva anual
| 2015 |
12 |
| 2016 |
5 |
| 2017 |
4 |
| 2018 |
6 |
| 2019 |
6 |
| 2020 |
6 |
Delitos por estado (Serie Mensual)
delitoMensual<-subset(delitos2, delitos2$Ano==2020)
losmeses<-unique(delitoMensual$meses)
delitoPorEstado2020=as.data.frame(order(unique(delitoMensual$Clave_Ent)))
for (i in 1:length(losmeses)) {
datos<-delitoMensual[delitoMensual$meses==losmeses[i],]
delitoAnual<-as.data.frame(aggregate(datos$value~datos$Clave_Ent,datos,sum))[2]
delitoPorEstado2020<-cbind(delitoPorEstado2020,delitoAnual)
}
names(delitoPorEstado2020)<-c("clave de la entidad",levels(losmeses))
tasaAnualDedelitoPorEstado2020<-delitoPorEstado2020
tasaAnualDedelitoPorEstado2020[,2:8]<-round(delitoPorEstado2020[,2:8]/ent$year2020*100000,2)
tasaDeCambio<-delitoPorEstado2020[,c(anterior,esteMes)]
tasaDeCambio$tasa<-NA
tasaDeCambio$tasa<-round((tasaDeCambio[2]-tasaDeCambio[1])/tasaDeCambio[1]*100,2)
#la tasa de cambio de QUerétaro
tq<-tasaDeCambio[22,3]
tq<-tq[1,1]
#Querétaro fue el iesimo estado que mas crecio
iesimo<-length(tasaDeCambio$tasa[tasaDeCambio$tasa>tq])+1
totN<-colSums(tasaDeCambio[,c(1,2)])
#El pais creció a una tasa de tmex en el periodo
tmex<-round((totN[2]-totN[1])/totN[1]*100,2)
tmex<-as.vector(tmex)[1]
# Pone nombre al estado
nomEnt<-c()
for (i in 1:32) {
nomEnt<-c(nomEnt,delitoMensual$Entidad[delitoMensual$Clave_Ent==i][1])
}
delitoPorEstado2020$`clave de la entidad`<-nomEnt
ent$Entidad<-nomEnt
tasaAnualDedelitoPorEstado2020[1]<-nomEnt
En esta sección mostramos cómo se ha comportado la incidencia delictiva mes a mes, estado por estado.
General
Entre Junio y Julio, el delito en Querétaro creció en 17.68%, en tanto que a nivel nacional lo hizo en 8.64%. Querétaro es en este periodo el 8 estado con la tasa de crecimiento más alta.
Serie Mensual 2020 (Absolutos)
kable(delitoPorEstado2020)
| Aguascalientes |
3254 |
3183 |
3429 |
2085 |
2305 |
2951 |
2925 |
0 |
0 |
0 |
0 |
0 |
| Baja California |
8384 |
8313 |
8862 |
5718 |
6247 |
6799 |
8088 |
0 |
0 |
0 |
0 |
0 |
| Baja California Sur |
1776 |
1664 |
1792 |
1039 |
1153 |
1603 |
1607 |
0 |
0 |
0 |
0 |
0 |
| Campeche |
202 |
185 |
198 |
134 |
141 |
128 |
135 |
0 |
0 |
0 |
0 |
0 |
| Coahuila de Zaragoza |
4445 |
4159 |
4126 |
3051 |
3375 |
4256 |
4715 |
0 |
0 |
0 |
0 |
0 |
| Colima |
2269 |
2157 |
2169 |
1693 |
1855 |
2102 |
2118 |
0 |
0 |
0 |
0 |
0 |
| Chiapas |
1730 |
1754 |
2001 |
1221 |
1117 |
979 |
1417 |
0 |
0 |
0 |
0 |
0 |
| Chihuahua |
5587 |
5717 |
5671 |
4699 |
5000 |
6139 |
6230 |
0 |
0 |
0 |
0 |
0 |
| Ciudad de México |
18579 |
20012 |
20640 |
11818 |
10941 |
13230 |
16046 |
0 |
0 |
0 |
0 |
0 |
| Durango |
2485 |
2590 |
2665 |
1583 |
1789 |
1892 |
2365 |
0 |
0 |
0 |
0 |
0 |
| Guanajuato |
11628 |
11212 |
11622 |
8065 |
8637 |
9718 |
9936 |
0 |
0 |
0 |
0 |
0 |
| Guerrero |
2306 |
2390 |
2339 |
1496 |
1396 |
1560 |
1863 |
0 |
0 |
0 |
0 |
0 |
| Hidalgo |
4162 |
4184 |
4478 |
2937 |
2266 |
2614 |
2945 |
0 |
0 |
0 |
0 |
0 |
| Jalisco |
11832 |
11026 |
11143 |
8527 |
9430 |
10898 |
10962 |
0 |
0 |
0 |
0 |
0 |
| México |
29429 |
29815 |
29960 |
24907 |
22884 |
25990 |
28263 |
0 |
0 |
0 |
0 |
0 |
| Michoacán de Ocampo |
3953 |
3865 |
4366 |
3049 |
3564 |
3570 |
3845 |
0 |
0 |
0 |
0 |
0 |
| Morelos |
3577 |
3603 |
3708 |
2543 |
2672 |
3018 |
3551 |
0 |
0 |
0 |
0 |
0 |
| Nayarit |
351 |
401 |
407 |
251 |
292 |
313 |
311 |
0 |
0 |
0 |
0 |
0 |
| Nuevo León |
6305 |
7266 |
6710 |
4850 |
5044 |
6165 |
5556 |
0 |
0 |
0 |
0 |
0 |
| Oaxaca |
3485 |
3718 |
3846 |
2708 |
2844 |
2724 |
3083 |
0 |
0 |
0 |
0 |
0 |
| Puebla |
5224 |
5216 |
5624 |
4532 |
4736 |
4784 |
5419 |
0 |
0 |
0 |
0 |
0 |
| Querétaro |
4656 |
4694 |
4846 |
3725 |
3589 |
3835 |
4513 |
0 |
0 |
0 |
0 |
0 |
| Quintana Roo |
4012 |
3753 |
4166 |
2025 |
2163 |
3201 |
3487 |
0 |
0 |
0 |
0 |
0 |
| San Luis Potosí |
4269 |
4226 |
4023 |
2722 |
3089 |
3859 |
4440 |
0 |
0 |
0 |
0 |
0 |
| Sinaloa |
1998 |
1980 |
1960 |
1231 |
1605 |
1869 |
1860 |
0 |
0 |
0 |
0 |
0 |
| Sonora |
2427 |
2313 |
2425 |
1859 |
2404 |
2217 |
2797 |
0 |
0 |
0 |
0 |
0 |
| Tabasco |
4466 |
4316 |
4315 |
2018 |
1958 |
3348 |
4026 |
0 |
0 |
0 |
0 |
0 |
| Tamaulipas |
2961 |
3023 |
3022 |
1855 |
2103 |
2684 |
2321 |
0 |
0 |
0 |
0 |
0 |
| Tlaxcala |
333 |
365 |
331 |
287 |
334 |
313 |
337 |
0 |
0 |
0 |
0 |
0 |
| Veracruz de Ignacio de la Llave |
6527 |
7552 |
7598 |
5287 |
4969 |
6248 |
6314 |
0 |
0 |
0 |
0 |
0 |
| Yucatán |
990 |
867 |
823 |
419 |
387 |
568 |
627 |
0 |
0 |
0 |
0 |
0 |
| Zacatecas |
2151 |
2059 |
2071 |
1441 |
1558 |
2201 |
1921 |
0 |
0 |
0 |
0 |
0 |
Serie Mensual 2020 (Tasa por 100 mil habitantes)
kable(tasaAnualDedelitoPorEstado2020)
| Aguascalientes |
226.82 |
221.87 |
239.02 |
145.33 |
160.67 |
205.70 |
203.88 |
0 |
0 |
0 |
0 |
0 |
| Baja California |
230.65 |
228.70 |
243.81 |
157.31 |
171.86 |
187.05 |
222.51 |
0 |
0 |
0 |
0 |
0 |
| Baja California Sur |
220.70 |
206.78 |
222.69 |
129.12 |
143.28 |
199.20 |
199.70 |
0 |
0 |
0 |
0 |
0 |
| Campeche |
20.19 |
18.49 |
19.79 |
13.39 |
14.09 |
12.79 |
13.49 |
0 |
0 |
0 |
0 |
0 |
| Coahuila de Zaragoza |
138.10 |
129.21 |
128.19 |
94.79 |
104.86 |
132.23 |
146.49 |
0 |
0 |
0 |
0 |
0 |
| Colima |
288.99 |
274.72 |
276.25 |
215.63 |
236.26 |
267.72 |
269.76 |
0 |
0 |
0 |
0 |
0 |
| Chiapas |
30.19 |
30.61 |
34.92 |
21.31 |
19.49 |
17.08 |
24.73 |
0 |
0 |
0 |
0 |
0 |
| Chihuahua |
146.97 |
150.39 |
149.18 |
123.61 |
131.53 |
161.49 |
163.88 |
0 |
0 |
0 |
0 |
0 |
| Ciudad de México |
206.01 |
221.90 |
228.86 |
131.04 |
121.32 |
146.70 |
177.92 |
0 |
0 |
0 |
0 |
0 |
| Durango |
132.96 |
138.58 |
142.59 |
84.70 |
95.72 |
101.23 |
126.54 |
0 |
0 |
0 |
0 |
0 |
| Guanajuato |
186.70 |
180.02 |
186.60 |
129.49 |
138.68 |
156.03 |
159.53 |
0 |
0 |
0 |
0 |
0 |
| Guerrero |
63.06 |
65.35 |
63.96 |
40.91 |
38.17 |
42.66 |
50.94 |
0 |
0 |
0 |
0 |
0 |
| Hidalgo |
134.85 |
135.56 |
145.09 |
95.16 |
73.42 |
84.69 |
95.42 |
0 |
0 |
0 |
0 |
0 |
| Jalisco |
140.69 |
131.11 |
132.50 |
101.39 |
112.13 |
129.59 |
130.35 |
0 |
0 |
0 |
0 |
0 |
| México |
168.86 |
171.08 |
171.91 |
142.92 |
131.31 |
149.13 |
162.17 |
0 |
0 |
0 |
0 |
0 |
| Michoacán de Ocampo |
81.92 |
80.10 |
90.48 |
63.19 |
73.86 |
73.98 |
79.68 |
0 |
0 |
0 |
0 |
0 |
| Morelos |
175.00 |
176.27 |
181.40 |
124.41 |
130.72 |
147.65 |
173.72 |
0 |
0 |
0 |
0 |
0 |
| Nayarit |
27.24 |
31.12 |
31.59 |
19.48 |
22.66 |
24.29 |
24.14 |
0 |
0 |
0 |
0 |
0 |
| Nuevo León |
112.39 |
129.52 |
119.60 |
86.45 |
89.91 |
109.89 |
99.03 |
0 |
0 |
0 |
0 |
0 |
| Oaxaca |
84.11 |
89.73 |
92.82 |
65.35 |
68.64 |
65.74 |
74.40 |
0 |
0 |
0 |
0 |
0 |
| Puebla |
79.10 |
78.98 |
85.15 |
68.62 |
71.71 |
72.44 |
82.05 |
0 |
0 |
0 |
0 |
0 |
| Querétaro |
204.24 |
205.91 |
212.58 |
163.40 |
157.44 |
168.23 |
197.97 |
0 |
0 |
0 |
0 |
0 |
| Quintana Roo |
232.81 |
217.79 |
241.75 |
117.51 |
125.52 |
185.75 |
202.35 |
0 |
0 |
0 |
0 |
0 |
| San Luis Potosí |
148.95 |
147.45 |
140.36 |
94.97 |
107.78 |
134.64 |
154.91 |
0 |
0 |
0 |
0 |
0 |
| Sinaloa |
63.29 |
62.72 |
62.09 |
39.00 |
50.84 |
59.21 |
58.92 |
0 |
0 |
0 |
0 |
0 |
| Sonora |
78.93 |
75.23 |
78.87 |
60.46 |
78.19 |
72.10 |
90.97 |
0 |
0 |
0 |
0 |
0 |
| Tabasco |
173.62 |
167.79 |
167.75 |
78.45 |
76.12 |
130.16 |
156.51 |
0 |
0 |
0 |
0 |
0 |
| Tamaulipas |
81.11 |
82.81 |
82.78 |
50.81 |
57.61 |
73.52 |
63.58 |
0 |
0 |
0 |
0 |
0 |
| Tlaxcala |
24.13 |
26.45 |
23.99 |
20.80 |
24.20 |
22.68 |
24.42 |
0 |
0 |
0 |
0 |
0 |
| Veracruz de Ignacio de la Llave |
76.43 |
88.43 |
88.97 |
61.91 |
58.19 |
73.16 |
73.94 |
0 |
0 |
0 |
0 |
0 |
| Yucatán |
43.82 |
38.38 |
36.43 |
18.55 |
17.13 |
25.14 |
27.75 |
0 |
0 |
0 |
0 |
0 |
| Zacatecas |
129.08 |
123.56 |
124.28 |
86.47 |
93.49 |
132.08 |
115.28 |
0 |
0 |
0 |
0 |
0 |
posición de queretaro por mes en el país
posicionMensual<-c()
for (i in 1:length(losmeses)) {
a<-tasaAnualDedelitoPorEstado2020[22,i+1]
if(a==0){b=0}else{b<-1+length(tasaAnualDedelitoPorEstado2020[tasaAnualDedelitoPorEstado2020[i+1]>a,i+1])}
posicionMensual<-c(posicionMensual,b)
}
posiciones<-data.frame(losmeses, posicionMensual)
names(posiciones)<-c("Mes","Posición de Querétaro a nivel nacional en el periodo")
kable(posiciones)
| Enero |
7 |
| Febrero |
7 |
| Marzo |
7 |
| Abril |
2 |
| Mayo |
4 |
| Junio |
6 |
| Julio |
6 |
| Agosto |
0 |
| Septiembre |
0 |
| Octubre |
0 |
| Noviembre |
0 |
| Diciembre |
0 |
Lugar de Querétaro en el año por delito
losDelitos<-unique(delitos2$Subtipo.de.delito)
losDelitos2020<-subset(delitos2,delitos2$Ano==2020)
delitoEstado2020=as.data.frame(order(unique(losDelitos2020$Clave_Ent)))
for (i in 1:length(losDelitos)) {
a<-subset(losDelitos2020,losDelitos2020$Subtipo.de.delito==losDelitos[i])
b<-as.data.frame(aggregate(a$value~a$Clave_Ent,a,sum))[2]
delitoEstado2020<-cbind(delitoEstado2020,b)
}
names(delitoEstado2020)<-c("claveEntidad",losDelitos)
tasaDelitoEstado2020<-delitoEstado2020
tasaDelitoEstado2020[,2:56]<-round(delitoEstado2020[,2:56]/ent$year2020*100000,2)
for (i in 1:length(nomEnt)) {
delitoEstado2020[i,1]<-nomEnt[i]
tasaDelitoEstado2020[i,1]<-nomEnt[i]
}
Incidencia en subtipos de Delito por estado en 2020
kable(delitoEstado2020)
| Aguascalientes |
53 |
87 |
2164 |
486 |
1 |
3 |
27 |
7 |
0 |
0 |
244 |
0 |
0 |
44 |
127 |
45 |
0 |
230 |
1399 |
1006 |
468 |
3 |
783 |
0 |
56 |
10 |
17 |
0 |
1145 |
101 |
2 |
1118 |
799 |
295 |
67 |
2121 |
177 |
160 |
1271 |
3 |
93 |
24 |
35 |
2 |
4 |
1843 |
1931 |
280 |
0 |
31 |
456 |
19 |
217 |
0 |
678 |
| Baja California |
1468 |
224 |
3221 |
875 |
21 |
23 |
1078 |
5 |
1 |
0 |
355 |
761 |
0 |
132 |
318 |
181 |
1 |
126 |
2282 |
6094 |
34 |
20 |
2217 |
3 |
4 |
9 |
5 |
6 |
2610 |
39 |
0 |
3400 |
779 |
244 |
77 |
3669 |
613 |
455 |
5808 |
0 |
296 |
269 |
425 |
34 |
28 |
6062 |
2180 |
1365 |
2 |
43 |
156 |
12 |
504 |
0 |
3877 |
| Baja California Sur |
31 |
30 |
809 |
180 |
2 |
6 |
89 |
1 |
0 |
0 |
93 |
165 |
66 |
9 |
104 |
25 |
0 |
56 |
662 |
389 |
12 |
0 |
103 |
43 |
9 |
2 |
4 |
0 |
429 |
56 |
5 |
1744 |
542 |
159 |
54 |
717 |
206 |
71 |
1405 |
5 |
332 |
147 |
28 |
0 |
1 |
268 |
776 |
86 |
0 |
43 |
48 |
2 |
152 |
0 |
468 |
| Campeche |
42 |
28 |
43 |
38 |
3 |
0 |
11 |
0 |
0 |
0 |
12 |
28 |
0 |
0 |
28 |
71 |
0 |
10 |
104 |
224 |
4 |
3 |
22 |
0 |
0 |
0 |
2 |
0 |
113 |
11 |
0 |
54 |
4 |
0 |
7 |
68 |
3 |
26 |
21 |
0 |
0 |
0 |
2 |
0 |
3 |
50 |
22 |
9 |
0 |
0 |
11 |
1 |
3 |
0 |
42 |
| Coahuila de Zaragoza |
122 |
113 |
2140 |
299 |
14 |
0 |
26 |
5 |
0 |
0 |
27 |
325 |
140 |
5 |
77 |
83 |
0 |
13 |
1154 |
332 |
80 |
8 |
190 |
14 |
10 |
3 |
11 |
1 |
606 |
24 |
30 |
1286 |
562 |
256 |
23 |
3287 |
225 |
564 |
5339 |
248 |
143 |
85 |
15 |
7 |
0 |
6145 |
2503 |
305 |
2 |
12 |
70 |
0 |
383 |
1 |
814 |
| Colima |
344 |
64 |
697 |
324 |
7 |
1 |
0 |
5 |
0 |
0 |
214 |
194 |
0 |
19 |
71 |
6 |
0 |
27 |
1034 |
512 |
0 |
0 |
75 |
0 |
0 |
0 |
0 |
0 |
398 |
24 |
0 |
1402 |
744 |
274 |
73 |
1366 |
227 |
140 |
2482 |
0 |
434 |
0 |
19 |
0 |
66 |
752 |
1546 |
103 |
0 |
28 |
78 |
2 |
172 |
2 |
437 |
| Chiapas |
244 |
355 |
373 |
307 |
14 |
6 |
74 |
5 |
1 |
0 |
88 |
89 |
59 |
13 |
287 |
0 |
0 |
370 |
137 |
1156 |
1 |
7 |
129 |
57 |
0 |
3 |
2 |
0 |
181 |
36 |
5 |
399 |
158 |
62 |
47 |
451 |
92 |
269 |
2745 |
1 |
95 |
2 |
26 |
4 |
57 |
597 |
271 |
46 |
0 |
14 |
38 |
25 |
155 |
1 |
665 |
| Chihuahua |
1413 |
159 |
2409 |
646 |
20 |
7 |
262 |
11 |
2 |
0 |
423 |
790 |
0 |
108 |
488 |
131 |
0 |
200 |
1303 |
2360 |
432 |
25 |
195 |
60 |
4 |
0 |
16 |
3 |
1096 |
130 |
79 |
2149 |
1472 |
457 |
10 |
4486 |
466 |
362 |
6733 |
22 |
873 |
11 |
53 |
17 |
0 |
4342 |
1742 |
478 |
8 |
92 |
365 |
49 |
905 |
0 |
1179 |
| Ciudad de México |
719 |
353 |
2561 |
1977 |
41 |
52 |
123 |
41 |
0 |
8 |
1026 |
1837 |
654 |
0 |
624 |
225 |
0 |
348 |
2455 |
6067 |
4020 |
113 |
5993 |
991 |
177 |
2170 |
1578 |
15 |
9627 |
0 |
21 |
11902 |
7288 |
2145 |
244 |
4739 |
2015 |
2563 |
14965 |
0 |
178 |
9 |
123 |
52 |
1062 |
3442 |
7806 |
434 |
12 |
213 |
2275 |
313 |
2870 |
4 |
2796 |
| Durango |
83 |
98 |
1058 |
449 |
9 |
0 |
40 |
0 |
0 |
0 |
220 |
210 |
52 |
9 |
121 |
2 |
0 |
163 |
1670 |
601 |
79 |
7 |
248 |
8 |
6 |
4 |
4 |
1 |
698 |
79 |
4 |
1887 |
596 |
188 |
59 |
1265 |
168 |
84 |
3232 |
1 |
47 |
109 |
4 |
1 |
13 |
444 |
693 |
111 |
0 |
8 |
39 |
0 |
53 |
0 |
444 |
| Guanajuato |
1980 |
919 |
6433 |
15 |
10 |
17 |
114 |
6 |
0 |
0 |
0 |
677 |
123 |
19 |
286 |
26 |
0 |
15 |
2448 |
2556 |
0 |
5 |
110 |
0 |
0 |
0 |
0 |
0 |
3922 |
164 |
0 |
11392 |
1482 |
691 |
10 |
5138 |
642 |
6 |
5772 |
0 |
830 |
8 |
110 |
1 |
0 |
7977 |
5057 |
225 |
1 |
72 |
219 |
20 |
51 |
0 |
11269 |
| Guerrero |
714 |
89 |
1181 |
153 |
10 |
2 |
7 |
12 |
0 |
0 |
217 |
166 |
38 |
7 |
108 |
79 |
0 |
0 |
206 |
1280 |
12 |
0 |
104 |
14 |
0 |
7 |
1 |
7 |
314 |
23 |
2 |
1397 |
297 |
136 |
153 |
919 |
238 |
9 |
1648 |
173 |
174 |
90 |
9 |
10 |
0 |
399 |
1204 |
95 |
0 |
27 |
143 |
3 |
106 |
2 |
1365 |
| Hidalgo |
189 |
129 |
2548 |
622 |
11 |
14 |
163 |
15 |
0 |
3 |
1082 |
381 |
0 |
29 |
245 |
183 |
0 |
20 |
1345 |
2007 |
67 |
13 |
420 |
92 |
31 |
12 |
40 |
1 |
962 |
53 |
0 |
1826 |
590 |
243 |
89 |
1270 |
387 |
58 |
3489 |
0 |
308 |
3 |
11 |
8 |
11 |
196 |
1564 |
126 |
1 |
36 |
76 |
1 |
265 |
14 |
2337 |
| Jalisco |
1041 |
487 |
4373 |
1417 |
33 |
10 |
0 |
8 |
1 |
0 |
598 |
1282 |
148 |
35 |
199 |
0 |
0 |
199 |
2778 |
7972 |
1186 |
237 |
6287 |
27 |
54 |
40 |
0 |
17 |
6135 |
95 |
56 |
6589 |
3757 |
1147 |
454 |
3999 |
1083 |
0 |
7200 |
0 |
0 |
615 |
79 |
2 |
7 |
595 |
5873 |
146 |
0 |
73 |
946 |
64 |
221 |
3 |
6250 |
| México |
1444 |
565 |
24738 |
5924 |
80 |
90 |
666 |
87 |
1 |
0 |
1554 |
1506 |
572 |
74 |
614 |
378 |
0 |
61 |
4489 |
22450 |
1619 |
2812 |
8985 |
162 |
527 |
3767 |
5237 |
23 |
10266 |
137 |
26 |
18881 |
5875 |
1786 |
1650 |
7009 |
2412 |
82 |
8928 |
1080 |
879 |
5 |
87 |
46 |
2080 |
2032 |
0 |
994 |
7 |
35 |
636 |
278 |
2134 |
2 |
35476 |
| Michoacán de Ocampo |
1128 |
567 |
3797 |
570 |
10 |
6 |
100 |
24 |
0 |
0 |
254 |
293 |
4 |
70 |
203 |
62 |
0 |
60 |
841 |
3361 |
25 |
606 |
380 |
70 |
24 |
95 |
11 |
10 |
511 |
51 |
81 |
2173 |
1072 |
340 |
9 |
1691 |
483 |
172 |
707 |
0 |
61 |
0 |
22 |
8 |
3 |
1132 |
2227 |
207 |
0 |
16 |
209 |
88 |
223 |
1 |
2154 |
| Morelos |
475 |
129 |
508 |
1380 |
25 |
4 |
283 |
41 |
0 |
3 |
128 |
245 |
13 |
30 |
224 |
9 |
0 |
50 |
809 |
2181 |
768 |
255 |
452 |
39 |
21 |
35 |
23 |
13 |
1511 |
25 |
4 |
2631 |
726 |
289 |
74 |
1088 |
540 |
166 |
2852 |
0 |
112 |
192 |
20 |
1 |
9 |
514 |
2470 |
156 |
1 |
33 |
146 |
7 |
30 |
1 |
931 |
| Nayarit |
90 |
64 |
81 |
31 |
8 |
0 |
6 |
1 |
0 |
0 |
69 |
0 |
3 |
0 |
62 |
9 |
0 |
70 |
72 |
197 |
15 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
102 |
4 |
1 |
79 |
107 |
13 |
3 |
48 |
14 |
0 |
468 |
0 |
164 |
4 |
4 |
2 |
2 |
83 |
38 |
10 |
1 |
3 |
2 |
0 |
8 |
0 |
387 |
| Nuevo León |
521 |
281 |
1947 |
713 |
39 |
57 |
144 |
11 |
1 |
54 |
1147 |
724 |
246 |
27 |
453 |
190 |
0 |
408 |
1465 |
1060 |
75 |
398 |
549 |
294 |
36 |
11 |
23 |
3 |
1215 |
59 |
19 |
4141 |
1523 |
359 |
239 |
2679 |
517 |
33 |
9889 |
0 |
203 |
2929 |
93 |
25 |
8 |
2091 |
1586 |
123 |
1 |
103 |
499 |
0 |
1142 |
9 |
1534 |
| Oaxaca |
498 |
476 |
2398 |
503 |
21 |
5 |
139 |
16 |
0 |
0 |
114 |
324 |
111 |
29 |
236 |
135 |
0 |
45 |
730 |
1400 |
104 |
33 |
926 |
104 |
42 |
117 |
17 |
18 |
769 |
49 |
17 |
1868 |
788 |
241 |
63 |
1498 |
419 |
205 |
3579 |
2 |
64 |
129 |
29 |
8 |
312 |
159 |
2351 |
161 |
1 |
98 |
146 |
1 |
258 |
22 |
630 |
| Puebla |
536 |
215 |
2394 |
411 |
38 |
4 |
242 |
15 |
0 |
0 |
146 |
426 |
149 |
33 |
260 |
181 |
0 |
384 |
1106 |
6342 |
150 |
588 |
1016 |
0 |
43 |
112 |
339 |
21 |
1858 |
83 |
204 |
2829 |
1137 |
491 |
90 |
1423 |
769 |
165 |
5516 |
0 |
139 |
464 |
18 |
2 |
271 |
583 |
2181 |
230 |
2 |
29 |
137 |
37 |
657 |
6 |
1063 |
| Querétaro |
98 |
171 |
2886 |
460 |
1 |
15 |
576 |
6 |
0 |
0 |
59 |
314 |
333 |
0 |
250 |
87 |
0 |
35 |
1581 |
2087 |
413 |
0 |
812 |
63 |
83 |
227 |
224 |
0 |
1718 |
95 |
13 |
5815 |
1319 |
307 |
140 |
764 |
473 |
25 |
2141 |
10 |
301 |
112 |
0 |
0 |
138 |
665 |
2163 |
173 |
0 |
48 |
173 |
1 |
0 |
12 |
2471 |
| Quintana Roo |
363 |
456 |
1166 |
368 |
7 |
8 |
164 |
8 |
0 |
0 |
316 |
320 |
100 |
24 |
325 |
0 |
0 |
89 |
1111 |
1562 |
23 |
26 |
877 |
74 |
67 |
30 |
48 |
6 |
2723 |
24 |
118 |
2557 |
222 |
991 |
161 |
1754 |
324 |
138 |
2616 |
0 |
213 |
305 |
43 |
16 |
1 |
641 |
1170 |
120 |
2 |
112 |
129 |
40 |
288 |
14 |
547 |
| San Luis Potosí |
337 |
220 |
2257 |
289 |
20 |
8 |
143 |
11 |
0 |
0 |
337 |
301 |
108 |
13 |
388 |
0 |
0 |
171 |
713 |
1991 |
676 |
199 |
468 |
17 |
15 |
29 |
3 |
4 |
828 |
138 |
47 |
2390 |
1055 |
395 |
90 |
2542 |
381 |
630 |
4607 |
0 |
230 |
1 |
22 |
7 |
0 |
822 |
1705 |
289 |
3 |
0 |
54 |
34 |
397 |
1 |
1242 |
| Sinaloa |
422 |
354 |
1216 |
320 |
15 |
2 |
310 |
6 |
1 |
0 |
718 |
196 |
51 |
0 |
78 |
45 |
0 |
21 |
322 |
1892 |
2 |
3 |
9 |
0 |
3 |
2 |
7 |
11 |
426 |
18 |
0 |
810 |
213 |
101 |
29 |
897 |
152 |
19 |
2657 |
0 |
53 |
39 |
24 |
4 |
27 |
185 |
527 |
45 |
1 |
9 |
56 |
0 |
137 |
3 |
65 |
| Sonora |
741 |
206 |
768 |
408 |
12 |
2 |
151 |
1 |
0 |
2 |
245 |
281 |
30 |
6 |
111 |
30 |
0 |
42 |
762 |
1607 |
44 |
9 |
176 |
174 |
2 |
0 |
9 |
2 |
514 |
59 |
54 |
2344 |
268 |
75 |
28 |
955 |
144 |
148 |
2665 |
5 |
537 |
82 |
19 |
1 |
32 |
1594 |
283 |
127 |
0 |
7 |
6 |
0 |
31 |
0 |
643 |
| Tabasco |
301 |
163 |
2103 |
424 |
9 |
2 |
360 |
19 |
0 |
0 |
260 |
89 |
0 |
124 |
147 |
0 |
0 |
320 |
1031 |
1395 |
6 |
8 |
2188 |
0 |
8 |
4 |
12 |
1 |
871 |
385 |
0 |
1462 |
418 |
289 |
60 |
1183 |
229 |
70 |
3397 |
0 |
412 |
10 |
24 |
2 |
0 |
46 |
2080 |
234 |
2 |
11 |
87 |
0 |
153 |
1 |
4047 |
| Tamaulipas |
378 |
370 |
1176 |
451 |
6 |
20 |
123 |
12 |
0 |
0 |
248 |
299 |
41 |
25 |
234 |
0 |
0 |
58 |
829 |
1362 |
7 |
0 |
64 |
0 |
0 |
0 |
0 |
2 |
733 |
46 |
1 |
2112 |
549 |
221 |
74 |
1701 |
248 |
15 |
3681 |
0 |
480 |
366 |
21 |
1 |
0 |
115 |
861 |
118 |
0 |
37 |
77 |
4 |
230 |
0 |
573 |
| Tlaxcala |
63 |
25 |
129 |
49 |
1 |
0 |
5 |
11 |
0 |
0 |
4 |
20 |
2 |
1 |
21 |
0 |
0 |
0 |
146 |
867 |
3 |
76 |
43 |
1 |
0 |
1 |
1 |
1 |
160 |
16 |
18 |
54 |
42 |
6 |
1 |
121 |
23 |
11 |
7 |
0 |
13 |
1 |
0 |
9 |
0 |
149 |
13 |
26 |
2 |
1 |
4 |
0 |
0 |
0 |
153 |
| Veracruz de Ignacio de la Llave |
748 |
499 |
3778 |
899 |
52 |
13 |
109 |
83 |
0 |
0 |
420 |
377 |
16 |
164 |
224 |
8 |
1 |
736 |
1559 |
3841 |
65 |
123 |
1229 |
177 |
43 |
39 |
42 |
13 |
3180 |
292 |
53 |
2252 |
1746 |
604 |
446 |
3585 |
1131 |
525 |
5814 |
617 |
572 |
929 |
18 |
4 |
0 |
336 |
3699 |
320 |
1 |
70 |
246 |
106 |
227 |
5 |
2459 |
| Yucatán |
26 |
63 |
131 |
29 |
4 |
0 |
143 |
0 |
0 |
0 |
2 |
38 |
3 |
0 |
18 |
0 |
0 |
2 |
207 |
81 |
1 |
0 |
39 |
0 |
0 |
0 |
0 |
0 |
69 |
2 |
3 |
0 |
216 |
214 |
0 |
790 |
5 |
179 |
362 |
0 |
102 |
24 |
2 |
12 |
0 |
101 |
1202 |
41 |
0 |
8 |
13 |
1 |
14 |
0 |
534 |
| Zacatecas |
404 |
77 |
1131 |
311 |
5 |
1 |
129 |
21 |
0 |
0 |
235 |
119 |
55 |
14 |
85 |
62 |
0 |
55 |
213 |
886 |
22 |
5 |
12 |
10 |
0 |
1 |
7 |
0 |
103 |
102 |
18 |
2229 |
567 |
178 |
205 |
1176 |
198 |
46 |
1931 |
0 |
255 |
60 |
12 |
6 |
0 |
184 |
713 |
105 |
6 |
56 |
45 |
1 |
142 |
3 |
1201 |
Tasa por cada 100 mil habitantes
kable(tasaDelitoEstado2020)
| Aguascalientes |
3.69 |
6.06 |
150.84 |
33.88 |
0.07 |
0.21 |
1.88 |
0.49 |
0.00 |
0.00 |
17.01 |
0.00 |
0.00 |
3.07 |
8.85 |
3.14 |
0.00 |
16.03 |
97.52 |
70.12 |
32.62 |
0.21 |
54.58 |
0.00 |
3.90 |
0.70 |
1.18 |
0.00 |
79.81 |
7.04 |
0.14 |
77.93 |
55.69 |
20.56 |
4.67 |
147.84 |
12.34 |
11.15 |
88.59 |
0.21 |
6.48 |
1.67 |
2.44 |
0.14 |
0.28 |
128.46 |
134.60 |
19.52 |
0.00 |
2.16 |
31.79 |
1.32 |
15.13 |
0.00 |
47.26 |
| Baja California |
40.39 |
6.16 |
88.61 |
24.07 |
0.58 |
0.63 |
29.66 |
0.14 |
0.03 |
0.00 |
9.77 |
20.94 |
0.00 |
3.63 |
8.75 |
4.98 |
0.03 |
3.47 |
62.78 |
167.65 |
0.94 |
0.55 |
60.99 |
0.08 |
0.11 |
0.25 |
0.14 |
0.17 |
71.80 |
1.07 |
0.00 |
93.54 |
21.43 |
6.71 |
2.12 |
100.94 |
16.86 |
12.52 |
159.79 |
0.00 |
8.14 |
7.40 |
11.69 |
0.94 |
0.77 |
166.77 |
59.97 |
37.55 |
0.06 |
1.18 |
4.29 |
0.33 |
13.87 |
0.00 |
106.66 |
| Baja California Sur |
3.85 |
3.73 |
100.53 |
22.37 |
0.25 |
0.75 |
11.06 |
0.12 |
0.00 |
0.00 |
11.56 |
20.50 |
8.20 |
1.12 |
12.92 |
3.11 |
0.00 |
6.96 |
82.27 |
48.34 |
1.49 |
0.00 |
12.80 |
5.34 |
1.12 |
0.25 |
0.50 |
0.00 |
53.31 |
6.96 |
0.62 |
216.72 |
67.35 |
19.76 |
6.71 |
89.10 |
25.60 |
8.82 |
174.60 |
0.62 |
41.26 |
18.27 |
3.48 |
0.00 |
0.12 |
33.30 |
96.43 |
10.69 |
0.00 |
5.34 |
5.96 |
0.25 |
18.89 |
0.00 |
58.16 |
| Campeche |
4.20 |
2.80 |
4.30 |
3.80 |
0.30 |
0.00 |
1.10 |
0.00 |
0.00 |
0.00 |
1.20 |
2.80 |
0.00 |
0.00 |
2.80 |
7.10 |
0.00 |
1.00 |
10.39 |
22.39 |
0.40 |
0.30 |
2.20 |
0.00 |
0.00 |
0.00 |
0.20 |
0.00 |
11.29 |
1.10 |
0.00 |
5.40 |
0.40 |
0.00 |
0.70 |
6.80 |
0.30 |
2.60 |
2.10 |
0.00 |
0.00 |
0.00 |
0.20 |
0.00 |
0.30 |
5.00 |
2.20 |
0.90 |
0.00 |
0.00 |
1.10 |
0.10 |
0.30 |
0.00 |
4.20 |
| Coahuila de Zaragoza |
3.79 |
3.51 |
66.49 |
9.29 |
0.43 |
0.00 |
0.81 |
0.16 |
0.00 |
0.00 |
0.84 |
10.10 |
4.35 |
0.16 |
2.39 |
2.58 |
0.00 |
0.40 |
35.85 |
10.31 |
2.49 |
0.25 |
5.90 |
0.43 |
0.31 |
0.09 |
0.34 |
0.03 |
18.83 |
0.75 |
0.93 |
39.95 |
17.46 |
7.95 |
0.71 |
102.12 |
6.99 |
17.52 |
165.87 |
7.70 |
4.44 |
2.64 |
0.47 |
0.22 |
0.00 |
190.91 |
77.76 |
9.48 |
0.06 |
0.37 |
2.17 |
0.00 |
11.90 |
0.03 |
25.29 |
| Colima |
43.81 |
8.15 |
88.77 |
41.27 |
0.89 |
0.13 |
0.00 |
0.64 |
0.00 |
0.00 |
27.26 |
24.71 |
0.00 |
2.42 |
9.04 |
0.76 |
0.00 |
3.44 |
131.69 |
65.21 |
0.00 |
0.00 |
9.55 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
50.69 |
3.06 |
0.00 |
178.56 |
94.76 |
34.90 |
9.30 |
173.98 |
28.91 |
17.83 |
316.12 |
0.00 |
55.28 |
0.00 |
2.42 |
0.00 |
8.41 |
95.78 |
196.90 |
13.12 |
0.00 |
3.57 |
9.93 |
0.25 |
21.91 |
0.25 |
55.66 |
| Chiapas |
4.26 |
6.20 |
6.51 |
5.36 |
0.24 |
0.10 |
1.29 |
0.09 |
0.02 |
0.00 |
1.54 |
1.55 |
1.03 |
0.23 |
5.01 |
0.00 |
0.00 |
6.46 |
2.39 |
20.17 |
0.02 |
0.12 |
2.25 |
0.99 |
0.00 |
0.05 |
0.03 |
0.00 |
3.16 |
0.63 |
0.09 |
6.96 |
2.76 |
1.08 |
0.82 |
7.87 |
1.61 |
4.69 |
47.90 |
0.02 |
1.66 |
0.03 |
0.45 |
0.07 |
0.99 |
10.42 |
4.73 |
0.80 |
0.00 |
0.24 |
0.66 |
0.44 |
2.70 |
0.02 |
11.60 |
| Chihuahua |
37.17 |
4.18 |
63.37 |
16.99 |
0.53 |
0.18 |
6.89 |
0.29 |
0.05 |
0.00 |
11.13 |
20.78 |
0.00 |
2.84 |
12.84 |
3.45 |
0.00 |
5.26 |
34.28 |
62.08 |
11.36 |
0.66 |
5.13 |
1.58 |
0.11 |
0.00 |
0.42 |
0.08 |
28.83 |
3.42 |
2.08 |
56.53 |
38.72 |
12.02 |
0.26 |
118.01 |
12.26 |
9.52 |
177.11 |
0.58 |
22.96 |
0.29 |
1.39 |
0.45 |
0.00 |
114.22 |
45.82 |
12.57 |
0.21 |
2.42 |
9.60 |
1.29 |
23.81 |
0.00 |
31.01 |
| Ciudad de México |
7.97 |
3.91 |
28.40 |
21.92 |
0.45 |
0.58 |
1.36 |
0.45 |
0.00 |
0.09 |
11.38 |
20.37 |
7.25 |
0.00 |
6.92 |
2.49 |
0.00 |
3.86 |
27.22 |
67.27 |
44.57 |
1.25 |
66.45 |
10.99 |
1.96 |
24.06 |
17.50 |
0.17 |
106.75 |
0.00 |
0.23 |
131.97 |
80.81 |
23.78 |
2.71 |
52.55 |
22.34 |
28.42 |
165.93 |
0.00 |
1.97 |
0.10 |
1.36 |
0.58 |
11.78 |
38.17 |
86.55 |
4.81 |
0.13 |
2.36 |
25.23 |
3.47 |
31.82 |
0.04 |
31.00 |
| Durango |
4.44 |
5.24 |
56.61 |
24.02 |
0.48 |
0.00 |
2.14 |
0.00 |
0.00 |
0.00 |
11.77 |
11.24 |
2.78 |
0.48 |
6.47 |
0.11 |
0.00 |
8.72 |
89.35 |
32.16 |
4.23 |
0.37 |
13.27 |
0.43 |
0.32 |
0.21 |
0.21 |
0.05 |
37.35 |
4.23 |
0.21 |
100.96 |
31.89 |
10.06 |
3.16 |
67.68 |
8.99 |
4.49 |
172.93 |
0.05 |
2.51 |
5.83 |
0.21 |
0.05 |
0.70 |
23.76 |
37.08 |
5.94 |
0.00 |
0.43 |
2.09 |
0.00 |
2.84 |
0.00 |
23.76 |
| Guanajuato |
31.79 |
14.76 |
103.29 |
0.24 |
0.16 |
0.27 |
1.83 |
0.10 |
0.00 |
0.00 |
0.00 |
10.87 |
1.97 |
0.31 |
4.59 |
0.42 |
0.00 |
0.24 |
39.31 |
41.04 |
0.00 |
0.08 |
1.77 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
62.97 |
2.63 |
0.00 |
182.91 |
23.80 |
11.09 |
0.16 |
82.50 |
10.31 |
0.10 |
92.68 |
0.00 |
13.33 |
0.13 |
1.77 |
0.02 |
0.00 |
128.08 |
81.20 |
3.61 |
0.02 |
1.16 |
3.52 |
0.32 |
0.82 |
0.00 |
180.94 |
| Guerrero |
19.52 |
2.43 |
32.29 |
4.18 |
0.27 |
0.05 |
0.19 |
0.33 |
0.00 |
0.00 |
5.93 |
4.54 |
1.04 |
0.19 |
2.95 |
2.16 |
0.00 |
0.00 |
5.63 |
35.00 |
0.33 |
0.00 |
2.84 |
0.38 |
0.00 |
0.19 |
0.03 |
0.19 |
8.59 |
0.63 |
0.05 |
38.20 |
8.12 |
3.72 |
4.18 |
25.13 |
6.51 |
0.25 |
45.06 |
4.73 |
4.76 |
2.46 |
0.25 |
0.27 |
0.00 |
10.91 |
32.92 |
2.60 |
0.00 |
0.74 |
3.91 |
0.08 |
2.90 |
0.05 |
37.33 |
| Hidalgo |
6.12 |
4.18 |
82.56 |
20.15 |
0.36 |
0.45 |
5.28 |
0.49 |
0.00 |
0.10 |
35.06 |
12.34 |
0.00 |
0.94 |
7.94 |
5.93 |
0.00 |
0.65 |
43.58 |
65.03 |
2.17 |
0.42 |
13.61 |
2.98 |
1.00 |
0.39 |
1.30 |
0.03 |
31.17 |
1.72 |
0.00 |
59.16 |
19.12 |
7.87 |
2.88 |
41.15 |
12.54 |
1.88 |
113.04 |
0.00 |
9.98 |
0.10 |
0.36 |
0.26 |
0.36 |
6.35 |
50.67 |
4.08 |
0.03 |
1.17 |
2.46 |
0.03 |
8.59 |
0.45 |
75.72 |
| Jalisco |
12.38 |
5.79 |
52.00 |
16.85 |
0.39 |
0.12 |
0.00 |
0.10 |
0.01 |
0.00 |
7.11 |
15.24 |
1.76 |
0.42 |
2.37 |
0.00 |
0.00 |
2.37 |
33.03 |
94.80 |
14.10 |
2.82 |
74.76 |
0.32 |
0.64 |
0.48 |
0.00 |
0.20 |
72.95 |
1.13 |
0.67 |
78.35 |
44.67 |
13.64 |
5.40 |
47.55 |
12.88 |
0.00 |
85.62 |
0.00 |
0.00 |
7.31 |
0.94 |
0.02 |
0.08 |
7.08 |
69.84 |
1.74 |
0.00 |
0.87 |
11.25 |
0.76 |
2.63 |
0.04 |
74.32 |
| México |
8.29 |
3.24 |
141.95 |
33.99 |
0.46 |
0.52 |
3.82 |
0.50 |
0.01 |
0.00 |
8.92 |
8.64 |
3.28 |
0.42 |
3.52 |
2.17 |
0.00 |
0.35 |
25.76 |
128.82 |
9.29 |
16.14 |
51.56 |
0.93 |
3.02 |
21.61 |
30.05 |
0.13 |
58.91 |
0.79 |
0.15 |
108.34 |
33.71 |
10.25 |
9.47 |
40.22 |
13.84 |
0.47 |
51.23 |
6.20 |
5.04 |
0.03 |
0.50 |
0.26 |
11.93 |
11.66 |
0.00 |
5.70 |
0.04 |
0.20 |
3.65 |
1.60 |
12.24 |
0.01 |
203.56 |
| Michoacán de Ocampo |
23.38 |
11.75 |
78.69 |
11.81 |
0.21 |
0.12 |
2.07 |
0.50 |
0.00 |
0.00 |
5.26 |
6.07 |
0.08 |
1.45 |
4.21 |
1.28 |
0.00 |
1.24 |
17.43 |
69.65 |
0.52 |
12.56 |
7.87 |
1.45 |
0.50 |
1.97 |
0.23 |
0.21 |
10.59 |
1.06 |
1.68 |
45.03 |
22.22 |
7.05 |
0.19 |
35.04 |
10.01 |
3.56 |
14.65 |
0.00 |
1.26 |
0.00 |
0.46 |
0.17 |
0.06 |
23.46 |
46.15 |
4.29 |
0.00 |
0.33 |
4.33 |
1.82 |
4.62 |
0.02 |
44.64 |
| Morelos |
23.24 |
6.31 |
24.85 |
67.51 |
1.22 |
0.20 |
13.85 |
2.01 |
0.00 |
0.15 |
6.26 |
11.99 |
0.64 |
1.47 |
10.96 |
0.44 |
0.00 |
2.45 |
39.58 |
106.70 |
37.57 |
12.48 |
22.11 |
1.91 |
1.03 |
1.71 |
1.13 |
0.64 |
73.92 |
1.22 |
0.20 |
128.71 |
35.52 |
14.14 |
3.62 |
53.23 |
26.42 |
8.12 |
139.53 |
0.00 |
5.48 |
9.39 |
0.98 |
0.05 |
0.44 |
25.15 |
120.84 |
7.63 |
0.05 |
1.61 |
7.14 |
0.34 |
1.47 |
0.05 |
45.55 |
| Nayarit |
6.98 |
4.97 |
6.29 |
2.41 |
0.62 |
0.00 |
0.47 |
0.08 |
0.00 |
0.00 |
5.35 |
0.00 |
0.23 |
0.00 |
4.81 |
0.70 |
0.00 |
5.43 |
5.59 |
15.29 |
1.16 |
0.00 |
0.00 |
0.00 |
0.08 |
0.00 |
0.00 |
0.00 |
7.92 |
0.31 |
0.08 |
6.13 |
8.30 |
1.01 |
0.23 |
3.73 |
1.09 |
0.00 |
36.32 |
0.00 |
12.73 |
0.31 |
0.31 |
0.16 |
0.16 |
6.44 |
2.95 |
0.78 |
0.08 |
0.23 |
0.16 |
0.00 |
0.62 |
0.00 |
30.03 |
| Nuevo León |
9.29 |
5.01 |
34.70 |
12.71 |
0.70 |
1.02 |
2.57 |
0.20 |
0.02 |
0.96 |
20.45 |
12.91 |
4.38 |
0.48 |
8.07 |
3.39 |
0.00 |
7.27 |
26.11 |
18.89 |
1.34 |
7.09 |
9.79 |
5.24 |
0.64 |
0.20 |
0.41 |
0.05 |
21.66 |
1.05 |
0.34 |
73.81 |
27.15 |
6.40 |
4.26 |
47.75 |
9.22 |
0.59 |
176.27 |
0.00 |
3.62 |
52.21 |
1.66 |
0.45 |
0.14 |
37.27 |
28.27 |
2.19 |
0.02 |
1.84 |
8.89 |
0.00 |
20.36 |
0.16 |
27.34 |
| Oaxaca |
12.02 |
11.49 |
57.87 |
12.14 |
0.51 |
0.12 |
3.35 |
0.39 |
0.00 |
0.00 |
2.75 |
7.82 |
2.68 |
0.70 |
5.70 |
3.26 |
0.00 |
1.09 |
17.62 |
33.79 |
2.51 |
0.80 |
22.35 |
2.51 |
1.01 |
2.82 |
0.41 |
0.43 |
18.56 |
1.18 |
0.41 |
45.08 |
19.02 |
5.82 |
1.52 |
36.15 |
10.11 |
4.95 |
86.37 |
0.05 |
1.54 |
3.11 |
0.70 |
0.19 |
7.53 |
3.84 |
56.74 |
3.89 |
0.02 |
2.37 |
3.52 |
0.02 |
6.23 |
0.53 |
15.20 |
| Puebla |
8.12 |
3.26 |
36.25 |
6.22 |
0.58 |
0.06 |
3.66 |
0.23 |
0.00 |
0.00 |
2.21 |
6.45 |
2.26 |
0.50 |
3.94 |
2.74 |
0.00 |
5.81 |
16.75 |
96.03 |
2.27 |
8.90 |
15.38 |
0.00 |
0.65 |
1.70 |
5.13 |
0.32 |
28.13 |
1.26 |
3.09 |
42.83 |
17.22 |
7.43 |
1.36 |
21.55 |
11.64 |
2.50 |
83.52 |
0.00 |
2.10 |
7.03 |
0.27 |
0.03 |
4.10 |
8.83 |
33.02 |
3.48 |
0.03 |
0.44 |
2.07 |
0.56 |
9.95 |
0.09 |
16.10 |
| Querétaro |
4.30 |
7.50 |
126.60 |
20.18 |
0.04 |
0.66 |
25.27 |
0.26 |
0.00 |
0.00 |
2.59 |
13.77 |
14.61 |
0.00 |
10.97 |
3.82 |
0.00 |
1.54 |
69.35 |
91.55 |
18.12 |
0.00 |
35.62 |
2.76 |
3.64 |
9.96 |
9.83 |
0.00 |
75.36 |
4.17 |
0.57 |
255.08 |
57.86 |
13.47 |
6.14 |
33.51 |
20.75 |
1.10 |
93.92 |
0.44 |
13.20 |
4.91 |
0.00 |
0.00 |
6.05 |
29.17 |
94.88 |
7.59 |
0.00 |
2.11 |
7.59 |
0.04 |
0.00 |
0.53 |
108.39 |
| Quintana Roo |
21.06 |
26.46 |
67.66 |
21.35 |
0.41 |
0.46 |
9.52 |
0.46 |
0.00 |
0.00 |
18.34 |
18.57 |
5.80 |
1.39 |
18.86 |
0.00 |
0.00 |
5.16 |
64.47 |
90.64 |
1.33 |
1.51 |
50.89 |
4.29 |
3.89 |
1.74 |
2.79 |
0.35 |
158.01 |
1.39 |
6.85 |
148.38 |
12.88 |
57.51 |
9.34 |
101.78 |
18.80 |
8.01 |
151.81 |
0.00 |
12.36 |
17.70 |
2.50 |
0.93 |
0.06 |
37.20 |
67.89 |
6.96 |
0.12 |
6.50 |
7.49 |
2.32 |
16.71 |
0.81 |
31.74 |
| San Luis Potosí |
11.76 |
7.68 |
78.75 |
10.08 |
0.70 |
0.28 |
4.99 |
0.38 |
0.00 |
0.00 |
11.76 |
10.50 |
3.77 |
0.45 |
13.54 |
0.00 |
0.00 |
5.97 |
24.88 |
69.47 |
23.59 |
6.94 |
16.33 |
0.59 |
0.52 |
1.01 |
0.10 |
0.14 |
28.89 |
4.81 |
1.64 |
83.39 |
36.81 |
13.78 |
3.14 |
88.69 |
13.29 |
21.98 |
160.74 |
0.00 |
8.02 |
0.03 |
0.77 |
0.24 |
0.00 |
28.68 |
59.49 |
10.08 |
0.10 |
0.00 |
1.88 |
1.19 |
13.85 |
0.03 |
43.33 |
| Sinaloa |
13.37 |
11.21 |
38.52 |
10.14 |
0.48 |
0.06 |
9.82 |
0.19 |
0.03 |
0.00 |
22.75 |
6.21 |
1.62 |
0.00 |
2.47 |
1.43 |
0.00 |
0.67 |
10.20 |
59.94 |
0.06 |
0.10 |
0.29 |
0.00 |
0.10 |
0.06 |
0.22 |
0.35 |
13.50 |
0.57 |
0.00 |
25.66 |
6.75 |
3.20 |
0.92 |
28.42 |
4.82 |
0.60 |
84.17 |
0.00 |
1.68 |
1.24 |
0.76 |
0.13 |
0.86 |
5.86 |
16.69 |
1.43 |
0.03 |
0.29 |
1.77 |
0.00 |
4.34 |
0.10 |
2.06 |
| Sonora |
24.10 |
6.70 |
24.98 |
13.27 |
0.39 |
0.07 |
4.91 |
0.03 |
0.00 |
0.07 |
7.97 |
9.14 |
0.98 |
0.20 |
3.61 |
0.98 |
0.00 |
1.37 |
24.78 |
52.26 |
1.43 |
0.29 |
5.72 |
5.66 |
0.07 |
0.00 |
0.29 |
0.07 |
16.72 |
1.92 |
1.76 |
76.23 |
8.72 |
2.44 |
0.91 |
31.06 |
4.68 |
4.81 |
86.67 |
0.16 |
17.46 |
2.67 |
0.62 |
0.03 |
1.04 |
51.84 |
9.20 |
4.13 |
0.00 |
0.23 |
0.20 |
0.00 |
1.01 |
0.00 |
20.91 |
| Tabasco |
11.70 |
6.34 |
81.76 |
16.48 |
0.35 |
0.08 |
14.00 |
0.74 |
0.00 |
0.00 |
10.11 |
3.46 |
0.00 |
4.82 |
5.71 |
0.00 |
0.00 |
12.44 |
40.08 |
54.23 |
0.23 |
0.31 |
85.06 |
0.00 |
0.31 |
0.16 |
0.47 |
0.04 |
33.86 |
14.97 |
0.00 |
56.84 |
16.25 |
11.24 |
2.33 |
45.99 |
8.90 |
2.72 |
132.06 |
0.00 |
16.02 |
0.39 |
0.93 |
0.08 |
0.00 |
1.79 |
80.86 |
9.10 |
0.08 |
0.43 |
3.38 |
0.00 |
5.95 |
0.04 |
157.33 |
| Tamaulipas |
10.35 |
10.14 |
32.21 |
12.35 |
0.16 |
0.55 |
3.37 |
0.33 |
0.00 |
0.00 |
6.79 |
8.19 |
1.12 |
0.68 |
6.41 |
0.00 |
0.00 |
1.59 |
22.71 |
37.31 |
0.19 |
0.00 |
1.75 |
0.00 |
0.00 |
0.00 |
0.00 |
0.05 |
20.08 |
1.26 |
0.03 |
57.85 |
15.04 |
6.05 |
2.03 |
46.60 |
6.79 |
0.41 |
100.83 |
0.00 |
13.15 |
10.03 |
0.58 |
0.03 |
0.00 |
3.15 |
23.59 |
3.23 |
0.00 |
1.01 |
2.11 |
0.11 |
6.30 |
0.00 |
15.70 |
| Tlaxcala |
4.57 |
1.81 |
9.35 |
3.55 |
0.07 |
0.00 |
0.36 |
0.80 |
0.00 |
0.00 |
0.29 |
1.45 |
0.14 |
0.07 |
1.52 |
0.00 |
0.00 |
0.00 |
10.58 |
62.83 |
0.22 |
5.51 |
3.12 |
0.07 |
0.00 |
0.07 |
0.07 |
0.07 |
11.59 |
1.16 |
1.30 |
3.91 |
3.04 |
0.43 |
0.07 |
8.77 |
1.67 |
0.80 |
0.51 |
0.00 |
0.94 |
0.07 |
0.00 |
0.65 |
0.00 |
10.80 |
0.94 |
1.88 |
0.14 |
0.07 |
0.29 |
0.00 |
0.00 |
0.00 |
11.09 |
| Veracruz de Ignacio de la Llave |
8.76 |
5.84 |
44.24 |
10.53 |
0.61 |
0.15 |
1.28 |
0.97 |
0.00 |
0.00 |
4.92 |
4.41 |
0.19 |
1.92 |
2.62 |
0.09 |
0.01 |
8.62 |
18.26 |
44.98 |
0.76 |
1.44 |
14.39 |
2.07 |
0.50 |
0.46 |
0.49 |
0.15 |
37.24 |
3.42 |
0.62 |
26.37 |
20.45 |
7.07 |
5.22 |
41.98 |
13.24 |
6.15 |
68.08 |
7.22 |
6.70 |
10.88 |
0.21 |
0.05 |
0.00 |
3.93 |
43.31 |
3.75 |
0.01 |
0.82 |
2.88 |
1.24 |
2.66 |
0.06 |
28.79 |
| Yucatán |
1.15 |
2.79 |
5.80 |
1.28 |
0.18 |
0.00 |
6.33 |
0.00 |
0.00 |
0.00 |
0.09 |
1.68 |
0.13 |
0.00 |
0.80 |
0.00 |
0.00 |
0.09 |
9.16 |
3.59 |
0.04 |
0.00 |
1.73 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
3.05 |
0.09 |
0.13 |
0.00 |
9.56 |
9.47 |
0.00 |
34.97 |
0.22 |
7.92 |
16.02 |
0.00 |
4.52 |
1.06 |
0.09 |
0.53 |
0.00 |
4.47 |
53.21 |
1.81 |
0.00 |
0.35 |
0.58 |
0.04 |
0.62 |
0.00 |
23.64 |
| Zacatecas |
24.24 |
4.62 |
67.87 |
18.66 |
0.30 |
0.06 |
7.74 |
1.26 |
0.00 |
0.00 |
14.10 |
7.14 |
3.30 |
0.84 |
5.10 |
3.72 |
0.00 |
3.30 |
12.78 |
53.17 |
1.32 |
0.30 |
0.72 |
0.60 |
0.00 |
0.06 |
0.42 |
0.00 |
6.18 |
6.12 |
1.08 |
133.76 |
34.02 |
10.68 |
12.30 |
70.57 |
11.88 |
2.76 |
115.88 |
0.00 |
15.30 |
3.60 |
0.72 |
0.36 |
0.00 |
11.04 |
42.79 |
6.30 |
0.36 |
3.36 |
2.70 |
0.06 |
8.52 |
0.18 |
72.07 |
Posicion de queretaro en 2020 por tipo de delito
posicionAnualporDelito<-c()
for (i in 1:length(losDelitos)) {
a<-tasaDelitoEstado2020[22,i+1]
if(a==0){b=0}else{b<-1+length(tasaDelitoEstado2020[tasaDelitoEstado2020[i+1]>a,i+1])}
posicionAnualporDelito<-c(posicionAnualporDelito,b)
}
posicionesAnualporDelito<-data.frame(losDelitos, posicionAnualporDelito)
posicionesAnualporDelito<-posicionesAnualporDelito[order(posicionesAnualporDelito$posicionAnualporDelito),]
names(posicionesAnualporDelito)<-c("Subtipo de delito", "Posición que ocupa Querétaro a nivel nacional en ese delito")
kable(posicionesAnualporDelito[posicionesAnualporDelito[2]>0,])
| 13 |
Acoso sexual |
1 |
| 32 |
Otros robos |
1 |
| 7 |
Otros delitos que atentan contra la vida y la integridad corporal |
2 |
| 54 |
Electorales |
2 |
| 3 |
Lesiones dolosas |
3 |
| 6 |
Aborto |
3 |
| 25 |
Robo en transporte público individual |
3 |
| 26 |
Robo en transporte público colectivo |
3 |
| 27 |
Robo en transporte individual |
3 |
| 16 |
Violación equiparada |
4 |
| 29 |
Robo a negocio |
4 |
| 33 |
Fraude |
4 |
| 55 |
Otros delitos del Fuero Común |
4 |
| 15 |
Violación simple |
5 |
| 19 |
Robo a casa habitación |
5 |
| 21 |
Robo de autopartes |
5 |
| 37 |
Despojo |
5 |
| 45 |
Otros delitos contra la sociedad |
5 |
| 47 |
Amenazas |
5 |
| 20 |
Robo de vehículo automotor |
6 |
| 35 |
Extorsión |
6 |
| 24 |
Robo a transeúnte en espacio abierto al público |
7 |
| 30 |
Robo de ganado |
7 |
| 40 |
Violencia de género en todas sus modalidades distinta a la violencia familiar |
7 |
| 51 |
Falsificación |
7 |
| 12 |
Abuso sexual |
8 |
| 23 |
Robo a transeúnte en vía pública |
8 |
| 41 |
Incumplimiento de obligaciones de asistencia familiar |
8 |
| 2 |
Homicidio culposo |
9 |
| 34 |
Abuso de confianza |
9 |
| 50 |
Falsedad |
9 |
| 4 |
Lesiones culposas |
10 |
| 48 |
Allanamiento de morada |
10 |
| 42 |
Otros delitos contra la familia |
11 |
| 46 |
Narcomenudeo |
12 |
| 31 |
Robo de maquinaria |
13 |
| 39 |
Violencia familiar |
16 |
| 8 |
Secuestro |
18 |
| 18 |
Otros delitos que atentan contra la libertad y la seguridad sexual |
20 |
| 52 |
Contra el medio ambiente |
21 |
| 38 |
Otros delitos contra el patrimonio |
23 |
| 36 |
Daño a la propiedad |
24 |
| 11 |
Otros delitos que atentan contra la libertad personal |
25 |
| 1 |
Homicidio doloso |
26 |
| 5 |
Feminicidio |
32 |
Lugar a nivel nacional de los municipios Queretanos en incidencia delictiva
Top 50 municipios en el año
pop2020<-subset(pop,pop$ANO==2020)
delitos2020<-subset(delitos2,delitos2$Ano==2020)
popMun<-aggregate(pop2020$POB~pop2020$MUN,pop2020,sum)
delMun<-aggregate(delitos2020$value~delitos2020$Cve..Municipio,delitos2020,sum)
delMun$estado<-NA
delMun$municipio<-NA
for (i in 1:nrow(delMun)) {
delMun$estado[i]<-unique(delitos2020$Entidad[delitos2020$Cve..Municipio==delMun$`delitos2020$Cve..Municipio`[i]])
delMun$municipio[i]<-unique(delitos2020$Municipio[delitos2020$Cve..Municipio==delMun$`delitos2020$Cve..Municipio`[i]])
}
delMun$poblacion<-NA
for (i in 1:nrow(delMun)) {
delMun$poblacion[delMun$municipio==popMun$`pop2020$MUN`[i]]<-popMun$`pop2020$POB`[i]
}
delMun$incidencia<-NA
delMun$incidencia<-round(delMun$`delitos2020$value`/delMun$poblacion*100000,2)
delMun<-delMun[order(delMun$incidencia,decreasing = TRUE),]
delMun$posicion<-NA
for (i in 1:nrow(delMun)) {
delMun$posicion[i]<-i
}
names(delMun)[c(1,2,6,7)]<-c("Clave del municipio","Número de carpetas de investigación", "Incidencia por cada 100 mil habitantes","Posición a nivel nacional")
kable(head(delMun[,c(7,3,4,2,5,6)],50),caption = "Top 50 en el año")
Top 50 en el año
| 72 |
1 |
Colima |
Colima |
5209 |
169188 |
3078.82 |
| 227 |
2 |
Chihuahua |
Santa Isabel |
100 |
4293 |
2329.37 |
| 969 |
3 |
Nuevo León |
Doctor Coss |
41 |
1845 |
2222.22 |
| 1556 |
4 |
Oaxaca |
Tlacolula de Matamoros |
504 |
24027 |
2097.64 |
| 908 |
5 |
Morelos |
Cuernavaca |
8348 |
399426 |
2090.00 |
| 284 |
6 |
Ciudad de México |
Cuauhtémoc |
15805 |
776217 |
2036.16 |
| 1821 |
7 |
Quintana Roo |
Tulum |
743 |
36866 |
2015.41 |
| 1072 |
8 |
Oaxaca |
Oaxaca de Juárez |
5000 |
258636 |
1933.22 |
| 501 |
9 |
Hidalgo |
Pachuca de Soto |
5322 |
280312 |
1898.60 |
| 16 |
10 |
Baja California |
Playas de Rosarito |
2031 |
107859 |
1883.01 |
| 913 |
11 |
Morelos |
Jojutla |
1138 |
61366 |
1854.45 |
| 285 |
12 |
Ciudad de México |
Miguel Hidalgo |
7021 |
379624 |
1849.46 |
| 333 |
13 |
Guanajuato |
Celaya |
9697 |
530820 |
1826.80 |
| 77 |
14 |
Colima |
Manzanillo |
3656 |
203306 |
1798.27 |
| 14 |
15 |
Baja California |
Tecate |
2018 |
113857 |
1772.40 |
| 1343 |
16 |
Oaxaca |
Villa de Etla |
198 |
11426 |
1732.89 |
| 264 |
17 |
Chihuahua |
Satevó |
58 |
3381 |
1715.47 |
| 1807 |
18 |
Querétaro |
Querétaro |
16691 |
976939 |
1708.50 |
| 6 |
19 |
Aguascalientes |
Pabellón de Arteaga |
854 |
50032 |
1706.91 |
| 907 |
20 |
Morelos |
Cuautla |
3503 |
210529 |
1663.90 |
| 11 |
21 |
Aguascalientes |
San Francisco de los Romo |
858 |
51568 |
1663.82 |
| 769 |
22 |
México |
Toluca |
15461 |
948950 |
1629.27 |
| 13 |
23 |
Baja California |
Mexicali |
17632 |
1087478 |
1621.37 |
| 2469 |
24 |
Zacatecas |
Zacatecas |
2469 |
155533 |
1587.44 |
| 576 |
25 |
Jalisco |
Guadalajara |
23289 |
1503505 |
1548.98 |
| 1851 |
26 |
San Luis Potosí |
San Luis Potosí |
13351 |
870578 |
1533.58 |
| 1820 |
27 |
Quintana Roo |
Solidaridad |
3666 |
239850 |
1528.46 |
| 331 |
28 |
Guanajuato |
Apaseo el Grande |
1495 |
99036 |
1509.55 |
| 1 |
29 |
Aguascalientes |
Aguascalientes |
14493 |
961977 |
1506.58 |
| 341 |
30 |
Guanajuato |
Guanajuato |
2970 |
198035 |
1499.73 |
| 514 |
31 |
Hidalgo |
Tepeapulco |
871 |
58776 |
1481.90 |
| 1394 |
32 |
Oaxaca |
Santa Inés Yatzeche |
14 |
945 |
1481.48 |
| 672 |
33 |
México |
Amecameca |
804 |
54548 |
1473.93 |
| 1639 |
34 |
Puebla |
Esperanza |
232 |
15794 |
1468.91 |
| 696 |
35 |
México |
Ecatepec de Morelos |
24931 |
1707754 |
1459.87 |
| 74 |
36 |
Colima |
Coquimatlán |
321 |
22167 |
1448.10 |
| 784 |
37 |
México |
Cuautitlán Izcalli |
8353 |
577190 |
1447.18 |
| 773 |
38 |
México |
Valle de Bravo |
1007 |
70192 |
1434.64 |
| 80 |
39 |
Colima |
Villa de Álvarez |
2163 |
151019 |
1432.27 |
| 762 |
40 |
México |
Texcoco |
3748 |
262015 |
1430.45 |
| 788 |
41 |
México |
Tonanitla |
156 |
10960 |
1423.36 |
| 1804 |
42 |
Querétaro |
El Marqués |
2537 |
178672 |
1419.92 |
| 688 |
43 |
México |
Chalco |
5598 |
397344 |
1408.85 |
| 76 |
44 |
Colima |
Ixtlahuacán |
85 |
6078 |
1398.49 |
| 720 |
45 |
México |
Naucalpan de Juárez |
12675 |
910187 |
1392.57 |
| 1575 |
46 |
Oaxaca |
Zimatlán de Álvarez |
290 |
20892 |
1388.09 |
| 346 |
47 |
Guanajuato |
León |
23154 |
1679610 |
1378.53 |
| 739 |
48 |
México |
San Mateo Atenco |
1106 |
80903 |
1367.07 |
| 10 |
49 |
Aguascalientes |
El Llano |
300 |
21947 |
1366.93 |
| 767 |
50 |
México |
Tlalnepantla de Baz |
10319 |
756537 |
1363.98 |
Top 50 municipios en el mes
pop2020<-subset(pop,pop$ANO==2020)
delitos2020Mes<-subset(delitos2,delitos2$Ano==2020 & delitos2$meses== esteMes)
popMun<-aggregate(pop2020$POB~pop2020$MUN,pop2020,sum)
delMunMes<-aggregate(delitos2020Mes$value~delitos2020Mes$Cve..Municipio,delitos2020Mes,sum)
delMunMes$estado<-NA
delMunMes$municipio<-NA
for (i in 1:nrow(delMunMes)) {
delMunMes$estado[i]<-unique(delitos2020Mes$Entidad[delitos2020Mes$Cve..Municipio==delMunMes$`delitos2020Mes$Cve..Municipio`[i]])
delMunMes$municipio[i]<-unique(delitos2020Mes$Municipio[delitos2020Mes$Cve..Municipio==delMunMes$`delitos2020Mes$Cve..Municipio`[i]])
}
delMunMes$poblacion<-NA
for (i in 1:nrow(delMunMes)) {
delMunMes$poblacion[delMunMes$municipio==popMun$`pop2020$MUN`[i]]<-popMun$`pop2020$POB`[i]
}
delMunMes$incidencia<-NA
delMunMes$incidencia<-round(delMunMes$`delitos2020Mes$value`/delMunMes$poblacion*100000,2)
delMunMes<-delMunMes[order(delMunMes$incidencia,decreasing = TRUE),]
delMunMes$posicion<-NA
for (i in 1:nrow(delMunMes)) {
delMunMes$posicion[i]<-i
}
names(delMunMes)[c(1,2,6,7)]<-c("Clave del municipio","Número de carpetas de investigación", "Incidencia por cada 100 mil habitantes","Posición a nivel nacional")
kable(head(delMunMes[,c(7,3,4,2,5,6)],50),caption = "Top 50 en el mes")
Top 50 en el mes
| 72 |
1 |
Colima |
Colima |
797 |
169188 |
471.07 |
| 1173 |
2 |
Oaxaca |
San José Estancia Grande |
4 |
1092 |
366.30 |
| 908 |
3 |
Morelos |
Cuernavaca |
1400 |
399426 |
350.50 |
| 1320 |
4 |
Oaxaca |
San Pedro Mártir |
6 |
1727 |
347.42 |
| 732 |
5 |
México |
Papalotla |
15 |
4367 |
343.49 |
| 1563 |
6 |
Oaxaca |
Valerio Trujano |
5 |
1464 |
341.53 |
| 16 |
7 |
Baja California |
Playas de Rosarito |
362 |
107859 |
335.62 |
| 227 |
8 |
Chihuahua |
Santa Isabel |
14 |
4293 |
326.11 |
| 1394 |
9 |
Oaxaca |
Santa Inés Yatzeche |
3 |
945 |
317.46 |
| 1821 |
10 |
Quintana Roo |
Tulum |
114 |
36866 |
309.23 |
| 956 |
11 |
Nuevo León |
Agualeguas |
8 |
2599 |
307.81 |
| 913 |
12 |
Morelos |
Jojutla |
187 |
61366 |
304.73 |
| 14 |
13 |
Baja California |
Tecate |
323 |
113857 |
283.69 |
| 1072 |
14 |
Oaxaca |
Oaxaca de Juárez |
722 |
258636 |
279.16 |
| 994 |
15 |
Nuevo León |
Parás |
3 |
1083 |
277.01 |
| 284 |
16 |
Ciudad de México |
Cuauhtémoc |
2133 |
776217 |
274.79 |
| 1556 |
17 |
Oaxaca |
Tlacolula de Matamoros |
66 |
24027 |
274.69 |
| 77 |
18 |
Colima |
Manzanillo |
553 |
203306 |
272.00 |
| 11 |
19 |
Aguascalientes |
San Francisco de los Romo |
140 |
51568 |
271.49 |
| 1003 |
20 |
Nuevo León |
Santiago |
125 |
46955 |
266.21 |
| 1381 |
21 |
Oaxaca |
Santa Cruz de Bravo |
1 |
377 |
265.25 |
| 1851 |
22 |
San Luis Potosí |
San Luis Potosí |
2303 |
870578 |
264.54 |
| 1343 |
23 |
Oaxaca |
Villa de Etla |
30 |
11426 |
262.56 |
| 285 |
24 |
Ciudad de México |
Miguel Hidalgo |
992 |
379624 |
261.31 |
| 1901 |
25 |
Sonora |
Agua Prieta |
231 |
88530 |
260.93 |
| 907 |
26 |
Morelos |
Cuautla |
547 |
210529 |
259.82 |
| 1152 |
27 |
Oaxaca |
San Francisco Nuxaño |
1 |
387 |
258.40 |
| 1807 |
28 |
Querétaro |
Querétaro |
2506 |
976939 |
256.52 |
| 1054 |
29 |
Oaxaca |
Magdalena Ocotlán |
3 |
1209 |
248.14 |
| 1820 |
30 |
Quintana Roo |
Solidaridad |
595 |
239850 |
248.07 |
| 333 |
31 |
Guanajuato |
Celaya |
1310 |
530820 |
246.79 |
| 769 |
32 |
México |
Toluca |
2302 |
948950 |
242.58 |
| 263 |
33 |
Chihuahua |
Santa Bárbara |
28 |
11572 |
241.96 |
| 1575 |
34 |
Oaxaca |
Zimatlán de Álvarez |
50 |
20892 |
239.33 |
| 1628 |
35 |
Puebla |
Chigmecatitlán |
3 |
1259 |
238.28 |
| 264 |
36 |
Chihuahua |
Satevó |
8 |
3381 |
236.62 |
| 1926 |
37 |
Sonora |
Fronteras |
22 |
9356 |
235.14 |
| 337 |
38 |
Guanajuato |
Cortazar |
238 |
101319 |
234.90 |
| 1449 |
39 |
Oaxaca |
Santa María Yolotepec |
1 |
426 |
234.74 |
| 1416 |
40 |
Oaxaca |
Santa María Guelacé |
2 |
855 |
233.92 |
| 60 |
41 |
Coahuila de Zaragoza |
Sabinas |
162 |
69500 |
233.09 |
| 773 |
42 |
México |
Valle de Bravo |
163 |
70192 |
232.22 |
| 13 |
43 |
Baja California |
Mexicali |
2506 |
1087478 |
230.44 |
| 2469 |
44 |
Zacatecas |
Zacatecas |
356 |
155533 |
228.89 |
| 1684 |
45 |
Puebla |
Oriental |
44 |
19352 |
227.37 |
| 576 |
46 |
Jalisco |
Guadalajara |
3418 |
1503505 |
227.34 |
| 1804 |
47 |
Querétaro |
El Marqués |
404 |
178672 |
226.11 |
| 1541 |
48 |
Oaxaca |
San Vicente Nuñú |
1 |
447 |
223.71 |
| 784 |
49 |
México |
Cuautitlán Izcalli |
1275 |
577190 |
220.90 |
| 218 |
50 |
Chihuahua |
Coyame del Sotol |
4 |
1819 |
219.90 |
Posición de los municipios de Queretaro en el año
kable(delMun[delMun$estado=="Querétaro",c(7,3,4,2,5,6)])
| 1807 |
18 |
Querétaro |
Querétaro |
16691 |
976939 |
1708.50 |
| 1804 |
42 |
Querétaro |
El Marqués |
2537 |
178672 |
1419.92 |
| 1809 |
76 |
Querétaro |
San Juan del Río |
3936 |
316169 |
1244.90 |
| 1799 |
127 |
Querétaro |
Corregidora |
2187 |
208076 |
1051.06 |
| 1801 |
185 |
Querétaro |
Huimilpan |
396 |
42305 |
936.06 |
| 1810 |
196 |
Querétaro |
Tequisquiapan |
721 |
78742 |
915.65 |
| 1802 |
280 |
Querétaro |
Jalpan de Serra |
241 |
29625 |
813.50 |
| 1800 |
282 |
Querétaro |
Ezequiel Montes |
372 |
45877 |
810.86 |
| 1805 |
286 |
Querétaro |
Pedro Escobedo |
616 |
76411 |
806.17 |
| 1794 |
316 |
Querétaro |
Amealco de Bonfil |
523 |
68441 |
764.16 |
| 1798 |
353 |
Querétaro |
Colón |
496 |
69112 |
717.68 |
| 1797 |
525 |
Querétaro |
Cadereyta de Montes |
442 |
76829 |
575.30 |
| 1806 |
627 |
Querétaro |
Peñamiller |
112 |
21988 |
509.37 |
| 1795 |
648 |
Querétaro |
Pinal de Amoles |
140 |
28189 |
496.65 |
| 1796 |
683 |
Querétaro |
Arroyo Seco |
71 |
14789 |
480.09 |
| 1803 |
731 |
Querétaro |
Landa de Matamoros |
93 |
20313 |
457.83 |
| 1808 |
740 |
Querétaro |
San Joaquín |
47 |
10323 |
455.29 |
| 1811 |
835 |
Querétaro |
Tolimán |
169 |
42391 |
398.67 |
| 1812 |
2463 |
Querétaro |
No Especificado |
68 |
NA |
NA |
Posición de los municipios de Queretaro en el mes
kable(delMunMes[delMunMes$estado=="Querétaro",c(7,3,4,2,5,6)])
| 1807 |
28 |
Querétaro |
Querétaro |
2506 |
976939 |
256.52 |
| 1804 |
47 |
Querétaro |
El Marqués |
404 |
178672 |
226.11 |
| 1809 |
65 |
Querétaro |
San Juan del Río |
656 |
316169 |
207.48 |
| 1801 |
150 |
Querétaro |
Huimilpan |
67 |
42305 |
158.37 |
| 1799 |
162 |
Querétaro |
Corregidora |
317 |
208076 |
152.35 |
| 1805 |
247 |
Querétaro |
Pedro Escobedo |
103 |
76411 |
134.80 |
| 1810 |
297 |
Querétaro |
Tequisquiapan |
99 |
78742 |
125.73 |
| 1802 |
305 |
Querétaro |
Jalpan de Serra |
37 |
29625 |
124.89 |
| 1794 |
462 |
Querétaro |
Amealco de Bonfil |
67 |
68441 |
97.89 |
| 1800 |
498 |
Querétaro |
Ezequiel Montes |
43 |
45877 |
93.73 |
| 1797 |
499 |
Querétaro |
Cadereyta de Montes |
72 |
76829 |
93.71 |
| 1798 |
548 |
Querétaro |
Colón |
61 |
69112 |
88.26 |
| 1803 |
742 |
Querétaro |
Landa de Matamoros |
14 |
20313 |
68.92 |
| 1806 |
747 |
Querétaro |
Peñamiller |
15 |
21988 |
68.22 |
| 1808 |
754 |
Querétaro |
San Joaquín |
7 |
10323 |
67.81 |
| 1796 |
757 |
Querétaro |
Arroyo Seco |
10 |
14789 |
67.62 |
| 1795 |
1097 |
Querétaro |
Pinal de Amoles |
12 |
28189 |
42.57 |
| 1811 |
1177 |
Querétaro |
Tolimán |
16 |
42391 |
37.74 |
| 1812 |
2463 |
Querétaro |
No Especificado |
7 |
NA |
NA |
Delitos en Querétaro
delitosQRO2020<-subset(delitos2, delitos2$Clave_Ent==22)
delitosQRO2020$periodo<-NA
delitosQRO2020$mes<-NA
m<-unique(delitosQRO2020$meses)
for (i in m) {
delitosQRO2020$mes[delitosQRO2020$meses==i]<-switch (i,"Enero"="01","Febrero"="02","Marzo"="03", "Abril"="04","Mayo"="05","Junio"="06","Julio"="07","Agosto"="08","Septiembre"="09","Octubre"="10","Noviembre"="11", "Diciembre"="12")
}
delitosQRO2020$periodo<-paste0(delitosQRO2020$Ano,delitosQRO2020$mes)
catalogoDelitos<-as.data.frame(sort(unique(delitosQRO2020$Subtipo.de.delito)))
losMeses2020<-sort(unique(delitosQRO2020$periodo))
for (i in 1:length(losMeses2020)){
a<-subset(delitosQRO2020, delitosQRO2020$periodo==losMeses2020[i])
b<-as.data.frame(aggregate(a$value~a$Subtipo.de.delito,a,sum))[2]
catalogoDelitos<-cbind(catalogoDelitos,b)
}
names(catalogoDelitos)<-c("Delito", losMeses2020)
stop1<-0
dondeBuscar<-colSums(catalogoDelitos[2:ncol(catalogoDelitos)])
for (i in 1:length(dondeBuscar)) {
if(dondeBuscar[i]==0){
stop1<-i;
break;
}
}
if(stop1==0){stop1=ncol(catalogoDelitos)}
stop2=stop1-12
#Superior al mismo périodo del año anterior
comparaAniAnterior<-catalogoDelitos[,c(1,stop2,stop1)]
comparaAniAnteriorTasa<-comparaAniAnterior
comparaAniAnteriorTasa[2]<-round(comparaAniAnteriorTasa[2]/ent$year2019[22]*1000,3)
comparaAniAnteriorTasa[3]<-round(comparaAniAnteriorTasa[3]/ent$year2020[22]*1000,3)
names(comparaAniAnteriorTasa)<-c("Delito", "Tasa 2019", "Tasa 2020")
comparaAniAnteriorTasa$cambio<-NA
comparaAniAnteriorTasa$cambio<-round((comparaAniAnteriorTasa[3]-comparaAniAnteriorTasa[2])/comparaAniAnteriorTasa[2],2)
aumentoContraUnAno<-comparaAniAnteriorTasa$Delito[comparaAniAnteriorTasa$cambio>0 & !is.na(comparaAniAnteriorTasa$cambio)]
maximoAbsoluto<-apply(X = catalogoDelitos[,2:stop1], MARGIN = 1,max)
estePeriodo<-catalogoDelitos[,stop1]
DelitosEnMaximoAbsoluto<-catalogoDelitos$Delito[estePeriodo!=0 & estePeriodo>=maximoAbsoluto]
names(DelitosEnMaximoAbsoluto)<-c(paste0("Delitos que alcanzan su máximo histórico en ",esteMes ,"(Números absolutos)"))
Delitos que aumentaron entre Junio y Julio
cambioMes<-cbind(catalogoDelitos[,1], catalogoDelitos[,(stop1-1):stop1])
cambioMes$tasadeCambio<-NA
cambioMes$tasadeCambio<-round((cambioMes[,3]-cambioMes[,2])/cambioMes[,2]*100,2)
cambioMes<-cambioMes[order(cambioMes$tasadeCambio,decreasing = TRUE),]
cambioMes1<-cambioMes[!is.infinite(cambioMes[,4]) & !is.nan(cambioMes[,4]), ]
cambioMes1<-cambioMes1[order(cambioMes1[3], decreasing = TRUE),]
names(cambioMes1)<-c("Delito", paste0("Carpetas en ", anterior), paste0("Carpetas en ", esteMes),"Tasa de cambio (%)")
kable(cambioMes1)
| 34 |
Otros robos |
706 |
845 |
19.69 |
| 25 |
Lesiones dolosas |
397 |
474 |
19.40 |
| 55 |
Violencia familiar |
261 |
344 |
31.80 |
| 45 |
Robo de vehículo automotor |
238 |
338 |
42.02 |
| 6 |
Amenazas |
278 |
320 |
15.11 |
| 30 |
Otros delitos del Fuero Común |
331 |
308 |
-6.95 |
| 38 |
Robo a negocio |
232 |
259 |
11.64 |
| 18 |
Fraude |
201 |
241 |
19.90 |
| 36 |
Robo a casa habitación |
190 |
227 |
19.47 |
| 40 |
Robo a transeúnte en vía pública |
110 |
133 |
20.91 |
| 11 |
Despojo |
70 |
104 |
48.57 |
| 9 |
Daño a la propiedad |
106 |
103 |
-2.83 |
| 26 |
Narcomenudeo |
72 |
82 |
13.89 |
| 33 |
Otros delitos que atentan contra la vida y la integridad corporal |
83 |
75 |
-9.64 |
| 24 |
Lesiones culposas |
73 |
58 |
-20.55 |
| 23 |
Incumplimiento de obligaciones de asistencia familiar |
18 |
57 |
216.67 |
| 3 |
Abuso sexual |
46 |
56 |
21.74 |
| 2 |
Abuso de confianza |
33 |
54 |
63.64 |
| 46 |
Robo en transporte individual |
42 |
50 |
19.05 |
| 4 |
Acoso sexual |
50 |
48 |
-4.00 |
| 42 |
Robo de autopartes |
49 |
37 |
-24.49 |
| 53 |
Violación simple |
26 |
35 |
34.62 |
| 47 |
Robo en transporte público colectivo |
34 |
35 |
2.94 |
| 5 |
Allanamiento de morada |
21 |
29 |
38.10 |
| 28 |
Otros delitos contra la familia |
14 |
26 |
85.71 |
| 19 |
Homicidio culposo |
23 |
25 |
8.70 |
| 29 |
Otros delitos contra la sociedad |
13 |
23 |
76.92 |
| 14 |
Extorsión |
18 |
19 |
5.56 |
| 16 |
Falsificación |
20 |
19 |
-5.00 |
| 20 |
Homicidio doloso |
8 |
15 |
87.50 |
| 43 |
Robo de ganado |
12 |
10 |
-16.67 |
| 52 |
Violación equiparada |
20 |
10 |
-50.00 |
| 15 |
Falsedad |
2 |
8 |
300.00 |
| 32 |
Otros delitos que atentan contra la libertad y la seguridad sexual |
3 |
8 |
166.67 |
| 39 |
Robo a transeúnte en espacio abierto al público |
9 |
7 |
-22.22 |
| 48 |
Robo en transporte público individual |
9 |
7 |
-22.22 |
| 27 |
Otros delitos contra el patrimonio |
4 |
5 |
25.00 |
| 44 |
Robo de maquinaria |
1 |
4 |
300.00 |
| 31 |
Otros delitos que atentan contra la libertad personal |
8 |
4 |
-50.00 |
| 12 |
Electorales |
3 |
3 |
0.00 |
| 54 |
Violencia de género en todas sus modalidades distinta a la violencia familiar |
1 |
2 |
100.00 |
Querétaro: Los delitos que han alcanzado su máximo histórico (en números absolutos) en este mes
kable(DelitosEnMaximoAbsoluto)
| Aborto |
| Despojo |
| Robo en transporte individual |
Querétaro: Los delitos más frecuentes en Julio
elMes<-catalogoDelitos[,c(1,stop1)]
elMes<-elMes[order(elMes[2], decreasing =TRUE),]
names(elMes)<-c(paste0("Delitos más frecuentes en ",esteMes),esteMes)
kable(elMes)
| 34 |
Otros robos |
845 |
| 25 |
Lesiones dolosas |
474 |
| 55 |
Violencia familiar |
344 |
| 45 |
Robo de vehículo automotor |
338 |
| 6 |
Amenazas |
320 |
| 30 |
Otros delitos del Fuero Común |
308 |
| 38 |
Robo a negocio |
259 |
| 18 |
Fraude |
241 |
| 36 |
Robo a casa habitación |
227 |
| 40 |
Robo a transeúnte en vía pública |
133 |
| 11 |
Despojo |
104 |
| 9 |
Daño a la propiedad |
103 |
| 26 |
Narcomenudeo |
82 |
| 33 |
Otros delitos que atentan contra la vida y la integridad corporal |
75 |
| 24 |
Lesiones culposas |
58 |
| 23 |
Incumplimiento de obligaciones de asistencia familiar |
57 |
| 3 |
Abuso sexual |
56 |
| 2 |
Abuso de confianza |
54 |
| 46 |
Robo en transporte individual |
50 |
| 4 |
Acoso sexual |
48 |
| 42 |
Robo de autopartes |
37 |
| 47 |
Robo en transporte público colectivo |
35 |
| 53 |
Violación simple |
35 |
| 5 |
Allanamiento de morada |
29 |
| 28 |
Otros delitos contra la familia |
26 |
| 19 |
Homicidio culposo |
25 |
| 29 |
Otros delitos contra la sociedad |
23 |
| 14 |
Extorsión |
19 |
| 16 |
Falsificación |
19 |
| 20 |
Homicidio doloso |
15 |
| 43 |
Robo de ganado |
10 |
| 52 |
Violación equiparada |
10 |
| 15 |
Falsedad |
8 |
| 32 |
Otros delitos que atentan contra la libertad y la seguridad sexual |
8 |
| 39 |
Robo a transeúnte en espacio abierto al público |
7 |
| 48 |
Robo en transporte público individual |
7 |
| 1 |
Aborto |
5 |
| 27 |
Otros delitos contra el patrimonio |
5 |
| 31 |
Otros delitos que atentan contra la libertad personal |
4 |
| 44 |
Robo de maquinaria |
4 |
| 12 |
Electorales |
3 |
| 54 |
Violencia de género en todas sus modalidades distinta a la violencia familiar |
2 |
| 49 |
Secuestro |
1 |
| 7 |
Contra el medio ambiente |
0 |
| 8 |
Corrupción de menores |
0 |
| 10 |
Delitos cometidos por servidores públicos |
0 |
| 13 |
Evasión de presos |
0 |
| 17 |
Feminicidio |
0 |
| 21 |
Hostigamiento sexual |
0 |
| 22 |
Incesto |
0 |
| 35 |
Rapto |
0 |
| 37 |
Robo a institución bancaria |
0 |
| 41 |
Robo a transportista |
0 |
| 50 |
Tráfico de menores |
0 |
| 51 |
Trata de personas |
0 |
Serie Mensual por delito en Querétaro
kable(catalogoDelitos)
| Aborto |
0 |
2 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
2 |
0 |
1 |
1 |
0 |
0 |
0 |
2 |
1 |
0 |
1 |
0 |
0 |
4 |
1 |
1 |
0 |
2 |
3 |
1 |
1 |
0 |
1 |
0 |
0 |
2 |
4 |
0 |
3 |
1 |
0 |
2 |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
1 |
1 |
3 |
2 |
3 |
1 |
3 |
4 |
0 |
3 |
3 |
1 |
3 |
0 |
5 |
0 |
0 |
0 |
0 |
0 |
| Abuso de confianza |
36 |
23 |
30 |
33 |
40 |
39 |
60 |
32 |
39 |
41 |
54 |
32 |
44 |
31 |
52 |
42 |
54 |
54 |
44 |
35 |
67 |
52 |
39 |
50 |
46 |
59 |
49 |
54 |
60 |
44 |
60 |
61 |
64 |
46 |
48 |
44 |
42 |
53 |
55 |
64 |
58 |
45 |
68 |
55 |
44 |
49 |
46 |
43 |
53 |
64 |
55 |
44 |
53 |
48 |
75 |
59 |
61 |
69 |
47 |
53 |
54 |
48 |
55 |
38 |
25 |
33 |
54 |
0 |
0 |
0 |
0 |
0 |
| Abuso sexual |
20 |
13 |
14 |
25 |
25 |
17 |
21 |
23 |
20 |
29 |
26 |
17 |
22 |
14 |
16 |
20 |
28 |
24 |
31 |
28 |
34 |
25 |
30 |
22 |
27 |
25 |
34 |
27 |
43 |
35 |
30 |
23 |
27 |
32 |
32 |
23 |
19 |
29 |
35 |
43 |
31 |
39 |
46 |
27 |
37 |
34 |
39 |
34 |
29 |
47 |
48 |
54 |
59 |
44 |
50 |
57 |
34 |
39 |
39 |
40 |
34 |
40 |
69 |
22 |
47 |
46 |
56 |
0 |
0 |
0 |
0 |
0 |
| Acoso sexual |
5 |
1 |
0 |
0 |
3 |
3 |
0 |
4 |
1 |
2 |
2 |
2 |
1 |
4 |
3 |
6 |
4 |
5 |
6 |
2 |
2 |
6 |
0 |
1 |
1 |
4 |
1 |
2 |
7 |
3 |
3 |
9 |
4 |
4 |
4 |
2 |
2 |
16 |
9 |
18 |
9 |
10 |
13 |
13 |
11 |
12 |
14 |
1 |
11 |
14 |
14 |
19 |
17 |
19 |
22 |
37 |
31 |
33 |
44 |
33 |
33 |
54 |
52 |
54 |
42 |
50 |
48 |
0 |
0 |
0 |
0 |
0 |
| Allanamiento de morada |
10 |
10 |
9 |
5 |
12 |
6 |
5 |
4 |
5 |
9 |
16 |
10 |
10 |
10 |
10 |
12 |
9 |
11 |
15 |
16 |
13 |
20 |
11 |
12 |
11 |
17 |
17 |
11 |
17 |
15 |
12 |
13 |
15 |
18 |
12 |
14 |
15 |
10 |
16 |
18 |
27 |
26 |
31 |
13 |
23 |
14 |
8 |
31 |
26 |
20 |
26 |
25 |
25 |
20 |
39 |
32 |
17 |
28 |
30 |
27 |
23 |
28 |
22 |
24 |
26 |
21 |
29 |
0 |
0 |
0 |
0 |
0 |
| Amenazas |
78 |
81 |
95 |
94 |
88 |
85 |
103 |
98 |
95 |
102 |
103 |
86 |
71 |
67 |
89 |
106 |
113 |
189 |
187 |
223 |
159 |
184 |
148 |
174 |
169 |
186 |
176 |
189 |
294 |
231 |
208 |
281 |
241 |
245 |
230 |
215 |
233 |
210 |
287 |
263 |
315 |
276 |
315 |
297 |
273 |
341 |
278 |
273 |
319 |
307 |
333 |
376 |
417 |
344 |
399 |
391 |
308 |
367 |
353 |
328 |
343 |
390 |
380 |
251 |
201 |
278 |
320 |
0 |
0 |
0 |
0 |
0 |
| Contra el medio ambiente |
2 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Corrupción de menores |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Daño a la propiedad |
167 |
153 |
186 |
178 |
172 |
168 |
158 |
132 |
165 |
143 |
186 |
174 |
155 |
183 |
159 |
185 |
213 |
391 |
430 |
461 |
477 |
402 |
394 |
412 |
407 |
387 |
432 |
395 |
477 |
447 |
385 |
455 |
412 |
522 |
437 |
444 |
478 |
395 |
433 |
426 |
436 |
510 |
487 |
462 |
473 |
465 |
430 |
426 |
451 |
436 |
484 |
481 |
506 |
452 |
272 |
116 |
120 |
112 |
102 |
128 |
113 |
127 |
105 |
114 |
96 |
106 |
103 |
0 |
0 |
0 |
0 |
0 |
| Delitos cometidos por servidores públicos |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Despojo |
43 |
40 |
45 |
51 |
35 |
33 |
48 |
38 |
34 |
38 |
28 |
50 |
41 |
47 |
47 |
38 |
36 |
46 |
38 |
49 |
41 |
46 |
37 |
45 |
36 |
48 |
51 |
55 |
56 |
64 |
51 |
61 |
51 |
41 |
54 |
29 |
45 |
57 |
65 |
47 |
68 |
60 |
60 |
72 |
61 |
78 |
58 |
49 |
69 |
71 |
83 |
72 |
73 |
73 |
81 |
66 |
65 |
69 |
66 |
62 |
66 |
77 |
58 |
45 |
53 |
70 |
104 |
0 |
0 |
0 |
0 |
0 |
| Electorales |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
3 |
0 |
0 |
5 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
3 |
26 |
12 |
2 |
2 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
3 |
0 |
3 |
0 |
0 |
3 |
3 |
0 |
0 |
0 |
0 |
0 |
| Evasión de presos |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Extorsión |
2 |
0 |
1 |
0 |
0 |
0 |
0 |
3 |
0 |
0 |
0 |
0 |
4 |
0 |
1 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
3 |
2 |
0 |
3 |
2 |
1 |
0 |
1 |
0 |
0 |
1 |
3 |
5 |
4 |
5 |
7 |
13 |
4 |
13 |
4 |
13 |
7 |
12 |
12 |
10 |
14 |
33 |
20 |
13 |
23 |
19 |
38 |
35 |
14 |
20 |
16 |
14 |
26 |
16 |
25 |
15 |
21 |
18 |
19 |
0 |
0 |
0 |
0 |
0 |
| Falsedad |
1 |
2 |
2 |
4 |
5 |
5 |
3 |
2 |
5 |
2 |
4 |
2 |
6 |
3 |
18 |
8 |
9 |
8 |
2 |
10 |
11 |
7 |
6 |
7 |
4 |
4 |
4 |
10 |
14 |
7 |
7 |
6 |
6 |
7 |
3 |
7 |
4 |
6 |
6 |
11 |
13 |
6 |
5 |
12 |
8 |
9 |
4 |
4 |
7 |
6 |
8 |
12 |
4 |
11 |
6 |
11 |
13 |
8 |
8 |
7 |
9 |
13 |
8 |
3 |
5 |
2 |
8 |
0 |
0 |
0 |
0 |
0 |
| Falsificación |
65 |
40 |
48 |
40 |
59 |
63 |
47 |
44 |
61 |
56 |
63 |
56 |
48 |
40 |
42 |
45 |
52 |
45 |
64 |
52 |
33 |
44 |
47 |
44 |
33 |
38 |
48 |
28 |
43 |
34 |
40 |
25 |
30 |
51 |
33 |
35 |
34 |
35 |
27 |
56 |
56 |
56 |
57 |
52 |
60 |
70 |
38 |
39 |
65 |
42 |
61 |
73 |
63 |
58 |
73 |
49 |
57 |
68 |
46 |
40 |
47 |
36 |
29 |
11 |
11 |
20 |
19 |
0 |
0 |
0 |
0 |
0 |
| Feminicidio |
2 |
1 |
0 |
1 |
0 |
0 |
1 |
0 |
2 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
2 |
0 |
2 |
1 |
1 |
0 |
0 |
1 |
2 |
1 |
2 |
0 |
2 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Fraude |
115 |
113 |
138 |
114 |
134 |
138 |
134 |
106 |
110 |
124 |
130 |
130 |
104 |
106 |
117 |
141 |
172 |
153 |
153 |
167 |
167 |
132 |
161 |
119 |
157 |
171 |
181 |
159 |
192 |
186 |
152 |
188 |
143 |
195 |
184 |
126 |
143 |
157 |
211 |
156 |
189 |
172 |
189 |
209 |
185 |
182 |
174 |
152 |
189 |
164 |
221 |
207 |
222 |
180 |
257 |
224 |
206 |
210 |
192 |
208 |
241 |
192 |
170 |
123 |
151 |
201 |
241 |
0 |
0 |
0 |
0 |
0 |
| Homicidio culposo |
23 |
29 |
24 |
20 |
30 |
25 |
24 |
20 |
30 |
25 |
32 |
34 |
22 |
23 |
30 |
28 |
33 |
23 |
33 |
24 |
18 |
23 |
21 |
25 |
20 |
27 |
18 |
30 |
28 |
26 |
24 |
27 |
27 |
28 |
14 |
27 |
30 |
20 |
30 |
27 |
25 |
34 |
29 |
21 |
22 |
18 |
33 |
21 |
25 |
32 |
33 |
27 |
28 |
20 |
23 |
26 |
27 |
21 |
34 |
31 |
24 |
27 |
22 |
24 |
26 |
23 |
25 |
0 |
0 |
0 |
0 |
0 |
| Homicidio doloso |
9 |
9 |
12 |
11 |
11 |
10 |
12 |
13 |
10 |
13 |
13 |
8 |
12 |
9 |
12 |
8 |
14 |
7 |
7 |
6 |
15 |
8 |
12 |
8 |
12 |
12 |
14 |
21 |
8 |
21 |
10 |
20 |
19 |
14 |
9 |
15 |
14 |
10 |
15 |
12 |
14 |
16 |
14 |
18 |
22 |
7 |
16 |
22 |
13 |
16 |
18 |
13 |
15 |
11 |
17 |
17 |
21 |
9 |
12 |
15 |
11 |
11 |
24 |
11 |
18 |
8 |
15 |
0 |
0 |
0 |
0 |
0 |
| Hostigamiento sexual |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Incesto |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Incumplimiento de obligaciones de asistencia familiar |
67 |
75 |
59 |
61 |
64 |
66 |
67 |
85 |
66 |
79 |
76 |
47 |
58 |
57 |
79 |
69 |
92 |
50 |
103 |
75 |
74 |
63 |
63 |
46 |
63 |
50 |
84 |
77 |
78 |
87 |
76 |
68 |
85 |
79 |
57 |
44 |
69 |
72 |
50 |
63 |
52 |
74 |
38 |
56 |
43 |
64 |
53 |
29 |
43 |
39 |
73 |
53 |
63 |
66 |
58 |
71 |
52 |
82 |
52 |
45 |
75 |
71 |
60 |
11 |
9 |
18 |
57 |
0 |
0 |
0 |
0 |
0 |
| Lesiones culposas |
37 |
42 |
45 |
45 |
42 |
45 |
32 |
37 |
44 |
53 |
51 |
68 |
44 |
46 |
45 |
59 |
41 |
83 |
70 |
72 |
83 |
82 |
95 |
64 |
76 |
53 |
71 |
71 |
78 |
61 |
52 |
60 |
70 |
72 |
62 |
67 |
59 |
65 |
75 |
74 |
81 |
69 |
83 |
85 |
70 |
91 |
77 |
64 |
78 |
70 |
71 |
65 |
80 |
69 |
77 |
91 |
105 |
87 |
83 |
96 |
56 |
80 |
90 |
63 |
40 |
73 |
58 |
0 |
0 |
0 |
0 |
0 |
| Lesiones dolosas |
176 |
194 |
205 |
244 |
236 |
240 |
235 |
246 |
227 |
245 |
290 |
266 |
172 |
173 |
219 |
239 |
286 |
322 |
320 |
405 |
357 |
366 |
304 |
409 |
367 |
315 |
366 |
356 |
561 |
458 |
389 |
422 |
375 |
399 |
355 |
371 |
325 |
335 |
448 |
459 |
504 |
432 |
519 |
419 |
421 |
509 |
400 |
423 |
402 |
413 |
503 |
483 |
614 |
522 |
499 |
448 |
498 |
461 |
380 |
467 |
351 |
416 |
488 |
432 |
328 |
397 |
474 |
0 |
0 |
0 |
0 |
0 |
| Narcomenudeo |
21 |
22 |
18 |
19 |
18 |
18 |
10 |
7 |
10 |
30 |
30 |
21 |
62 |
84 |
79 |
63 |
42 |
61 |
72 |
74 |
72 |
68 |
71 |
78 |
97 |
74 |
91 |
66 |
81 |
84 |
91 |
70 |
58 |
67 |
82 |
81 |
85 |
79 |
85 |
98 |
92 |
83 |
106 |
112 |
97 |
115 |
88 |
109 |
139 |
133 |
138 |
139 |
165 |
158 |
152 |
119 |
117 |
126 |
107 |
86 |
133 |
121 |
102 |
77 |
78 |
72 |
82 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos contra el patrimonio |
2 |
0 |
3 |
4 |
4 |
2 |
4 |
2 |
2 |
2 |
5 |
3 |
1 |
3 |
2 |
2 |
6 |
1 |
2 |
3 |
3 |
2 |
2 |
1 |
1 |
5 |
5 |
4 |
3 |
2 |
4 |
2 |
5 |
3 |
1 |
3 |
1 |
4 |
4 |
5 |
6 |
1 |
3 |
3 |
3 |
4 |
2 |
1 |
1 |
3 |
9 |
2 |
3 |
5 |
7 |
4 |
6 |
4 |
1 |
3 |
4 |
2 |
5 |
3 |
2 |
4 |
5 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos contra la familia |
3 |
4 |
3 |
6 |
5 |
4 |
5 |
4 |
10 |
8 |
4 |
10 |
4 |
8 |
5 |
15 |
11 |
4 |
10 |
14 |
8 |
12 |
10 |
11 |
9 |
5 |
11 |
13 |
17 |
12 |
13 |
23 |
11 |
14 |
10 |
26 |
21 |
17 |
16 |
14 |
16 |
14 |
19 |
26 |
15 |
18 |
18 |
17 |
12 |
6 |
13 |
17 |
28 |
15 |
20 |
29 |
15 |
18 |
14 |
20 |
14 |
13 |
22 |
12 |
11 |
14 |
26 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos contra la sociedad |
12 |
8 |
14 |
9 |
5 |
13 |
8 |
6 |
10 |
11 |
4 |
8 |
12 |
7 |
18 |
15 |
16 |
13 |
8 |
7 |
9 |
6 |
9 |
4 |
6 |
12 |
11 |
14 |
14 |
6 |
13 |
12 |
16 |
9 |
9 |
10 |
3 |
17 |
11 |
7 |
16 |
11 |
14 |
5 |
10 |
9 |
13 |
16 |
8 |
9 |
11 |
15 |
12 |
11 |
7 |
8 |
25 |
39 |
23 |
15 |
15 |
17 |
29 |
16 |
25 |
13 |
23 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos del Fuero Común |
106 |
112 |
121 |
96 |
107 |
142 |
130 |
120 |
114 |
136 |
166 |
163 |
122 |
133 |
163 |
148 |
202 |
233 |
268 |
245 |
267 |
269 |
236 |
275 |
252 |
259 |
317 |
252 |
304 |
350 |
300 |
323 |
287 |
321 |
262 |
305 |
302 |
348 |
388 |
382 |
366 |
355 |
349 |
339 |
373 |
428 |
318 |
346 |
376 |
364 |
359 |
397 |
469 |
424 |
465 |
461 |
414 |
453 |
401 |
339 |
403 |
405 |
399 |
297 |
328 |
331 |
308 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos que atentan contra la libertad personal |
3 |
1 |
2 |
3 |
1 |
8 |
2 |
3 |
2 |
3 |
3 |
2 |
3 |
0 |
2 |
2 |
1 |
2 |
3 |
2 |
1 |
6 |
3 |
1 |
8 |
3 |
3 |
4 |
0 |
8 |
6 |
0 |
1 |
7 |
1 |
3 |
1 |
2 |
2 |
2 |
1 |
2 |
1 |
4 |
4 |
3 |
5 |
3 |
3 |
1 |
4 |
3 |
10 |
5 |
7 |
4 |
7 |
2 |
4 |
2 |
4 |
8 |
15 |
13 |
7 |
8 |
4 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos que atentan contra la libertad y la seguridad sexual |
7 |
1 |
7 |
4 |
6 |
6 |
2 |
2 |
4 |
4 |
6 |
4 |
3 |
4 |
4 |
2 |
9 |
1 |
5 |
8 |
1 |
2 |
2 |
4 |
1 |
4 |
5 |
2 |
6 |
1 |
7 |
7 |
2 |
3 |
6 |
3 |
4 |
7 |
2 |
3 |
2 |
1 |
2 |
2 |
3 |
2 |
0 |
1 |
3 |
4 |
6 |
4 |
6 |
3 |
5 |
5 |
5 |
5 |
2 |
3 |
6 |
7 |
2 |
5 |
4 |
3 |
8 |
0 |
0 |
0 |
0 |
0 |
| Otros delitos que atentan contra la vida y la integridad corporal |
50 |
36 |
60 |
58 |
47 |
52 |
44 |
44 |
53 |
72 |
59 |
84 |
24 |
35 |
44 |
40 |
48 |
74 |
55 |
81 |
66 |
54 |
56 |
49 |
54 |
70 |
69 |
67 |
61 |
63 |
58 |
61 |
48 |
83 |
63 |
67 |
64 |
54 |
66 |
59 |
80 |
64 |
62 |
55 |
57 |
61 |
66 |
79 |
67 |
70 |
72 |
76 |
73 |
72 |
95 |
84 |
80 |
85 |
80 |
86 |
77 |
92 |
93 |
76 |
80 |
83 |
75 |
0 |
0 |
0 |
0 |
0 |
| Otros robos |
573 |
539 |
543 |
542 |
560 |
557 |
534 |
563 |
580 |
627 |
556 |
494 |
556 |
480 |
559 |
591 |
551 |
649 |
719 |
788 |
731 |
822 |
724 |
649 |
716 |
710 |
797 |
710 |
777 |
877 |
805 |
898 |
887 |
912 |
946 |
844 |
816 |
795 |
866 |
887 |
926 |
947 |
903 |
929 |
865 |
931 |
800 |
828 |
963 |
940 |
1015 |
942 |
884 |
938 |
967 |
978 |
871 |
1029 |
950 |
1018 |
939 |
913 |
940 |
738 |
734 |
706 |
845 |
0 |
0 |
0 |
0 |
0 |
| Rapto |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Robo a casa habitación |
165 |
161 |
215 |
220 |
236 |
202 |
184 |
212 |
223 |
192 |
201 |
206 |
194 |
204 |
213 |
218 |
213 |
285 |
292 |
317 |
289 |
383 |
309 |
365 |
308 |
291 |
351 |
282 |
304 |
331 |
327 |
327 |
327 |
345 |
356 |
303 |
369 |
271 |
344 |
331 |
312 |
270 |
352 |
361 |
357 |
320 |
282 |
360 |
340 |
260 |
278 |
303 |
320 |
265 |
303 |
290 |
266 |
267 |
264 |
253 |
317 |
261 |
218 |
188 |
180 |
190 |
227 |
0 |
0 |
0 |
0 |
0 |
| Robo a institución bancaria |
1 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Robo a negocio |
177 |
129 |
192 |
191 |
140 |
152 |
153 |
157 |
136 |
151 |
140 |
132 |
154 |
132 |
154 |
152 |
185 |
220 |
294 |
244 |
249 |
296 |
264 |
269 |
257 |
256 |
314 |
252 |
261 |
229 |
271 |
295 |
294 |
294 |
292 |
348 |
296 |
267 |
262 |
249 |
262 |
215 |
224 |
237 |
223 |
292 |
299 |
226 |
257 |
248 |
268 |
281 |
312 |
272 |
312 |
319 |
252 |
297 |
299 |
262 |
286 |
238 |
270 |
214 |
219 |
232 |
259 |
0 |
0 |
0 |
0 |
0 |
| Robo a transeúnte en espacio abierto al público |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
1 |
2 |
2 |
3 |
1 |
1 |
2 |
3 |
9 |
7 |
6 |
7 |
6 |
5 |
4 |
18 |
8 |
8 |
6 |
16 |
27 |
22 |
24 |
17 |
13 |
14 |
30 |
0 |
11 |
20 |
31 |
11 |
13 |
21 |
13 |
45 |
14 |
22 |
16 |
14 |
14 |
7 |
14 |
14 |
8 |
22 |
7 |
12 |
16 |
8 |
22 |
11 |
14 |
7 |
9 |
6 |
9 |
7 |
0 |
0 |
0 |
0 |
0 |
| Robo a transeúnte en vía pública |
101 |
58 |
110 |
80 |
97 |
80 |
83 |
88 |
116 |
118 |
108 |
90 |
87 |
64 |
114 |
104 |
110 |
149 |
158 |
186 |
185 |
172 |
150 |
176 |
140 |
147 |
157 |
157 |
151 |
161 |
141 |
169 |
169 |
195 |
194 |
195 |
199 |
178 |
159 |
135 |
181 |
160 |
178 |
170 |
137 |
203 |
153 |
147 |
124 |
133 |
115 |
145 |
145 |
122 |
113 |
137 |
146 |
162 |
156 |
116 |
110 |
134 |
149 |
85 |
91 |
110 |
133 |
0 |
0 |
0 |
0 |
0 |
| Robo a transportista |
8 |
20 |
9 |
10 |
10 |
13 |
8 |
10 |
6 |
16 |
17 |
14 |
20 |
22 |
8 |
10 |
15 |
14 |
10 |
1 |
7 |
11 |
4 |
3 |
10 |
7 |
8 |
2 |
3 |
6 |
10 |
11 |
11 |
16 |
4 |
10 |
33 |
17 |
21 |
18 |
11 |
2 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Robo de autopartes |
33 |
26 |
39 |
34 |
37 |
30 |
52 |
34 |
34 |
41 |
40 |
28 |
36 |
22 |
16 |
16 |
14 |
49 |
62 |
52 |
46 |
49 |
43 |
40 |
55 |
64 |
57 |
72 |
55 |
76 |
53 |
70 |
76 |
86 |
87 |
57 |
78 |
86 |
100 |
94 |
116 |
90 |
104 |
86 |
104 |
90 |
73 |
73 |
110 |
96 |
75 |
68 |
63 |
69 |
76 |
58 |
61 |
71 |
39 |
45 |
70 |
62 |
81 |
68 |
46 |
49 |
37 |
0 |
0 |
0 |
0 |
0 |
| Robo de ganado |
26 |
24 |
24 |
22 |
19 |
28 |
42 |
34 |
32 |
22 |
14 |
32 |
30 |
26 |
21 |
20 |
26 |
18 |
13 |
20 |
18 |
26 |
26 |
22 |
14 |
20 |
20 |
7 |
20 |
18 |
27 |
17 |
16 |
21 |
21 |
23 |
28 |
31 |
12 |
9 |
15 |
19 |
16 |
21 |
11 |
16 |
13 |
14 |
17 |
33 |
19 |
19 |
29 |
19 |
19 |
27 |
19 |
22 |
13 |
22 |
22 |
11 |
15 |
7 |
18 |
12 |
10 |
0 |
0 |
0 |
0 |
0 |
| Robo de maquinaria |
0 |
1 |
1 |
3 |
2 |
2 |
3 |
1 |
1 |
2 |
3 |
1 |
2 |
3 |
2 |
2 |
3 |
3 |
0 |
1 |
3 |
2 |
1 |
1 |
0 |
0 |
4 |
0 |
6 |
3 |
1 |
1 |
1 |
2 |
0 |
4 |
1 |
1 |
0 |
1 |
3 |
2 |
1 |
1 |
0 |
2 |
2 |
2 |
1 |
1 |
2 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
1 |
3 |
0 |
2 |
2 |
1 |
1 |
4 |
0 |
0 |
0 |
0 |
0 |
| Robo de vehículo automotor |
313 |
275 |
274 |
273 |
326 |
340 |
320 |
364 |
389 |
350 |
311 |
337 |
326 |
305 |
378 |
347 |
372 |
415 |
408 |
504 |
458 |
478 |
440 |
449 |
423 |
412 |
457 |
411 |
500 |
516 |
513 |
516 |
496 |
510 |
494 |
490 |
508 |
398 |
468 |
474 |
535 |
525 |
556 |
632 |
480 |
527 |
468 |
594 |
472 |
442 |
441 |
473 |
426 |
399 |
402 |
372 |
355 |
357 |
376 |
407 |
347 |
338 |
329 |
272 |
225 |
238 |
338 |
0 |
0 |
0 |
0 |
0 |
| Robo en transporte individual |
22 |
12 |
16 |
12 |
16 |
22 |
11 |
23 |
26 |
19 |
29 |
28 |
26 |
24 |
25 |
15 |
35 |
19 |
22 |
32 |
19 |
25 |
36 |
28 |
17 |
25 |
41 |
25 |
27 |
22 |
27 |
29 |
37 |
31 |
33 |
41 |
33 |
33 |
28 |
21 |
34 |
38 |
24 |
30 |
37 |
31 |
37 |
29 |
22 |
20 |
19 |
36 |
35 |
42 |
27 |
28 |
35 |
43 |
23 |
27 |
27 |
27 |
28 |
17 |
33 |
42 |
50 |
0 |
0 |
0 |
0 |
0 |
| Robo en transporte público colectivo |
29 |
26 |
51 |
33 |
27 |
20 |
38 |
60 |
60 |
54 |
41 |
48 |
28 |
38 |
35 |
47 |
53 |
57 |
55 |
75 |
61 |
66 |
46 |
32 |
38 |
31 |
33 |
33 |
34 |
65 |
52 |
33 |
24 |
16 |
17 |
24 |
15 |
12 |
10 |
2 |
7 |
6 |
5 |
7 |
5 |
5 |
9 |
9 |
16 |
7 |
4 |
13 |
12 |
16 |
13 |
21 |
24 |
47 |
51 |
27 |
30 |
42 |
21 |
28 |
37 |
34 |
35 |
0 |
0 |
0 |
0 |
0 |
| Robo en transporte público individual |
6 |
3 |
7 |
6 |
3 |
7 |
5 |
2 |
4 |
5 |
2 |
5 |
1 |
4 |
5 |
4 |
1 |
6 |
9 |
7 |
6 |
6 |
0 |
6 |
7 |
5 |
12 |
10 |
8 |
12 |
14 |
12 |
10 |
5 |
5 |
2 |
7 |
11 |
8 |
5 |
12 |
8 |
8 |
6 |
11 |
6 |
6 |
6 |
8 |
14 |
15 |
8 |
6 |
6 |
12 |
8 |
17 |
8 |
13 |
20 |
11 |
10 |
21 |
14 |
11 |
9 |
7 |
0 |
0 |
0 |
0 |
0 |
| Secuestro |
1 |
0 |
2 |
2 |
3 |
1 |
2 |
3 |
0 |
2 |
0 |
3 |
1 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
2 |
0 |
3 |
3 |
2 |
0 |
1 |
0 |
2 |
0 |
3 |
0 |
1 |
1 |
0 |
1 |
2 |
0 |
1 |
0 |
0 |
0 |
2 |
0 |
2 |
3 |
1 |
1 |
1 |
2 |
1 |
0 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
2 |
1 |
0 |
2 |
2 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
| Tráfico de menores |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Trata de personas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
2 |
0 |
1 |
0 |
0 |
1 |
3 |
0 |
1 |
0 |
0 |
0 |
3 |
1 |
2 |
1 |
3 |
1 |
1 |
2 |
0 |
0 |
1 |
0 |
0 |
1 |
1 |
3 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| Violación equiparada |
1 |
3 |
3 |
2 |
1 |
2 |
3 |
1 |
3 |
4 |
1 |
5 |
1 |
1 |
4 |
1 |
3 |
6 |
7 |
4 |
6 |
3 |
7 |
6 |
6 |
8 |
4 |
1 |
11 |
10 |
7 |
9 |
9 |
6 |
6 |
4 |
10 |
5 |
6 |
5 |
6 |
9 |
4 |
4 |
6 |
5 |
9 |
4 |
3 |
9 |
8 |
7 |
12 |
5 |
9 |
12 |
5 |
7 |
11 |
14 |
11 |
14 |
4 |
12 |
16 |
20 |
10 |
0 |
0 |
0 |
0 |
0 |
| Violación simple |
17 |
11 |
30 |
25 |
31 |
22 |
29 |
28 |
24 |
28 |
28 |
21 |
16 |
20 |
21 |
24 |
34 |
22 |
25 |
25 |
37 |
24 |
28 |
9 |
12 |
21 |
31 |
23 |
36 |
31 |
25 |
27 |
23 |
24 |
24 |
19 |
18 |
25 |
18 |
18 |
22 |
32 |
23 |
23 |
20 |
22 |
27 |
14 |
20 |
24 |
29 |
33 |
44 |
47 |
49 |
33 |
28 |
44 |
43 |
51 |
46 |
39 |
49 |
30 |
25 |
26 |
35 |
0 |
0 |
0 |
0 |
0 |
| Violencia de género en todas sus modalidades distinta a la violencia familiar |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
1 |
1 |
2 |
0 |
1 |
0 |
1 |
3 |
0 |
3 |
1 |
2 |
0 |
0 |
0 |
0 |
0 |
| Violencia familiar |
49 |
67 |
81 |
74 |
86 |
76 |
73 |
82 |
86 |
106 |
83 |
79 |
59 |
72 |
80 |
82 |
75 |
76 |
83 |
95 |
89 |
103 |
82 |
69 |
85 |
63 |
96 |
83 |
123 |
92 |
106 |
126 |
86 |
111 |
103 |
112 |
113 |
97 |
136 |
178 |
179 |
154 |
177 |
175 |
182 |
188 |
142 |
144 |
150 |
159 |
221 |
236 |
245 |
216 |
385 |
354 |
286 |
338 |
283 |
262 |
259 |
296 |
377 |
298 |
306 |
261 |
344 |
0 |
0 |
0 |
0 |
0 |
Delitos que aumentaron respecto del mismo mes en el año anterior(en tasa por cada 1000 habitantes)
kable(aumentoContraUnAno)
| Aborto |
| Abuso sexual |
| Acoso sexual |
| Despojo |
| Electorales |
| Falsedad |
| Homicidio culposo |
| Otros delitos contra la familia |
| Otros delitos contra la sociedad |
| Otros delitos que atentan contra la libertad y la seguridad sexual |
| Robo a transeúnte en vía pública |
| Robo de maquinaria |
| Robo en transporte individual |
| Robo en transporte público colectivo |
| Violencia de género en todas sus modalidades distinta a la violencia familiar |
Delitos en su máximo del año en Querétaro
#MAximo en el año
stop3<-stop1-(stop1 %% 12)+2
soloEsteAno<-catalogoDelitos[,c(1,stop3:stop1)]
maxAno<-apply(X = soloEsteAno[,2:ncol(soloEsteAno)],MARGIN = 1,FUN = max)
delitosEnmaximoAnual<-soloEsteAno$Delito[soloEsteAno[,ncol(soloEsteAno)]>=maxAno & soloEsteAno[ncol(soloEsteAno)]!=0]
kable(delitosEnmaximoAnual)
| Aborto |
| Allanamiento de morada |
| Despojo |
| Electorales |
| Fraude |
| Otros delitos contra el patrimonio |
| Otros delitos contra la familia |
| Otros delitos que atentan contra la libertad y la seguridad sexual |
| Robo de maquinaria |
| Robo en transporte individual |
Municipal
Municipios que aumentaron respecto del mismo mes del año anterior (Julio )
#Superior al mismo périodo del año anterior
catalogoMunicipios<-as.data.frame(sort(unique(delitosQRO2020$Cve..Municipio)))
losMeses2020<-sort(unique(delitosQRO2020$periodo))
for (i in 1:length(losMeses2020)){
a<-subset(delitosQRO2020, delitosQRO2020$periodo==losMeses2020[i])
b<-as.data.frame(aggregate(a$value~a$Cve..Municipio,a,sum))[2]
catalogoMunicipios<-cbind(catalogoMunicipios,b)
}
names(catalogoMunicipios)<-c("cveMun", losMeses2020)
catalogoMunicipios<-catalogoMunicipios[1:18,]
pop2020Qro<-subset(pop2020,pop2020$CLAVE_ENT==22)
popQro20<- aggregate(pop2020Qro$POB~ pop2020Qro$CLAVE,pop2020Qro,sum)
pop2019Qro<-subset(pop,pop$CLAVE_ENT==22 & pop$ANO==2019)
popQro19<- aggregate(pop2019Qro$POB~ pop2019Qro$CLAVE,pop2019Qro,sum)
comparaAnoAnteriorMUN<-catalogoMunicipios[,c(1,stop2,stop1)]
comparaAnoAnteriorMUNTasa<-comparaAnoAnteriorMUN
comparaAnoAnteriorMUNTasa[2]<-round(comparaAnoAnteriorMUNTasa[2]/popQro19[2]*1000,3)
comparaAnoAnteriorMUNTasa[3]<-round(comparaAnoAnteriorMUNTasa[3]/popQro20[2]*1000,3)
names(comparaAnoAnteriorMUNTasa)<-c("Delito", "Tasa 2019", "Tasa 2020")
comparaAnoAnteriorMUNTasa$cambio<-NA
comparaAnoAnteriorMUNTasa$cambio<-round((comparaAnoAnteriorMUNTasa[3]-comparaAnoAnteriorMUNTasa[2])/comparaAnoAnteriorMUNTasa[2],2)
misMuns<-catalogoMunicipios[,1]
catalogoMunicipios$nomMun<-NA
nomMun<-c()
for (i in 1:length(misMuns)) {
catalogoMunicipios$nomMun[i]<-unique(delitosQRO2020$Municipio[delitosQRO2020$Cve..Municipio==misMuns[i]])
nomMun[i]<-unique(delitosQRO2020$Municipio[delitosQRO2020$Cve..Municipio==misMuns[i]])
}
aumento<-comparaAnoAnteriorMUNTasa$Delito[comparaAnoAnteriorMUNTasa$cambio>0 & !is.na(comparaAnoAnteriorMUNTasa$cambio)]
aumentoContraUnAnoMUNICIPAL<-NA
for (i in 1:length(aumento)) {
aumentoContraUnAnoMUNICIPAL[i]<-catalogoMunicipios$nomMun[catalogoMunicipios$cveMun==aumento[i]]
}
names(aumentoContraUnAnoMUNICIPAL)<-c("Municipios")
kable(aumentoContraUnAnoMUNICIPAL,caption = "Municipios cuya tasa por cada 1000 habitantes aumentó respecto del mismo mes del año anterior")
Municipios cuya tasa por cada 1000 habitantes aumentó respecto del mismo mes del año anterior
| Huimilpan |
| Jalpan de Serra |
| Landa de Matamoros |
| Peñamiller |
Cambio respecto del mes anterior por municipio
stop4<-stop1-1
municipio<-as.data.frame(cbind(catalogoMunicipios$cveMun, catalogoMunicipios$nomMun,catalogoMunicipios[,stop4],catalogoMunicipios[,stop1]))
municipio$tasa<-NA
municipio$tasa<-round((as.numeric(municipio[,4])-as.numeric(municipio[,3]) )/as.numeric(municipio[,3])*100,2)
names(municipio)<-c("cveMun","Municipio",anterior, esteMes,"Tasa de cambio respecto del mes anterior (%)")
kable(municipio[2:5])
| Amealco de Bonfil |
76 |
67 |
-11.84 |
| Pinal de Amoles |
21 |
12 |
-42.86 |
| Arroyo Seco |
8 |
10 |
25.00 |
| Cadereyta de Montes |
60 |
72 |
20.00 |
| Colón |
82 |
61 |
-25.61 |
| Corregidora |
254 |
317 |
24.80 |
| Ezequiel Montes |
56 |
43 |
-23.21 |
| Huimilpan |
56 |
67 |
19.64 |
| Jalpan de Serra |
36 |
37 |
2.78 |
| Landa de Matamoros |
13 |
14 |
7.69 |
| El Marqués |
346 |
404 |
16.76 |
| Pedro Escobedo |
86 |
103 |
19.77 |
| Peñamiller |
37 |
15 |
-59.46 |
| Querétaro |
2070 |
2506 |
21.06 |
| San Joaquín |
7 |
7 |
0.00 |
| San Juan del Río |
488 |
656 |
34.43 |
| Tequisquiapan |
104 |
99 |
-4.81 |
| Tolimán |
27 |
16 |
-40.74 |
Municipios en Máximo Anual
soloEsteAnoMUN<-catalogoMunicipios[,c(1,stop3:stop1)]
maxAnoMun<-apply(X = soloEsteAnoMUN[,2:ncol(soloEsteAnoMUN)],MARGIN = 1,FUN = max)
municipiosEnmaximoAnual<-soloEsteAnoMUN
municipiosEnmaximoAnual<-soloEsteAnoMUN$cveMun[soloEsteAnoMUN[,ncol(soloEsteAnoMUN)]>=maxAnoMun & soloEsteAnoMUN[ncol(soloEsteAnoMUN)]!=0]
munmax<-c()
for (i in 1:length(municipiosEnmaximoAnual)) {
munmax[i]<-catalogoMunicipios$nomMun[catalogoMunicipios$cveMun==municipiosEnmaximoAnual[i]]
}
names(munmax)<-c("Municipios en Máximo Anual")
kable(munmax)
| Cadereyta de Montes |
| Huimilpan |
| El Marqués |
| San Juan del Río |
Municipios en su nivel máximo (absoluto) registrado
maximoAbsolutoMUNICIPAL<-apply(X = catalogoMunicipios[,2:stop1], MARGIN = 1,max)
estePeriodoMunicipal<-catalogoMunicipios[,stop1]
municipiosEnmaximoAbsoluto<-catalogoMunicipios$nomMun[estePeriodoMunicipal!=0 & estePeriodoMunicipal>=maximoAbsolutoMUNICIPAL]
names(municipiosEnmaximoAbsoluto)<-c("Municipios en máximo histórico (absoluto) registrado")
kable(municipiosEnmaximoAbsoluto)
| Municipios en máximo histórico (absoluto) registrado |
Huimilpan |
Serie de tiempo municipal (Absolutos)
catalogoMunicipios2<-catalogoMunicipios
names(catalogoMunicipios2[2:73])<-paste0(substr(names(catalogoMunicipios2[2:73]),5,6),"-",substr(names(catalogoMunicipios2[2:73]),1,4))
catalogoMunicipios2<-cbind(catalogoMunicipios2[,1],catalogoMunicipios2[,74],catalogoMunicipios2[,2:stop1])
names(catalogoMunicipios2)[c(1,2)]<-c("Clave","Municipio")
kable(catalogoMunicipios2)
| 22001 |
Amealco de Bonfil |
45 |
47 |
52 |
40 |
52 |
51 |
50 |
59 |
42 |
52 |
55 |
70 |
48 |
51 |
53 |
52 |
71 |
50 |
59 |
49 |
43 |
44 |
40 |
40 |
51 |
36 |
45 |
43 |
43 |
81 |
48 |
42 |
48 |
40 |
48 |
37 |
54 |
46 |
43 |
34 |
38 |
80 |
90 |
80 |
53 |
70 |
73 |
70 |
92 |
89 |
92 |
101 |
101 |
105 |
80 |
74 |
65 |
87 |
87 |
88 |
75 |
84 |
93 |
75 |
53 |
76 |
67 |
| 22002 |
Pinal de Amoles |
14 |
3 |
10 |
10 |
11 |
12 |
11 |
6 |
10 |
11 |
3 |
11 |
10 |
17 |
9 |
14 |
10 |
9 |
5 |
10 |
8 |
17 |
4 |
13 |
8 |
7 |
17 |
13 |
14 |
11 |
13 |
12 |
11 |
21 |
13 |
6 |
9 |
11 |
9 |
18 |
26 |
28 |
28 |
15 |
18 |
15 |
12 |
7 |
9 |
21 |
16 |
25 |
15 |
21 |
21 |
21 |
15 |
18 |
18 |
19 |
21 |
20 |
19 |
19 |
28 |
21 |
12 |
| 22003 |
Arroyo Seco |
5 |
4 |
5 |
4 |
4 |
5 |
7 |
8 |
5 |
4 |
5 |
3 |
2 |
2 |
4 |
3 |
7 |
6 |
6 |
2 |
5 |
2 |
3 |
9 |
6 |
6 |
3 |
5 |
4 |
2 |
9 |
7 |
2 |
3 |
5 |
3 |
3 |
6 |
6 |
8 |
12 |
8 |
12 |
5 |
2 |
8 |
3 |
9 |
17 |
7 |
7 |
9 |
7 |
9 |
12 |
8 |
5 |
7 |
20 |
10 |
11 |
4 |
17 |
11 |
10 |
8 |
10 |
| 22004 |
Cadereyta de Montes |
41 |
37 |
37 |
37 |
38 |
47 |
40 |
36 |
50 |
29 |
37 |
40 |
47 |
48 |
47 |
58 |
52 |
41 |
51 |
45 |
42 |
50 |
37 |
38 |
43 |
44 |
46 |
48 |
51 |
59 |
47 |
48 |
49 |
49 |
40 |
27 |
38 |
50 |
46 |
59 |
48 |
74 |
84 |
60 |
109 |
75 |
53 |
64 |
58 |
88 |
87 |
60 |
66 |
62 |
75 |
80 |
71 |
60 |
74 |
72 |
51 |
69 |
61 |
65 |
64 |
60 |
72 |
| 22005 |
Colón |
40 |
30 |
46 |
42 |
40 |
56 |
45 |
40 |
48 |
38 |
47 |
36 |
49 |
53 |
50 |
49 |
57 |
63 |
56 |
60 |
71 |
66 |
55 |
52 |
45 |
60 |
53 |
50 |
57 |
67 |
48 |
48 |
36 |
54 |
55 |
56 |
47 |
53 |
49 |
62 |
54 |
61 |
80 |
53 |
67 |
77 |
54 |
65 |
62 |
59 |
71 |
76 |
67 |
70 |
75 |
82 |
86 |
80 |
59 |
71 |
74 |
65 |
81 |
69 |
64 |
82 |
61 |
| 22006 |
Corregidora |
176 |
166 |
186 |
179 |
200 |
218 |
189 |
183 |
189 |
210 |
211 |
182 |
185 |
192 |
183 |
213 |
226 |
297 |
338 |
359 |
302 |
304 |
308 |
306 |
321 |
311 |
368 |
283 |
361 |
304 |
343 |
306 |
342 |
352 |
298 |
330 |
345 |
329 |
397 |
362 |
379 |
387 |
390 |
373 |
349 |
365 |
329 |
373 |
353 |
333 |
396 |
374 |
399 |
379 |
382 |
364 |
346 |
394 |
377 |
333 |
395 |
359 |
349 |
260 |
253 |
254 |
317 |
| 22007 |
Ezequiel Montes |
30 |
38 |
25 |
23 |
22 |
42 |
37 |
31 |
40 |
47 |
43 |
43 |
26 |
35 |
68 |
55 |
57 |
40 |
41 |
48 |
48 |
47 |
37 |
39 |
28 |
22 |
36 |
26 |
32 |
38 |
47 |
38 |
39 |
43 |
48 |
30 |
51 |
52 |
42 |
51 |
46 |
58 |
64 |
75 |
63 |
46 |
59 |
54 |
54 |
46 |
90 |
59 |
61 |
62 |
54 |
60 |
63 |
55 |
55 |
46 |
62 |
55 |
57 |
35 |
64 |
56 |
43 |
| 22008 |
Huimilpan |
36 |
33 |
42 |
28 |
25 |
39 |
30 |
33 |
32 |
26 |
52 |
35 |
29 |
36 |
42 |
21 |
33 |
28 |
34 |
31 |
28 |
29 |
40 |
22 |
22 |
21 |
23 |
24 |
37 |
29 |
26 |
41 |
32 |
35 |
20 |
35 |
29 |
37 |
33 |
28 |
28 |
34 |
49 |
49 |
44 |
46 |
55 |
38 |
48 |
44 |
55 |
54 |
53 |
43 |
59 |
50 |
58 |
61 |
35 |
49 |
58 |
61 |
65 |
38 |
51 |
56 |
67 |
| 22009 |
Jalpan de Serra |
18 |
32 |
35 |
15 |
27 |
21 |
16 |
23 |
13 |
24 |
16 |
18 |
25 |
16 |
32 |
29 |
25 |
24 |
32 |
27 |
20 |
24 |
15 |
15 |
22 |
25 |
21 |
23 |
21 |
24 |
23 |
18 |
14 |
38 |
32 |
20 |
29 |
27 |
30 |
34 |
41 |
31 |
49 |
36 |
38 |
42 |
32 |
31 |
50 |
47 |
42 |
40 |
57 |
29 |
36 |
36 |
23 |
36 |
31 |
21 |
39 |
30 |
33 |
38 |
28 |
36 |
37 |
| 22010 |
Landa de Matamoros |
1 |
4 |
3 |
2 |
5 |
4 |
6 |
2 |
6 |
9 |
8 |
5 |
3 |
1 |
1 |
3 |
4 |
9 |
6 |
8 |
6 |
2 |
6 |
4 |
6 |
5 |
5 |
6 |
9 |
4 |
6 |
8 |
4 |
4 |
6 |
7 |
10 |
3 |
8 |
6 |
7 |
11 |
10 |
9 |
5 |
10 |
15 |
10 |
7 |
6 |
7 |
9 |
5 |
9 |
10 |
12 |
9 |
11 |
11 |
10 |
13 |
10 |
18 |
19 |
6 |
13 |
14 |
| 22011 |
El Marqués |
133 |
161 |
158 |
184 |
158 |
171 |
166 |
168 |
197 |
169 |
158 |
173 |
152 |
151 |
148 |
170 |
189 |
222 |
279 |
322 |
287 |
289 |
276 |
266 |
262 |
279 |
294 |
313 |
338 |
325 |
328 |
325 |
285 |
291 |
268 |
307 |
365 |
308 |
334 |
352 |
390 |
376 |
378 |
381 |
337 |
393 |
347 |
372 |
441 |
457 |
437 |
474 |
488 |
392 |
473 |
387 |
380 |
417 |
379 |
408 |
377 |
395 |
404 |
320 |
291 |
346 |
404 |
| 22012 |
Pedro Escobedo |
44 |
32 |
54 |
48 |
49 |
59 |
49 |
57 |
32 |
45 |
45 |
44 |
47 |
50 |
68 |
57 |
70 |
46 |
52 |
66 |
55 |
37 |
48 |
49 |
59 |
68 |
101 |
83 |
101 |
113 |
107 |
123 |
106 |
131 |
87 |
117 |
112 |
102 |
110 |
116 |
135 |
112 |
123 |
117 |
106 |
129 |
111 |
90 |
151 |
111 |
103 |
130 |
140 |
113 |
131 |
114 |
115 |
105 |
91 |
80 |
80 |
77 |
115 |
66 |
89 |
86 |
103 |
| 22013 |
Peñamiller |
10 |
4 |
3 |
4 |
9 |
5 |
9 |
3 |
6 |
6 |
8 |
7 |
8 |
5 |
4 |
7 |
13 |
11 |
7 |
7 |
17 |
6 |
11 |
5 |
5 |
4 |
6 |
5 |
10 |
14 |
3 |
9 |
6 |
5 |
6 |
10 |
11 |
8 |
10 |
15 |
12 |
14 |
9 |
15 |
15 |
17 |
14 |
11 |
13 |
7 |
14 |
11 |
10 |
14 |
11 |
15 |
8 |
19 |
4 |
17 |
7 |
20 |
14 |
13 |
6 |
37 |
15 |
| 22014 |
Querétaro |
1556 |
1414 |
1611 |
1557 |
1605 |
1541 |
1601 |
1713 |
1747 |
1830 |
1762 |
1704 |
1557 |
1414 |
1712 |
1686 |
1873 |
2461 |
2647 |
2936 |
2769 |
2983 |
2550 |
2561 |
2489 |
2447 |
2772 |
2517 |
2992 |
2962 |
2701 |
3073 |
2829 |
3031 |
2941 |
2833 |
2684 |
2500 |
2873 |
2791 |
3003 |
2802 |
2975 |
3026 |
2859 |
3126 |
2648 |
2702 |
2816 |
2748 |
2915 |
2976 |
3184 |
2959 |
3101 |
2900 |
2671 |
2971 |
2717 |
2755 |
2617 |
2678 |
2744 |
2066 |
2010 |
2070 |
2506 |
| 22015 |
San Joaquín |
1 |
1 |
1 |
2 |
2 |
2 |
1 |
3 |
3 |
1 |
3 |
3 |
3 |
4 |
3 |
3 |
4 |
4 |
2 |
5 |
7 |
4 |
5 |
6 |
3 |
4 |
3 |
4 |
8 |
3 |
2 |
5 |
3 |
3 |
5 |
2 |
1 |
0 |
4 |
8 |
4 |
5 |
5 |
8 |
4 |
6 |
5 |
10 |
8 |
6 |
4 |
7 |
7 |
3 |
13 |
9 |
6 |
10 |
7 |
7 |
8 |
6 |
6 |
7 |
6 |
7 |
7 |
| 22016 |
San Juan del Río |
371 |
314 |
404 |
425 |
440 |
410 |
366 |
312 |
354 |
400 |
406 |
364 |
339 |
380 |
400 |
437 |
430 |
392 |
398 |
356 |
392 |
371 |
403 |
479 |
518 |
478 |
569 |
457 |
543 |
558 |
514 |
560 |
537 |
617 |
574 |
535 |
594 |
604 |
695 |
677 |
714 |
650 |
631 |
605 |
594 |
664 |
589 |
611 |
624 |
561 |
684 |
702 |
788 |
664 |
736 |
687 |
631 |
660 |
579 |
579 |
619 |
611 |
626 |
490 |
446 |
488 |
656 |
| 22017 |
Tequisquiapan |
47 |
40 |
32 |
34 |
43 |
37 |
52 |
46 |
53 |
47 |
54 |
57 |
35 |
35 |
55 |
52 |
42 |
54 |
52 |
50 |
58 |
41 |
42 |
49 |
49 |
57 |
50 |
50 |
119 |
98 |
109 |
93 |
107 |
100 |
91 |
86 |
123 |
81 |
103 |
127 |
132 |
102 |
100 |
103 |
94 |
109 |
84 |
100 |
73 |
90 |
103 |
95 |
94 |
94 |
114 |
134 |
84 |
108 |
87 |
101 |
110 |
117 |
111 |
100 |
80 |
104 |
99 |
| 22018 |
Tolimán |
11 |
10 |
24 |
18 |
11 |
21 |
17 |
12 |
13 |
18 |
16 |
11 |
18 |
14 |
17 |
20 |
21 |
21 |
15 |
16 |
13 |
16 |
12 |
18 |
13 |
9 |
16 |
17 |
24 |
14 |
11 |
11 |
15 |
15 |
12 |
17 |
8 |
10 |
6 |
18 |
19 |
43 |
44 |
41 |
41 |
42 |
39 |
38 |
33 |
36 |
53 |
36 |
19 |
43 |
56 |
46 |
29 |
16 |
29 |
23 |
24 |
22 |
18 |
27 |
35 |
27 |
16 |
Top 5 delitos por municipio
En lo que va del año
mm<-unique(delitosQRO2020$Cve..Municipio)
mm<-mm[1:18]
top5<-as.data.frame(c("Primero","Segundo","Tercero","Cuarto","Quinto"))
for (i in 1:length(mm)){
mimu<-subset(delitosQRO2020,delitosQRO2020$Cve..Municipio==mm[i] & delitosQRO2020$Ano==2020)
a<-aggregate(mimu$value~mimu$Subtipo.de.delito,data = mimu, FUN = sum)
a<-as.data.frame(a)
names(a)<-c(nomMun[i],"Carpetas")
a<-a[order(a$Carpetas, decreasing = TRUE),]
top5<-cbind(top5,a[1:5,])
}
names(top5)[1]<-c("Posicion")
kable(top5,caption="Top 5 delitos en carpetas de investigación por municipio en lo que va del año ")
Top 5 delitos en carpetas de investigación por municipio en lo que va del año
| 34 |
Primero |
Otros robos |
74 |
Lesiones dolosas |
35 |
Violencia familiar |
14 |
Lesiones dolosas |
79 |
Otros robos |
90 |
Otros robos |
390 |
Otros robos |
58 |
Otros robos |
58 |
Violencia familiar |
50 |
Violencia familiar |
20 |
Otros robos |
507 |
Otros robos |
105 |
Lesiones dolosas |
22 |
Otros robos |
3557 |
Otros robos |
8 |
Otros robos |
718 |
Otros robos |
120 |
Violencia familiar |
45 |
| 25 |
Segundo |
Lesiones dolosas |
69 |
Violencia familiar |
23 |
Amenazas |
12 |
Violencia familiar |
56 |
Violencia familiar |
83 |
Otros delitos del Fuero Común |
205 |
Violencia familiar |
39 |
Amenazas |
52 |
Otros robos |
40 |
Lesiones dolosas |
12 |
Lesiones dolosas |
295 |
Lesiones dolosas |
88 |
Violencia familiar |
20 |
Lesiones dolosas |
1439 |
Amenazas |
7 |
Amenazas |
382 |
Robo a casa habitación |
85 |
Lesiones dolosas |
23 |
| 6 |
Tercero |
Amenazas |
59 |
Amenazas |
16 |
Otros robos |
9 |
Amenazas |
44 |
Lesiones dolosas |
61 |
Lesiones dolosas |
198 |
Otros delitos del Fuero Común |
36 |
Lesiones dolosas |
50 |
Lesiones dolosas |
29 |
Amenazas |
11 |
Violencia familiar |
215 |
Violencia familiar |
54 |
Amenazas |
12 |
Otros delitos del Fuero Común |
1365 |
Robo a casa habitación |
5 |
Otros delitos del Fuero Común |
374 |
Lesiones dolosas |
79 |
Amenazas |
12 |
| 55 |
Cuarto |
Violencia familiar |
56 |
Otros robos |
14 |
Lesiones dolosas |
6 |
Otros robos |
38 |
Otros delitos del Fuero Común |
38 |
Amenazas |
184 |
Lesiones dolosas |
34 |
Daño a la propiedad |
35 |
Amenazas |
26 |
Otros robos |
10 |
Amenazas |
202 |
Otros delitos del Fuero Común |
50 |
Otros robos |
9 |
Robo a negocio |
1334 |
Robo a negocio |
4 |
Lesiones dolosas |
363 |
Amenazas |
62 |
Daño a la propiedad |
10 |
| 30 |
Quinto |
Otros delitos del Fuero Común |
55 |
Daño a la propiedad |
8 |
Otros delitos que atentan contra la vida y la integridad corporal |
4 |
Otros delitos del Fuero Común |
31 |
Amenazas |
36 |
Fraude |
156 |
Robo de vehículo automotor |
29 |
Violencia familiar |
35 |
Otros delitos del Fuero Común |
19 |
Despojo |
6 |
Otros delitos del Fuero Común |
171 |
Amenazas |
46 |
Otros delitos del Fuero Común |
8 |
Robo de vehículo automotor |
1334 |
Violencia familiar |
4 |
Violencia familiar |
353 |
Otros delitos del Fuero Común |
49 |
Lesiones culposas |
9 |
Top 5 municipal durante Julio
top5mes<-as.data.frame(c("Primero","Segundo","Tercero","Cuarto","Quinto"))
for (i in 1:length(mm)) {
mimume<-subset(delitosQRO2020,delitosQRO2020$Cve..Municipio==mm[i] & delitosQRO2020$Ano==2020 & delitosQRO2020$meses==esteMes)
a<-aggregate(mimume$value~mimume$Subtipo.de.delito,data = mimume, FUN = sum)
a<-as.data.frame(a)
names(a)<-c(nomMun[i],"Carpetas")
a<-a[order(a$Carpetas, decreasing = TRUE),]
top5mes<-cbind(top5mes,a[1:5,])
}
names(top5mes)[1]<-c("Posicion")
kable(top5mes,caption=paste0("Top 5 delitos en carpetas de investigación por municipio en ",esteMes))
Top 5 delitos en carpetas de investigación por municipio en Julio
| 25 |
Primero |
Lesiones dolosas |
13 |
Lesiones dolosas |
6 |
Abuso sexual |
2 |
Violencia familiar |
13 |
Otros robos |
13 |
Otros robos |
49 |
Otros robos |
12 |
Violencia familiar |
9 |
Violencia familiar |
9 |
Violencia familiar |
5 |
Otros robos |
71 |
Otros robos |
19 |
Violencia familiar |
3 |
Otros robos |
510 |
Robo a negocio |
2 |
Otros robos |
122 |
Otros robos |
14 |
Violencia familiar |
7 |
| 34 |
Segundo |
Otros robos |
11 |
Otros robos |
2 |
Amenazas |
2 |
Amenazas |
9 |
Lesiones dolosas |
8 |
Lesiones dolosas |
32 |
Violencia familiar |
7 |
Amenazas |
7 |
Amenazas |
4 |
Despojo |
2 |
Lesiones dolosas |
60 |
Lesiones dolosas |
16 |
Abuso sexual |
2 |
Robo de vehículo automotor |
238 |
Violencia familiar |
2 |
Violencia familiar |
71 |
Amenazas |
12 |
Lesiones dolosas |
3 |
| 55 |
Tercero |
Violencia familiar |
9 |
Violencia familiar |
2 |
Violencia familiar |
2 |
Lesiones dolosas |
9 |
Otros delitos del Fuero Común |
8 |
Amenazas |
27 |
Amenazas |
3 |
Daño a la propiedad |
7 |
Lesiones dolosas |
4 |
Lesiones dolosas |
2 |
Violencia familiar |
35 |
Violencia familiar |
14 |
Daño a la propiedad |
2 |
Lesiones dolosas |
232 |
Amenazas |
1 |
Amenazas |
69 |
Robo a casa habitación |
11 |
Abuso sexual |
1 |
| 6 |
Cuarto |
Amenazas |
7 |
Amenazas |
1 |
Lesiones dolosas |
1 |
Otros robos |
8 |
Violencia familiar |
8 |
Fraude |
27 |
Fraude |
3 |
Otros delitos del Fuero Común |
7 |
Despojo |
3 |
Amenazas |
1 |
Otros delitos del Fuero Común |
29 |
Amenazas |
8 |
Otros robos |
2 |
Robo a negocio |
196 |
Lesiones dolosas |
1 |
Lesiones dolosas |
67 |
Lesiones dolosas |
10 |
Acoso sexual |
1 |
| 30 |
Quinto |
Otros delitos del Fuero Común |
5 |
Lesiones culposas |
1 |
Otros delitos del Fuero Común |
1 |
Robo a casa habitación |
5 |
Amenazas |
4 |
Robo de vehículo automotor |
24 |
Lesiones dolosas |
3 |
Lesiones dolosas |
6 |
Otros robos |
3 |
Fraude |
1 |
Amenazas |
25 |
Despojo |
6 |
Despojo |
1 |
Otros delitos del Fuero Común |
177 |
Otros robos |
1 |
Otros delitos del Fuero Común |
51 |
Fraude |
7 |
Amenazas |
1 |
El futuro
Delitos para preocuparse en Septiembre
Aqu se presentan los delitos que en promedio aumentan durante Septiembre; hemos calculado el promedio de los logaritmos de la tasa por cada 100 mil habitantes de cada mes, de cada ao, por cada delito. Presentamos los delitos que, en promedio, alcanzan su mximo en Septiembre.
anos2<-unique(delitosQRO2020$Ano)
meses2<-unique(delitosQRO2020$meses)
canalDelitos<-catalogoDelitos[,1]
for(i in 1:length(meses2)){
mediamesEntodo<-c()
for(j in 1:length(catalogoDelitos[,1])){
delmes<-subset(delitosQRO2020,delitosQRO2020$meses==meses2[i] & delitosQRO2020$Subtipo.de.delito==catalogoDelitos[j,1])
delmesano<-aggregate(formula=delmes$value~delmes$Ano, data = delmes, FUN = sum)
cuantos<-nrow(delmesano)-1
delmesano<-delmesano[1:cuantos,]
pobs<-ent[22,2:(2+cuantos-1)]
pobs<-t(pobs)
delmesano$pob<-NA
delmesano$tasa<-NA
delmesano$logtasa<-NA
delmesano$pob<-pobs[,1]
delmesano$tasa<-(delmesano[,2])/delmesano$pob*100000
delmesano$logtasa<-log(delmesano$tasa+1)
mimedia<-mean(delmesano$logtasa)
miEE<-1.96*(sd(delmesano$logtasa)/sqrt(cuantos))
mediamesEntodo<-c(mediamesEntodo,mimedia)
}
canalDelitos<-cbind(canalDelitos,as.numeric(mediamesEntodo))
}
canalDelitos<-as.data.frame(canalDelitos)
names(canalDelitos)<-c("Delito",levels(meses2))
for(i in 1:55){
for(j in 2:13){
canalDelitos[i,j]<-as.numeric(canalDelitos[i,j])
}
}
aumentan<-canalDelitos[c("Delito", esteMes,proximo)]
aumentan$aumentan<-NA
aumentan$max<-NA
aumentan$max<-apply(canalDelitos[,2:13],1, max)
aumentan$aumentan<-aumentan[,3]>aumentan[,2]
aumentan$enMaximoAnual<-NA
aumentan$enMaximoAnual<-aumentan$enMaximoAnual<-aumentan$max==aumentan[c(proximo)]
alerta<-cbind(aumentan$Delito[aumentan$enMaximoAnual==TRUE & aumentan$enMaximoAnual!=0],aumentan[aumentan$enMaximoAnual ==TRUE & aumentan$enMaximoAnual!=0,3])
alerta<-as.data.frame(alerta)
names(alerta)<-c("Delito","logTasaPromedio")
miAlerta<-alerta[alerta$logTasaPromedio!=0,]
kable(miAlerta[,1], caption=paste0("Delitos que, en promedio, aumentan en ",proximo))
Delitos que, en promedio, aumentan en Septiembre
| Homicidio doloso |
| Otros delitos contra la sociedad |
| Robo a transente en espacio abierto al pblico |
| Violencia de gnero en todas sus modalidades distinta a la violencia familiar |
cual<-miAlerta$Delito[miAlerta$logTasaPromedio==max(miAlerta$logTasaPromedio)]
Comportamiento mensual del delito de mayor riesgo (Homicidio doloso)
esteDelito<-subset(delitosQRO2020,delitosQRO2020$Subtipo.de.delito==cual)
mismeses2<-as.data.frame(levels(delitosQRO2020$meses))
for (i in 1:length(anos2)) {
miano1<-subset(esteDelito,esteDelito$Ano==anos2[i])
aggregate(miano1$value~miano1$meses,miano1,sum)
mismeses2<-cbind(mismeses2,as.data.frame(aggregate(miano1$value~miano1$meses,miano1,sum))[2])
}
names(mismeses2)<-c("Mes",paste0("ao ",anos2))
kable(mismeses2, caption = paste0("Serie de tiempo anual y mensual para ",cual))
Serie de tiempo anual y mensual para Homicidio doloso
| Enero |
9 |
12 |
12 |
14 |
13 |
11 |
| Febrero |
9 |
9 |
12 |
10 |
16 |
11 |
| Marzo |
12 |
12 |
14 |
15 |
18 |
24 |
| Abril |
11 |
8 |
21 |
12 |
13 |
11 |
| Mayo |
11 |
14 |
8 |
14 |
15 |
18 |
| Junio |
10 |
7 |
21 |
16 |
11 |
8 |
| Julio |
12 |
7 |
10 |
14 |
17 |
15 |
| Agosto |
13 |
6 |
20 |
18 |
17 |
0 |
| Septiembre |
10 |
15 |
19 |
22 |
21 |
0 |
| Octubre |
13 |
8 |
14 |
7 |
9 |
0 |
| Noviembre |
13 |
12 |
9 |
16 |
12 |
0 |
| Diciembre |
8 |
8 |
15 |
22 |
15 |
0 |