Proyecto Final
Noe Buenrostro Domínguez
January 27, 2019
Introducción
En Beijin y gran parte de China se esta experimentando una contaminación crónica del aire. Esto es de interés general ya que siendo la primer potencia económica del mundo establece estándares que repercuten en el mundo entero.
PM2.5 consiste en particulas que se encuentran el el aire con diametros menor a 2.5. Influye en la visibilidad, salud publica y el clima. Evidencia empidemiológica muestra que una exposición al PM2.5 puede causar morbilidad pulmonar, serios problemas respiratorios, al igual que problemas cardiovasculares, incluso la muerte.
Se ha descubierto que las condiciones metereológicas, aereosoles secundarios, emisiones locales y el transporte regional son los principales factores que contribuyen a la formación de PM2.5 en Beijing. También hay factores que no pueden ser ignorados al momento de analizar este tema, ya que la variabilidad en los patrones de distribución y transmision de PM2.5 se confunden con condiciones metereológcas, emisiones quimicas, etc.
Debido a esta variabilidad en las condiciones climatologicas, se cree que para realizar un análisis eficiente se ocupa un dataset con la longitud suficiente y una alta frecuencia temporal para generar una evaluación confiable del patron y tendencia, en gravedad y cantidad, de la contaminación del aire.
Para realizar este análisis se cuenta con un dataset generado por Song Xi Chen, de Guanghua School of Management, Center for Statistical Science, Univesidad de Peking [1].
En este trabajo se pretende responder a la siguiente pregunta:
- ¿Cómo influeyen las condiciones metereológicas en la concentración de particulas contaminantes PM2.5?
- ¿Se puede determinar una relación o aproximación de la concentración de PM2.5 respecto a las condiciones metereológicas con un modelo de regresión lineal?
Descripción de dataset
El dataset se compone de 43825 observaciones con 13 características. Recopilando información de lecturas u observaciones realizadas del 2010 a 2015.
data<- read.csv("~/Documents/MCPI/Estadistica/Tarea Final/datasets/PRSA_data_2010.1.1-2014.12.31.csv", header=FALSE)
summary(data)## V1 V2 V3 V4
## 1 : 1 2010:8760 1 : 3720 1 : 1440
## 10 : 1 2011:8760 10 : 3720 10 : 1440
## 100 : 1 2012:8784 12 : 3720 11 : 1440
## 1000 : 1 2013:8760 3 : 3720 12 : 1440
## 10000 : 1 2014:8760 5 : 3720 13 : 1440
## 10001 : 1 year: 1 7 : 3720 14 : 1440
## (Other):43819 (Other):21505 (Other):35185
## V5 V6 V7 V8
## 0 : 1826 16 : 626 18 : 1323 24 : 1566
## 1 : 1826 11 : 596 17 : 1294 23 : 1538
## 10 : 1826 13 : 589 19 : 1290 22 : 1433
## 11 : 1826 12 : 578 16 : 1238 21 : 1400
## 12 : 1826 17 : 572 20 : 1219 25 : 1397
## 13 : 1826 (Other):38797 15 : 1162 20 : 1364
## (Other):32869 NA's : 2067 (Other):36299 (Other):35127
## V9 V10 V11 V12
## 1014 : 1504 cbwd: 1 0.89 : 6266 0 :43456
## 1006 : 1445 cv : 9387 1.79 : 4807 1 : 66
## 1013 : 1443 NE : 4997 3.13 : 1932 2 : 46
## 1012 : 1382 NW :14150 1.78 : 1836 3 : 37
## 1025 : 1375 SE :15290 4.92 : 1251 4 : 31
## 1015 : 1374 3.58 : 1197 5 : 27
## (Other):35302 (Other):26536 (Other): 162
## V13
## 0 :42016
## 1 : 529
## 2 : 316
## 3 : 214
## 4 : 136
## 5 : 113
## (Other): 501Como puede observarse este dataset tiene varias filas con elementos NA y además todas las variables están asignadas como factor por lo que se realizó un preprocesamiento del dataset para eliminar las observaciones incompletas que tengan NA asignados. Las caracteristicas No, Month, day, hour, PM2.5, DEWP, TEMP, PRES, Iws, Is e Ir se asignaron como númericas. Después de este preprocesamiento el dataset quedó con 41757 observaciones.
knitr::kable(head(data[1:11,1:13]), format="markdown")| No | year | month | day | hour | pm2.5 | DEWP | TEMP | PRES | cbwd | Iws | Is | Ir | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 26 | 25 | 2010 | 1 | 2 | 0 | 129 | -16 | -4 | 1020 | SE | 1.79 | 0 | 0 |
| 27 | 26 | 2010 | 1 | 2 | 1 | 148 | -15 | -4 | 1020 | SE | 2.68 | 0 | 0 |
| 28 | 27 | 2010 | 1 | 2 | 2 | 159 | -11 | -5 | 1021 | SE | 3.57 | 0 | 0 |
| 29 | 28 | 2010 | 1 | 2 | 3 | 181 | -7 | -5 | 1022 | SE | 5.36 | 1 | 0 |
| 30 | 29 | 2010 | 1 | 2 | 4 | 138 | -7 | -5 | 1022 | SE | 6.25 | 2 | 0 |
| 31 | 30 | 2010 | 1 | 2 | 5 | 109 | -7 | -6 | 1022 | SE | 7.14 | 3 | 0 |
Características
A continuación se describen las 13 características:
- No: número de obsrvación. Cuantitativas, numericas, variable discreta.
- year: año que se tomó la observación. Cualitativa.
- month: mes que se tomó la observación. Cuantitativas, numericas, variable discreta.
- day: día que se tomó la observación. Cuantitativas, numericas, variable discreta.
- hour: hora que se tomó la observación. Cuantitativas, numericas, variable discreta.
- pm2.5: Concentración de PM2.5 (ug/m^3). Cuantitativas, numericas, variable continua.
- DEWP: Temperatura del punto de rocío. Cuantitativas, numericas, variable continua.
- TEMP: Temperatura. Cuantitativas, numericas, variable continua.
- PRES: Presión (hPa). Cuantitativas, numericas, variable continua.
- cbwd: Dirección combinada del viento. Cualitativa.
- Iws: Velocidad acumulada del viento (m/s). Cuantitativas, numericas, variable continua.
- Is: Horas acumuladas de nieve. Cuantitativas, numericas, variable discreta.
- Ir: Horas acumuladas de lluvia. Cuantitativas, numericas, variable discreta.
El tipo de dato de las observaciones y su contenido se describe a continuación:
## 'data.frame': 41757 obs. of 13 variables:
## $ No : num 25 26 27 28 29 30 31 32 33 34 ...
## $ year : Factor w/ 6 levels "2010","2011",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ month: num 1 1 1 1 1 1 1 1 1 1 ...
## $ day : num 2 2 2 2 2 2 2 2 2 2 ...
## $ hour : num 0 1 2 3 4 5 6 7 8 9 ...
## $ pm2.5: num 129 148 159 181 138 109 105 124 120 132 ...
## $ DEWP : num -16 -15 -11 -7 -7 -7 -7 -7 -8 -7 ...
## $ TEMP : num -4 -4 -5 -5 -5 -6 -6 -5 -6 -5 ...
## $ PRES : num 1020 1020 1021 1022 1022 ...
## $ cbwd : Factor w/ 5 levels "cbwd","cv","NE",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Iws : num 1.79 2.68 3.57 5.36 6.25 ...
## $ Is : num 0 0 0 1 2 3 4 0 0 0 ...
## $ Ir : num 0 0 0 0 0 0 0 0 0 0 ...Como puede observarse las caracteristicas No, Month, day, hour, PM2.5, DEWP, TEMP, PRES, Iws, Is e Ir se asignaron como númericas. Esto nos permitirá analizar las variables con herramientas de tendencia central, la correlación entre ellas y llegar a un modelo que describa el fenómeno de la inluencia de las variables metereológicas en relación a las particulas contaminantes PM2.5.
Descripción de dataset usando medidas de tendencia central
Este tipo de herramientas estadísticas ayudarán el en la comprensión del comportamiento del fenónmeno de la contaminación del aire.
## No month day hour
## Min. : 25 Min. : 1.000 Min. : 1.00 Min. : 0.0
## 1st Qu.:11464 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 5.0
## Median :22435 Median : 7.000 Median :16.00 Median :12.0
## Mean :22279 Mean : 6.514 Mean :15.69 Mean :11.5
## 3rd Qu.:33262 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:18.0
## Max. :43824 Max. :12.000 Max. :31.00 Max. :23.0
## pm2.5 DEWP TEMP PRES
## Min. : 0.00 Min. :-40.00 Min. :-19.0 Min. : 991
## 1st Qu.: 29.00 1st Qu.:-10.00 1st Qu.: 2.0 1st Qu.:1008
## Median : 72.00 Median : 2.00 Median : 14.0 Median :1016
## Mean : 98.61 Mean : 1.75 Mean : 12.4 Mean :1016
## 3rd Qu.:137.00 3rd Qu.: 15.00 3rd Qu.: 23.0 3rd Qu.:1025
## Max. :994.00 Max. : 28.00 Max. : 42.0 Max. :1046
## Iws Is Ir
## Min. : 0.45 Min. : 0.00000 Min. : 0.0000
## 1st Qu.: 1.79 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median : 5.37 Median : 0.00000 Median : 0.0000
## Mean : 23.87 Mean : 0.05534 Mean : 0.1949
## 3rd Qu.: 21.91 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :565.49 Max. :27.00000 Max. :36.0000El factor que más nos interesa es ver como se comporta es ver el comportamiento de las particulas PM2.5 como puede obaservarse con una media en 98.61 y calculando la desviación estándar que resulta en 92.05039 y una varianza de 8473.274 se puede observar que los datos no tenderán a tener una distribución uniforme por lo que se analizará más a detalle esta variable.
A continuación se muestra el comportamiento de las muestras en el periodo que comprende del año 2010 al año 2015,
Como puede observarse en los gráficos de arriba el comportamiento por año de la presencia de particulas contaminantes PM2.5 en Bejing y su alrededores es relativamente parecida, lo que pude destacarse que en la tendencia baja en los meses cercanos a verano y en invierno sube la presencia de estas partículas. Esto es claro ínidice que en efecto, las variables metereológicas can a tener influencia en un modelo que intente predecir la candtidad de particulas PM2.5 en el aíre.
Para llegar a este modelo es conveniente también corroborar la distribución de esta variable y así analizar que tanto la incidencia de las cantidades en el aíre es determinada por un comportamiento lineal, con lo que se podrá analizar la convencia de un modelo lineal.
Como se puede observar en la figura anterior se muestra que la distribucion no tiene una forma gaussiana uniforme, por lo que que se es probable que modelo lineal no describa correctamente el fenómeno.
Correlación de características
El estadístico paramétrico Coeficiente de Correlación de Pearson es adecuada cuando las observaciones de unidades maestreadas aleatoriamente, están medidas en escalas de intervalos [3]. mediante este parámetro se busca estimar la relación entre las variables metereológicas la precencia de partículas PM2.5.
## corrplot 0.84 loaded
Este gráfico muestra poca correlación de las partículas PM2.5 respecto a otras características. Pero se analizarán las variables o características DEWP, TEMP, mont, hour, Ir y pm2.5 para ver si se puede llegar a un modelo para predecir el comportamiento.
Correlación de variables más correlacionadas
## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
##
## legend
Estos gráficos muestran que efectivamente la cantidad de partículas PM2.5 no estan muy correlacionadas con los factores metereológicos por lo que es probable que con un modelo de regresión lineal no se describa en forma correcta el fenómeno.
Modelo
Se implementará un modelo de regresión lineal de tal manera que se modele la presencia de partículas PM2.5 usando las características temperatura, temperatura de punto de rocío, mes, hora, horas acumuladas de lluvia y de nieve. A continuación se muestra el modelo resultante:
# Modelo regresión multiple
model <- lm(pm2.5 ~ TEMP + DEWP +month + Ir + hour + Is , data = data)
summary(model)##
## Call:
## lm(formula = pm2.5 ~ TEMP + DEWP + month + Ir + hour + Is, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -166.35 -54.17 -16.39 34.20 891.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 164.20788 1.19822 137.043 < 2e-16 ***
## TEMP -6.15556 0.06080 -101.237 < 2e-16 ***
## DEWP 5.60396 0.05152 108.771 < 2e-16 ***
## month -2.33091 0.11812 -19.733 < 2e-16 ***
## Ir -7.59930 0.28254 -26.896 < 2e-16 ***
## hour 1.54921 0.05986 25.880 < 2e-16 ***
## Is -3.96792 0.51296 -7.735 1.05e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.83 on 41750 degrees of freedom
## Multiple R-squared: 0.2291, Adjusted R-squared: 0.229
## F-statistic: 2068 on 6 and 41750 DF, p-value: < 2.2e-16Podemos ver por el p-value que todas las características que se utilizaron para este modelado resultaron ser significativas para la descripción de la variable dependiente, en este caso la cantidad de particulas PM2.5 en el aire. Con el valor error residual de 80.83 es capaz este modelo de describir lo datos pero hay que evaluar el modelo para validar la forma en que describe el modelado de la cantidad de partíclas PM2.5 a partir de las características metereológicas.
Evaluación del modelo
En la gráfica donde se imprimieron los Fitted values contra Residuals se puede ver que los datos no presentan homosteacidad ya que al estar difiriendo la dispersión de los datos respecto a la linea. Al encontrarse con perturbaciones heteroscedásticas se comprueba que un modelo lineal no es ideal para describir este fenómeno.
En la gráfica Normal Q-Q podemos ver que efectivamente como ya se había demostrado, los datos no siguen una distribución normal ya que los valores residuales no se ajustan a la recta.
En el gráfico Scale-Location podemos ver nuevamente que el modelo no presenta homoesticidad, los residuos no estan igualmente esparcidos en los rangos de predicción.
En la gráfica Residuals vs Leverage podemos ver que en caso de tomarse como bueno el modelo los outliers no influyen en el modelado.
Regresión lineal usando la caracteristica DEWP (la más correlacionada)
A pesar de que el modelo lineal demostró no ser eficiente en la descripción del fenómeno, se evaluara un modelo lineal usando solo la variable que más esta correlacionada con la característica de interés, para llegar a la probabilidad en la cantidad de particulas PM2.5 debido a un valor dado de DEWP (temperatura del punto de rocío).
##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggplot2':
##
## ggsave## DEWP
## pm2.5 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24
## 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
## 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
## 3 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0
## 4 0 1 0 0 0 0 0 1 1 0 1 0 2 1 0 2 0
## 5 0 0 0 0 0 0 0 1 0 0 1 1 1 2 1 1 1
## 6 1 0 2 1 2 0 1 3 5 1 2 1 1 1 2 2 2
## 7 0 0 0 0 1 1 1 0 1 1 2 3 3 2 6 3 5
## 8 0 0 0 1 0 1 1 1 1 0 1 3 3 3 2 5 6
## 9 0 0 0 0 0 0 1 2 2 2 1 3 7 9 6 14 20
## 10 0 0 0 1 0 2 0 1 2 0 0 5 6 10 5 7 15
## 11 0 0 0 0 0 0 0 3 0 1 0 2 2 11 8 14 15
## 12 0 0 0 0 0 0 1 0 1 0 2 3 5 9 5 16 16
## 13 0 0 0 0 0 0 0 1 1 0 2 2 7 8 10 19 16
## 14 0 0 0 0 0 0 0 0 1 0 1 0 2 7 10 13 17
## 15 0 0 0 0 0 0 0 0 0 0 1 0 4 5 7 7 13
## 16 0 0 0 0 0 0 0 0 1 0 0 0 3 9 9 8 17
## 17 0 0 0 0 0 0 0 0 0 1 1 0 6 3 9 10 13
## 18 0 0 0 0 0 0 0 1 0 0 0 0 3 2 4 11 14
## 19 0 0 0 0 0 0 0 0 0 0 0 0 5 5 3 3 5
## 20 0 0 0 0 0 0 0 0 0 0 0 0 5 2 9 1 12
## 21 0 0 0 0 0 0 0 0 0 0 0 1 2 3 8 4 6
## 22 0 0 0 0 0 0 0 0 0 0 0 0 1 1 7 6 2
## 23 0 0 0 0 0 0 0 0 0 0 1 1 0 3 6 5 8
## 24 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 3 10
## 25 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 2 7
## 26 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 4 4
## 27 0 0 0 0 0 0 1 0 0 0 0 1 0 3 7 4 5
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 3 1
## 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 3
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 4
## 31 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 2
## 32 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 2 4
## 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1
## 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0
## 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2
## 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
## 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2
## 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5
## 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 3
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 3
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 44 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 1 1
## 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1
## 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 50 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 3
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1
## 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 3
## 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1
## 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
## 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1
## 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 73 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 107 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 109 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 111 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 117 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 128 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 144 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 149 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 152 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 153 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 155 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 156 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 158 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 159 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 160 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 161 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 162 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 163 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 164 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 165 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 166 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 167 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 168 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 169 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 170 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 171 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 172 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 173 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 174 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 175 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 176 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 177 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 178 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 179 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 181 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 182 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 184 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 185 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 186 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 187 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 188 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 189 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 190 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 191 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 192 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 193 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 194 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 195 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 196 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 197 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 198 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 199 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 202 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 203 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 204 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 205 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 206 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 207 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 208 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 209 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 211 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 212 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 213 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 214 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 215 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 216 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 217 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 218 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 219 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 221 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 222 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 223 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 224 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 225 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 226 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 228 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 229 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 231 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 232 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 233 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 234 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 235 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 236 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 237 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 238 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 239 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 241 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 242 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 243 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 244 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 245 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 247 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 248 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 249 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 251 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 253 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 254 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 255 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 256 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 257 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 258 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 259 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 265 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 266 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 267 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 268 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 269 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 271 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 272 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 273 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 274 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 275 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 276 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 277 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 278 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 279 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 281 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 282 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 283 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 284 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 285 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 286 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 287 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 288 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 289 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 291 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 293 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 294 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 295 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 296 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 297 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 298 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 299 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 301 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 302 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 303 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 304 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 305 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 306 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 307 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 308 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 309 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 310 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 311 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 312 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 313 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 314 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 315 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 316 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 317 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 318 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 319 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 321 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 323 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 324 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 325 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 326 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 327 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 328 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 329 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 330 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 331 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 332 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 334 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 336 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 337 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 338 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 339 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 341 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 342 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 343 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 344 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 345 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 346 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 347 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 348 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 349 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 351 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 352 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 353 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 354 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 355 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 356 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 357 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 358 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 359 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 360 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 361 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 362 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 363 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 364 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 365 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 366 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 367 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 368 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 369 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 370 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 371 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 372 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 373 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 374 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 375 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 376 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 377 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 378 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 379 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 380 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 381 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 382 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 383 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 384 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 385 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 386 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 387 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 389 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 390 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 391 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 392 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 393 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 394 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 395 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 396 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 397 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 398 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 399 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 401 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 402 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 403 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 404 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 405 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 406 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 407 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 408 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 409 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 410 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 411 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 412 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 413 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 414 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 415 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 416 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 417 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 419 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 420 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 421 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 422 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 424 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 425 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 426 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 427 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 428 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 430 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 431 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 432 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 433 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 434 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 435 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 436 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 437 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 438 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 439 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 440 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 441 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 442 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 443 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 444 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 445 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 447 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 448 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 449 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 451 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 452 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 453 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 454 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 455 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 456 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 457 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 458 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 459 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 460 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 461 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 462 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 463 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 464 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 465 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 466 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 467 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 468 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 469 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 470 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 472 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 473 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 474 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 475 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 476 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 477 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 478 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 479 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 480 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 481 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 482 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 483 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 484 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 485 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 486 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 487 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 488 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 489 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 490 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 491 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 492 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 493 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 494 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 495 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 496 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 498 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 499 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 501 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 502 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 503 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 504 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 505 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 506 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 507 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 508 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 510 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 512 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 513 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 514 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 515 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 516 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 517 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 519 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 521 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 522 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 523 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 524 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 525 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 526 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 527 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 528 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 529 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 532 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 534 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 535 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 539 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 540 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 541 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 542 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 545 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 548 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 551 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 557 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 558 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 559 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 562 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 563 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 567 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 569 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 573 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 577 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 579 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 580 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 584 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 587 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 595 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 599 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 603 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 607 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 611 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 618 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 623 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 648 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 649 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 651 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 659 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 671 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 673 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 684 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 722 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 731 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 744 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 776 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 784 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 802 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 805 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 810 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 824 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 845 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 852 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 886 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 972 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 980 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## DEWP
## pm2.5 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 2 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 1
## 3 1 0 1 0 2 0 0 2 1 1 0 0 2 0 1 2 1 0
## 4 0 1 1 2 5 2 1 0 2 1 3 0 0 3 0 2 3 1
## 5 2 3 0 5 1 1 2 2 6 1 2 2 1 0 0 1 4 6
## 6 0 8 12 11 5 9 3 10 4 3 5 4 11 4 4 6 6 2
## 7 4 7 13 10 9 16 10 10 5 7 8 13 10 2 3 3 9 2
## 8 11 21 13 20 12 18 15 10 9 18 6 15 11 4 8 11 7 4
## 9 20 26 23 17 17 10 18 22 21 14 13 12 11 10 12 11 7 5
## 10 18 24 20 17 11 20 15 22 16 19 14 19 15 11 11 22 4 6
## 11 17 31 26 31 20 25 18 24 25 20 14 21 17 13 8 8 10 7
## 12 20 29 26 25 29 27 26 21 20 18 16 12 13 8 7 9 8 6
## 13 29 29 28 33 25 18 24 28 21 12 20 17 11 8 9 6 7 3
## 14 11 23 21 26 26 30 24 24 17 12 11 9 14 15 2 5 8 4
## 15 18 24 11 21 21 23 17 21 20 19 12 11 15 6 7 9 10 9
## 16 31 27 26 28 31 27 32 29 22 15 19 13 6 8 11 14 6 5
## 17 28 20 17 29 31 29 26 29 21 17 10 13 14 7 7 8 7 7
## 18 20 13 26 30 31 17 17 18 15 14 13 10 12 3 4 6 6 7
## 19 19 22 19 16 18 27 12 14 17 11 14 5 9 9 3 8 3 2
## 20 17 13 20 17 16 20 20 12 18 8 13 10 5 5 1 7 6 3
## 21 12 17 23 16 19 15 11 18 8 5 13 11 5 10 2 7 2 7
## 22 10 16 12 9 17 18 12 5 16 16 8 16 9 4 4 6 4 4
## 23 6 10 14 18 16 15 11 9 15 8 4 8 6 4 4 7 6 4
## 24 12 8 12 12 11 13 9 13 9 8 7 7 8 7 4 3 3 4
## 25 7 15 7 8 10 14 13 7 8 8 6 5 3 7 4 8 7 5
## 26 5 16 12 6 8 12 14 11 6 8 5 8 6 5 4 6 5 6
## 27 9 19 7 14 14 7 13 14 13 11 11 17 9 7 2 8 9 6
## 28 1 6 6 4 11 6 3 5 7 8 7 5 3 5 4 5 2 5
## 29 5 6 8 6 7 6 11 9 5 1 9 7 3 8 3 10 2 10
## 30 3 3 10 10 8 9 4 6 3 7 5 10 2 5 4 0 2 1
## 31 3 4 5 4 7 8 7 6 5 6 2 4 6 9 9 5 4 4
## 32 5 4 6 4 3 9 8 3 7 5 6 9 8 4 2 7 5 4
## 33 4 5 8 10 8 4 3 12 6 7 4 5 4 2 4 2 4 3
## 34 5 4 7 1 6 6 5 7 9 3 3 5 4 5 7 7 4 4
## 35 1 4 2 2 2 6 5 7 8 4 6 4 8 2 5 5 8 1
## 36 4 11 8 2 8 6 8 7 4 2 5 4 7 4 4 1 6 3
## 37 3 8 7 3 4 5 12 3 7 2 6 5 7 5 3 5 1 1
## 38 0 3 3 3 4 6 5 6 6 10 5 7 5 5 3 4 8 5
## 39 2 5 5 3 5 9 3 8 1 7 3 3 7 4 4 5 5 9
## 40 2 5 5 5 5 9 8 4 4 9 5 3 4 7 4 6 6 4
## 41 3 4 6 3 7 6 5 5 4 6 5 6 5 5 3 6 3 6
## 42 1 5 3 4 6 6 3 5 6 5 4 4 5 3 3 6 2 2
## 43 3 4 8 3 6 8 5 5 2 2 6 7 6 3 3 6 5 5
## 44 0 1 1 5 7 4 5 4 3 4 3 3 3 5 2 6 4 6
## 45 1 5 6 3 4 4 4 2 4 2 3 6 8 4 4 3 3 11
## 46 0 2 0 3 8 7 8 6 5 6 3 4 7 8 4 4 5 5
## 47 2 5 2 3 6 8 5 6 6 2 2 4 7 2 9 3 4 5
## 48 1 5 2 3 5 6 3 7 7 3 5 2 1 2 6 8 3 6
## 49 0 9 3 4 4 5 11 2 3 7 4 7 3 6 8 4 5 5
## 50 1 5 4 1 6 1 3 7 4 6 4 2 2 7 5 6 7 2
## 51 1 4 4 3 6 4 4 3 2 4 4 5 7 5 7 1 1 2
## 52 4 3 1 9 3 2 3 3 3 5 4 5 3 2 5 8 7 11
## 53 0 1 1 4 4 3 4 10 4 4 3 4 5 3 6 9 2 4
## 54 2 0 0 4 6 2 5 0 3 5 3 8 6 2 4 7 4 1
## 55 1 0 3 3 5 6 4 2 2 3 4 3 4 7 6 5 8 9
## 56 2 2 2 3 5 6 1 3 1 4 3 6 4 1 4 3 2 5
## 57 1 2 1 1 2 2 2 5 3 6 3 3 2 6 3 6 4 2
## 58 2 2 0 1 4 6 3 2 5 4 2 3 4 4 4 3 2 3
## 59 3 3 2 4 5 1 0 3 3 3 3 4 4 5 3 6 7 7
## 60 3 3 2 1 2 4 1 6 0 5 3 4 5 2 3 4 4 7
## 61 4 0 3 1 5 2 5 5 3 4 3 2 4 5 3 6 0 4
## 62 0 1 3 0 2 2 4 1 2 6 2 2 2 9 2 4 11 3
## 63 1 2 0 1 3 2 1 1 8 8 3 7 6 6 6 4 5 5
## 64 2 1 2 4 3 3 2 3 4 4 4 6 2 5 1 6 4 3
## 65 0 0 1 1 2 8 1 5 4 4 2 3 6 5 4 7 3 2
## 66 2 5 0 5 2 2 10 3 7 3 3 4 2 2 3 6 6 7
## 67 2 0 1 4 2 9 1 1 5 5 5 2 4 3 7 1 2 5
## 68 2 0 0 1 1 4 2 4 3 5 6 4 1 4 4 5 2 5
## 69 3 0 2 3 8 2 2 7 3 3 8 10 6 7 2 7 3 3
## 70 0 1 2 4 3 5 1 4 5 5 1 7 5 6 3 3 6 4
## 71 0 1 2 3 2 7 1 6 5 4 3 6 4 7 6 4 5 3
## 72 1 0 0 3 4 4 3 2 4 3 7 4 6 8 5 4 6 4
## 73 0 2 3 3 3 2 3 5 3 7 5 4 5 12 1 3 1 4
## 74 0 1 2 0 5 7 0 1 2 3 5 2 6 4 3 5 3 4
## 75 1 1 1 2 1 4 4 1 2 6 1 6 2 5 7 2 4 4
## 76 1 0 0 1 4 1 1 5 4 0 3 5 5 4 9 1 2 5
## 77 0 0 0 3 3 4 3 2 3 2 6 4 7 4 3 2 3 5
## 78 0 0 1 1 6 3 2 3 4 7 3 6 6 4 2 2 4 5
## 79 2 2 2 0 6 1 2 7 2 2 5 4 4 9 5 2 7 3
## 80 1 1 1 2 3 1 2 2 3 2 5 6 5 3 4 4 3 4
## 81 0 1 1 1 1 2 1 4 2 2 6 6 1 5 3 2 5 4
## 82 1 2 1 1 3 0 4 0 2 1 3 11 3 2 6 3 5 3
## 83 0 0 1 2 2 0 3 2 4 2 1 1 4 1 7 6 2 2
## 84 0 2 0 1 2 6 3 2 3 5 3 7 1 6 1 5 0 5
## 85 1 2 1 0 2 5 1 0 3 5 2 5 3 2 1 6 2 4
## 86 0 0 0 1 2 0 4 3 1 3 1 1 4 6 1 2 6 4
## 87 0 0 0 5 2 5 5 4 3 3 2 4 6 2 5 1 4 6
## 88 3 1 1 1 2 2 4 2 4 1 1 4 5 3 2 2 3 5
## 89 0 1 2 1 1 3 4 1 3 3 4 2 3 5 2 4 6 4
## 90 0 0 0 0 2 2 2 1 5 3 6 7 3 2 5 2 9 8
## 91 0 1 0 3 5 1 3 1 2 1 4 4 3 5 3 0 5 3
## 92 1 0 0 1 2 2 1 5 1 3 2 4 4 4 1 3 2 3
## 93 0 1 1 0 2 1 3 3 2 2 4 2 6 3 2 6 7 4
## 94 0 1 1 0 1 2 1 2 2 4 1 2 4 2 1 0 5 3
## 95 0 0 2 3 7 6 2 0 3 3 4 3 4 2 2 2 5 2
## 96 1 2 0 2 2 1 3 5 1 4 2 1 6 2 4 4 6 5
## 97 1 0 1 1 4 1 0 2 0 0 7 2 2 5 3 4 6 0
## 98 0 0 1 0 1 0 1 2 2 2 1 2 1 4 6 4 4 5
## 99 0 0 1 0 1 2 1 4 0 4 1 4 3 4 9 1 2 6
## 100 0 0 2 3 1 5 0 2 1 3 4 2 5 6 3 6 6 8
## 101 0 0 0 2 1 1 0 0 1 2 3 2 3 1 3 2 4 2
## 102 0 0 1 1 1 0 3 2 3 4 5 1 1 0 2 3 5 2
## 103 1 1 1 0 2 0 1 2 2 4 2 0 0 5 3 3 4 2
## 104 0 2 1 1 1 0 0 1 1 2 2 3 3 3 4 4 2 3
## 105 0 1 1 2 2 1 2 1 1 4 3 3 1 2 3 2 8 3
## 106 0 0 0 1 6 1 0 2 1 2 2 1 1 1 5 5 4 4
## 107 0 0 0 1 0 5 3 1 0 2 2 3 3 2 4 1 4 3
## 108 0 0 0 1 2 2 3 2 0 4 1 4 2 2 2 4 4 3
## 109 0 0 0 1 1 2 0 2 3 4 4 5 3 1 3 4 2 5
## 110 0 0 0 0 1 2 0 1 1 1 1 1 2 1 3 5 1 1
## 111 0 2 1 2 2 0 4 6 1 3 1 2 3 2 4 5 3 3
## 112 0 0 0 0 1 1 2 1 5 2 1 3 6 1 5 3 5 2
## 113 0 0 0 1 3 2 1 0 2 4 2 0 3 2 5 9 0 2
## 114 0 2 1 2 2 1 1 0 2 1 4 1 2 2 3 3 4 5
## 115 0 0 1 2 1 0 2 1 2 4 0 2 4 2 1 1 3 4
## 116 0 0 0 0 1 1 1 0 1 2 2 1 5 5 4 2 2 2
## 117 0 0 0 2 2 3 1 0 0 6 4 2 5 1 2 5 1 2
## 118 0 0 0 0 3 1 1 0 3 4 1 1 4 5 5 0 1 2
## 119 0 0 0 2 0 1 0 0 2 1 1 2 9 2 1 2 2 2
## 120 0 0 1 0 0 2 1 1 4 2 2 1 4 3 2 3 3 3
## 121 0 0 1 0 2 0 3 3 2 2 0 2 1 4 3 3 4 2
## 122 0 1 1 0 2 0 1 1 2 2 1 5 9 3 1 7 1 3
## 123 0 0 0 0 2 3 1 1 2 4 2 1 4 0 2 2 2 2
## 124 0 0 1 1 1 0 0 1 1 1 2 5 6 3 0 3 2 2
## 125 0 2 0 0 0 0 3 3 2 3 4 3 2 2 4 3 6 1
## 126 0 0 1 1 1 1 2 1 1 5 4 1 0 2 6 4 2 0
## 127 0 0 1 1 0 2 1 0 0 2 0 5 3 6 2 0 2 2
## 128 0 1 0 0 2 0 1 2 4 0 2 5 4 3 1 5 5 2
## 129 0 0 1 0 1 2 0 3 1 0 1 0 2 7 5 4 2 2
## 130 0 0 2 0 0 1 2 1 3 4 2 5 1 4 3 4 2 4
## 131 0 0 1 0 0 1 0 1 0 2 0 0 2 2 4 4 2 0
## 132 0 0 1 0 1 2 0 0 0 2 1 4 4 6 3 2 5 5
## 133 0 0 0 0 1 0 0 1 0 1 0 5 3 0 7 4 3 2
## 134 0 0 0 0 1 0 0 0 0 0 2 1 4 5 3 3 2 3
## 135 1 0 0 2 0 1 5 1 1 2 3 2 2 3 2 3 3 0
## 136 0 0 0 0 0 0 1 0 3 3 4 3 0 0 0 0 4 2
## 137 0 0 1 0 2 0 1 1 1 1 1 1 1 3 4 1 4 3
## 138 0 0 0 0 0 1 1 1 3 4 2 2 5 5 5 1 3 2
## 139 0 0 1 1 1 0 2 1 0 1 1 3 1 1 0 4 2 2
## 140 0 1 0 0 0 1 0 0 3 1 1 1 4 2 1 3 5 0
## 141 1 0 0 0 1 0 0 1 2 1 0 2 2 2 4 2 1 3
## 142 0 0 0 0 0 2 0 1 1 0 1 3 0 4 5 3 2 2
## 143 0 0 0 0 0 3 1 1 1 0 0 1 1 0 4 4 2 5
## 144 0 0 1 0 1 0 5 0 1 2 4 2 2 5 2 3 1 4
## 145 0 0 0 0 0 0 0 0 1 1 2 1 4 2 7 1 6 5
## 146 0 0 0 0 1 0 2 1 0 2 1 5 5 4 2 1 4 1
## 147 0 0 1 0 0 1 1 0 1 3 0 3 1 2 3 0 4 1
## 148 0 1 0 0 0 0 0 1 2 2 1 1 4 3 0 3 2 1
## 149 0 0 0 0 1 0 0 1 1 4 0 3 3 4 3 5 3 0
## 150 0 0 0 0 1 0 0 1 2 2 2 2 1 1 2 7 2 4
## 151 0 0 0 0 1 0 1 1 0 0 1 3 1 0 2 6 2 5
## 152 0 0 0 0 1 1 1 0 1 1 1 0 3 2 3 3 4 2
## 153 0 1 0 1 0 1 0 1 1 2 0 1 4 2 2 2 2 2
## 154 0 0 0 0 0 1 0 0 0 1 1 1 4 4 5 5 4 3
## 155 0 0 0 0 1 0 1 2 3 2 0 2 1 3 4 5 3 0
## 156 0 0 0 0 0 1 2 2 4 2 0 0 0 3 1 3 5 2
## 157 1 0 0 0 0 0 0 0 1 0 1 0 2 4 2 0 0 1
## 158 0 0 0 0 0 1 1 2 1 0 2 1 2 3 3 4 3 0
## 159 0 0 0 0 2 0 1 1 2 0 2 3 5 1 1 3 1 5
## 160 0 0 0 0 0 1 0 0 1 2 1 0 3 1 1 2 3 0
## 161 1 0 0 1 1 1 2 3 2 0 3 5 1 3 3 2 1 1
## 162 0 0 0 0 1 1 2 1 1 0 1 1 2 4 2 3 1 1
## 163 0 0 0 1 1 1 0 2 0 1 0 2 3 6 5 5 2 2
## 164 0 0 0 1 1 1 0 0 1 2 1 2 1 0 1 2 2 3
## 165 0 0 0 0 0 0 2 2 0 0 2 0 1 0 3 3 1 0
## 166 0 0 0 0 0 2 0 0 1 0 0 1 1 1 2 2 6 4
## 167 0 0 0 0 0 1 2 0 1 0 2 5 4 6 1 0 0 2
## 168 1 0 0 0 0 1 0 1 2 1 2 0 1 2 0 0 2 0
## 169 0 0 0 0 1 0 2 2 1 0 0 0 1 1 1 2 2 3
## 170 0 0 0 0 0 1 1 0 1 1 2 3 4 1 2 3 2 0
## 171 0 0 0 0 0 0 0 1 0 0 3 1 0 0 4 0 1 2
## 172 0 0 0 0 0 0 1 0 2 1 1 1 2 2 2 4 0 6
## 173 0 0 0 0 0 0 1 0 2 1 0 0 1 0 2 1 1 2
## 174 0 0 0 1 0 0 0 1 0 2 1 6 1 1 3 3 0 2
## 175 0 0 0 0 0 1 0 0 1 2 2 3 3 1 4 4 1 1
## 176 0 0 0 1 1 0 0 0 1 1 0 3 2 0 1 2 5 1
## 177 0 0 0 1 0 1 0 1 2 1 1 0 1 3 2 2 0 2
## 178 0 0 0 0 0 0 0 0 2 0 0 4 1 0 2 2 2 1
## 179 0 0 0 0 0 1 0 1 0 1 0 0 3 1 1 0 6 1
## 180 0 0 0 0 0 0 0 0 2 2 1 0 2 1 1 2 2 2
## 181 0 0 0 0 0 0 0 0 2 1 0 1 0 1 3 0 4 4
## 182 0 0 0 0 0 0 0 0 0 1 0 1 1 0 3 2 0 1
## 183 0 0 0 0 0 0 1 1 0 0 3 0 0 3 2 2 1 4
## 184 0 0 0 1 0 0 0 0 2 0 2 3 3 2 1 2 2 3
## 185 0 0 0 0 0 0 0 0 2 1 3 1 0 0 3 4 1 3
## 186 0 0 0 0 0 1 0 0 0 0 0 1 0 2 1 0 0 0
## 187 0 0 0 0 1 0 0 0 0 0 1 1 2 3 3 1 5 3
## 188 0 0 0 0 0 0 0 0 3 1 0 1 0 1 1 2 0 2
## 189 0 0 1 0 0 0 1 1 0 0 1 1 0 1 0 0 2 5
## 190 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 4 0 2
## 191 0 0 0 0 1 0 0 0 2 1 0 0 0 2 0 1 1 8
## 192 0 0 0 0 0 0 0 1 0 1 2 2 1 2 2 2 0 2
## 193 0 0 0 0 0 1 0 1 0 0 2 0 2 3 0 0 0 2
## 194 0 0 0 0 0 0 0 0 0 3 0 0 1 4 0 0 0 1
## 195 1 0 0 0 1 0 0 0 0 0 0 0 0 1 2 1 1 1
## 196 0 0 0 0 0 0 2 0 1 1 1 1 0 0 1 0 3 3
## 197 0 0 0 0 0 0 0 2 0 0 0 2 0 1 1 3 1 1
## 198 0 0 1 0 0 1 0 1 0 0 0 3 1 0 1 2 0 0
## 199 0 0 0 1 1 0 0 0 0 0 1 2 1 2 1 1 1 1
## 200 1 0 0 0 0 0 0 1 0 2 1 0 1 2 0 2 2 1
## 201 0 0 0 0 0 0 0 1 0 0 0 1 2 2 2 2 5 2
## 202 0 0 0 0 0 0 0 0 2 1 0 1 0 0 2 2 3 2
## 203 0 0 0 0 0 0 0 0 0 1 2 0 0 1 3 2 5 0
## 204 0 0 0 0 2 0 1 0 0 1 1 2 1 1 1 2 2 3
## 205 0 0 0 0 0 1 0 0 0 1 1 3 1 1 2 2 3 2
## 206 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 6 1 1
## 207 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1 0 1
## 208 0 0 0 0 0 0 0 0 0 1 0 2 1 4 2 1 4 1
## 209 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 3 2 3
## 210 0 0 1 0 0 0 0 2 0 0 1 2 0 0 1 0 2 3
## 211 0 0 0 0 0 0 0 1 0 0 0 1 3 2 1 3 2 3
## 212 0 0 0 0 0 0 1 0 0 0 1 0 0 2 1 2 2 1
## 213 0 0 0 0 0 0 1 1 1 0 1 0 2 0 0 5 2 3
## 214 0 0 0 0 0 1 0 0 0 1 2 2 0 1 1 1 1 1
## 215 0 0 0 0 0 0 0 0 0 0 2 0 1 0 2 0 3 2
## 216 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 3 1 3
## 217 0 0 0 0 0 0 0 0 0 0 0 1 2 1 0 2 0 6
## 218 0 0 0 0 1 0 0 0 0 0 0 0 0 1 3 2 4 1
## 219 0 0 0 0 0 0 1 0 2 0 0 0 1 1 0 1 2 1
## 220 0 0 0 0 0 0 0 0 0 0 1 1 1 1 3 0 2 3
## 221 0 0 0 0 1 0 1 0 0 0 2 1 1 0 3 3 1 1
## 222 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 4 1 2
## 223 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 3
## 224 0 0 0 0 0 0 0 0 0 0 1 0 0 1 3 1 2 1
## 225 0 0 0 0 0 1 0 0 1 1 0 0 2 0 1 0 4 3
## 226 0 0 0 0 0 0 1 0 1 1 1 0 1 1 2 0 3 1
## 227 0 0 0 0 1 0 1 1 1 0 0 2 1 4 1 0 1 3
## 228 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 3
## 229 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 2 2
## 230 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 1 4 2
## 231 0 0 0 0 0 0 0 1 0 1 2 1 0 0 0 0 2 0
## 232 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 2 1
## 233 0 0 0 0 0 0 0 0 1 1 0 1 2 0 1 1 2 5
## 234 0 0 1 0 0 0 0 0 3 2 0 1 0 0 2 1 0 3
## 235 0 0 0 0 0 0 0 1 1 0 0 1 1 3 0 1 2 2
## 236 0 0 0 0 0 0 1 1 1 0 0 0 3 0 2 0 0 2
## 237 0 0 0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 2
## 238 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 2 3 2
## 239 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0
## 240 0 0 0 0 0 0 0 1 2 0 0 0 2 1 0 1 3 3
## 241 0 0 0 0 0 0 0 1 1 1 0 1 0 1 1 0 2 6
## 242 0 0 0 0 0 0 0 2 1 0 1 0 0 0 1 2 0 2
## 243 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0
## 244 0 0 0 1 1 0 0 0 0 0 0 2 0 1 1 3 1 1
## 245 0 0 0 0 0 0 0 0 0 2 0 0 0 1 4 0 0 1
## 246 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 2 4
## 247 0 0 0 0 0 0 0 0 1 2 0 2 1 0 0 0 0 2
## 248 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 2 0
## 249 0 0 0 0 0 0 0 0 1 0 0 1 1 0 2 0 1 2
## 250 0 0 0 0 0 1 0 2 1 1 0 0 1 2 0 1 2 0
## 251 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 2 1
## 252 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 1
## 253 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 4
## 254 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 2 3 3
## 255 0 0 0 0 0 0 0 1 0 1 2 0 0 0 1 1 0 0
## 256 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 2
## 257 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 2 1 0
## 258 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2 0 1 0
## 259 0 0 0 0 0 0 0 0 1 0 1 1 2 0 1 2 1 1
## 260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 3
## 261 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 2 0
## 262 0 0 0 0 1 0 0 0 0 1 0 0 0 4 0 0 0 1
## 263 0 0 0 0 0 0 0 1 1 0 1 0 0 2 1 1 1 3
## 264 0 0 0 0 0 0 0 0 1 0 0 0 1 0 2 2 3 2
## 265 0 0 0 1 0 0 0 0 0 0 1 2 0 0 0 0 0 0
## 266 0 0 0 0 0 0 0 1 0 1 0 0 0 1 2 1 0 2
## 267 0 0 0 0 0 0 0 0 1 0 1 1 3 1 1 2 1 4
## 268 0 0 0 0 0 0 0 0 0 1 0 1 2 0 1 1 1 1
## 269 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 0 1 1
## 270 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 3
## 271 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2 2 1 2
## 272 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 273 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 4 5
## 274 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 2 1
## 275 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 1
## 276 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1
## 277 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1
## 278 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 1
## 279 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1
## 280 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 2 0
## 281 0 0 0 0 0 0 0 0 0 0 0 0 1 3 0 2 3 0
## 282 0 0 0 0 0 0 0 0 0 1 2 2 1 1 1 2 3 3
## 283 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 2
## 284 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1
## 285 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 2
## 286 0 0 0 0 0 0 0 0 1 1 0 0 2 0 1 0 0 1
## 287 0 0 0 0 0 0 1 0 0 0 0 1 0 0 3 0 0 0
## 288 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 2 0 4
## 289 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 2 0 0
## 290 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 2 1 1
## 291 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1
## 292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 1
## 293 0 1 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0
## 294 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 2
## 295 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
## 296 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 2
## 297 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2
## 298 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0
## 299 0 0 0 0 1 0 0 0 1 0 1 1 1 2 0 0 1 0
## 300 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0
## 301 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 3 0
## 302 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1
## 303 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1
## 304 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0
## 305 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 0 0
## 306 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
## 307 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 1 1
## 308 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 2 1 1
## 309 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 2 0
## 310 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 0
## 311 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 2 2 1
## 312 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 0 0 1
## 313 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1
## 314 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## 315 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0
## 316 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 2 3
## 317 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
## 318 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 1 1
## 319 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0
## 320 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1
## 321 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 1 0
## 322 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0
## 323 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 2
## 324 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1
## 325 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 0
## 326 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 2
## 327 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 0 5
## 328 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 3
## 329 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 330 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
## 331 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0
## 332 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 2 0
## 333 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 1 1 2
## 334 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 3
## 335 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 1 1
## 336 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 2
## 337 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0
## 338 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 339 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1
## 340 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 2
## 341 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 342 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 343 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1
## 344 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1
## 345 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 1
## 346 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0
## 347 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0
## 348 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 2 1
## 349 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
## 350 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1
## 351 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0
## 352 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2
## 353 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1
## 354 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 355 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 356 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 2 0
## 357 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0
## 358 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0
## 359 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 360 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## 361 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0
## 362 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0
## 363 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
## 364 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 365 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1
## 366 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1
## 367 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 2 0
## 368 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 369 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 370 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 371 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 372 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 2 0 0
## 373 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0
## 374 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0
## 375 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
## 376 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0
## 377 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0
## 378 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2
## 379 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1
## 380 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0
## 381 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
## 382 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0
## 383 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 2
## 384 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1
## 385 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 386 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 387 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 1
## 389 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 390 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0
## 391 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1
## 392 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2
## 393 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## 394 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
## 395 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 396 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 397 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0
## 398 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## 399 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1
## 400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 401 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
## 402 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2
## 403 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 404 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0
## 405 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## 406 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 407 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1
## 408 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0
## 409 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 410 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 411 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 412 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
## 413 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 414 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 415 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 416 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 417 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1
## 418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## 419 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
## 420 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 421 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 422 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## 424 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 425 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1
## 426 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0
## 427 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 428 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 430 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 431 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 432 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 433 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 434 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 435 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 436 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 1
## 437 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4
## 438 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 439 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 440 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 441 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 442 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0
## 443 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 444 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## 445 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 447 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 448 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1
## 449 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 450 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0
## 451 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 452 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 453 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 454 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 455 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 456 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 457 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1
## 458 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## 459 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 460 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 461 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 462 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 463 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 464 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 465 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 466 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 467 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 468 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 469 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
## 470 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 472 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 473 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 474 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 475 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 476 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 477 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 478 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 479 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## 480 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 481 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 482 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 483 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 484 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 485 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 486 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0
## 487 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 488 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 489 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0
## 490 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 491 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 492 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 493 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 494 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1
## 495 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 496 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 498 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 499 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 501 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 502 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 503 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 504 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 505 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 506 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 507 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 508 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 510 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 512 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 513 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0
## 514 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 515 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 516 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 517 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 519 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 521 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 522 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 523 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 524 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 525 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 526 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 527 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 528 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
## 529 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 532 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## 534 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 535 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 539 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 540 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 541 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 542 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 545 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 548 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 551 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 557 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 558 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 559 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 562 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 563 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 567 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 569 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 573 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0
## 577 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 579 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 580 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 584 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 587 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 595 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 599 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 603 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 607 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 611 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 618 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 623 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 648 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 649 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 651 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
## 659 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 671 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 673 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 684 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 722 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 731 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 744 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 761 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 776 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 784 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 802 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 805 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 810 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 824 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 845 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 852 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 858 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 886 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 972 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 980 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DEWP
## pm2.5 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 0 1 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 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0
## 2 0 0 2 1 3 0 2 0 0 0 1 1 0 1 0 2 0 0 0 0 0 1 0
## 3 2 2 2 1 0 2 2 2 1 0 0 3 2 5 2 0 1 0 1 0 0 0 0
## 4 0 3 2 3 2 3 0 1 2 0 1 2 3 2 1 2 2 1 0 0 0 2 3
## 5 2 1 4 7 0 8 1 3 1 0 3 4 2 3 3 6 4 4 1 1 0 0 1
## 6 1 4 4 5 4 4 2 8 3 2 6 6 4 6 2 9 10 6 3 1 3 4 3
## 7 3 8 6 8 8 3 5 8 3 6 8 9 4 8 4 1 7 3 2 0 4 3 2
## 8 4 7 5 4 4 13 5 9 6 7 6 7 11 4 7 6 6 6 4 1 4 2 6
## 9 2 5 7 9 4 10 6 6 14 9 8 12 4 8 7 4 5 7 3 9 6 10 5
## 10 3 8 4 9 8 7 8 8 9 11 8 10 11 4 6 8 14 12 5 7 4 6 2
## 11 7 9 12 9 8 7 11 12 10 6 5 8 10 4 9 10 8 7 14 7 5 5 7
## 12 6 5 8 10 10 12 9 8 9 5 7 6 8 10 7 12 9 12 13 5 3 5 6
## 13 2 6 12 5 4 7 12 8 12 6 5 7 10 6 6 7 9 8 12 12 9 7 5
## 14 7 7 7 4 8 6 4 10 4 3 4 5 2 5 8 6 6 7 6 12 7 7 3
## 15 2 8 9 7 15 3 4 8 3 4 6 3 5 7 10 5 11 9 8 9 8 9 5
## 16 12 9 3 12 9 8 6 9 10 6 5 4 6 9 15 10 5 12 15 10 11 10 9
## 17 11 3 3 4 7 6 9 6 2 7 6 6 6 7 4 12 9 16 9 18 10 11 11
## 18 8 7 11 9 10 4 4 5 7 7 3 1 4 4 6 6 4 8 8 12 14 10 10
## 19 6 6 8 8 10 4 3 7 6 5 5 2 3 6 3 3 6 8 9 10 9 13 7
## 20 8 6 8 5 7 6 7 6 8 4 2 1 6 6 6 5 4 11 3 9 11 7 8
## 21 5 5 5 10 4 3 5 5 2 1 6 9 7 2 4 7 5 7 10 12 8 6 7
## 22 8 5 6 9 10 13 8 4 4 5 5 10 5 2 4 8 2 8 10 12 8 13 8
## 23 7 5 7 5 8 5 5 7 3 3 9 11 3 6 5 3 0 8 8 9 5 15 13
## 24 9 8 4 10 8 3 3 3 1 5 7 4 6 7 4 5 4 8 12 11 3 8 11
## 25 7 6 7 7 2 9 6 2 3 2 1 6 1 7 3 6 4 7 6 12 10 11 8
## 26 2 5 10 4 7 6 6 4 2 5 5 7 7 4 4 6 3 5 10 11 11 5 10
## 27 4 5 8 15 6 7 9 6 2 7 7 6 8 8 11 2 3 6 10 13 4 11 17
## 28 7 7 9 5 8 4 4 4 8 6 7 7 7 4 4 7 3 7 5 9 10 8 9
## 29 3 3 4 3 4 4 3 4 5 3 4 5 8 1 6 5 8 3 9 12 7 13 6
## 30 6 2 2 10 6 6 1 4 3 5 4 3 5 3 1 8 4 5 9 6 11 7 6
## 31 6 4 2 3 5 7 4 1 7 6 2 4 5 1 2 4 7 9 9 10 8 8 16
## 32 6 5 6 3 3 7 1 7 6 4 2 6 7 4 3 4 4 11 8 3 7 5 15
## 33 5 3 3 5 6 5 1 4 5 3 5 7 5 5 4 1 5 7 9 9 12 3 8
## 34 9 7 7 5 4 8 4 4 1 3 5 6 6 3 3 5 2 8 3 4 11 8 9
## 35 8 8 5 0 3 3 6 7 3 4 4 2 3 2 3 3 6 5 12 7 9 11 12
## 36 6 6 12 7 2 9 7 2 3 4 1 7 1 1 1 1 4 7 7 6 11 13 9
## 37 9 5 2 5 10 8 1 4 1 3 5 8 4 4 8 3 5 6 6 7 6 7 10
## 38 7 15 16 8 5 4 4 1 1 4 0 9 2 5 8 5 3 4 11 6 4 14 11
## 39 6 5 9 9 1 4 4 6 1 6 7 6 3 5 5 4 6 3 9 5 11 7 12
## 40 4 3 7 4 5 4 4 1 5 3 10 6 2 2 7 5 3 7 6 5 8 12 10
## 41 6 6 8 9 8 2 4 4 3 1 5 6 5 4 2 5 3 4 6 5 9 9 6
## 42 9 4 4 4 2 6 3 2 4 4 7 6 7 2 3 9 2 7 5 4 8 14 7
## 43 7 2 3 2 6 3 5 2 3 4 5 10 5 4 6 5 4 5 8 8 5 8 9
## 44 3 6 4 7 3 4 9 5 2 5 5 4 2 4 6 3 3 4 6 4 9 4 11
## 45 7 3 2 1 3 5 8 5 6 5 3 8 4 3 4 4 3 5 9 5 9 8 6
## 46 4 8 3 3 4 2 5 4 3 4 10 3 8 2 1 4 4 6 5 7 12 7 6
## 47 3 5 2 1 4 7 8 4 8 4 3 8 5 4 2 4 3 7 8 6 9 11 8
## 48 9 5 5 5 3 8 6 6 3 7 8 7 2 4 8 4 7 6 10 7 14 11 13
## 49 6 1 2 2 2 4 4 1 5 2 4 7 5 4 7 5 4 6 7 5 8 5 15
## 50 8 2 8 0 5 3 4 5 2 6 5 8 4 4 5 5 4 5 5 8 9 14 16
## 51 6 5 7 4 7 9 5 7 6 4 0 6 4 4 4 3 3 2 5 6 7 9 12
## 52 4 3 7 3 6 9 5 4 4 3 2 4 0 3 4 3 5 2 8 6 8 6 13
## 53 4 3 2 6 3 5 4 6 1 6 6 3 1 7 4 7 0 2 5 4 8 7 7
## 54 2 6 9 3 2 5 3 5 2 3 6 2 0 5 7 2 2 9 7 5 6 10 13
## 55 3 1 2 4 6 6 0 3 3 5 6 8 8 4 2 6 5 8 9 11 7 4 5
## 56 3 3 6 3 3 6 7 7 5 2 3 2 5 5 2 3 4 10 7 2 11 3 11
## 57 3 5 5 4 4 7 6 0 5 5 4 6 7 2 9 4 5 5 3 4 5 13 11
## 58 5 3 5 1 4 2 1 6 2 6 3 3 5 1 2 8 7 5 5 9 5 9 11
## 59 5 2 6 2 5 6 4 4 2 6 7 3 7 7 2 8 4 6 8 12 7 13 13
## 60 3 3 2 0 5 6 3 7 2 6 6 3 6 4 4 2 3 10 3 4 3 13 11
## 61 3 2 3 2 0 5 2 2 3 3 1 4 4 4 4 4 3 7 6 7 10 4 14
## 62 3 4 2 2 3 2 2 4 2 1 4 4 2 4 2 6 4 6 3 5 9 11 10
## 63 7 3 0 9 3 2 11 3 4 2 8 2 9 4 5 8 7 3 5 7 5 6 6
## 64 2 3 6 4 3 5 4 6 3 5 4 4 7 2 5 1 4 9 3 5 6 8 10
## 65 8 1 3 7 3 0 4 3 4 3 6 3 6 3 5 5 2 5 3 6 10 3 6
## 66 2 4 1 2 2 1 4 4 5 2 5 7 3 6 5 3 5 4 7 3 5 4 8
## 67 1 0 3 3 4 1 5 3 6 2 6 4 4 3 2 5 5 3 5 8 3 5 13
## 68 5 6 3 2 3 0 2 4 6 6 6 2 5 4 2 5 1 1 5 6 4 9 8
## 69 3 5 4 5 5 10 4 3 2 3 8 2 7 7 4 6 4 4 5 11 8 9 15
## 70 6 2 3 4 4 4 5 5 2 5 2 7 6 5 3 5 3 4 4 3 5 12 7
## 71 5 3 5 4 3 2 8 6 2 3 5 3 3 1 4 3 2 5 5 6 8 8 9
## 72 3 4 1 1 3 7 2 2 0 2 5 7 5 3 6 3 1 7 5 3 5 11 6
## 73 2 4 2 4 1 1 5 4 3 7 6 1 5 5 4 5 3 3 4 4 3 7 11
## 74 5 5 3 3 3 1 4 2 2 1 1 6 3 1 3 3 1 3 8 4 6 8 14
## 75 3 1 2 2 4 2 6 3 3 8 10 2 2 5 1 1 2 7 2 7 5 11 4
## 76 5 7 4 3 4 6 3 1 2 4 4 3 2 4 3 4 3 4 7 3 4 12 5
## 77 5 9 1 6 2 2 4 4 1 5 3 5 6 5 5 5 4 8 2 1 8 7 6
## 78 4 4 3 2 4 4 2 3 3 7 3 3 8 4 2 3 4 8 4 7 3 2 9
## 79 6 5 8 5 9 5 1 5 1 8 8 3 8 6 5 1 6 4 7 5 6 4 11
## 80 4 5 5 3 5 5 6 4 5 3 4 7 12 0 7 7 5 6 7 4 8 6 11
## 81 4 4 2 3 2 2 3 2 2 1 3 3 6 3 4 4 0 4 10 4 4 9 14
## 82 6 4 2 3 1 2 2 7 2 4 2 3 7 2 3 5 5 2 2 4 3 8 7
## 83 3 5 2 4 4 5 3 3 5 2 7 3 10 4 1 2 6 7 7 4 5 7 6
## 84 1 3 4 3 5 4 7 2 4 2 6 3 4 5 2 2 2 3 5 5 7 6 6
## 85 3 3 3 0 2 4 0 2 3 1 3 2 9 6 4 3 5 5 3 3 9 8 9
## 86 7 3 2 1 3 4 2 5 5 2 2 2 5 4 4 1 5 4 7 4 8 12 5
## 87 4 2 5 6 3 5 2 3 2 4 4 2 5 2 0 4 3 3 4 7 6 6 8
## 88 5 6 5 1 2 1 1 1 2 0 0 3 5 2 2 2 2 5 1 2 4 9 13
## 89 6 4 4 1 2 3 1 2 8 4 1 5 4 4 5 2 2 5 2 3 6 14 9
## 90 3 10 6 4 1 3 2 7 8 3 4 5 2 3 3 7 2 5 4 7 4 10 6
## 91 8 7 2 5 4 2 7 9 1 5 5 3 6 3 2 4 3 1 4 5 8 4 6
## 92 6 5 1 2 1 2 4 5 1 1 3 4 3 7 2 3 3 2 5 6 7 7 4
## 93 3 1 3 4 4 1 1 4 1 4 2 5 7 2 3 4 5 3 2 3 2 11 9
## 94 3 8 1 1 2 1 3 2 4 1 2 1 5 4 5 0 4 1 7 3 8 10 4
## 95 5 4 1 2 3 2 3 1 4 4 5 2 6 3 3 6 1 2 0 4 6 7 7
## 96 6 2 3 3 2 3 0 3 3 1 1 7 9 2 5 4 4 2 4 6 6 11 12
## 97 4 8 4 3 4 1 0 5 4 4 1 2 2 1 2 3 7 2 7 3 7 5 4
## 98 3 4 1 3 5 3 2 3 4 5 1 4 4 4 5 5 4 7 4 4 14 8 5
## 99 7 0 5 0 3 0 2 3 2 2 4 3 7 3 2 3 3 6 3 3 8 8 3
## 100 3 5 3 4 3 4 2 0 2 0 0 3 8 6 3 8 1 8 4 3 6 9 4
## 101 7 6 1 4 1 4 2 2 3 2 3 2 3 2 3 1 3 5 4 4 9 11 8
## 102 2 1 2 2 3 0 2 4 4 2 4 3 8 7 3 2 0 1 4 3 3 7 7
## 103 2 4 2 1 3 2 2 0 2 8 0 2 5 5 7 2 0 1 4 5 6 8 7
## 104 0 2 2 1 1 2 0 0 2 3 1 2 4 1 4 1 6 9 4 3 5 7 5
## 105 3 3 4 2 3 2 4 1 3 4 1 4 2 6 4 1 4 6 6 2 4 4 6
## 106 2 2 1 3 4 3 5 0 2 2 1 5 1 4 2 2 2 4 4 3 4 6 5
## 107 3 1 3 1 2 5 3 2 4 2 0 1 7 1 3 4 6 2 2 7 4 3 2
## 108 5 6 3 2 0 1 0 0 2 4 3 1 6 2 5 3 4 3 3 2 7 4 10
## 109 3 3 3 0 2 2 2 2 0 0 2 1 4 3 1 2 3 2 2 4 7 6 4
## 110 0 3 4 5 4 2 0 1 2 5 4 2 4 5 2 3 0 3 3 2 4 4 3
## 111 1 3 5 3 3 2 4 2 6 2 7 2 6 2 2 2 0 1 4 3 5 8 4
## 112 5 2 1 1 4 2 1 0 2 2 2 3 2 0 3 2 6 2 4 2 2 7 2
## 113 2 0 0 1 5 2 3 3 1 6 1 3 3 2 3 8 4 3 3 2 3 3 7
## 114 2 8 1 3 1 3 4 3 2 1 1 3 4 2 2 5 3 2 0 1 2 5 7
## 115 2 5 3 1 2 4 0 2 0 1 2 1 1 4 4 4 7 2 2 1 10 6 1
## 116 2 2 4 0 4 1 3 2 3 3 3 1 2 2 4 1 3 1 5 1 4 6 3
## 117 1 1 1 1 0 2 1 1 1 1 2 1 1 1 2 1 0 2 2 4 7 7 7
## 118 4 1 5 2 4 2 3 0 0 4 1 0 2 4 2 3 4 2 2 5 4 4 7
## 119 3 3 1 2 2 1 1 2 0 2 1 0 3 4 1 3 4 5 2 2 5 3 8
## 120 0 1 0 2 1 4 3 0 0 2 0 1 1 1 2 2 0 2 3 2 6 2 9
## 121 4 4 3 0 3 3 1 4 6 3 3 2 2 1 0 2 2 6 3 1 6 5 8
## 122 2 3 1 1 4 0 2 2 1 5 5 0 2 1 3 1 2 6 2 4 2 6 3
## 123 5 4 3 2 2 2 2 3 1 3 2 2 2 4 0 4 4 1 2 3 5 7 4
## 124 2 4 2 2 2 4 1 2 2 2 0 2 2 2 0 2 2 4 3 6 4 2 5
## 125 1 1 2 1 1 0 0 1 1 2 2 2 2 3 5 2 2 4 1 3 5 6 4
## 126 6 5 3 0 5 5 0 2 1 2 1 2 1 3 1 2 4 3 0 2 3 3 3
## 127 2 2 2 3 1 2 6 2 2 4 1 2 1 3 3 4 0 3 4 4 3 4 6
## 128 4 1 7 3 0 1 3 2 4 2 2 2 0 2 2 1 3 0 1 8 5 4 4
## 129 3 2 1 2 3 1 2 1 2 2 5 0 1 2 4 1 1 6 3 2 8 1 2
## 130 3 1 0 4 2 1 4 5 0 4 1 0 2 2 1 2 2 2 3 5 4 2 1
## 131 2 2 2 2 1 0 3 2 1 1 0 1 2 2 2 2 2 3 1 2 3 7 5
## 132 2 4 1 2 2 2 1 4 2 1 1 1 3 1 4 2 1 4 0 6 8 7 1
## 133 1 4 2 0 0 1 0 1 1 0 3 3 1 2 0 1 2 2 5 3 3 4 4
## 134 2 1 1 3 0 1 2 0 4 4 3 3 3 2 1 0 0 0 3 5 1 4 6
## 135 4 5 4 2 3 2 3 1 2 1 0 3 1 3 2 4 1 2 2 2 1 4 4
## 136 6 3 2 3 2 1 2 2 2 3 4 2 2 2 3 0 4 3 2 3 6 2 5
## 137 1 0 0 1 2 2 1 0 2 1 0 4 2 2 2 3 1 2 4 4 3 6 4
## 138 4 4 2 1 2 1 1 2 1 4 3 1 1 2 1 3 1 1 4 1 4 1 5
## 139 1 4 0 0 1 2 2 2 4 0 1 1 0 1 3 4 6 4 2 5 2 1 1
## 140 1 3 1 1 1 1 1 0 0 1 2 1 3 2 1 2 6 4 1 2 2 8 4
## 141 3 2 1 1 4 1 0 5 0 2 1 5 2 0 2 1 1 3 2 2 1 7 4
## 142 1 4 1 1 2 2 2 3 0 2 1 4 1 1 1 2 1 4 0 1 4 4 4
## 143 3 4 4 2 3 2 3 2 1 3 1 3 3 5 2 4 3 1 5 3 7 3 5
## 144 3 5 3 1 3 2 2 3 0 3 1 1 1 3 1 5 3 3 2 1 2 6 6
## 145 2 1 0 0 2 3 1 1 1 0 0 3 2 0 3 1 2 2 1 1 2 3 4
## 146 2 0 3 1 2 1 1 1 1 0 2 4 2 2 2 2 1 1 2 4 5 3 5
## 147 1 3 2 0 4 0 2 2 3 1 2 0 1 0 2 2 2 4 0 1 2 4 5
## 148 2 5 3 2 1 2 1 0 2 1 2 1 2 1 2 2 5 2 2 4 1 1 3
## 149 4 0 1 0 2 1 3 2 0 3 2 4 2 1 0 0 3 5 2 2 2 8 5
## 150 6 4 0 0 2 1 2 0 1 1 1 2 3 0 1 2 1 1 2 2 4 5 2
## 151 2 1 2 1 0 2 1 3 1 3 1 1 2 2 1 3 6 2 0 2 2 3 7
## 152 3 6 1 2 1 1 1 2 0 2 0 2 1 2 2 4 1 3 5 4 6 3 6
## 153 6 2 2 6 2 3 4 0 2 1 1 0 1 1 2 0 4 3 3 0 5 4 7
## 154 2 0 3 1 2 1 1 1 1 3 4 2 1 4 3 1 0 1 1 0 3 4 3
## 155 2 2 3 2 2 1 1 2 0 0 1 0 1 2 2 1 3 3 2 0 1 3 4
## 156 3 4 0 2 0 2 1 2 2 1 1 1 1 1 2 3 3 2 2 1 1 1 3
## 157 3 0 1 1 3 0 0 2 3 2 2 1 2 0 0 4 1 2 2 4 1 2 4
## 158 2 1 1 1 2 2 0 3 3 1 1 1 4 2 1 5 2 0 2 0 2 2 0
## 159 2 4 1 3 2 4 0 4 3 2 0 0 1 2 3 1 2 0 1 2 4 4 3
## 160 2 3 0 1 3 0 1 1 1 2 3 0 1 1 0 3 1 1 2 3 0 4 8
## 161 1 1 2 1 0 3 0 2 0 1 1 2 2 1 1 2 2 3 2 4 1 2 2
## 162 2 1 1 1 0 1 1 2 1 1 0 3 1 1 2 2 4 3 4 2 1 0 1
## 163 1 0 2 2 2 2 1 4 0 2 1 3 0 3 0 3 2 2 3 0 0 3 1
## 164 3 2 8 4 3 2 1 4 1 1 1 0 2 1 2 7 1 4 2 2 4 3 6
## 165 1 1 4 2 3 1 3 2 3 0 1 1 0 3 2 2 1 1 3 0 3 2 1
## 166 3 2 1 3 0 2 0 4 3 0 3 3 1 0 0 3 2 2 3 1 4 1 5
## 167 4 2 2 1 0 1 0 2 3 1 1 1 1 2 2 2 6 0 0 2 0 1 3
## 168 0 1 0 3 2 0 1 2 1 1 2 0 2 1 0 3 2 5 1 3 4 3 3
## 169 2 1 1 2 0 0 3 2 1 0 1 0 0 0 0 2 3 0 2 0 2 0 3
## 170 4 0 1 2 1 5 0 0 2 1 1 0 1 1 3 1 4 4 4 1 4 0 2
## 171 3 1 1 1 1 4 2 0 0 2 0 0 2 0 1 0 2 0 2 3 1 0 0
## 172 4 2 1 3 2 1 1 0 0 2 0 1 1 2 0 2 1 4 4 0 1 2 1
## 173 1 0 4 0 1 3 1 1 2 0 1 1 0 0 0 1 2 2 0 1 2 1 4
## 174 2 1 2 3 4 1 1 3 1 2 1 2 0 2 1 2 1 3 2 1 2 4 0
## 175 3 2 1 2 1 2 1 1 2 0 4 1 2 1 2 1 0 1 0 0 1 2 2
## 176 3 1 0 0 3 2 1 2 1 1 1 0 3 1 3 0 4 1 3 1 0 2 1
## 177 2 0 0 3 1 1 2 0 1 0 0 4 1 0 0 1 3 2 2 4 0 2 2
## 178 0 2 1 4 4 1 2 2 0 0 0 0 3 0 0 1 0 2 3 1 3 0 2
## 179 0 1 2 1 3 1 0 2 1 1 0 2 0 1 3 0 2 3 3 2 0 1 3
## 180 1 2 2 1 2 1 2 3 4 2 1 1 1 1 1 1 1 0 1 0 0 1 5
## 181 3 2 1 1 1 4 0 1 1 3 1 0 0 0 1 1 1 0 3 3 1 1 0
## 182 2 0 2 0 2 2 1 1 0 2 1 2 0 0 1 2 2 2 0 2 1 1 4
## 183 2 3 3 3 1 3 1 1 3 1 0 0 5 1 0 0 0 5 0 2 2 1 2
## 184 3 2 0 2 2 4 3 2 3 0 2 3 2 2 2 2 1 1 0 0 0 1 3
## 185 2 1 0 3 1 1 2 0 4 2 0 0 1 1 0 2 4 1 5 2 0 0 3
## 186 2 0 0 0 4 0 1 3 1 0 0 0 1 1 0 0 0 3 0 1 0 2 1
## 187 3 6 1 0 1 2 1 2 4 2 1 0 0 1 1 2 2 1 2 2 0 2 1
## 188 3 6 2 1 0 2 2 2 2 2 0 0 1 0 0 1 3 1 1 1 1 3 1
## 189 1 0 1 0 2 3 1 0 2 2 0 0 0 3 0 0 2 2 2 2 0 2 2
## 190 1 0 2 1 2 1 1 2 0 2 0 1 2 1 1 0 0 2 1 0 1 1 2
## 191 3 1 1 1 2 0 0 0 1 1 1 2 2 0 1 1 0 0 2 2 0 1 2
## 192 1 0 0 1 1 3 3 0 0 0 1 0 2 1 1 0 2 2 1 1 0 0 1
## 193 2 3 0 1 1 3 2 3 0 1 0 2 1 2 0 0 4 0 2 0 0 3 2
## 194 0 0 1 0 1 2 1 2 1 3 1 1 3 3 1 0 1 4 0 2 2 0 0
## 195 2 0 1 0 0 3 1 3 1 2 1 2 3 2 0 1 0 1 2 2 0 2 1
## 196 3 5 2 1 1 2 2 0 4 1 0 1 1 1 1 0 2 3 2 0 1 2 3
## 197 1 1 1 0 1 3 1 0 1 0 0 1 2 0 0 0 3 3 1 1 1 0 0
## 198 4 0 1 3 1 1 1 0 1 1 0 0 2 0 1 1 1 0 0 3 0 2 1
## 199 1 1 1 1 2 1 2 2 0 2 1 3 0 2 0 1 1 0 3 3 0 0 2
## 200 5 2 2 1 3 4 0 1 0 0 0 1 2 0 0 1 2 2 1 3 0 1 0
## 201 1 0 1 2 4 3 1 0 1 2 0 0 2 0 1 1 2 1 1 1 0 1 3
## 202 3 1 2 1 1 1 4 1 0 0 0 0 3 0 0 0 1 2 2 1 4 0 1
## 203 5 1 3 2 1 1 0 2 0 1 0 0 1 0 1 0 2 3 2 0 1 2 2
## 204 1 1 2 0 1 1 3 0 2 1 0 0 1 0 0 1 0 2 4 2 1 0 3
## 205 0 3 2 1 1 2 1 1 2 0 1 0 0 2 1 0 2 3 1 0 1 2 3
## 206 6 2 2 1 1 1 4 3 0 5 1 1 0 0 3 0 2 0 2 2 0 4 1
## 207 0 2 0 1 2 0 0 0 1 0 1 1 0 2 2 0 1 0 2 4 0 1 2
## 208 3 3 1 2 1 0 1 0 2 1 0 2 0 2 0 0 0 2 0 5 0 0 1
## 209 2 2 1 0 1 1 1 1 1 0 1 3 0 0 2 0 0 0 6 1 1 0 1
## 210 2 0 1 2 4 2 0 2 1 0 0 2 1 1 1 0 0 2 2 2 1 1 0
## 211 0 1 0 1 3 1 1 0 2 1 0 0 0 1 3 0 0 0 5 0 0 1 0
## 212 3 0 0 1 0 0 2 2 0 1 1 1 1 1 1 1 0 2 2 2 3 0 0
## 213 0 2 0 1 2 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0
## 214 3 1 1 1 3 0 0 1 0 2 0 5 4 2 0 0 1 2 1 1 3 1 0
## 215 1 0 1 1 1 2 2 1 2 0 0 1 3 5 1 0 2 1 0 0 2 3 1
## 216 0 0 0 2 1 1 1 0 1 1 0 0 1 1 1 0 0 0 3 1 2 1 1
## 217 1 1 1 0 1 2 0 2 0 0 0 0 2 1 3 2 0 1 1 2 1 3 2
## 218 2 3 2 1 1 1 0 0 1 3 0 0 1 0 0 1 0 2 0 1 0 0 1
## 219 0 2 1 6 1 0 0 0 1 0 0 1 1 1 0 0 0 1 2 1 2 1 2
## 220 0 4 4 0 1 1 1 0 0 1 0 1 1 0 2 0 1 1 1 1 1 1 1
## 221 2 0 1 0 0 2 0 2 1 0 0 0 0 0 0 1 1 1 4 2 1 1 1
## 222 4 5 1 3 0 2 2 1 1 0 1 0 0 1 1 1 0 2 3 1 0 0 1
## 223 0 1 0 2 3 0 3 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0
## 224 2 0 0 0 3 1 1 1 1 1 0 2 1 1 1 2 2 1 0 1 1 0 0
## 225 3 0 3 0 2 1 1 2 1 2 0 0 0 2 0 1 0 2 2 2 1 1 1
## 226 2 2 0 2 1 1 3 0 1 1 0 0 0 0 2 0 1 1 1 2 0 0 0
## 227 4 5 0 3 2 3 0 1 3 1 0 0 1 2 1 0 0 3 1 2 1 0 1
## 228 1 0 1 1 0 2 0 1 0 1 0 0 2 0 0 0 0 0 3 0 1 0 0
## 229 2 4 0 2 1 2 0 1 0 0 0 1 0 1 3 0 0 3 2 0 1 0 1
## 230 2 2 1 0 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 3 2 0 0
## 231 0 1 2 1 2 1 1 2 3 0 0 0 1 0 0 0 3 1 0 1 1 1 2
## 232 1 0 0 2 0 4 1 1 3 0 0 1 0 2 0 0 1 1 1 0 0 0 0
## 233 1 1 1 0 2 0 1 0 2 1 0 0 0 0 0 1 1 0 1 0 1 0 2
## 234 2 0 2 1 0 2 1 1 1 0 0 1 1 0 0 1 0 1 2 1 0 1 2
## 235 0 3 2 1 4 3 1 1 2 0 3 1 0 0 0 0 1 4 2 2 0 0 0
## 236 0 1 2 1 1 2 0 0 0 0 0 0 2 1 0 1 1 0 0 1 0 1 3
## 237 2 3 1 1 4 1 1 0 0 0 1 0 0 0 0 0 1 1 1 1 1 3 1
## 238 2 2 0 2 2 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0
## 239 0 3 0 3 4 3 1 1 0 0 0 0 0 1 0 1 1 1 3 1 2 0 0
## 240 2 1 0 3 2 1 0 1 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0
## 241 0 3 1 1 2 4 2 2 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0
## 242 0 0 2 0 0 2 1 3 1 0 1 0 0 0 0 0 0 1 0 1 1 1 2
## 243 2 2 0 1 1 2 0 2 1 0 0 1 0 0 0 0 2 1 1 1 1 1 0
## 244 0 1 0 0 0 1 0 4 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0
## 245 2 1 0 0 1 1 1 0 4 0 1 0 0 0 0 0 0 2 2 0 0 0 0
## 246 0 3 2 1 0 0 0 2 1 0 0 1 0 0 1 1 0 2 1 0 2 1 0
## 247 3 3 3 1 3 4 1 3 1 1 0 0 1 1 0 0 1 1 3 2 0 0 1
## 248 1 3 0 4 4 1 0 0 1 1 0 4 1 1 0 2 0 3 1 1 1 0 0
## 249 1 1 2 1 1 4 0 0 2 1 0 0 1 0 0 1 0 1 1 0 0 0 1
## 250 3 1 0 1 0 2 0 1 2 1 1 0 0 0 1 0 1 2 1 1 0 1 0
## 251 0 2 0 0 1 1 0 0 2 0 0 1 0 0 0 1 1 0 2 0 0 3 2
## 252 0 1 1 1 1 2 3 1 0 2 0 0 0 0 0 2 2 2 4 0 2 0 0
## 253 1 1 2 0 2 0 1 0 1 0 0 0 0 0 1 1 0 5 4 0 1 0 1
## 254 1 1 1 2 2 0 0 2 2 1 1 2 0 1 0 1 0 3 0 0 1 0 0
## 255 0 3 0 2 0 1 0 1 1 1 0 2 0 0 1 0 2 0 1 1 0 1 1
## 256 0 3 0 1 1 3 0 1 2 0 0 1 0 0 0 1 2 1 2 1 3 2 0
## 257 2 2 2 1 1 1 0 0 0 0 1 0 1 0 3 2 0 1 1 1 0 0 0
## 258 0 5 0 1 1 0 2 0 1 0 0 0 1 0 1 2 1 2 1 1 1 0 0
## 259 3 7 0 1 1 2 0 2 2 0 1 1 0 0 0 0 1 4 4 3 0 2 0
## 260 3 0 0 0 0 1 2 1 0 0 1 1 0 2 0 0 0 3 1 0 1 1 0
## 261 0 1 0 2 2 2 0 0 0 0 1 0 0 0 1 0 0 2 0 0 3 0 1
## 262 1 1 0 0 2 2 1 0 1 0 0 0 1 0 0 1 0 2 1 0 0 0 0
## 263 0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0 2 1 3 1 1 0 0
## 264 3 1 0 0 2 1 0 1 0 0 0 0 1 0 0 1 1 1 2 1 2 0 0
## 265 0 2 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 2 0 0 1 0
## 266 3 1 2 0 1 2 2 0 1 1 0 0 0 1 1 0 1 2 1 2 0 1 1
## 267 0 2 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0
## 268 2 1 0 1 2 0 0 0 0 1 0 1 2 0 0 0 1 1 1 0 1 1 0
## 269 1 0 1 1 2 0 1 1 1 1 0 2 0 0 0 1 2 4 0 3 0 1 1
## 270 1 0 0 2 1 2 2 1 1 0 0 1 0 1 0 1 1 2 0 0 0 0 0
## 271 1 2 1 2 2 1 1 1 0 0 0 2 1 1 0 0 0 1 2 1 2 1 0
## 272 3 0 1 1 0 0 0 0 0 2 0 0 1 1 1 0 3 2 0 0 0 1 0
## 273 3 1 1 0 0 0 1 2 0 1 0 0 2 2 1 0 0 0 1 0 1 1 0
## 274 5 1 1 0 0 0 1 2 0 1 0 0 0 0 1 0 1 1 1 2 1 0 0
## 275 1 1 0 2 1 0 0 2 0 1 1 1 0 2 1 1 0 2 0 0 2 0 0
## 276 1 3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 277 4 2 1 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0 3 1
## 278 4 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 3 0 0 1 1 0
## 279 2 2 0 1 1 2 0 0 1 0 0 0 0 0 0 0 0 0 0 3 1 0 0
## 280 0 0 0 0 2 2 4 1 2 1 2 0 1 1 1 0 2 0 1 0 5 0 0
## 281 0 3 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 2 0 2 0 0 0
## 282 2 1 1 2 3 0 0 0 2 1 0 0 1 1 1 0 1 0 0 0 2 0 2
## 283 2 0 0 0 2 1 1 0 0 1 0 0 1 1 0 0 3 4 0 0 0 1 1
## 284 1 1 3 1 3 1 0 1 1 1 0 0 1 0 0 0 1 3 0 1 2 1 0
## 285 2 1 1 0 1 0 0 0 1 2 0 1 1 0 0 1 0 2 1 0 0 0 0
## 286 0 1 0 1 3 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 1 1 0
## 287 1 3 0 1 2 1 1 1 1 0 0 0 0 0 0 0 2 0 0 0 1 0 0
## 288 1 2 1 1 0 0 0 0 1 0 0 2 0 0 0 0 1 2 0 0 1 1 1
## 289 1 1 2 2 2 1 0 1 1 0 0 0 0 0 0 0 2 0 0 1 0 1 0
## 290 2 3 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 1 1 2 0 0
## 291 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1
## 292 1 2 0 0 1 1 0 0 0 1 1 0 0 0 0 0 3 0 0 0 0 0 0
## 293 3 1 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 4 0 0 1 0 0
## 294 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0
## 295 1 0 1 1 1 1 1 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0
## 296 1 1 1 3 1 0 1 0 0 0 0 1 0 0 0 0 3 1 0 0 1 0 0
## 297 2 2 0 0 0 0 1 0 0 1 0 2 0 0 0 0 2 1 1 0 1 0 0
## 298 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 3 1 1 2 1 0 0
## 299 1 0 1 0 2 3 1 1 0 0 0 0 0 0 0 0 0 2 0 0 1 0 1
## 300 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 1
## 301 2 2 0 1 0 0 1 0 0 1 0 0 0 0 0 1 2 0 0 1 0 1 0
## 302 1 3 1 0 1 0 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 0 1
## 303 1 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 3 0 0
## 304 1 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## 305 2 1 3 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 306 2 2 2 2 2 1 2 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0
## 307 0 0 0 2 0 0 1 1 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0
## 308 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 2 1 2 0 0 0
## 309 0 0 0 1 0 1 0 0 1 1 0 0 2 0 0 0 0 1 0 0 1 0 0
## 310 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 0 1 0 1
## 311 0 3 0 0 1 1 1 1 0 0 1 0 0 0 0 3 0 0 0 0 0 0 0
## 312 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 2 2 1 0 0 1 1 0
## 313 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0
## 314 3 2 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 3 0 0
## 315 3 0 0 2 0 3 0 1 0 1 0 0 0 1 0 0 2 0 0 1 0 0 0
## 316 2 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 1
## 317 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0
## 318 3 0 0 1 2 0 0 1 0 1 0 0 0 0 0 1 1 3 0 1 0 0 0
## 319 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0
## 320 3 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 1 1 1 0 1 0
## 321 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 322 4 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## 323 4 1 0 1 0 2 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0
## 324 1 0 1 0 3 0 2 2 0 0 0 0 1 1 0 0 1 2 1 2 1 0 0
## 325 2 0 0 0 0 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
## 326 2 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1
## 327 0 0 1 1 0 2 0 1 0 0 0 2 1 0 0 0 0 0 0 1 0 0 0
## 328 0 0 0 0 1 1 1 0 2 1 0 0 0 0 0 0 0 0 0 2 1 0 0
## 329 2 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0
## 330 0 2 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0
## 331 1 1 2 0 1 0 0 0 0 1 0 0 0 2 1 0 1 1 0 1 2 0 0
## 332 2 0 2 1 1 2 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0
## 333 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 334 1 1 0 0 2 0 0 0 0 2 0 0 1 0 0 0 0 0 0 1 0 0 0
## 335 2 2 0 0 2 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0
## 336 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0
## 337 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 1 1 0 0 0
## 338 1 0 0 0 1 1 0 2 0 0 0 0 0 0 0 1 2 0 0 1 0 0 1
## 339 4 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0
## 340 2 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
## 341 3 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 2 0 1 0 0 0
## 342 1 0 0 0 0 3 0 0 0 0 1 1 0 1 0 0 0 0 2 1 0 0 0
## 343 1 1 2 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 1 2 0 0 0
## 344 3 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0
## 345 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 346 0 1 0 1 1 3 0 0 0 0 1 0 1 0 0 0 2 0 1 0 0 0 1
## 347 4 2 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0
## 348 4 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 0 0
## 349 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 1
## 350 1 1 2 1 1 0 2 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 0
## 351 0 1 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
## 352 2 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0
## 353 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0
## 354 2 0 2 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0
## 355 1 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 356 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0
## 357 3 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0
## 358 3 2 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## 359 2 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0
## 360 2 1 0 2 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0
## 361 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 362 3 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 363 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 364 2 0 0 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 365 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 366 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## 367 2 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## 368 0 1 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 369 3 1 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 1 0 1 0 0
## 370 0 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 371 0 2 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 372 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 373 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1
## 374 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0
## 375 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 376 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 377 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 378 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 379 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 380 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
## 381 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0
## 382 1 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0
## 383 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0
## 384 3 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0
## 385 0 2 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0
## 386 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1
## 387 2 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0
## 388 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 3 0 0 0 0 0
## 389 1 2 0 1 2 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0
## 390 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0
## 391 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 392 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 393 0 1 0 0 0 1 0 2 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0
## 394 4 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0
## 395 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 0 0 1 0
## 396 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0
## 397 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 398 2 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## 399 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0
## 400 0 0 0 2 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0
## 401 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 402 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 2 1 0 0 0
## 403 2 1 1 1 2 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0
## 404 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 405 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
## 406 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
## 407 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## 408 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0
## 409 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 410 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 411 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 412 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 413 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0
## 414 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 1 0
## 415 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 416 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 417 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 418 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0
## 419 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
## 420 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 2 0 1 0
## 421 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 422 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## 424 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 425 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 426 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 427 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 428 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 429 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0
## 430 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 1 0
## 431 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0
## 432 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 433 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 434 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 435 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 2 0
## 436 0 3 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0
## 437 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 438 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## 439 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 440 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 441 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 442 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 443 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 444 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 445 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 446 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 447 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 448 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 449 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0
## 450 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 451 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 452 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 453 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## 454 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 455 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 456 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 457 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 458 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 459 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 460 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 461 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 462 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 463 2 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 464 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 465 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
## 466 0 0 0 1 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 467 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 468 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 469 0 0 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 470 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 472 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 473 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 474 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 475 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 476 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 477 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 478 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 479 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 480 0 2 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 481 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 482 1 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 483 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 484 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 485 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 486 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 487 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 488 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 489 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 490 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 491 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 492 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 493 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 494 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 495 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 496 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 498 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 499 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 500 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 501 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 502 0 0 0 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 503 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 504 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 505 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 506 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 507 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 508 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 510 2 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 512 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 513 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 514 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 515 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 516 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 517 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 519 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 521 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 522 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 523 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 524 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 525 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 526 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 527 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 528 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 529 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 532 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 534 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 535 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 539 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 540 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 541 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 542 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 545 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 548 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 551 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 557 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 558 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 559 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 562 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## 563 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 567 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 569 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 573 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 577 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 579 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 580 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 584 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 587 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 595 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 599 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 603 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 607 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 611 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 615 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 618 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 623 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 648 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 649 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 651 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 659 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 671 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 673 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 684 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 700 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 722 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 731 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 744 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 776 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 784 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 802 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 805 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 810 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 824 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 845 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 852 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 886 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 972 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 980 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## DEWP
## pm2.5 18 19 20 21 22 23 24 25 26 27 28
## 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 2 1 0 0 0 0 0 0 0 0 0
## 3 0 2 0 0 0 0 0 0 0 0 0
## 4 3 1 0 0 0 0 0 0 0 0 0
## 5 0 2 0 0 0 0 0 0 0 0 0
## 6 1 1 1 0 2 0 0 0 0 0 0
## 7 4 7 1 0 1 0 0 0 0 0 0
## 8 3 0 2 2 0 0 0 0 0 0 0
## 9 3 0 6 3 1 0 0 0 0 0 0
## 10 3 3 5 2 0 0 0 0 0 0 0
## 11 6 3 4 0 1 1 0 0 0 0 0
## 12 5 6 0 1 1 2 0 0 0 0 0
## 13 8 4 2 1 2 1 0 0 0 0 0
## 14 8 6 3 1 1 1 0 0 0 0 0
## 15 5 5 3 2 0 2 0 0 0 0 0
## 16 10 6 5 2 1 0 0 0 0 0 0
## 17 8 5 7 4 2 0 0 0 0 0 0
## 18 7 8 2 2 2 0 0 0 0 0 0
## 19 8 7 3 5 0 0 1 0 0 0 0
## 20 6 8 5 4 3 1 0 0 0 0 0
## 21 6 11 2 5 1 2 0 0 0 0 0
## 22 4 3 6 3 3 1 1 0 0 0 0
## 23 7 10 1 1 2 4 1 0 0 0 0
## 24 10 7 4 1 6 4 0 0 0 0 0
## 25 7 11 5 4 4 1 0 0 0 0 0
## 26 15 3 3 2 1 1 0 0 0 0 0
## 27 5 9 4 4 2 1 1 0 0 0 0
## 28 6 7 4 6 2 4 0 0 0 0 0
## 29 10 7 6 3 5 1 3 2 0 0 0
## 30 4 6 4 7 2 1 1 1 0 0 0
## 31 11 12 7 1 2 3 3 0 0 0 0
## 32 7 4 6 5 3 1 2 0 0 0 0
## 33 10 7 5 7 4 5 2 1 1 0 0
## 34 6 8 10 12 1 0 1 1 0 0 0
## 35 7 12 6 6 6 2 2 0 0 0 0
## 36 5 7 7 10 3 1 0 1 0 0 0
## 37 8 8 9 6 5 3 1 1 0 0 0
## 38 19 8 5 8 7 6 2 0 0 0 0
## 39 10 4 7 6 4 3 2 0 0 0 0
## 40 14 5 2 5 2 3 3 2 0 0 0
## 41 9 8 6 7 3 2 2 1 0 0 0
## 42 8 15 5 2 5 2 2 0 0 0 0
## 43 3 6 7 4 2 4 1 0 0 0 0
## 44 13 9 2 6 4 3 2 0 0 0 0
## 45 11 14 10 7 8 1 1 0 0 0 0
## 46 11 13 7 8 7 5 4 2 0 0 0
## 47 4 6 7 10 7 9 0 0 0 0 0
## 48 7 10 8 8 8 5 4 0 0 0 0
## 49 10 8 6 5 6 3 3 2 0 0 0
## 50 11 7 6 9 6 9 0 0 0 0 0
## 51 11 9 3 9 4 6 4 1 0 0 0
## 52 11 9 5 7 2 6 4 0 0 0 0
## 53 11 9 4 4 13 7 4 1 0 0 0
## 54 7 8 11 7 4 5 5 1 0 0 0
## 55 11 10 11 6 5 6 5 1 0 0 0
## 56 9 5 4 10 8 8 3 3 0 0 0
## 57 11 7 7 8 2 7 6 1 0 0 0
## 58 7 13 6 2 4 5 4 0 0 0 0
## 59 10 16 6 9 4 7 7 0 0 0 0
## 60 12 8 9 7 5 8 4 2 0 0 0
## 61 11 6 7 13 4 7 6 2 0 0 0
## 62 14 9 11 7 5 3 2 3 0 0 0
## 63 8 12 9 6 7 3 4 2 0 0 0
## 64 8 10 6 6 7 7 2 0 0 0 0
## 65 6 9 6 9 7 3 3 1 1 0 0
## 66 10 9 8 10 4 6 3 0 0 0 0
## 67 7 6 4 4 9 5 7 1 0 0 0
## 68 11 9 6 2 8 0 4 2 0 0 0
## 69 11 12 8 7 11 10 5 0 0 0 0
## 70 16 8 8 12 7 6 5 0 0 0 0
## 71 10 9 10 6 11 2 1 0 0 0 0
## 72 7 11 7 11 2 7 3 0 0 0 0
## 73 8 6 9 7 7 8 4 1 0 0 0
## 74 12 6 17 6 4 3 6 1 0 0 0
## 75 4 9 13 12 7 4 4 1 1 0 0
## 76 12 4 8 7 5 3 4 0 1 0 0
## 77 9 12 6 11 11 5 8 1 0 0 0
## 78 9 8 8 6 6 3 5 1 0 0 0
## 79 18 6 7 5 8 5 5 3 0 1 0
## 80 11 13 7 13 15 5 9 0 1 0 0
## 81 9 9 7 2 4 5 3 1 0 0 0
## 82 13 7 4 3 3 6 6 1 0 0 0
## 83 10 7 9 7 10 2 3 1 0 0 0
## 84 6 11 9 6 4 5 4 0 1 0 0
## 85 13 9 11 10 5 3 5 1 0 0 0
## 86 8 8 10 5 6 3 3 0 1 0 0
## 87 8 7 8 5 9 7 4 1 0 0 0
## 88 8 10 8 8 9 9 6 2 0 0 0
## 89 6 7 11 5 8 3 2 4 0 0 0
## 90 12 9 11 11 8 4 4 1 1 0 0
## 91 9 6 8 7 11 4 3 1 0 0 0
## 92 9 7 10 10 9 2 6 1 0 0 0
## 93 12 10 9 8 10 5 3 1 1 0 0
## 94 9 4 5 9 10 5 4 1 1 0 0
## 95 15 12 7 6 7 6 5 0 0 0 0
## 96 7 8 13 11 5 8 8 2 0 0 0
## 97 5 9 12 6 4 2 2 1 1 0 0
## 98 5 8 7 7 5 1 3 1 0 0 0
## 99 13 10 4 14 7 2 1 3 0 0 0
## 100 10 7 6 9 7 5 4 1 0 0 0
## 101 6 5 5 10 7 8 3 1 0 0 0
## 102 7 5 9 8 5 1 4 1 1 0 0
## 103 6 6 14 5 8 5 5 0 1 0 0
## 104 7 10 6 9 6 3 5 1 0 0 0
## 105 8 9 8 12 6 3 4 1 0 0 0
## 106 6 9 7 3 6 5 2 1 1 0 0
## 107 6 6 8 6 7 2 4 0 1 0 0
## 108 11 7 9 6 5 0 4 1 2 0 0
## 109 9 4 7 7 9 6 7 1 1 0 0
## 110 6 6 10 12 6 0 6 0 2 0 0
## 111 3 10 9 12 5 4 6 1 0 1 0
## 112 7 5 12 5 7 9 1 0 0 0 0
## 113 8 4 8 5 9 3 5 1 0 0 0
## 114 6 9 5 7 5 3 0 1 0 0 0
## 115 4 2 5 9 3 6 5 0 0 0 0
## 116 5 10 5 6 9 6 4 2 0 0 0
## 117 12 10 5 9 8 2 2 0 1 0 0
## 118 6 5 5 11 10 6 3 1 0 0 0
## 119 3 3 6 2 8 2 2 1 0 0 0
## 120 7 1 6 6 8 8 1 2 1 0 0
## 121 9 9 5 9 6 4 2 0 0 0 0
## 122 4 3 5 8 6 6 5 1 1 0 0
## 123 8 7 4 6 9 0 1 1 0 0 0
## 124 9 8 7 5 8 4 1 0 1 0 0
## 125 4 3 5 5 5 4 3 2 0 0 0
## 126 4 6 4 4 6 4 1 1 0 0 0
## 127 4 4 8 8 4 2 4 1 1 0 0
## 128 6 3 8 8 8 8 3 3 1 0 0
## 129 5 6 2 4 2 2 2 0 0 0 0
## 130 5 2 3 8 6 7 5 4 0 0 0
## 131 1 5 5 4 5 2 0 1 1 0 0
## 132 10 3 7 9 5 8 5 0 0 0 0
## 133 3 6 5 2 5 5 2 1 0 0 0
## 134 8 6 6 6 6 5 4 1 0 0 0
## 135 9 6 7 2 4 6 1 2 0 0 0
## 136 2 6 2 1 4 5 0 1 1 0 0
## 137 6 5 3 4 6 6 4 1 0 0 0
## 138 3 4 9 3 2 3 5 2 0 0 0
## 139 6 5 13 5 3 6 3 1 0 0 0
## 140 11 7 6 1 5 5 6 1 1 0 0
## 141 5 3 4 7 3 6 3 3 0 0 0
## 142 5 5 5 7 5 3 2 4 0 0 0
## 143 4 4 7 5 5 4 2 2 1 0 0
## 144 4 7 1 4 6 6 1 1 0 0 0
## 145 3 9 5 7 5 3 4 1 1 0 0
## 146 5 7 3 2 4 8 2 1 0 0 0
## 147 3 3 6 2 6 1 1 2 1 0 0
## 148 3 4 4 6 4 2 2 1 0 0 0
## 149 1 4 3 8 4 4 1 2 0 0 0
## 150 4 3 6 1 5 9 3 1 0 0 0
## 151 4 7 3 5 2 6 1 2 1 0 0
## 152 6 1 6 3 6 7 3 0 0 0 0
## 153 7 3 5 3 7 6 3 1 0 0 0
## 154 8 3 4 3 7 4 1 2 3 0 0
## 155 5 2 4 4 4 2 2 2 1 0 0
## 156 3 4 3 1 4 2 1 0 1 0 0
## 157 2 2 4 1 6 2 2 1 1 0 0
## 158 5 5 3 3 2 1 2 2 0 0 0
## 159 3 5 1 5 2 4 1 1 0 0 0
## 160 3 7 5 5 4 2 5 0 2 0 0
## 161 6 7 5 2 6 5 3 1 0 0 0
## 162 1 4 2 3 3 1 1 0 0 0 0
## 163 4 5 3 4 8 2 0 0 0 0 0
## 164 6 4 6 3 5 3 4 1 1 0 0
## 165 5 5 5 5 2 3 4 0 1 0 0
## 166 3 6 2 5 7 5 0 0 0 0 0
## 167 2 0 4 2 7 4 1 2 0 0 0
## 168 2 2 3 4 4 2 1 0 1 0 0
## 169 0 7 1 5 3 4 2 2 1 0 0
## 170 4 2 2 2 5 2 1 1 1 0 0
## 171 1 3 1 3 8 1 1 1 2 0 0
## 172 3 2 2 4 4 6 2 1 0 0 0
## 173 0 4 3 5 3 2 3 0 0 0 0
## 174 3 4 7 5 3 1 2 3 1 0 0
## 175 1 3 3 3 3 2 4 0 0 0 0
## 176 2 1 5 3 4 2 2 2 0 0 0
## 177 1 3 2 1 1 4 2 0 1 0 0
## 178 4 1 4 7 3 3 3 2 0 1 0
## 179 1 1 1 4 1 5 6 0 1 0 0
## 180 4 1 2 1 1 5 0 3 0 0 0
## 181 2 0 5 7 2 2 2 0 0 0 0
## 182 0 3 2 3 3 5 0 0 0 0 0
## 183 3 1 2 3 2 4 2 1 0 0 0
## 184 0 5 4 3 2 5 3 1 0 1 0
## 185 1 1 4 3 7 2 2 1 1 0 0
## 186 4 0 2 6 2 6 0 0 0 1 0
## 187 0 4 2 2 1 5 1 0 0 1 0
## 188 2 1 2 2 4 5 2 1 1 0 0
## 189 1 5 0 6 2 3 3 2 0 0 0
## 190 0 5 2 2 3 2 2 2 0 0 0
## 191 3 5 4 2 1 3 0 0 0 0 0
## 192 1 1 3 1 1 5 1 2 2 0 0
## 193 0 1 2 2 5 3 1 0 0 0 0
## 194 1 5 4 4 1 4 2 5 0 0 0
## 195 1 4 1 2 2 4 2 2 1 0 0
## 196 0 7 2 4 4 3 3 1 1 0 0
## 197 0 2 1 1 1 1 4 0 1 1 0
## 198 2 2 4 4 1 3 1 1 0 0 0
## 199 1 3 6 3 4 2 1 2 0 0 0
## 200 1 4 3 1 0 1 2 1 0 0 0
## 201 0 2 3 0 5 2 4 4 4 0 0
## 202 0 0 4 4 3 3 6 1 1 0 0
## 203 2 1 1 3 4 2 1 1 0 0 0
## 204 3 1 2 0 3 1 6 1 0 1 0
## 205 3 2 3 2 3 3 3 1 1 0 0
## 206 1 1 3 0 4 2 4 3 0 0 1
## 207 2 1 0 2 1 2 2 0 0 0 0
## 208 0 1 0 1 4 1 3 0 1 0 0
## 209 2 0 2 3 4 0 1 1 0 1 0
## 210 1 0 2 0 1 1 3 1 1 1 0
## 211 1 1 2 3 3 2 2 1 0 0 0
## 212 1 3 0 0 3 3 1 1 1 0 0
## 213 0 1 0 0 1 4 1 1 0 0 0
## 214 0 1 0 2 0 0 0 1 0 0 0
## 215 1 2 0 1 0 2 2 1 2 0 0
## 216 0 1 1 1 3 1 0 0 0 0 0
## 217 1 0 2 0 2 0 0 1 0 0 0
## 218 0 0 1 1 1 4 0 1 0 0 0
## 219 0 0 0 3 2 1 0 1 1 0 0
## 220 0 2 0 0 2 3 1 2 0 0 0
## 221 0 1 4 0 3 2 0 1 1 0 0
## 222 1 0 0 1 1 0 1 1 0 0 0
## 223 0 0 0 0 3 1 0 0 0 0 1
## 224 2 1 1 0 6 2 2 0 0 0 0
## 225 1 1 2 0 2 0 2 0 0 0 0
## 226 3 1 0 1 3 2 2 2 0 0 0
## 227 1 0 1 1 0 5 1 2 1 0 0
## 228 2 1 2 1 0 1 1 0 0 0 0
## 229 0 2 0 1 5 1 0 1 0 0 0
## 230 1 2 1 1 1 1 1 3 1 0 0
## 231 2 0 1 1 6 1 1 0 0 0 0
## 232 1 0 0 0 3 2 3 0 0 0 0
## 233 0 1 0 0 0 4 3 1 1 0 0
## 234 0 0 0 0 0 0 0 1 0 1 0
## 235 0 0 1 2 1 0 0 0 0 0 1
## 236 1 1 0 0 1 1 0 0 0 0 0
## 237 1 2 1 0 1 2 0 0 0 1 0
## 238 2 0 2 1 2 0 0 0 0 1 0
## 239 1 1 0 0 3 3 1 1 0 0 0
## 240 0 0 2 1 1 0 2 2 1 0 0
## 241 1 0 0 0 1 0 1 0 0 0 0
## 242 0 1 2 1 1 2 1 0 1 0 0
## 243 0 0 0 1 1 0 0 1 0 0 0
## 244 1 2 2 0 0 2 1 0 0 0 2
## 245 0 0 0 1 2 3 2 1 0 0 0
## 246 0 0 1 0 0 2 1 1 1 1 0
## 247 0 1 1 0 1 1 0 0 0 0 0
## 248 0 1 1 0 1 0 0 0 0 0 1
## 249 1 0 3 1 2 1 0 1 0 0 0
## 250 0 0 0 0 3 3 0 0 0 0 0
## 251 0 0 0 1 0 0 0 1 0 0 0
## 252 1 0 0 0 1 1 0 0 0 0 0
## 253 0 0 0 0 0 0 0 0 0 0 0
## 254 2 0 2 1 0 1 0 0 0 0 0
## 255 0 0 0 0 1 1 1 0 0 0 0
## 256 0 0 0 2 2 0 0 2 0 0 0
## 257 0 1 2 0 1 0 0 0 0 1 0
## 258 1 0 0 0 1 1 0 0 0 0 0
## 259 2 0 1 1 1 2 0 1 0 2 0
## 260 0 0 0 0 1 0 0 0 0 0 0
## 261 0 0 0 1 0 0 0 1 0 0 0
## 262 0 1 0 0 1 0 0 1 0 0 0
## 263 0 0 0 0 1 0 2 0 0 0 0
## 264 0 0 1 0 1 0 0 0 1 0 0
## 265 0 0 1 0 1 0 0 0 0 0 0
## 266 1 0 0 1 3 1 0 0 0 0 0
## 267 0 1 0 0 0 1 0 0 0 1 0
## 268 0 0 0 0 1 0 0 0 0 0 0
## 269 0 0 1 0 0 0 1 1 0 2 0
## 270 0 0 0 0 2 0 1 0 0 0 0
## 271 0 0 0 0 0 2 0 2 0 0 0
## 272 0 0 0 0 0 0 0 1 0 0 0
## 273 0 0 0 0 0 0 0 2 0 0 0
## 274 0 0 0 0 3 0 0 0 0 0 0
## 275 1 0 1 0 2 2 0 0 0 0 0
## 276 0 0 0 0 0 0 0 0 0 0 1
## 277 0 0 0 3 2 1 0 0 0 1 1
## 278 0 0 0 0 1 0 0 1 0 1 0
## 279 0 0 0 0 0 0 0 0 0 0 0
## 280 0 0 0 0 1 2 0 0 1 0 0
## 281 0 0 0 1 4 0 0 0 0 0 1
## 282 0 0 0 0 0 0 0 0 0 0 0
## 283 0 0 0 0 0 2 0 1 0 0 0
## 284 0 0 3 0 0 0 0 1 0 0 0
## 285 0 0 1 0 1 0 0 0 0 1 0
## 286 0 0 0 0 2 0 1 0 1 0 0
## 287 0 0 1 1 0 0 0 0 0 0 0
## 288 0 0 0 0 1 0 0 1 0 0 0
## 289 1 0 0 0 0 0 0 0 0 0 0
## 290 0 0 0 0 0 1 0 1 0 0 0
## 291 0 0 0 0 0 2 0 1 0 0 0
## 292 0 0 0 0 1 0 0 0 0 0 0
## 293 0 1 0 1 1 0 0 2 0 1 0
## 294 0 0 0 0 0 0 0 0 0 0 0
## 295 0 0 0 0 0 2 0 0 0 0 0
## 296 0 0 1 0 1 0 0 1 0 0 0
## 297 0 0 0 1 1 1 0 0 0 0 0
## 298 2 0 0 0 0 1 0 0 0 0 0
## 299 0 0 0 0 0 1 0 1 0 1 0
## 300 0 0 0 0 1 0 0 2 0 1 0
## 301 0 0 0 0 1 0 0 0 0 0 0
## 302 0 0 0 0 1 0 0 0 2 0 0
## 303 0 0 0 0 0 1 0 0 0 0 0
## 304 0 0 0 0 0 1 0 0 0 0 0
## 305 0 1 0 0 0 0 0 0 0 0 0
## 306 1 0 0 0 0 0 1 0 1 0 0
## 307 0 0 0 0 0 0 0 2 0 0 0
## 308 0 0 0 0 0 0 0 0 1 0 0
## 309 0 0 0 0 0 1 0 1 0 0 0
## 310 0 0 0 0 0 1 0 0 0 0 1
## 311 0 0 0 0 0 1 0 0 1 0 0
## 312 0 0 0 0 0 1 0 0 0 0 0
## 313 0 0 1 0 0 0 0 1 0 0 0
## 314 0 0 0 0 0 0 0 0 0 0 0
## 315 0 0 0 0 0 0 0 0 0 0 0
## 316 0 0 0 0 0 0 0 1 0 0 0
## 317 0 0 0 0 0 0 0 0 0 0 0
## 318 0 0 0 0 1 0 0 1 0 0 0
## 319 0 0 0 0 0 0 0 0 0 0 0
## 320 0 0 0 0 0 0 0 0 0 0 0
## 321 0 1 0 0 0 0 0 1 1 0 0
## 322 0 0 0 0 0 0 0 0 0 0 0
## 323 0 0 0 0 0 0 0 0 0 0 0
## 324 0 0 0 0 0 0 0 0 0 0 0
## 325 0 0 0 0 0 0 0 0 0 1 0
## 326 0 0 0 0 1 0 0 0 0 0 0
## 327 0 0 0 0 0 0 0 0 0 0 0
## 328 0 0 0 0 0 0 0 0 0 0 0
## 329 0 0 0 0 0 0 0 0 0 0 0
## 330 0 0 0 0 0 0 0 1 1 0 0
## 331 0 0 0 0 0 0 0 0 0 0 0
## 332 0 0 0 0 0 0 0 0 0 0 0
## 333 0 0 1 0 0 0 0 0 0 0 0
## 334 0 1 0 0 0 0 0 0 2 0 0
## 335 0 0 0 0 0 0 0 1 0 0 0
## 336 0 0 0 0 0 0 1 0 1 0 0
## 337 0 0 0 1 0 1 0 0 0 0 1
## 338 0 1 0 0 0 0 0 0 0 0 0
## 339 0 0 0 0 1 0 0 0 0 0 0
## 340 0 0 0 0 0 0 0 0 0 0 0
## 341 0 0 0 0 0 0 0 1 0 0 0
## 342 0 0 0 0 1 0 0 0 0 0 0
## 343 0 0 0 0 0 0 0 0 0 0 0
## 344 0 0 0 0 0 0 0 0 0 0 0
## 345 0 0 0 0 0 0 0 0 0 0 0
## 346 0 0 0 0 0 0 0 0 0 0 0
## 347 0 0 0 0 0 0 0 0 0 0 0
## 348 0 0 0 0 0 0 0 0 0 0 0
## 349 1 0 0 0 0 0 0 0 0 0 0
## 350 0 0 0 0 0 0 0 0 0 0 0
## 351 0 0 0 0 0 0 0 0 0 0 0
## 352 0 0 0 0 1 0 0 0 0 0 0
## 353 0 0 0 0 0 0 0 0 0 0 0
## 354 0 0 0 0 0 0 0 0 2 0 0
## 355 0 0 0 0 0 0 0 0 1 0 0
## 356 0 0 0 0 0 0 0 0 0 1 0
## 357 0 0 0 0 0 0 0 0 0 0 0
## 358 0 1 0 0 0 0 0 0 0 0 0
## 359 0 0 0 0 0 0 0 0 0 0 0
## 360 0 1 0 0 0 1 0 0 0 0 0
## 361 0 0 0 0 0 0 0 0 0 1 0
## 362 0 0 0 0 0 0 0 0 0 0 0
## 363 0 0 0 0 0 0 0 0 0 0 0
## 364 0 0 0 0 0 0 0 0 0 0 0
## 365 0 0 0 0 0 0 0 0 0 0 0
## 366 0 0 0 0 0 0 0 0 0 0 0
## 367 0 0 0 0 0 0 0 0 0 0 0
## 368 0 0 0 0 0 0 0 0 0 0 0
## 369 0 0 0 0 1 0 0 0 0 0 0
## 370 0 0 0 0 0 0 0 0 0 0 0
## 371 0 0 0 0 0 0 0 0 0 0 0
## 372 0 0 0 0 0 0 0 0 0 0 0
## 373 0 0 0 0 0 0 0 0 0 0 0
## 374 1 0 0 0 0 0 0 0 0 0 0
## 375 0 0 0 0 0 0 0 0 1 1 0
## 376 0 0 0 0 0 0 0 0 0 1 0
## 377 0 0 0 0 0 0 0 0 0 0 0
## 378 0 0 0 0 0 0 0 0 0 0 0
## 379 0 0 0 0 0 0 0 0 0 0 0
## 380 0 0 0 0 0 0 0 0 0 0 0
## 381 0 0 0 0 0 0 0 0 0 0 0
## 382 0 0 0 0 0 0 0 0 0 0 0
## 383 0 0 0 0 0 0 0 0 1 0 0
## 384 0 0 0 0 0 0 0 0 0 0 0
## 385 0 0 0 0 0 0 0 0 0 0 0
## 386 0 0 0 0 0 0 0 0 0 0 0
## 387 0 0 0 0 0 0 0 0 0 0 0
## 388 0 0 0 0 0 0 0 0 0 0 0
## 389 0 0 0 0 0 0 0 0 0 0 0
## 390 0 0 0 0 0 0 0 0 1 0 0
## 391 0 0 0 0 0 0 0 0 0 0 0
## 392 0 0 0 0 0 0 0 0 0 0 0
## 393 0 0 0 0 0 0 0 0 0 0 0
## 394 0 0 0 0 0 0 0 0 0 0 0
## 395 0 0 0 0 0 0 0 0 0 0 0
## 396 0 0 0 0 0 0 0 0 0 0 0
## 397 0 0 0 0 0 0 0 0 0 0 0
## 398 0 0 0 0 0 0 0 0 0 0 0
## 399 0 0 0 0 1 0 0 0 0 0 0
## 400 0 0 0 0 0 0 0 0 0 0 0
## 401 0 0 0 0 0 0 0 0 0 0 0
## 402 0 0 0 1 0 0 0 0 0 0 0
## 403 0 0 0 0 0 0 0 0 0 0 0
## 404 0 0 0 0 0 0 0 0 0 0 0
## 405 0 0 0 0 0 0 0 0 0 0 0
## 406 0 0 0 0 0 1 0 0 0 0 0
## 407 0 0 0 0 0 0 0 0 0 0 0
## 408 0 0 0 0 0 0 0 0 0 0 0
## 409 0 0 0 0 0 0 0 0 0 0 0
## 410 0 0 0 0 0 0 0 0 0 0 0
## 411 1 0 0 0 0 0 0 0 0 0 0
## 412 0 0 0 0 0 0 0 0 0 0 0
## 413 0 0 0 0 0 0 0 0 0 0 0
## 414 1 0 0 0 0 0 0 0 0 0 0
## 415 0 0 0 0 0 0 0 0 0 0 0
## 416 0 0 0 0 0 0 0 0 0 0 0
## 417 0 0 0 0 0 0 0 0 0 0 0
## 418 0 0 0 0 0 0 0 0 0 0 0
## 419 0 0 0 0 0 0 0 0 0 0 0
## 420 0 0 0 0 0 0 0 0 0 0 0
## 421 0 0 0 0 0 0 0 0 0 0 0
## 422 0 0 0 0 0 0 0 0 0 0 0
## 423 0 0 0 0 0 0 0 0 0 0 0
## 424 0 0 0 0 0 0 0 0 0 0 0
## 425 0 0 0 0 0 0 0 0 0 0 0
## 426 0 0 0 0 0 0 0 0 0 0 0
## 427 0 0 0 0 0 0 0 0 0 0 0
## 428 0 0 0 0 1 0 0 0 0 0 0
## 429 0 0 0 0 0 0 0 0 0 0 0
## 430 0 0 0 0 0 0 0 0 0 0 0
## 431 0 0 0 0 0 0 0 0 0 0 0
## 432 0 0 0 1 1 0 0 0 0 0 0
## 433 0 0 0 0 0 0 0 0 0 0 0
## 434 0 0 0 0 0 0 0 0 0 0 0
## 435 0 0 0 0 0 0 0 0 0 0 0
## 436 0 0 0 0 0 0 0 0 0 0 0
## 437 0 0 0 0 0 0 0 0 0 0 0
## 438 0 0 0 0 0 0 0 0 0 0 0
## 439 0 0 0 0 0 0 0 0 0 0 0
## 440 0 0 1 0 0 0 0 0 0 0 0
## 441 0 0 0 0 1 0 0 0 0 0 0
## 442 0 0 0 1 0 0 0 0 0 0 0
## 443 0 0 0 0 0 0 0 0 0 0 0
## 444 0 0 0 0 0 0 0 0 0 0 0
## 445 0 0 0 0 1 0 0 0 0 0 0
## 446 0 0 0 0 0 0 0 0 0 0 0
## 447 0 0 0 0 0 0 0 0 0 0 0
## 448 0 0 0 0 0 1 0 0 0 0 0
## 449 0 0 0 0 0 0 0 0 0 0 0
## 450 0 0 0 0 0 0 0 0 0 0 0
## 451 0 0 0 0 0 0 0 0 0 0 0
## 452 0 0 0 0 0 0 0 0 0 0 0
## 453 0 0 0 0 0 0 0 0 0 0 0
## 454 0 0 0 0 0 0 0 0 0 0 0
## 455 0 0 1 0 0 0 0 0 0 0 0
## 456 0 0 0 0 0 0 0 0 0 0 0
## 457 0 0 0 0 0 0 0 0 0 0 0
## 458 0 0 0 0 0 0 0 0 0 0 0
## 459 0 0 0 1 0 0 0 0 0 0 0
## 460 0 0 0 0 0 0 0 0 0 0 0
## 461 0 0 0 0 0 0 0 0 0 0 0
## 462 0 0 0 0 0 0 0 0 0 0 0
## 463 0 0 0 0 0 0 0 0 0 0 0
## 464 0 0 0 0 0 0 0 0 0 0 0
## 465 0 0 0 0 0 0 0 0 0 0 0
## 466 0 0 0 0 0 1 0 0 0 0 0
## 467 0 0 0 0 0 0 0 0 0 0 0
## 468 0 0 0 0 0 0 0 0 0 0 0
## 469 0 0 0 0 0 0 0 0 0 0 0
## 470 0 0 0 0 0 0 0 0 0 0 0
## 472 0 0 0 0 0 0 0 0 0 0 0
## 473 0 0 0 0 0 0 0 0 0 0 0
## 474 0 0 0 0 0 0 0 0 0 0 0
## 475 0 0 0 0 0 0 0 0 0 0 0
## 476 0 0 0 0 0 0 0 0 0 0 0
## 477 0 0 0 0 0 0 0 0 0 0 0
## 478 0 0 0 0 0 0 0 0 0 0 0
## 479 0 0 0 0 0 0 0 0 0 0 0
## 480 0 0 0 0 0 0 0 0 0 0 0
## 481 0 0 0 0 0 0 0 0 0 0 0
## 482 0 0 0 0 0 0 0 0 0 0 0
## 483 0 0 0 0 0 0 0 0 0 0 0
## 484 0 0 0 0 0 0 0 0 0 0 0
## 485 0 0 0 0 0 0 0 0 0 0 0
## 486 0 0 0 0 0 0 0 0 0 0 0
## 487 0 0 0 0 0 0 0 0 0 0 0
## 488 0 0 0 0 0 0 0 0 0 0 0
## 489 0 0 0 0 0 0 0 0 0 0 0
## 490 0 0 0 0 0 0 0 0 0 0 0
## 491 0 0 0 0 0 0 0 0 0 0 0
## 492 0 0 0 0 0 0 0 0 0 0 0
## 493 0 0 0 0 0 0 0 0 0 0 0
## 494 0 0 0 0 0 0 0 0 0 0 0
## 495 0 0 0 0 0 0 0 0 0 0 0
## 496 0 0 0 0 0 0 0 0 0 0 0
## 498 0 0 0 0 0 0 0 0 0 0 0
## 499 0 0 0 0 0 0 0 0 0 0 0
## 500 0 0 0 0 0 0 0 0 0 0 0
## 501 0 0 0 0 0 0 0 0 0 0 0
## 502 0 0 0 0 0 0 0 0 0 0 0
## 503 0 0 0 0 0 0 0 0 0 0 0
## 504 0 0 0 0 0 0 0 0 0 0 0
## 505 0 0 0 0 0 0 0 0 0 0 0
## 506 0 0 0 0 0 0 0 0 0 0 0
## 507 0 0 0 0 0 0 0 0 0 0 0
## 508 0 0 0 0 0 0 0 0 0 0 0
## 510 0 0 0 0 0 0 0 0 0 0 0
## 512 0 0 0 0 0 0 0 0 0 0 0
## 513 0 0 0 0 0 0 0 0 0 0 0
## 514 0 0 0 0 0 0 0 0 0 0 0
## 515 0 0 0 0 0 0 0 0 0 0 0
## 516 0 0 0 0 0 0 0 0 0 0 0
## 517 0 0 0 0 0 0 0 0 0 0 0
## 519 0 0 0 0 0 0 0 0 0 0 0
## 521 0 0 0 0 0 0 0 0 0 0 0
## 522 0 0 0 0 0 0 0 0 0 0 0
## 523 0 0 0 0 0 0 0 0 0 0 0
## 524 0 0 0 0 0 0 0 0 0 0 0
## 525 0 0 0 0 0 0 0 0 0 0 0
## 526 0 0 0 0 0 0 0 0 0 0 0
## 527 0 0 0 0 0 0 0 0 0 0 0
## 528 0 0 0 0 0 0 0 0 0 0 0
## 529 0 0 0 0 0 0 0 0 0 0 0
## 532 0 0 0 0 0 0 0 0 0 0 0
## 534 0 0 0 0 0 0 0 0 0 0 0
## 535 0 0 0 0 0 0 0 0 0 0 0
## 539 0 0 0 0 0 0 0 0 0 0 0
## 540 0 0 0 0 0 0 0 0 0 0 0
## 541 0 0 0 0 0 0 0 0 0 0 0
## 542 0 0 0 0 0 0 0 0 0 0 0
## 545 0 0 0 0 0 0 0 0 0 0 0
## 548 0 0 0 0 0 0 0 0 0 0 0
## 551 0 0 0 0 0 0 0 0 0 0 0
## 557 0 0 0 0 0 0 0 0 0 0 0
## 558 0 0 0 0 0 0 0 0 0 0 0
## 559 0 0 0 0 0 0 0 0 0 0 0
## 562 0 0 0 0 0 0 0 0 0 0 0
## 563 0 0 0 0 0 0 0 0 0 0 0
## 567 0 0 0 0 0 0 0 0 0 0 0
## 569 0 0 0 0 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0 0 0 0 0
## 573 0 0 0 0 0 0 0 0 0 0 0
## 577 0 0 0 0 0 0 0 0 0 0 0
## 579 0 0 0 0 0 0 0 0 0 0 0
## 580 0 0 0 0 0 0 0 0 0 0 0
## 584 0 0 0 0 0 0 0 0 0 0 0
## 587 0 0 0 0 0 0 0 0 0 0 0
## 595 0 0 0 0 0 0 0 0 0 0 0
## 599 0 0 0 0 0 0 0 0 0 0 0
## 603 0 0 0 0 0 0 0 0 0 0 0
## 607 0 0 0 0 0 0 0 0 0 0 0
## 611 0 0 0 0 0 0 0 0 0 0 0
## 615 0 0 0 0 0 0 0 0 0 0 0
## 618 0 0 0 0 0 0 0 0 0 0 0
## 623 0 0 0 0 0 0 0 0 0 0 0
## 648 0 0 0 0 0 0 0 0 0 0 0
## 649 0 0 0 0 0 0 0 0 0 0 0
## 651 0 0 0 0 0 0 0 0 0 0 0
## 659 0 0 0 0 0 0 0 0 0 0 0
## 671 0 0 0 0 0 0 0 0 0 0 0
## 673 0 0 0 0 0 0 0 0 0 0 0
## 684 0 0 0 0 0 0 0 0 0 0 0
## 700 0 0 0 0 0 0 0 0 0 0 0
## 722 0 0 0 0 0 0 0 0 0 0 0
## 731 0 0 0 0 0 0 0 0 0 0 0
## 744 0 0 0 0 0 0 0 0 0 0 0
## 761 0 0 0 0 0 0 0 0 0 0 0
## 776 0 0 0 0 0 0 0 0 0 0 0
## 784 0 0 0 0 0 0 0 0 0 0 0
## 802 0 0 0 0 0 0 0 0 0 0 0
## 805 0 0 0 0 0 0 0 0 0 0 0
## 810 0 0 0 0 0 0 0 0 0 0 0
## 824 0 0 0 0 0 0 0 0 0 0 0
## 845 0 0 0 0 0 0 0 0 0 0 0
## 852 0 0 0 0 0 0 0 0 0 0 0
## 858 0 0 0 0 0 0 0 0 0 0 0
## 886 0 0 0 0 0 0 0 0 0 0 0
## 972 0 0 0 0 0 0 0 0 0 0 0
## 980 0 0 0 0 0 0 0 0 0 0 0
## 994 0 0 0 0 0 0 0 0 0 0 0## [1] -1.043804## [1] 1.273667## numeric(0)## numeric(0)## numeric(0)## numeric(0)## numeric(0)
En este gráfico se puede ver la probabilidad de prediccion de la cantidad de particulas en el aire PM2.5 debido a la caracteristica más correlacionada DEWP, se puede observar nuevamente que el comportamiento no es lineal. Comprobando que este fenómeno no será correctamente descrito con un modelado lineal.
Conclusiones
Respondiendo a la primera de las preguntas planteadas el análisis que se hizo utilizando medidas de tendencia central muestra que el comportamiento en el volumen de partículas PM2.5 en realidad esta influenciado por variables climatologicas ya que en los meses cercanos a verano la tendencia era a disminuir el volumen y el comportamiento se presentaba inverso en los meses de invierno.
Respondiendo a la segunda de las preguntas planteadas puede observarse la determinación de establecer una relación de las partículas PM2.5 presentes en el aire no estan relacionadas linealmente por las variables metereológicas medidas en el dataset. Esto puede ser no por que no tengan relación si no por que como se vio, no presentan Homoestacidad por lo que no tienen una distribución normal por lo que hacer un modelo de predicción lineal no es eficiente ya que no describe el fenómeno. Como trabajo por hacer es realizar un modelado no lineal para determinar un posible modelo de predicción de cantidad de partículas PM2.5 en relación con las condiciones metereológicas
El dataset tampoco se presta para hacer una clasificación ya que los valores que se encuentran en la mayoría de las características de interes tienen extensos valores únicos.
Fuentes
[1] Beijing PM2.5 Data Data Set.Xi Chen, de Guanghua School of Management, Center for Statistical Science, Univesidad de Peking. Consultado: enero 2019. http://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
[2] Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H. and Chen, S. X. (2015). Assessing Beijing’s PM2.5 pollution: severity, weather impact, APEC and winter heating. Proceedings of the Royal Society A, 471, 20150257.
[3] Correlación y Regresión Lineal. Consultado en enero del 2019: https://rpubs.com/osoramirez/316691