Reading datasets and grouping observations
library(data.table)
library(plyr) #join
library(ggplot2) #fortify, ggplot
library(scales) #scale_fill_distiller
library(sp) #used by rgdal
library(rgdal) #readOGR
library(ggmap) #theme_nothing
library(rgeos) #gCentroids
library(forecast) #auto.arima, forecast
Read .csv containing monthly TESS Ontario works cases for January 2004 to November 2014. The size of the csv file is 2.68 GB.
CSVFILE <- "opendata_tess_ow.csv"
tess_dt <- fread(CSVFILE) #data.table
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Structure of datatable:
str(tess_dt)
Classes ‘data.table’ and 'data.frame': 9536341 obs. of 21 variables:
$ YEAR_NUM : int 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 ...
$ MNTH : int 20040101 20040101 20040101 20040101 20040101 20040101 20040101 20040101 20040101 20040101 ...
$ PROGRAM_NM : chr "Ontario Works" "Ontario Works" "Ontario Works" "Ontario Works" ...
$ OFFICE : chr "Application Centre" "Application Centre" "Application Centre" "Application Centre" ...
$ FAMILY_TYP_NM : chr "Families" "Families" "Families" "Families" ...
$ FAMILY_SIZE : chr "2" "2" "2" "2" ...
$ AGE : chr "18 to 29 yrs old" "18 to 29 yrs old" "18 to 29 yrs old" "30 to 39 yrs old" ...
$ EDUCATION_LEVEL : chr "High School Incomplete" "High School Incomplete" "Post Secondary" "High School Complete" ...
$ EARNINGS : chr "" "" "" "Earnings from employment" ...
$ IMMIGRATION_STATUS : chr "Permanent Resident" "Permanent Resident" "Canadian Citizen" "Canadian Citizen" ...
$ TIMES_ON_ASSISTANCE : chr "1" "4+" "3" "1" ...
$ MONTHS_ON_ASSISTANCE : chr "1 to 6 months" "1 to 6 months" "1 to 6 months" "1 to 6 months" ...
$ MONTHS_OFF_ASSISTANCE : chr "1 to 6 months" "1 to 6 months" "1 to 6 months" "7 to 24 months" ...
$ GENDER : chr "F" "F" "F" "F" ...
$ SHELTER_COSTS : chr "$600 to $999" "$400 to $599" "$200 to $399" "$400 to $599" ...
$ YOUNGEST_DEP_AGE_RANGE: chr "less than 5 yrs old" "less than 5 yrs old" "less than 5 yrs old" "5 to 10 yrs old" ...
$ WARD_SCODE : int 2 2 8 8 6 2 1 2 8 6 ...
$ CENSUS_NEIGH_SCODE : int 4 4 24 27 19 4 2 4 27 19 ...
$ NEW_CASES : int 0 0 0 0 0 0 0 0 0 0 ...
$ EXITS : int 1 0 0 0 1 0 0 0 0 0 ...
$ CASES : int 1 1 1 1 1 1 1 1 1 1 ...
- attr(*, ".internal.selfref")=<externalptr>
Select columns from tess_dt
ow_cases <- tess_dt[,.(YEAR_NUM, MNTH, WARD_SCODE, CENSUS_NEIGH_SCODE, CASES)]
Drop rows where at least one of CENSUS_NEIGH_SCODE and WARD_SCODE is missing
ow_cases_dropna <- na.omit(ow_cases, cols=c("CENSUS_NEIGH_SCODE", "WARD_SCODE"))
Calculate mean number of cases in 2010 by month and ward
ow_wards_2010 <- ow_cases_dropna[YEAR_NUM==2010,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2010_months <- ow_wards_2010[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2010_monthly <- ow_wards_2010_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
Add “id” column
ow_wards_2010_monthly$id <- ow_wards_2010_monthly$WARD_SCODE
Calculate mean number of cases in 2013 by month and ward
ow_wards_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2013_months <- ow_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2013_monthly <- ow_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
Add “id” column
ow_wards_2013_monthly$id <- ow_wards_2013_monthly$WARD_SCODE
Plotting data by wards
City Wards
https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#29b6fadf-0bd6-2af9-4a8c-8c41da285ad7
Owner: City Clerk’s Office
Currency: August 2018
44 Ward Model - May 2010 (WGS84 - Latitude / Longitude)
http://opendata.toronto.ca/gcc/wards_may2010_wgs84.zip
Shapefile: icitw_wgs84
readme.txt:
Wards: There are a total of 44 electoral wards in the City of Toronto
* GEO_ID = unique geographic identifier
* NAME = Name of the Ward with corresponding ward number
* SCODE_NAME = Ward Number
* LCODE_NAME = Ward Number and the community council area it is in (N,S, E or W)
* TYPE_DESC = Ward
* TYPE_CODE = City Ward
We shall input wards shapefile, create centroids of wards, and process shapefile as dataframe. First we input the shapefile
wards.sh <- readOGR("C:/Users/14165/Desktop/ArcGIS/SHAPEFILES/May2010_WGS84", "icitw_wgs84")
OGR data source with driver: ESRI Shapefile
Source: "C:\Users\14165\Desktop\ArcGIS\SHAPEFILES\May2010_WGS84", layer: "icitw_wgs84"
with 44 features
It has 10 fields
Add “id” column
wards.sh@data$id <- as.integer(wards.sh@data$SCODE_NAME)
Make centroids of each ward, for placing labels when plotting
wards.sh.centroids <- as.data.frame(gCentroid(wards.sh, byid = TRUE))
Add “id” column
wards.sh.centroids$id <- wards.sh@data$id
Shapefile processing
wards.sh.points = fortify(wards.sh, region="id")
wards.sh.df = join(wards.sh.points, wards.sh@data, by="id")
Merge ward shapefile and ow_wards_2010_monthly dataframe
wards.sh.ow_2010 <- merge(wards.sh.df, ow_wards_2010_monthly, by = "id")
Make graphics object for wards.sh.ow_2010
p.wards_2010 <- ggplot() +
geom_polygon(data = wards.sh.ow_2010,
aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH),
color = "black", size = 0.25) +
coord_map() +
scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) +
theme_nothing(legend = TRUE) +
labs(title="Mean monthly number of Ontario Works cases by ward in Toronto in 2010") +
geom_text(aes(x=x, y=y, group=NULL, label=id), data = wards.sh.centroids, size = 2)
Plot graphics object
p.wards_2010 + guides(fill = guide_legend(reverse = TRUE)) #ggplot2
![](data:image/png;base64,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)
Calculate mean number of cases in 2013 by month and ward
ow_wards_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2013_months <- ow_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]
ow_wards_2013_monthly <- ow_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
Add “id” column
ow_wards_2013_monthly$id <- ow_wards_2013_monthly$WARD_SCODE
Merge ward shapefile and ow_wards_2013_monthly dataframe
wards.sh.ow_2013 <- merge(wards.sh.df, ow_wards_2013_monthly, by = "id")
Make graphics object
p.wards_2013 <- ggplot() +
geom_polygon(data = wards.sh.ow_2013,
aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH),
color = "black", size = 0.25) +
coord_map() +
scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) +
theme_nothing(legend = TRUE) +
labs(title="Mean monthly number of Ontario Works cases by ward in Toronto in 2013") +
geom_text(aes(x=x,y=y, group=NULL, label=id), data = wards.sh.centroids, size=2)
Plot graphics object
p.wards_2013 + guides(fill = guide_legend(reverse = TRUE)) #ggplot2
![](data:image/png;base64,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)
Calculate mean number of cases in 2013 by month and neighbourhood
ow_nbds_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, CENSUS_NEIGH_SCODE)]
ow_nbds_2013_months <- ow_nbds_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, CENSUS_NEIGH_SCODE)]
ow_nbds_2013_monthly <- ow_nbds_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(CENSUS_NEIGH_SCODE)]
Add “id” column
ow_nbds_2013_monthly$id <- ow_nbds_2013_monthly$CENSUS_NEIGH_SCODE
Doing finer aggregation: mean monthly cases who are single (without dependents)
ow_singles <- tess_dt[FAMILY_TYP_NM=="Singles",.(YEAR_NUM, MNTH, WARD_SCODE, CENSUS_NEIGH_SCODE, CASES)]
ow_singles_dropna <- na.omit(ow_singles, cols=c("CENSUS_NEIGH_SCODE", "WARD_SCODE"))
ow_singles_wards_2013 <- ow_singles_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]
ow_singles_wards_2013_months <- ow_singles_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]
ow_singles_wards_2013_monthly <- ow_singles_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
Add “id” column
ow_singles_wards_2013_monthly$id <- ow_singles_wards_2013_monthly$WARD_SCODE
Merge ward shapefile and Ontario Works dataframe
wards.sh.ow_singles_2013 <- merge(wards.sh.df, ow_singles_wards_2013_monthly, by = "id")
We shall plot wards.sh.ow_singles_2013. Make graphics object
p.wards_singles_2013 <- ggplot() +
geom_polygon(data = wards.sh.ow_singles_2013,
aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH),
color = "black", size = 0.25) +
coord_map() +
scale_fill_distiller(name="Cases", palette = "PuRd", trans = "reverse", breaks = pretty_breaks(n = 8)) +
theme_nothing(legend = TRUE) +
labs(title="Mean monthly number of Ontario Works cases (singles) by ward in Toronto in 2013") +
geom_text(aes(x=x,y=y, group=NULL, label=id), data = wards.sh.centroids, size = 2)
Plot graphics object
p.wards_singles_2013 + guides(fill = guide_legend(reverse = TRUE))
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAIAAADE8iHyAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOy9e5RT5b3//07Xd63v+p7+cXp6LHWAuUEy6jACCm01Ga5HbDNUilax5SIXS1JFO6NHbMGRmyNUsDppRZ2hIMNNHawiSlKhcp1Je87BKjgOMgkzTADpsX572j/a7z+/tfL745ns2dnZe2ffb/m8FouVPNnZ+0mys/Oaz+fzPI8vm82CIAiCIAiiGF8ydneJqC8UisYSadFH04lYNBQKxcQfdT3pWDQUTXB3Qj6f5EuVf9ReElGfz8e9DhsOH/IxohKn0dB5NLSV3AknRd4npRaNn10i6it8WjoW8kk1azk9bP7sLCQRLf4GGfUtM2Y/xXrsus8uHQtJdVjfCSyJHe+O9muFrtMmnYjxLnEFl8L08GUyVPhojkRU5D1LJ4YvnqGolu4ZLA0Aksn2pobFIn1JxxY3NLUnk4Yf0SGkD3W2e/bFWUUi2sB7E/2FG6QT0VAg7zxKJtubGgLS3xyRfdjxSYXnRoBkbyqvMdWbBIBk56F0QXNdjcjLJwAAiWhDT2tHo6veoPDKVjSJXRgJB2PPVT0RDTQ08S5x7Q0Bvn0kooHhy2Qy2d4QENGadCzU0C7SGGgYvngm25sC6lXMeGkAgsGg8CoIIH2oMxkMBk04HuExIvFsNpvNtoULHknHFje0JxGMtMZT2SFS8dZIEMl2UVM1AX9jdzbbrf4HKzw3ArQf4H9DEwfaEWltjQisIXGgHYjMLXz5BABmlpHm4p+A1k/KJPyNzZFk0xYXxRI0o/mdD7dlOVKtQSDYmhpuEbkiOBjNb0I61tIOBHPXuFS8NQgMnzn5D7NH21vyLn7pRDQUaCqUncSWpuTwjrOpeCQovCYpIWso8QiASGtrMP/DHjoDIq0FpwHrNgAgGIzEBc+JR3iWEYwMP3HofIrHW3Mb8B/NJ3fu5Q6V2zJ3ZOEzeV1CXpdkDppq5dlQsDXFO+O5vYn0P8W9ZXHhuyh8/4q/6sLvWKo1mNu1ijch1yFexwWfi/SnNnTE3NtR8ETZdzge4Z+Y+W8J710We2cED6n6pHI9kjnT8l5RwfssecKI9ZH/xKHPmfcx8Q9ZfP+FfROcTOwR7n6qVVlH845Z8MWSfq+KHULuy668b8K3R/q5/De8+BWD37tIa5x3FLlPXN0LSYlcGXPIfu+UXiWEXS34yuS9e2pO+/zXHcx9EUS+plm177wEhRc0/oPKvxQKtldzrdD0fdfygyW/K+F1Pph/AUcwEm8tPGkUHKs4pkhDvPC7kWoN5j5J3rvP/1AKPprCB3lnu9iDsidwJJJ3AsTzd8A9Mx6R7pLMQaWkISLYXV7/g7nfbUHXpZyhyKtWIA2K3oR4BIAwJlRwHZJ5i7jnir4I6Xe4mDQIfgIL9ov875XST0rBmZb3ivLfZ7kTRvQF5P+kD58rvJ92pfsv7Bv/XMq9XdJnr+T1JP+T0POtFAqMzAtR2DdRxZLpT/FToujD+Z+Irhci80Mr/71TepUQ/QERPblUnvYF27OriUJpkH7npZH+QVP3pTDwqq7q+27Im8DbFf/6XOiPvN+w1qEvvciWgt3yL5rKMUcaCow6HuGa8/8c5Kkai7NIXPIEPxkFHsnui75B+dumchfEvE8z/6MVXmgFH7zEQUUu/eC/2gggfhkSfLaSzlCkA8WlQdmbMPRYfvhL8M7Lf2oyp6H8Oyx/nhf5DvAe1vBJFTnThNKk6IQRf/Uivch7ZfzPX9EJyTsatyPxvzCDgh3JXPG5Y+RdV2TfK7lDyJ42Kvom7gxyB1X05R3640xwykvvR/sLkT6J5b93iq8SeW8QZ3+FJ5fa017+LRLrhLLLpjRS0qD2S2H8VV3Z992QN2F4Y8nLvOQ7JXPB5CQm2KpUXIYxSRoE5/SQMxS+jWIvPj/ymErF462t3J/IAqUQOXIBwm0FdlXg3iJfcqmH8w4q/lMkvrHwr0mJ34wir0Run4IeKX8ThA/lP7nIpyZ7vZd4PO94RkqDwk8q1zWlZ5ryE0a8j/zzX+RDFwsWSOxf8mWK9GHoxy4YaW2Np2T/spD+A294E9H3SuYQ8qeN8r4Vfj3knlt41ZY4JWRP+cJPXM8LkX53i3RC8VVC8D0Itrbmn1yC80LhaS9xdVEsDcqu1ZI7ETar+FIYeFVX93035E3I/YEnEYiUbpM/RDxSJCAshxmFkACr+uKquxIH2gvrulK9SSDZFOAPqQk0JYGePvasdCzk8wUCgYaGpqZ2sQrWYG1g+E6gNijXn7xtwS9N99fU5T0i6Gh4bn6oVs1BlW7ML5FLHGhHcN5s6eoZVR2QeSrk3oT8HvhnzwsOfS5FPzWRwwgp8g4bhfI3St2ZJkTNy+F9LVK9yeGnDren+3oEu1RzQgIA2hsa2iEckuFvbI6wkSZNDYEAG20lM95EcuiGzHslcwj500ZF39J9PfkNql6XxCmR7usRP+UL0f1C/DV1hYNohjso9b2D8qtEeG4k96RUbxJ1NY1zI0NHzH+aqtO+cECP1Fsk/sK0X7VEUfulMPCqrv3ypfpNYEPF2hFsTRlcAxpuy3Z3dzMlSaod02OWNPAvj6LOUIxENNCUBIKRSGtrPB5PpQoSrR5i+HpQ1BlKmEBtUKbUN3GgvbiviD7RyjMtnLuAJw6083sbqA2y9jyX0EiQVQ8JKvXDbUMDTRjJ9ibRkVqyFHmvNB9CT9+MeF2GYWZnlF4luEtv7sIbqA2i/UCCyVHuh7+kLrBuZGh0ZLA1lTcEQ/j3HaBniLY/vLI1WDDkuwimSQM7dZu2JNKxFlFnCNSKxIiz2aFBKuwPrni2u62tsTEcDvv9BX9jmITgVylxoN2Cg+auB3qcofBvmKFZADSQ/6dQ+lBn7qSU/9SUofUdZn/aCAYX5brIhiFpeOt0n2nqXs7QBVz4Qftnzxtuzzcf1W9XpLkt7G/saA2ivSH/B8sfbmzrzmaz2VSKlVZLKhgvcMRDwXslfggFp43yvhWi57nIfXPyLpzslC/E5Bci+b0DoPwqMWSgib4edi75Z88LoqcvcahzWEjVnvaB2qDwrNB+edGP2i+FgVd1S34gElFfoCkZjMRTBRfWQK3wV55FyhT8vcTmktMtseZJw1CY7MCWTvE/ndhlsiE0HL9LJwSvqf1A7rF0Irq4KQmpi5lBDP0qNXDzBA3Nj6Hit0hr/8IrW4PoaWnRHWdoPxDjsjuFc3so301DbjKRdCK2uCmZi8op+NTk0PcOs1/CZFMgL+qbTsTYoOSgqul+8j4pjWeahpfDLuAHDvSIhaKF7XreLhYq57RhaELKoR35/YGa2qD4lcY/e16QF7JMxwRTWUq+VzKHkD9tlPet8O8sFc+Vhk281bR4aDe5U14E3S+kyAVe6ns31E9lVwnWyZaWzuTQZv6aOiQ7WwquxCpOeyZWTYtzp6K+y4se1H4pDLyqG7ArRSSiLCfR3RYu3G/uk+BO1ujipqSyLhT2n8Wb1PZfXQlEMQRjP4biXVJFHGLxMEHduMTDskV/AoTbCstD8oqDig/mkTworyA1VWxjkcqV4fpayXe32KsWvJ/8MVEq3oR4BNJjRQuPUvipyRfWyI9ZKl4eJPJ89mILx16r+KTUnmmahlzmH134Krnq+VR+q5oTUqTEXm7Ql1y9Zh4F75z0eyV1CLVfdrli4MJKSOn+KLtiiL8w6bJlzS9EfvSE3Pcu7wBFKuiEm4k8Te1pL3t5ET2+hmu15E7yUfelMPCqrm/IpZofLDGkLvRS54PYuab+elWIiZEGrkZEMkUbbsvmTYXCj8b4Gzvy5tBoZTPXSJYQGdXltm7h7CxKA+/+xmb2xGRvSkO4YaiuSE8+O9zG9T0YaU11r6zVvKvald3cvE/B1ji/DkfuU1PUSc3vMPf8eCtvUppgMNIaF5dyCfI/KX1nmvqXM/THcsEHPfRtEaQmdb1dLGHJggb+xm7FH1u4LW+mo+GPX/69kj2E/Jddcd9YHIT32ah5XdL4G7vzX3KrZIJf1wsprHPNR+Z7xw6g7CohPMdEnqb+tA+3DZdrBCNxPZcXnaj9Uhh4Vdd7+SqOVGaMR973M+8LWpT8/mv7tviytMqlQ0hEfQ3tkbjL5kolCGtJx0KBpjqzvyeJqK+hp9XwH4RYKNA5T89e6SpB2I2pkQZCMelEtKUdwdaVdC0gCDn8jc0RiXJYrQwViMWG6xSiDe0mLBmW2NKUVLJohhR0lSAcAEUabCcR9Q2VFAUN/8uGILxIIuprqTXwy5KOFa7vY/i3UVeYga4ShFOgSIPtDM3yEYzE6VpAEEoIt8XrjFxm2pjCCHkSW5qgfTlvukoQToEiDQRBEARBKIIiDQRBEARBKIKkgSAIgiAIRZA0EARBEAShCJIGgiAIgiAUQdJAEARBEIQiSBoIgiAIglAESQNBEARBEIogaSAIgiAIQhEkDQRBEARBKIKkgSAIgiAIRZA0EARBEAShCJIGgiAIgiAUQdJAEARBEIQiSBoIgiAIglAESQNBEARBEIogaSAIgiAIQhEkDQRBEARBKIKkgSAIgiAIRZA0EARBEAShCJIGgiAIgiAUQdJAEARBEIQiSBoIgiAIglAESQNBEARBEIogaSAIgiAIQhEkDQRBEKWCz+ezuwuEu/lfdneAIAiCMBdyBcIoSBoIgiC8CecKH+3/mN+YzWZt6hHhekgaCIIgvAM/qMB3BYIwBJIGgiAI1yMaVCAIw6E4FUEQhIthuqDKFSbecyNd+QltUKSBIAjClVB0gbAekgaCIAjXQCULhL1QeoIgCMLRmCEKlKEgtEGRBoIgCIeioV6BIEyFIg0EQRAOwrIEBAUbCA1QpIEgCMJ+qKqRcAUkDQRBELZBrkC4C5IGgiAIG6B6BcKNkDQQBEFYgWDVKNIFwo1QISRBEIS5ODaoQLWQhFoo0kAQBGE8NAsT4Uko0kAQBGEkjo0riELBBkIVFGkgCILQC8UViBKBIg0EQRAa8YYrULCBUA5FGgiCIFTjrhwEQRgFSQNBEERxaMAkQYCkgSAIQh4KKhAEB9U0EARBiFBSEzxTWQOhEIo0EARBDFNSrkAQaiFpIAii1PHGIAiCsABKTxAEUbpQvQIHZSgIJXzJ7g4QBEFYio/H2XfO2d0dgnATlJ4gCKJUYHEFgSicfefc+DtuoGADQSiBpIEgCI/DlSxQXEEen48S1kQRSBoIgvAm5Aqq+Gj/xxPvudHuXhBOh6SBIAhPQa5AEOZB0kAQhLsRTPBMrqCZj/Z/TBkKQh6SBoIg3IpoYSNBEOZB0kAQhMugBARB2AVJA0EQ7oBcgSBsh6SBIAjnwq9XIFewACprIOQhaSAIwnFYHFSg+Z0IQiEkDQRBOAVKQBCEwyFpIAjCZpzgChRsIAglkDQQBGEPTnAFohAqayBkIGkgCMI6qLCRIFwNSQNBEFZAcQWC8ABfsrsDBEF4Fh+P8ycHzp8csLtHcpx95xyt2MRgGQq7e0E4EYo0EARhMNzvTaElnD85cN3Uago2EIRLIWkgCMIA+H+YOjyiIM/Ee26kMRSgckhCAkpPEAShC372wfk5CHlsCYFcOrSo5UMAmb3ti+5r37D3Q+u7QBBKIWkgCEI17ipWcDR/2tORBIBLh57AnN27IkvwxoYuuztFEFJQeoIgCBWwNARZgkFk9h7E4rsDHUD57N0LAGBwAGOm2t0tgpCCIg0EQRTB2LjC+ZMD4++4wai+GY6VYyguHdqJOQvLhxsye59or36Y32InNIaCKIQiDQRBiCMzCIIwgswgMHBwQ8tgqg97Lt1U2fHEsekP766/1u5+EYQ0VBxLEIQQC3IQTh54afUiFB9uaMGa6f+1qB2BGgAYszjilGADgIn33Eg/EwQHSQNBEIDlYyadLA2w3hscDEkDwYfSEwRR6lBtI0EQCiFpIIhSxPa5mGhqSBdBszwRHDR6giBKCG4QxNXUF1dTX4ACDBKU+DoUE++5kXv5lKYh+FCkgSA8Dj+owETBIVCwweHwtYmCDQSDpIEgPAvTBRlRuJr6oixwDQUbCFEu/Ocl7vbYb5aTNxCg9ARBeAz+RExwWGjBdZRyhkIAEwia64mgSANBeAHuau4uS3ByhuLsO+ecPHOl9fC9gUIOJQtJA0G4G4ooEAZSNLhy4T8vUaqilCFpIAhXYlRogcoapCip+Z04V+DXMRSFog4lCEkDQbgGx46DINyIIKigXBe4YAO7S1GHkoKkgSBcgKk5CHuDDUaVNWQOzt85at+aSQDQtXUXVtxXb0DvPAjfFVTFFfjQwIqShaSBIBwKxRVUcHXXzlPADwBk9mz92eGLgYjdPXIgTBc0i4IU/MBDNpulnIW3IWkgCGfh0nEQtpLZ8yaW/CCwEwAwdcW+qq1rde7RG+MmNCcg1ML2TDmLUoCkgSAcgctdYbBjS1sCgQfnL506arBjy6MJfPfB+UunjlL0ZJ0ZiszB7bhrfcVnTBQqKoCMth15CJOCCvJQzqIUIGkgCNtwTgJCZ1nD4P42zN/42qjjax58BTPexfzfvDZqsGPLK5Url1Ya21ERMhmg/821Gy6m0tiVmXRfhRE7dd0kDZYFFRTCchb29oEwA5IGgrAalwcVRKi8Z+NiDJ7cfxgzorj4LoIAKquRGgQUSoOOYENF/Zz19QA+WLsBxhiDi9A2VNICxn6znMIMnoSkgSAswnuukE9lZfB27DtaOf+7Lz69+kI1+gbwoJXHn7R+Te5m/Yr1Vh7ZFhyrC4S3IWkgCBNxTgKiKHoyFIP/8crgt5ZOHTX9Nvzq+OXnnnlxaSUGO7a0Gd5JK2EZCmfO7zTxnhud7wpU0+BJSBoIwni8HlQQUjka2x9cPVB9PlH18Gujj67ZgrF490LVc4vV7MTJ61AQaqGaBq9C0kAQhuFqVygLXKP9yaOWbngRABYDwPQNKwEsNaRXhEshY/AqtDQ2QeiFW4r6auoL9s/uHqmjLHBNWeCaC3/IXPhD5rqp1fZ2xmljFpy5OvZH+z928q8y1zfBQu2EByBpIAiNuN0VkK8LdvcFAJy2bhblSnRy4T8vsX/Ir+9xHelYiOc/oVja7g7ZB6UnCEIpgqueGy2BgyUjHOIKhAZYsMGZ5ZCFvRKsceWqAsl0LBRoQmsq2+3n7gd8vfFsW9jmntkCSQNBFMHVlQqisOiC6EMX/pAZe0uFjX/xUzmkV+GbhIsGViSigSa0prob/bkWf2N3CqFAS2xleLixdKD0BEFI4vbsg+1cN7Wa/bO7IwShjcSBdkSahXLgb+zOchohlbrIb48muGfzH5BoznvAWZA0EIQQQbGC3d1xH5wrfHrswqfHLtjdHb04sxaS9crJ5ZBeIN3Xg2BtQGaLRDTQVBfPMlKtwWTTlgRYDqNzXirLPdDTwDwgHQsNPyHV2tPALENye8dB6QmCGIJLQ6T/kPHfUkG6oAp+OEEgCp8eu3Dd1GqnFTkqxFGLUPD1xZnVDMpxT31DXY1MEiIdWJnNDmcuZs8LNnX2pREWPNHf2J1tBIDElqZkJN4dzjU3R5oatiQa2wIS2zsPkgai1OG7gr09cQiqyhpkXEEzVNbA4bRlqIyCeyFuWAyzpy+NsJQ3+P1+AImor6E91xScB6YDvgZfOwBEhmsm0309QHKonXtCXxph8e0dCEkDUaLIu0L6D5mywDUUbBDFDFFwOBPvudHU+aSlMiAf7f/YFTNGexZ/TR2SvSlAKA2JqK+lNtXd6Ge+EGxNZRv9Q1kGtkW4LZttY1syGYjEs20BAMG8skoO0e0d6A4OVzyCMBL+mMmicQVPZij40z7KjLeUiTSwSgUNh75+xlhVGQpHBRvMWIRCIApS+/e2NPBrMrLZLPuGOupXaajYIP9nPleY0Bbm5IE9kIj6GnrEpCAR9TUgnm2D1AYS2zvPGijSQHgZwcwKJZuA4FxB4cQM/AyFYOxDiYQWCtEZbCiMJSjcm5PnY9CPIE8BhxkDAH9jR2tnIBAC90OfjoUC/MKEZOehdONQmKGhHQgCBT/66b4eBOcFAP/K1mCgaXFsNtsbJxkpie2dB0kD4U3YBcglltDf/uSKd3DDHbc9F5kGAAM7wy9WJ7ZM07tfta4ggOlCyVoCg1VBGqILzlww01E4zRgAsKLEmqgv4GvKteSSEQDCbfGIr2HosWBrKl4XaOhNAeG2bDzK+6OFy0kMzfKQ21uu3S+1veOg9AThEQRBhbMHe8fPqYUOb7AsPTGwM3xkRiJS2d8+7wV/53MzB19eufJtrNAoDQoTEEUZe0uF4bqgKkNhY3qCP1zCCbrg7QwFckkK+jFyBRRpIFwP04WzB3sF7Zw3aMaaWsjqJYkIAPSnUDML/e2/xoMrbnhR5U50BhUKYRkKG8MMFo+hMEoUOJgxGLIrb2coGGQMboGkgXAxUrpgCGy2BjP2LEZ/+7xfBLYksPNR/Oi56ouPKn8mrSKhE/0JiEIMNAbPM/ab5WQMLoKkgXANhavkmaQLlvO7lfPi4S2JmZX9R4HUrx9def7cp3h5YNqPRadf5icg4CpdcMgsT4LJmkwaFkHGoASa0dJ1kDQQjoYvCl5RBCFHn/zFp9fdgF8/mkDNg089twXAiUdXYtgYBJYAq0TB9gyF4ZgRVOBjni54OENBYQZ3QdJAOBHOFbwqCnxmPpWYKWia9twWE8oUXIcZZQ1mGwOhHAozuBGSBsJBGO4KZw/2+m+pcMnAyzxkVq+2EsODDfZmKNhCEuZN03TmNz0Tvl9HYyyVQ2EG10HSQNiMkxMQdk0mXZiP8BjchFF8exBtNCPYYOCc0Hxd4N+Y8P06Q/YvwEsZCgozuBRaGpuwDbb8dAzTAJw92Os0Y7ALp42GuPCHzPUzxhq4Q7ZeNotecIto8yeSEsxBaSxG+cfEe25kxnDmNz2cMfBxV5hh8LW5zb8XuW0SLpqYwWcEdr8II6FIA2ED7FvEdMFs3JihcI4xmIpo1uPTYxeunzHWCYMsROEXLoi6An9L13jD5e3bjgA/KrhtJq4wBsau/zVLz9Pv+/+OGNUTJ0CRBsI6OO+OYZo1xuAcPJ9xMJDCeINgkKQtsNACiytIRRcKn2JgB0xTkIs7d2P5j64vuE0QIpA0EObCj9ExVyhBXVBrDGOtm1TKofC9wfCQw9l3zin/OWeuwOmCwmdxG7MDOXlgxeBrL2PR/ZUFt82DZnNyNZSeIEyBS+M5QRF0Zig01ELyLYEdV/nkkldTXzgtLGHLhA2CPMX4O26wcikK5WkIDTt0Utri4gCQ3v2z5vSn5/GzY7O429sHbzXFHqj+0e2QNBAGY2W9ggUon0y6UBQE+1ElH2NvqSiRygYZ+N5ganUkw3BR4I/AZLd17tCEKZ6qpv/g59MB/P5nzfj5kluBodsmxhsozOBqSBoIA+CXBztKF7gFq8wrhORcwahDcKMnuCSFPnu4uPOZtkMIrFiwbPpo/m0j+mo+5o2n4AoVC4dNGg7bMzca04k1krf+vEX0tqFQmMEDkDQQ2nFUDoKDv7KlqYMm2K+7SYdgxsBSFey2NnUYfL0NCzbtH32sefkO3JbibldvW2Z26tpAWMjB8AwFFwMwcJ8CCnfOqYPjvMESKMzgdkgaCNUY7goxTBs/p9aQeRrGz6k1e3Sl4aEFAYIUBlfioC1hUXnvpiW4ePz1w7gtOv3eZcjdVmsMtq9DwbxBzx74QzBMFQV5TJr3yflQ/aM06Vgo0NucbQvn7jQlcw9F4kPNiaivoR1AsDXV3egfelS00VxIGghFODYBYQ3y9QpmwzRCR3VkVXXoduw9OohllXm3vY8DRUFngMGlK1eRMciQji1uSiKSu5vqTXKmMLxJqAHxbDaMdCwUiNZk28ISjaZD0kDI4cwEhMU4YWIozcYw+PsdA7cumz56xm345bbXcdu9Q7ePXV62RGVZg+3BBoUIJnWw0RWQ04XSzERwUCmDHOnY4s66SJALLaT7eoK1KwUbpXqTkblhAPDPnhdsOpBoC4fFG02HpIEQUuJBBY9RWY5ty1dd9H96qOon+0MDzdxtqwohB9+4Z0f5/vXfGjz5xraXjuOBVS1TR2ncFctQSJU18F3hg1c+ADBp6SSNRzIC0gWGi2aMtoN0bHHnvI4OLG7PZWdTvclke8DXBIBLTvBFwl9Th86+NMIQazQ/RUHSQAzhjaCCkyeN1rn8lcYxFKOXtWwDgCUAMIN32xKu7NxxHFiEwTe2vV/e8uoLg7t+sbPy35cYmxlhusBEgc8Hr3xgrzeQMTDIGKRgytDtR2y4qa9nuEIhHQuFYoHuxlRvErXCJ4s2mg9JA+G1mRW8igtnbhjc9RqWLbpuBzA4eH7srQAqq5AeBHRKgyABUagL3sZFZQ2UmJCDUwakhxv9jd3ZRu5OTV3yQAoI1AYLny7aaD4kDaWLN0ILJQI39tJeb1BV1jD4xjb8oKXycjMA4LoqrVkJPixDUWqWIIrzvYESE/KkD3Umk8lcIgJo8PVIjYHIyYMfLBRRN9cPQLTRdGjtidLCsStBsFGXdvfCBbhnWYrBQeDCa81rd5/v270Tlbh4BcDgRbu7ZRlnftNj3pITzk98kDEUxd/YnR0i1RpEJJ7tbvQjEfWFYrnIQ7qvh5U6BmqD7S2xNJhqDJU/ijaaDkUaSoKSCio4uaxBD9zASz1zPelHcbChcurdLVMB/EfzWiyZOvr4+5uad1Wfv1D57H0W9DLHB698MOH7dfYOoChByBi0E25L9YVy4Ydga6qbDY9o7I73+gK+Jl6beKPpkDR4mZJyBVegsxYSuTyFm4obvtWyHgCmr39hus098RYOr2wgY1CDv7E7m3ensXCbcFs226ao0VwoPeE1HJuAKIpjMhQXdz5z57xnVu38Pbu9at4zO05ctrtT+bgnSWE/LNhgy6FNzVA4Fip+9DYUafACNLOCgQy+/ggWvNU5+uLO5at2ZoAFmzpHH2tevqPKMYs1OKQo0i6oFtLJUGLC8yXihBYAACAASURBVFCkwcVwEYUzm44DcFFQwclU3vvWktEABtIIzLh305LRF0+8fhi3zXSIMTD0JDj0c+EPGZ3LQBCehIyhFKBIg5vgRxQAMFcgTODizuW/9K99qxIAqqocuVgDt5BVacYbCDiyrIGMwfP46DN2PpwryFvChFXT3R5paMQJQ9a6hK7lLo81Lz88a+2maaMx+PsdF29dNg048cydAwveUrtYgyj+WyoMjBPY6A32rkOhKkPB5oW0awyFqatgT7znRidIg4djDII/1bThpXeGIg3ORaErEMZy4plf9vmvx95VRxBYvgBHlq8a8H8ar/pJp3GLNegcQMGnxOsbFGL7fNKmYnuwwcPGQBRC0uA4yBXsZdpP3+KHawxfrCH9h4zf0LEPXJ7Ceq6fMdauYAOVQzI+2v+xEwZoeNsY/utf7tHz9G/8z36jeuIESBrshyoVCLugoZuETmiAZalB0mAPfFEgSyD0I5qhKOoEZ976RM9BJ9w5Ts/TS4Qzv+kxtazBRigxUYKQNFiKqamHM5uOW1IL+f/O4dP38OWbUGP4kdj8TkbVQnoVqWREoSLodALHomHAp/Pnk750aFHHyN3NN2X2tu98D2MicxbWX2t3n2QhYyhNSBosgumCB4IKX+DTs7ipCf/vBK58gVH25NJLAJkyhb9+8Xfu9leu+bKNZnDmrU+sLGvgXMGDpQx/2tORBO7GpUM7MWfNrmtPtTyxp/LphUpC/7YMvCRjKFlIGszCq5UK12AEcOUc/v4nlDt5fKe7lq0qVAS+Gcgz4c5xXo0oMGx1hczel185jDHLv7eovizT9fLqbQjc/r21C8rU7aVYhiKz9yAW3x3oAMpnr1mATNehYwgucWyxABlDKUPSYDyeCSqI8gX+DpR/Df8H+AtFGrTBX7aK6YJyRRDw1y/+/pVrvmxk5xyDE+IKmXdfwffWdpSdemr1bgTfO/atPR03Zfa+vDvz40UGVpCy6EL51Q25horKSTNw8OQlKIo0WAwZQ4lD00gbBn9SZ68aA4A/A+Pxf67BV8fjczP+vHXMslUmwoZclgWuKQtc89cv/q7ZGLzH9TPGcv8+eOUD9s+onWtYuariu2sXlGW63j2K4LT6Sd/Gf+zueveV/tHTtBmDxNjIzCAwcHBDyxupvjf2dH24pwsov3bKdLx38k9K98wyFJo6pQ4yBnNIx0K+aIK7m4iy35JQLA0NjeZCkQa9OGpaBQtqIW/Al19H3+fAh6hsMu8wXoSbnsFLlmBUWQOLKziyWKGiYtJMvH2iC/0YvbRiUhXePpGB6kgDy1CI7r9+9pp6AB9uaMHC+rI9LU9sGKxMvTcqssuRhZBkDIaTji1uSiLC3Qs1IJ7NhpGOhQLRmmxbWE2j6VCkQQv85acH7n+TNU5YNX3Cqum29ssaRt2LmmmoacJX7e4JAHRt3d0FAJmug+vue3xd19Xhh1hZg10d4+O/pYL1hOIKAgRxBbu7IyTz4e4uoKJsygy8ty2JGd+tYLdPXS3+XNXctKb5JuDahc1Pr1kQ2b1r9hQTjqELmpLBFNKxxZ11kSB3P9WbjMwNA4B/9rxg+4GEukbToUiDCrigAicK3N3q7XfBGcGGUiKzZ+uqw4OBCJA5uOPo6HW7Nmf2bN1dsSLvr0AbyyE9GVowBLvqFdQOvKy4Fh2r1w9WpA6PjnZ87+JTq9cPBnEY0Q6VhZBq0yIOhBIT5pCOLe6c19GBxe1DA83TfT3B2pXstr+mDp19aYShuNFveo9JGooj5QocZAx2MXXF3qqt6wBkLqXGfANARRUuZABOGs4e7B0/p9YWb+BCCxYf13qUZyicUNuojrJFT24EgAUAMIV3WxGcK1gws5MFAy/JGAyHKUO3HzGuKdWbREFRl/JG8yFpkIPpgpQr8CFjsIOKCiDnAoEqiT/+mDdY1ieGlcbABlA4dtQlfyIm17iCDvhBBc/MAkmJCVPglAG8KsZAbbBwS+WN5kPSIIQ/v4ISXSCcQEU5Tl5FfVnmIlCV/xAzBvYrbkG8gVISKD1REOAQV9AZeBCIAoUZDCd9qDOZTAZ8uYLyBl9Pa6q7saYueSAF+AGk+3pQN9cPQHmj6ZA0DFE0ByGApSQIh1DxjX/rf37dnspUf/mmhfkPcZNSW5Cn8N9SYYsr2B5s4DIUDh4EMYTz55PWjGDgJXdXuToURhTIFczD39idbWQ307FQoLd5aPRDbbC9JbYy3OhPH+pMRprDABBQ3Gg6pSsNghkbtQUVHJiVsGoFCqdQv2IdAJRNWbO5SLU5v77BjMCDXcbgHGitaifAj3Ow20VDDnxXIEuwG39jd7zXF/A1AcHWVHdYZaPplKI0KK9UkMLtYYZGnGA3SsctGJw3sLsGBh5sNwbbgw1wdoChRBDNjPAjEJw9kCg4CX9jN/8jCLdls23CbZQ3mkupSIMhcQU+DowxFIVzBdb50phVQgh/CU2jCiRtNwaG7d4waekkV3iDqRkKZy6EzfrDpqTkdIFcgdCAx6VBbaWCQgbuf7N61XTnewNnCQxBh+1KZAh6VdgBKxfI1h9scIgx2M6Ztz6ZcOc4u3tBFIGmWyB04k1pMMkVXAT7YXaa1rBeCf7Wb8QJu7IkOkdjOnAmBtuDDYQz4Za9IF0gdOIpabDYFSY4ONjgqI5xoYXC4AFrYb/cdqmDtmCDYwMM5A2uhv26G5jdIF0gjMUL0mBLXIGbOtqBONAY5HMN7C9+W0IO2qaMZDGGr1zzZWd6A2EvGsoaJBbA1AvbLemCTr7xP/vt7oKDcLE0OCEH4eRggwUIqhMgES1QUp1gy7yN/EMr9wb/LRV/+8s/APzzV//J5K5pxK5gAytroFpIVXC6wDpjVBEl6QJhEi6TBkdN18iCDWwMQgmqg1SBArQmGtiPN3uulbWQyL0KKW8QLJXJjIEgdCLQBZAxOJX+6sf1PH3MwGajeuIEnC4NgqGScIAr8OE644rBFAYimnTgFyhog+8NtiDlDVwgxC3GQJUN9lI0Q8F+1w0PdVAFA2E2jpYGn8/nKEWQp3DaA+drhIZRlzJVjYZge55C0MgPM7jFGDgm3DnOYm8wIUNxad/OXUdQ/aOG+RVnnt51hTVWT2+YHxqha7+2ZCj4tQuCQ+sPM1CAgbAA50qDu4xhOOSw/S44u0xSA/zaBQtSBvojFnoOLUhGICcTrjMGFmywuxd6uXRkFxqeeGVE99NP77vviSeeAPD5vqfjlTqNwWIKMxECyBgIt+Bcachms+7yBobrOiyAP4TBYldwCFy8gb/Sla090o4HkhTls56Yj0vdR47jm/eVA8ClffGB6Uvm292v4rAMBf+u1JZkDISLcK40ENbTiBP8EL3tomBlLSSfwqmmXRdm8BblFROmI37qEuaXf7xrYNR9RimDNRkKs/dPxkBYiaOlwaXBBtchKFOw3RUYTF/s8gY+rjYG64MNxpY1XPp4X+bG+aERoen49anP51d+kK5uEC7f7GSKGoOeMAOVPRLW8yW7O+BlJqyazv2zuy9FOHuw16jf5rMHewvnb9C8K9iaHZA/9N/+8g8PFA04nPKv4/jTT+/bufTXo340f8SlwSv+SldVM8ij3xiy2SwZg4tJRH1DRBO5tnQs5BuGa89tGoqlC5+f12gujo40uBp+gISbzoGhZ1RFpvvZjs/ZzaoZU++u/1fNexrujHlOo98eOG+wPt7gmcSEpmBDZu9LOw5j7PK5i+rLACDzzoKOUXufvNmcLsowYv4TTwAAS0kM3Sl5KCXhBdKxUAPi2WwYSMdCgWgi2xYGkOpNRuLspsim6VgoEK3JtoUlGk3H6ZGGbDbrgWEIA/e/yf2DvjWpK0KPPXnnY09OrQLK9RuDeTTihJIJpBVi1zhMDxgDh6olKDPv7MDcdR0PVB2L7c4AuLq7o9u0ntkKK2swaeeCWkjRDTRMIE3G4BH8jd2533n/7HnB9gMJAEj39QRrA4JNU73JyNywYEvRRtNxujR4D84b5NVBdoMre09enHHntwzvm4GwfIex4QHLvIHVUnjJGNgaGcq9oeKOdQvKMl3vvI/QtApk9h7A4nnCy5gMZ976ZNLSSVo6ShSDjMGTpA915gQg1ZtMNgXykxN8kfDX1KGnLy3RaD4ukAZvBBv4yJd28nVB3BvOv9o/4of1xvfLMMxIJVhW38B0wUvGwOC8QbE6VFRM/jdcPtH1zg7MXSScvIJQRtFggyrIGDxIOhby+QJNaF0ZBoB0Xw+CrSlWrJKqbQnF0kCqN1n4RNFG83GBNNjC8a53j1tyIE4R+CWTXCJDlK7egTHXj7Kkd87CAm/wWIBBwF+/+LvCZTkzf9zdBVSUTZmBn2+7jP4D657qTKU7d6teQdwNmJehmPD9uqJ7Vp6hIGPwJv7G7mw2m23uDYRiaXa3u9E/9FhNXbI3BQRqg4VPFG00H3cUQlo79vLqK12/fPevlQ+bfyROEapzNxQ86crgf1dXOriawVRMLW7wtjGooqIMHT9bN1iZOjz65Y47pgDAH9c9BYo3KIW5gpIZGhT6ChmD1wnPjTQcSAF+sQf9NXXJ3IPpvh7UzfUDEG00HYo0iDCzftPDXzH9KGc2HWfDKKSSLxLVD6MW/PgxY3MTZzYdd9GkhyZ5Q+kYw1+/+HvxDEXZoid/vm7BA3uHjAHAzetsGDrhKlhcgf0785se5XM6FR11ScbgTRJR3lDJxIH2YG1A0Jju62GVDoHaYHtLLI286gfRRtNxR6QBlgYbyiqBAfMPI0D0pZXsKpryMG8wsHKidIzBMkxYucpctE0NqXCiaP2QMXiQcFuqLxTwNQEAgq2p7kY/4Bc2suERjd3xXl/A18RrE280HddIg/fgxw9o1ksNGOINOidj+Ntf/vHPX/0nhYUCxbjw0qORt1B7Z8OvHrjtwkuPtp4HUHPHT358+xgj9k7I8MErH6gd7sHpgjWzRBOexN/YnW1U1AiE27LZNkWN5kLSYA/KLcFjI0eMwpAkhaMCDP0vR/DQ+4fHXHgp/PCRMdefr2lq/fFYuztFCOFc4T9f/MM3H7xFpzEonBGSwgyEc3CTNFhZDjm9/scWHEUhlJgwA0cZA4AxP37/AQC4cB7Xf7v/0963P216uxf/3t56m1vVwXUZChn4rsBu6DeGolApA+FA3CQNpYabYwyZPVtXHcZ3IncNTUJsBnoqG5xa+HnhpfAz1219fwwOv5y4fQxw5NGHj9z2q1lGH8YDS2YbjmDFS8GgBs4VAHzzwVtgRFZCPsxAxkA4E/dJA/+n1POlAJbFGNgACqNKCzMHV+GuvbvKMnu27q5Y4bhxesbGGIwrazjcFH5n9tb3Z41B/8upC7ffPmYMgN50P2ZRUYNVFEYUBBgVYJAfaUnGQDgW90nDZ5OfZzdGnn6ECYQz1aGk5EZA5hLwDQAVVbiQARwlDU7LSnAcefSZ3htq8cLDh3D9Tx7ClhUPp2/ofavmp4fdbAxuyVBwVZBSrsAw1hikwgxkDE5jzMBmu7vgIFwmDaysgXkD+9+Z6sC6xO+htu5NcOdIy/q7vtP+/Lr+SqQHEbG7M3ycmpUAgFnPvc9PQxxOAMADNnWmFOAPl5B3BRiXkuAoNIaJ99z40f6PyRgIh+MyaUC+N4D3w2xrp/Ko3n4X1z0An01+XrM3WGkMhmYoprVsXlSBzJ6tO4zYmyTKyxqYLnz0xscT76YBbCWNwqACH2NrHkUTE8wVJt5zI+mCA7kybauep486scKonjgB90kDY+TpR/g/zAA0/zVvIPwAAx/mDXb0yC5O7NyKMfhtf/mmhXZ3hYsufPRG8bFtpUOp1UJqcAWGGcYgCDNQdIFwEa6UBhZs4HuDvb/KXPlCoS4INlOrNS5NT6Bs0ZoVABbZ2wvSBSdgY1mDqgSEKGQMBCHAldKAnDfY3Yth5HUBmrRm4P43q7ffxSaOPLPpOHdDWw9LDS4fYXdHvABbq8JFYQnNcQU+rI7BWMgYCLfjqQWrnD+xgYYeMh3h5pzmTz5dsJZVKSI6NaSMMXz0xsf//NV/MrYPf/vLP75yzZeN3adzmHDnuLMHe88e7J1w57jiK13ZyqSlk9i//3zxD+yf5l0ZXvlYOCsDGQPhRtwaaYBYRaQzR1JwDNVsqu8hP0pBkQYZKB9hOMwY2G2+n3H2YHv4QX8OohDDjUEKMgbCdbhYGgoZnsLBWnUYuP/NkfkjJmRQKDeCmAQ3SISrcqAYA4P9jFEywnCYFoiOTFFrDGaUNZjhCgwzjEE0zEDGQLgRT0kDh8NHK8jM38B3BalRGObpgrHzQppEYTKCXEEbMgMo+AEGDvbOczIxfk7thDvHWRxpMKRYoSiGG4OBeyM8RSLqa2gHAETi2bawoDW3XLbKRnPxpjQwTBqEKYgBsHJFDfspzFZIjdgsfBbDyWJkBg7PPnzlmi8btEa2zYgaAwqiDqq8QWewwRpXgDnFjxCrf6QwA4F0LNSAeDYbBtKxUCCayLaF+a3pWCgQrVHZaDo+V5+7bACF1K8s+0011hsEEzexQyhMTEjB/+1Xu6uRpx8xtsphwqrpDow0GK4LE+++0fDJpA1agcJSCiMNUsYggH0iqsIMqqSBn32A+a7AYfjalVyYgfMGqn90Fz6fT//kTsU/7nQsFOhtzraFkYj6Dsxlv/9aGk3H3ZEG+YGX3J/ySryBixbI1xkIftR16gK3E0Pkw2Pw0xDODC14DJkiBj7c52JSYoLpgmWWwMeMMIPosplkDISA9KHOZKQ5DCDd1xOsXcla/TV16OxLIwzFjeanKNwtDUbB5QVk5nsuNAYD0bZnM9IT9pY1kCjYhcIAA0ObLkhlKOwKKohi3ogJtmeqbyCEpGOhQFMSwdZUGABSvUkULJGjvNF8XC8NgoGXqpCvOhTdzCF4bASmY4sVjjzyAp5/aBZw5OWHNr+FO1e+8OBtdvfJBFQZAytlMCTMYGNQwRaYMVCYgcjD39idbQQSUV8olupuDNQGC7dR3mg+rpcGzSiMHCgpTrQF0gWTufDiI8vf6q19HOh/+aF3/S8cee/Ciy+/0H/bQ25eqloIG0BhcWDJvNGSejC8mkEAGQMhS3hupOFACgjX1CUPpAA/gHRfD+rm+gEobzQdL8wImc1mjQrUO1AOPMn4ObXsH4CP3vjYecYAAN95/ujjtQBw4TyuSz8069vP49tyxuDGeSHtMgb9Mza6ETIGIo9E1BeKpXN3DrQHawMAArXB9pZYGkOFDnPDUNVoOiURaSicSUkm4/DZ5OdHik2s5Cg+m/z8SLetZcWvVzi9/TS7Mfn+ybZ0hk0mLTuAYuwY4MLQ7R3n/f848t6FF7/90JH3XphlSQ/NhvmNBmMonCdDOayswYGuYE2YgSDyCLel+kIBXxMA3kwL/sbueK8v4GtibcwElDeajvelgZtJCYqLGLinOFAXXAf3G8OJggu55bqxAMYGbKg6MgXNxsCwfepoYzHbGBgUZiAKGapnEBJuy2bbtDeai0ekoWg5JBc/UO4BTjYGs6d1MmQABdMFN7vCELN+cte7P37oxVq8hTlH7O6MfqxPSZQ4FGYgvIRHpEEJTpYAVTBjMC83wS3DrRnv6MLzLwBA9UOx9wDgQXt7Y8Skk5qrLsyensEuLEhMUIyB8BLekQY9Yy9dh9OqGQR5buW6cHr76Yl33+jMQkinwX7vBb/6Mg7BtizcQGeYQed6E44tazADijEQ3sM70lAi2L7ehFQdnAfiCs5BKh4g+L0fP6dWVeRAT2KCTc9g1zpVJmFqmIHGWBKexFPSUCLBBrvCDJ4oaRxGwQAKG+AkQMmvO38brgyFb3XGroVBlRDKIWPwEqNOrLC7Cw7CU9JAGI7oOElCir/95R/6l63S9tvMPYu7IYgJGVj/qDPM4JAMhUkLWoKMgfA0JA1uwvA1LfkIih8tK2aksgY+xk7RyBVOum7WKWswLzdBxuAlLupbKrnKeQsR6MFr0uDtDAWb04nd1mkPheMjKJDgSXROyaAKtk4mxEIRRpdBXH5t394jqPrRbffeOuLya/v2DgAom7poxq2jlT3fvGoGGi5BeBuvSYPn4XxI+YyQouMnSRFKB8ONQSAH3N0/7vkQwM0LbxLdWMA3H7xFc4bi8rG9uO2n20f8ftOzr5ffh4GyBatmKLQFgiB0QdLgWfiu8MmavEmJxm3wxlTIhBwmRRf45ZZMCJgrcPxxz4d8UeBMQtAocAtVjJ7x0x/g8u+PncSkBaP/vDf9wcVNH6QRXrtqnCJ1kA0zZPa+/MphjFn+vUX1Zeh6d/22JG6/Z+0CZZ2lMAPheTwoDd7OUPBhWiCIN3CuIBAFAZPvn0zBBpMGUOifgsn5cOogMAaGwkY9wQZgdPm4qfhd9+VxU9c/duto4Pf7nvn9uJ/eqnV3jMy7r+B7azvKTj21ejeC/cdGre3YmNn77u7MTYsq9O2ZILyBB6WhROCvqQHFrsDfZtz9k0F5CqNhAyjs7oXjKDQGPcGGy5+8fmncvbeOuHU6duz+3W3Tb7t19AgA6cHPceuIIs+Vr2ao+O7aBch0vXsUwaW43D8G6xfvx+0Pr1ViDDSVE1EKeFMaSifYgJwuKHEFAW5Xh662PYgurEdmb9vOAQCjZyyePaXc7l45icyerasOI3D7resW3oyug+vau3D7D9YtvNnufulm9New+9lnBkemj5Qt2z4uc/+u3O1ixqCMiopJM/H2iUHs7x/V07Exs3f1+q6Na+sVPJNyE4Tn8aY0lBQadKHw6eN4S1S7QSAye9ueeC8TiAC4enJg9JLm2a4PHvNHRRqyYFjm4CrctXdXWWbP4+v2XEb/6HW7Nmf2HNydudn9kfYR9656DAB+AAC3bh++rZfMh7szNy2qL5syA23bMhNv/x6AikrZ94sfYPD5fCB1IJSTjoUCTUkAQCSebQsL2vjNiaivoR28NbQlG83Fs9JQCsEGA6eU5sxj3IZZk90Qe5ga3V3ZtgEA/tTfl+xvSaZw99PNN7ng11BmyoTP+v7Mboys+Zr+A1XM2bsQAAb7MXbMpQtjsO6+13D7I+uMeo+kJhR3MkVHWlZci47V6wcrUodHRzu+d/GpX63fW4HDmNlhWReJEiIRDXTOS2W7/cwUQrFUd6Mfqd4kZwo50rFQA+LZbBjpWCgQrcm2hSUaTcez0lAi6AwzyOzQyrSF+vmdKsqBQXbz2hlPPz2lHOhq29B10xolMWRb4FyBbwbcbdPI7Hn85TGP7K16s/bo6N5dmzN7Hl/XtXmd5neJLwqi1Y6qYGUNtk8NmUfZoic3AsACAJjCuy0O1TEQ2kkcaI80Z1l8wN/YHGk6kAL86b6eYO1Kwaap3mRkbhgA/LPnBZsOJNrCYfFG0/GyNJRCsME8uLSFw0MOl/44OHjzlPIyAKnBq6gvU/d0s1egKHQFDvON4dSGx9+f+cje+jJ04aYxZQAqqiq17IhzBf2iYCOGT+gkagyUmyCUEm7LDv/IJw60M1dI9SaT7QFfEwAuOcEXCX9NHTr70ghDrNH8FIWXpYEoBcpvxhMvbBisSL03OrJLpTGYBH8AhflmIEnX1pfTlQG8ue4oxi656ztHn1+3pxKH8W+71OyE6YKprqBv4KVtUIyBMI50LNTQ05pq8wPpvp7hCoV0LBSKBbobU71JFGQDRRvNx+PS4Plgw7gNswzPULiF+ugaAChbuOtpQDaGbAt6dOGzvj+PrPmazlrI+hV7+WmINZsBYKGaPVhgDDpneVKOqatgE4QOElFfQw9XyOhv7M425h7y19QlD6SAQG2w8HmijebzJTsO6mG+2DYYmzPYue1vVhyMydC4DbNohkfNTLz7Rru74GhcnY8gCKeTjoV8DYhniwx98NfUJXtTQ0/p60FdjV+i0XxIGoxk4L/34trGg5Uz8VnnUUuO+Nnk59k/t3sDq4W05lgT776R+3fl3OfWHNR1WDY44o97PjRvlWqGeWEGfgUDVTMQ6kjHQoGmOsFAiUTUF4qlc1v09bBSx0BtsL0llgaQPtSZK38UbTQdj6cnLKb6643LAeDPKVx7m92dMYRP1hxxfi2kEvhGYqooeGk6SAozSMEVNPh8PpYDZXfJGwjlpA91JoFkg6891xKJZ9vCbam+UK4OMtia6mbDIxq7472+gK+J1ybeaDrelwbLyxq+2HbucGBMY7VVxyNk4FxBRhSunPtc/wAKJ1Q+EqKYF2Y485ueCd+vowmdCG3kVS8Ubw+3ZbNtihrNxfvSAEu94dzj5z7+zpjGmf/b/EPxGHn6kU/WHOEyFCVbGimKwriCBm/gBxUMdwVDaiEJA2HRBSYK/BaCKClKQhos4+jg4U//qQx/6vwtrn2gcqqVwQZmDEOrWG2YBVvVobvjVSz+YQiXXu1YewT+WTev/qGzyw2ZWHASIG8PFFQwHPNmeTIqzCAQBUFcgUtPEITnIWkwkpmVjTPtOK4giPLZ5OdHnn7EptGYl17tWHvkM//9wKUja/GdHTu+funVTRu7b1wdsrwrauFiEkwLBOpQUq7gximizUMQUSjMRFBugigdSkUaPD9hgwDmDbYcun7xjoqOjQDKZ+34IQBkBlBt1+zOH73x8agbRqitfBQEHhjWu4LFGQrviYIhYQYutEDhBIJA6UhDaTLOiDyFygEU5eVAZvjupVc3ba9etkPhitUqV6BQtEM9T3dXUEH0V1+hc7DnppKDXEsgqGnGaa04cR0KCWNgIybs6xRhNVXb77K7Cw6ihKShBIMN7IZ9JQ7dGzedmLZsR+jrirY+vf30ZN4K3frROQ3DlXOfW7KmlDGMn1PL/8lnBIKVSlbZLjQGhmCuxmIjMC/t29FxGGN+NHt+/dcBoGvHPiybb1eQSWeYQVC7wG5QsIEgSkgaShauxAHWqkN3x/b0SD9+u/EEqhct/qHCYAMhQOGvfuFPPoBUcrBowEDKGARRh6JzNlz6bQdmrH1/nQAAIABJREFUN+/8evfT6/dVrp1yakfz4UuBH8k/x6mIVjuKthClwNkWXXP1jW+2pdTNLEgaPAKrYJCKowyNqrBKHUKLVwPA4h22Fz9qKGjgY3uwgZU1yG8jZQz8DdiNQvmQMgY+SowBQPl3mufjUtdvj+Fbi8uBKcs6Kna0FH2WACcsXiVlDARBoNSmkc5ms3aVB5rEyNOPsH+Zs1eLbswmnLagVw7B2PIIl8KEgP0/fk4tJxDcbXljUEl55U0z8NmpSyjXEFgycAJKzbkJMgaCkIciDe6D7z1KXEE/lk0mzVagoB975RQNMyDfG1iVA79dHoVhBgCXzuwbnDC//uuhaWg/9d/z5ysrZHEO3LhKMgaCkKHkpMG95ZCcK+gUhc8mPz+ylBbUdnuGQgYlxiDA0LhCHuXXYtf6lkx56vDIyE6txmDIGAoNYQYKMBCEQkpOGuBCb2C6UNQVMmevjhxfpuR1MW8QfchjMvHRGx9bvvh1/7YnH3oHN9wx6xfLp/Jva9yd1GwNGozBXL4+/4m1ADA/11C/rNm+3qiAjIEglFOK0uAKzM5BFLqFx6o97GKg4yH8KH6wsn/bvf++beAcd/vo1F+oLaHmSiBtXIGCjb9w0XKXqsIMlJIgCLWUViEkh2MrIvmFjdw/5U/PnL2q53W5KMww8e4buX/yW7JJIfUci2UoFG5cvTi+vBLAQAo1t/FuK1yIZGTN17h/Zw/2sn+FmzkuzGAcf9zz4TcfvEXbc5Ubw4Tv13EBBjIGwjbSsZBviGiCa01EWVMoloaGRnOhSIMjMKpewVQmO6AWklOED18/y27cdO94s7uknv5t9/4isDleLbwtDt9IHLispVuCDaqMARRdIOwnEQ10zktlu/1AOhYKhGKp7kZ/OhZqQDybDSMdCwWiNdm2MKC80XRKVxocUtmgsF7BdlgQwpoxFIXwwwmcLjiV9x+/N/6dzfGZlYLbefBF4czG49ztolM5mTBO0guoLX4kYyDsJ3GgPdKc9QMA/I3NkaYDKcCf6k1G5oYBwD97XrDpQKItHIbyRtMpXWmwC0H6wAxdGHn6EbUyNPL0I0pyE5aNvWQUxhUK+fD1s44apXn0yV98WnMDfv3vv0VNAG9ztx94KsqCDUwX+KLA58zG4/JzMZEuFKKtjoEgbCbclh3+kU8caA/WrgTSfT3B2pWszV9Th86+NMJQ3Og3vdckDRZhWQIic/Zqxfgy8/ZvjTcwXTAqqKBtrUs+ygdeznwqzit4jC4X20bKGASPMnvg1KF0jEH5wEtW/aDWGCjMQDiMdCzU0NOaavMDid4kClaeSyluNJ+SlgZrMhRuSUAoh3kDAJPUga1c5fg0hBZYmGHC6uny3sBg2wjUwRacVtbAVUqqSkmQMRCOJBH1NfS0prob/QAQqA0WbqK80XxKWhosw2JjUD5hg2a4EgeYpg7KcVqGQh4lulC4veiy155EsLQmH/6oCm2zRJMxEM4iHQsFmuri2W4uT+GvqUseSAF+AOm+HtTN9QNQ3mg6JTrkksPssZfOHNhZyGeTnx8nMd2TDJ+sOfLJmiPGrmfNOL39tCXDIga2rW2Ys/axbaeG7h9d2ya1np2qgZeGw9Sh6JKVYlzcufmueZtX7fw9d3vHicvG9s4Abl54E/t39p1z7B+nCN988Bb2D8CZ3/Swf2r3T6UMhOPIGUP+oIdAbbC9JZYGkD7Umat0VN5oOhRpMB0vJSZEMTtbYQiiZQ0Xd63A/fGDFQPb5j92dMqKC2tXvJOqbTK5JwpzE4Wc2Xh8wurpgWClqsqGwdcfxfw3O0df3BlZtTNzfuj25h1Vjy/TICCGw8UVzr5zrvBRbWkIKSjMQDiK9KHOJJBs8LXnWiLxbFvY39gd7/UFfE1AsDU1FIJQ3mg6JA0monDxSTOwIEPBx94BmZqpui++HAD60wjMAsasj49d+5ipR2RzQuv0BlVPqbz3zSUAMHABgbGD5xECUFWN1EVAlTQYW9Yg7wochrgCgAnfryNjIJyGv7E72yj6SLgtm23T3mguJA2ApjGKnmScxEJWp959E9+9a0rBbQGGD6y46d7xBpZDSoyhGNg2/xf+n8erAAD9Rh1MGjYEY2TN17R5gyYu7oz8auyaN5cA8zasuuDH+TRWWHVsDn6xgrwrcNuMv+MG/d5AiQmCMBCShqExFHb3wrF8tvvdZ377efWPhbfFMTBVwcZQKNxYphZStkby/Z/OT3z75/GZFYqOon/FS35VhOZ4g0qOPRk5fNuaN6eNBi7P3NK+rBIXd2627o8ThUEFk6AREwRhLCQNQxgebLAxN6GEwgpNqQkepn03VvVua+FtKZyQqhAsSCG1PsXRtb84F6jF9sfeQ+CB9dEqk3tlbXRhiBObf3Xefx32rfodAj+aj19vxli8e6HyuSWa9nbzwpuUZyjs1QUOMgaCMBCSBsCjwQbRsgb5OaaYN+RnKEZWAIMit4tgfXUkCzZwt0W3EYzImLk+Llh5cub6Z03pHIBcQYPF3jDt8Ten8e4+9TiAZdp2xcoa5LdRm4MwFUpMEIThkDQMY2CwwWlhBlsWxLI+5FC0AOLD189aNjWkFKySUbM6nNl4XO0ACgswKaigp6yBEhOEUYxvVrusvZchaRjCw8EG2DrsU2fIwdhaSHvhChqsT1IYBX8MhaOCCgLIGAjCJEgaPI4hujDlu02itxWiOeSgqhbS4dhS0GASDilWkIKMgTCW06/oipVOXuqRixiDpMF4nJabUIVYWYMxqAo5ONkV9Gco9DBh9XTrcxOCUgYrXUFbhoKMgSBMgqRhGGvWr3I+gmEUBgrEJ2uOjNswa7J0yIFzBc+kJDhsqYLUCecKH3XmfRzj77jBmTEGUPEjQZgMSQMhAhcpMXyV7aLe4D1dcB1SruB8KDFBEGZD0pCH/mCDq3MT1iBf4qC88rF72x4sX1gR39BxhTWMmXHHwtC1xZ+ocwCFNmxc7EoJ/ASEA11hwvfrimYoyBgIwgJIGghxDI8xCBDMOa2yiCGzb1vzexl/BChvWNMM4E97Wt6pUmIMH75+Vv/6marKGpguODAx4XBR4GBlDUq2JGMgCLMhaRBClQ0cZpRDCvY/TmsRw5Tluyq3bcjdy+x7p3/G8oWG9s4ATNIF/o+9hqJI/dmHjzrPOq2sgUoZCMIavmR3BzwF5SbUwrxEfR1DRTn/3kc7+0ctCSl+MpviSeURhxl1w4hRN4woGmZgZY+GGwPboYY9B4KV7N9HnWfZP2M7ZiOUmCDcTToW8kUT/HvDcO2JKGsIxdLDzxRtNBeSBhdzcc8dq7pM2bPZuQk+n6w5ojNf0P1f6TE3K1tyyiC4lSpFHx1Z8zVTKxiU6wInCp50BT5kDIRbSUR9gaYkryHVm4zEsznawgCQjoUaEM9ms9nUvM7AkEiINpoOSYMI2Wy2cD2nomh4igwV48uK/HJn2tsOG3hAIWbnJgTH0uENmcGMv1JBNYOBSGkB1/7Bc6fM7oPohA18S+CLguGuwDIUxu5ThrPvnJNKQFBignAviajP19DTGm8NDrel+3qCtQHBhqneZGRuGAD8s+cF2w8kJBtNh2oajERnboJvCedPDlw3tVp624EdOxGNXG/GCsdWhhn4qJ0xOrR8DQCgYv5Ta0zqkijcyAuW4xDYgwW6wMEtQuHeQZKGQGEGwqWE27LZNiAd6xxuS/Umk+0BH5t5NxLPtoWHRGIle9xfU4fOvjTCEGv0m95nkgZx1JZD6gkzcD/S508OKHzKxT0vYsmWqsxKzQeV74yVYQYGm7/BssPpX7mKMwYpS/jguVMTHp1i3qCJMxuPT1g9nelCaboCgAnfryNjIDxFuq8HwdZUd6MfQDoWCsUC3Y2p3iRqhVuKNpoPSYNhKAwzFP4dL+UK508OVIwvE9vtwACQ2rlyVfrTT9F+sT5Spbav0n2zXhc4PllzZJxVy1Npy4bwyycNiihc2fvWxsOovn3cY1P+/GzHkMNUzZh6d/2/Kt1FiRhD4XzSlJggPIi/sTvbyN2pqUseSAGB2mDhlqKN5kPSYABSYQapOL/yiIIE1TMWbpkBoGvlKnjEGCxG1WwNnCsYnnrIdG/E1K0d/3plb9uzmehjTwL4v288dbJcoTGwYIOxXTKOzJ6t2w9jbOSu++rLMnu2/uwwArffsn7hJCOPQWEGoiTIyYMfLBRRN9cPQLTRdEgaJFGYoWDGwI8HCEoTTOoeANRv2WTi3m2ABRtg92TSggGZ5pUpVIS2LgCAy/2omgIAV/aevDjjzrtV7WTivPG2BBvkZ2vIHNyOu9bvKju1YeUuTPkt7tq3qyyzZ+Xarknr6404OoUZCG+SiPpaaoeyE0j39UTmtgEI1AbbW2Irw43+9KHOZKQ5DKlG0yFp0AXfGDSUJhCFWFzcwGFC6kFhWcOVvW27xty9tQJA36v9I364ABDED9geWKNgbyzYYL03TJw3HrLLXVbMWb8Qma6Dv8OU++vn3FcPAIP9GDtVx0G5DAVNzEB4lnBbqi+Uq4MMtqa62fCIxu54ry/ga+K1iTeaDkmDHDLBBn5KgumCGa4gXdbgZawpbhBM8XT27V7u9vjv1Vo1COI/nmo7NePurSwf0fXJwJipo9gDFx44yG00dvV01jL2pTmcT3D2YFmSgokCQ9l0kBUV37gNbx7P4L4KZPasfGnMo/v0z6dBMQbCa/gbu7P59xoLNxoaaaGg0VxIGrTANwbPxBXYitilU9bAahr4rmA9XW/tSo+oxslnj6Fq8Z23Dn5eXZmrZhj70hzOGwpvIGcSnDqYGmwoGlcoJPPBrsyk++rLpszESyevVvc/97uZj+6r1zGYlz8tBMUYCMIufPT1KwoXbOBcwWJRuG5qtTXBBkdJw7gNswwPNvCLH2V0wdhIwyRNAy8nrJ7OVwQpxr40h7ttqjSoXmni6q4Nz10YU5U6XP5A5NJL7Qj4AWDskhX3qQ02cLpAFyvCenw+3+lXRNbjVc7kpZO9dOpSpEERdukCYQicK5zecRrA5GWT7Q0wGAgnFmNfmmNXRaQ4Zfet2QIACwFgiobiRwotEIQDIWlQCumCxeisbOAHFZgruIszG4+PVRZsYLAtxzrKG7RCoQWCcCwkDcVh5ZA2dqA0yyGh3huMEoWzb/daWAvpAqxZC5tcgXAmk5dOtrsLDoKkgRjCvCUndA6hVD4Lk0JROL3j9Pjv1VqWoTB7PmlvwIyBdIEgHA5JgyJYsMHzGQq1VZAKbUDtEg+jbhhxZtNxfsuEVdOtTzFMenSK64INFx44aFKGwtRgAxkD4WR6Euf1PL0ufJ1RPXECJA3uwOwMhei4iaJOoGfBJ3kmrJou8AaLYRkKGzsA9WUNbkF0TW0yBoJwBSQNSvF2sIE/SQPfFczTAhmunPtcMJHzmU3HJyybbHs946RHp7AbrotAOATBgAhWKkS6QBAugqSByIMZgy2uII+x3qCqrIFzBba97REIW+BPB6kB0SJH0gWCcB0kDUrxcJiBw7HGYAtchsLGSR1sz1BwrqCnmoFKFgjCM3zJ7g64g1IwBgYzhlE3jOD+2d2jYc5sOj55maVjn86+3VtoDGff7uViDw7kwgMHdUYFBJx95xwZA0EQDJKG4jjEGFgtpHn7Z1WWhaJgizpcOff5hFXTTT0Ey1CYegg+1qwpZSPj77hBtMIRZAwE4SFIGorgEGOwhszZq9wAjb6ui9w/WzuVh/XBBkPQUzh5ZuNx/gITToO5wvg7bmBmIPAGrp0gCEnSsZAvmhi+n4j6fD6fzxeKpbU0mgtJgxwlZQwAKsaXVYwvKxSFvq6Lzgk2MG9wnTp88NypCaunuy7ewKZnKLqZjBn4chjaL4LwComoL9CUHL6fjoUaEM9ms9nUvM7AkEsobzQdkgY3YV6GQkoXOJg3OKTEwd4pHKC1rMHKgZrGljXIwCoemBYUBhvMnnmaIFxNIurzNfS0xluDw22p3mRkbhgA/LPnBdsPJNQ1mg5JgySODTMY6w1FdYHDe3kKi8saLMP60RZMDjhvKHyIIIhCwm3ZbLa7McBrSvf1BGuHGvw1dejpS6tpNB+SBnEcawzG9or5h3IbsDhPIV8OWdQbujte7QaAS90dy5Z1bHzVtSNJTShryOxt33Bf+56uPwHIdMU33Ne8oetP4psqzFBw3gBeoQP3KFU2EIQiUr1JPY3mQ9IggmONwVgUBhj41NRXmdUbg7n0asey7VcGAFw6svbEzTt2LF6ExKuX7O4Wq2ywuxe4FN+JOWt2RaqOv7CnK77z+Mg1u1qWDB7cI/X+FPWGwqET2Ww2m81y7VTTQBCKCNQG9TSaD0mDEOcbg9ljL6VgxuCoqZ9kgg31i3fcPwoAyifMwh9f7T6ye2BkfXnBZpozFBbP1qA22CA/W0N5w5oF12a64kcRnIrL6eprAVRUon9QRw+zOdhdsgSC0IK/pi7Zm2K30309qKvxq2k0H5KGPJxvDEbBwgzKt3egMTAkvKGc84NLZwYwsr5iwjR81mV7pMFKis3yVFF580xcPjkIf+W1Rh6XvIEgdBCoDba3xNIA0oc6c5WOyhtNh6RhGNcZA6thtOxwdi1epXOWp8xnmDarvHxEaBqOdJn7Cq7s2//A0v1b9p0DcKX75Jalv9zS/YVwI4szFKLecOmjPV1A+bX103F4ABj8E4CMfJhBJkMhn7kQRCAIgpDF39gdr2sK+Hy+QOe8VFtYZaPpkDS4FS5UYIE3OLyUQb4iMhSuPrFx46tHNm7H/T8UK+I0KkNx6WQLZrz0yj3zcWTLvpP7jn9t5Ss/mT94bL8h4Q1tniE1jKL8Whxv3rC3/b720ZHmOTMHXtiwt715YPSSevWHEJ0lmiyBIFTgb+zO8n/zw23sK9Td6NfSaC60YNUQJoQZBju2tCUQeHD+0qmjDN0xcP7kwHVTq2vqq/q6Llrzi+7AxERRQotXA8CIH65eDQA/NPlw5VNfmg8AlwZQVf3fR6trAYyqxMUMUFhLoRymC5pHUV544ODYeeMBfNR5drj12oXNLQCwAADQ3KLIFliwoXAIJfkBQZQOFGkAzElMDO5vw/yNr62s/t3Tr+ipL5NB88QJagsaDBlpeXRt21EAGNi29rE5a9uOZpQ+UUmGwq7ppQvKIa/s+2VH9fx7KjGm8hrJZxXNULC5I9m/Cw8c1Dnvgl2LZFK8gSC8B0UazKLyno2LMXhy/2HMiFaafCx+hoJbPMJw+N5w5dznAo2QDUUMbFu74p1UbRNwcddW3P/swYr3fzq/bcy+aJVx3Tuz6fiEZZNP7zht3C7V8oenf3lq+vyXQtfg0tdx6guErrkyCCj89PkaYfjPPIs35AUbNMFVNrB4g5L5GwiC8BIkDabWP1ZWBm/HvqODWGq4N5w/OcDSE4KYQcX4MjO8gX+UmvoqZgxcI2uR8YZZ6+Nj1z4GoOq+Z5dj4OiuBL6zosroTmr2BlbWULgKtiq693eky8bg2JbjqLpvxpSBfVv2lfUPfL15frEn6kxAWMxHnWf5xZUUSyCIkqLUpcE8Yxj8j1cGv7V06qjpt+FXx68sXWxoWcN1U6tlHi0sjTRWIwpTG8VKK6qrgH7e3THTw9h++CKURhpYYEPJkhM2xhtC97wU4t194ie3yGz8wXOnJuTyGtboglHBBkgXNxCEJ6kLX2d3FxxESUuDqWMsK0dj+4OrB6rPJ6oefs0gY+BcQdU6ETX1Vfzwg9qCBmO5eKqtf0p0ZsW/fRu/OJKJLq8w5SiTjfCGrhd348FF9cjseXHVYXwncueieqPHqVgZXXDy+toEQbiFkpYGcxm1dMOLALBY956Uu4LoNiwMwLzBlqkk+VRV4qX5j10I9L4z9t8PmmMMZzYdn7Bquj5vyOx5cdXhwUAEyLyzCnfu3VWW2fPi7ooHFxV2+ezbveO/V2vlCpZqYbpgSIyBQ+GCFAThAT7r+7Oep4+s+ZpRPXECpSsNzp/KSZUryMN5g0kxhpr6qqJjMmeufxYAKqLP7AOA5Wb0I4d+b5j64N6qF9cByFwCJgOoqMKFDGCg53zw3Kmxj04xNdhghi7woYIGgig1SlQanGwMBroCH8tmdHAIar0hvxayogJgY0Lr7/xOe+u6/kqkBxExr7tGY6AuTJSuhBBdC5sgCA9TitLgQGPgFzaaV3BgY5hBP8prITk4bwCgI1UxreWZRRXI7Hlxh9QWjspQGBtdYAMluOESgt2SMRBEqVFy0uC0RXRMiiuUIF1vvYE772ZTG2a6V3Rcs/XJ68AkQ3nIQWzg5YmdL2IMfttfvmmh0X02NkNheDKCuQIzA9EvDkUaCKLUKDlpyGazTvAGj7mC/CQN5nNl71sbD/939VCdxP99o+NjYMbwwwpTFad3nOZPK1n/4DoAKFu05kEAi4zvtaGMfWmOsbULfGOAY744BEHYS8lJA0Mwz4FMtoJtaUY6wxu6AAurJWQyFFPu3Fr51rNsq70nsXhGdUf+BkMhB72pCjkmPTpFQ4aCBRvgsMmdBMaAXKRBsGYmhRkIotQoxbUnCtfqvW5qNfvH34zfIj+ZEmHI4hQ6GMUNash0v4qpd0uNcWDqYMYqFXpmkzSkGOLCAwdFV8HWgMAYfD4fMwYWyaAVJQjCKNKxkG+YaGKoORFlDaFYenhb0UYbKEVp4MjmA54csBv8R431BjYJtIE7JAAAVzJA/8lnnzo2kD72huiSWJw3iKqD5mWyPUahFhTmPkgdCEInqd5kJM79yLDVsdOxUAPi2Ww2m5rXGRgSCdFGeyhpaRDAeQNnDIINKN4gjwXBhmIrXo6qDz325J2PPTmj2j9DLt7A/kmpg/V88Nwp/TM2GhJsmDhvvODMZ3cnzhvPPUTFDQRhBOm+nmBtQNCY6k1G5oYBwD97XrD9QEKy0R5IGvIQBB4ED9nSJbfAlWiMumEE98/KDtTf+Vg9d+e6x55UMFu8sdmKgmWybUCnN8g8tzDSQOpAEPpI9SaTTYH85ARfJPw1dejpS0s02gRJgzoo2CAPt/qlW8o8+SEHh0Qd7KKw+FHwKHkzQRhJuq8HwdYU+ys1VdsSiqWBVG+ycEvRRpsgaVCBsRdN75U1MFcwWxeKZSi0wNQBuaiDXWUN+RmKqztPPH7Pia07MwCuHme3/1Z8J9qCDTLGwEUUuIpIBjkEQejC39id7W70D92pqUv2poBAbbBwS9FGmyBpUA0FG7wKV+tgy9EFYygGzz6PiZv3T7sLH2zdefb531Vv3j/tLnx0cFDBrjQnKQRaAMpBEITF5OQBYKGIuhq/RKNNkDSog/66ImQwsKyhcvzmJf8M4Goa5TMq6zFw8PjZN9P/8o1KQ/aeg5U38g2jcG4Grp1f60NfBILQSyLKGz+Z7uthpY6B2mB7SywNIH2oM1f+KNpoDyQNWjAw2OCxDIUAflGksdWRhmco+JzZdNwZAy+v7nzrNf/MORi8hH/5RnXlrfif/1ISaYCyYAOXkpCp/+XmZuBaKPZAEMYQbkvN68zVQS5GBxty6W/sjtc1BXw+X6BzXoq1iTfaA00drwUDl7y6bmq1W2oGDYFJkrY5pwuFw7xUwoRV07XN18RsQ/N8TZOGl6L4Y/Nbv79t5orp/4zjJ7Zi2orpwPETj1+cyCIQipCZW1q+7JHB+UGhNNB1gygRfD7fZ31/1rOHkTVf89L3pUSnkSbsQu2c03xRKPzpYvEGtrSEXbUIAtiKl/r3c/zEa+e/WomPtv4O5csnlm97a+vFsTiEH+xXbAwyKDEGiK03QWEGgihxSBoIh8LpguhvG3+SY6YOnEAY1QGxFS/lNjbquIzp0zZP591tuRMAlqjcyYUHDo6dN14QbFBoDIxCUfuo86xR81UTBOE6SBpUY/gfWzX1VaZlKAY7NrelAVTN+tG8GcbW0OlBflVMeV0QINiGfTo6Yw8KCyb4omDI+hGWoSFY6vP5mHyQMRBEKUM1DaphP0sGrntpYlnD5R1PJv/tqXnOsYUhauqrRKVBlS5IIbA6VerAdEFJgEFn7YIMvLIGY+AqG7jfe23SAF6Yga4bRIlANQ0CKNKgmsJEr3O5nO47mn7y6KdY8vxTtzhLHQqDDcwY9H+7NJT6c6EFVcWP8sZg+5TSEM4WBeh4e9lpz2mHz0d/bxBEKULffC0YOHoC5kYajg2OnlEJnNy8Go9vnGrKMTTClUMydTDKGARw2QqpDZSHFgQIihgEAsEZg7ExA7WMfWmOwHFVvcP80ROF+kWXDqIUMORPRC99WWieBkdg0mwNg8mBwcvs5qcDl804gkbY6+1Z9z67a5IxcPucsGq6TJmCtqGVyJ/hgLOESY9OmfToFHZce42BdYCFBKSmYZCicGpIAV66CBKEDFkjsPtFGAmlJ+zn/MkBk6amrgzi8ZbVA2M+jVc9/PpoM46gBb4x9Kx73zxjYGRzqzkbOLyicKwE/29xdtt2Y2Bw3qBwe+5VsPIF9r98yIEgiNKBpMHTjF72+ssAsNjujgjgYgx16/7NmiMarg4yA0GdYwwM5d7AFwJWvvD/t3fHOnITYRzALR4BoTRI6AjSUZA34IAiBR0SFU063iBPcE9wLRVVUiGhVBQUREJpUiJEiZBIC+9wFKOMBtvrnfV6x2PP71eclM3K59td7fz9zTfj8DNEh51dMAEzCA0UdX1zFRNDUHIoSnd3iA+G/RiGxYPpaYvhMByvwitJDLELMvN8htswxAwUHlFjADRCnmzxJZfd25tZ7Hs/6V4fQ6wxrP4J7O2L3BsaR6NDSBjDZRqVxIXubWLoTZqMPnM0CqSvxuhuTqu/a8AqVBpOFr6IwzC/bHTYsWGBoatm4BmeRs5W0JUnhnh6sVpw6MlHg8Ww8LDs2QIbIjTMEb89YwPj0fTQa3UcNj/uuMwwXBtSrJVhnnTCIvxM6w2jeaKexDDqaD6LDY+9MDS6m1MlaQ8oT2iYLy2vk0HZAAAF00lEQVThfvz5h4dyQwwHvefvOCUEaVaotsxwSEwG4UK8FxRqPvnhhk75fvvh95gbQkUh3c2pq/sPBwrQ07CMtGYb0kOaFYYXcDtODDErDINC8Oj2cf2fupP2MkqfvPpuTt0Zt5ZIl0hMt3rU/w4Cl6DSsIxeFaH3YAvfsEezQqp3OdvV9xKddD7pu//Rd1+tmxtmv5LDJRLDKQkNDdA4oWFhR7+y9/e121sW0bJQVVorN6T9jznSWkI48+F6y55h2QxoitBQ1M4Sw5lxYWevRrBWbjg1MRwyff/rXb5lQD6hobStdzNMtzceNVw3EQ6yp+vX8rlhRmKIw3945UOZ4QKnBuyK0MBxZwaFKE0McdCqfPnlPMVyw+zOxyismEgf6a2YGNpTwgNOIjRw0Em9jRN6sSDdFHLHY0+B3DBvSuLo3afSm1TZDhJIuWIoJ3wL1zw90duFaanextEpifDg7j9+l9ss8pwmhrQFMk5MxHDQm6eYWIcJtEaloagKE8NSUw8ztDD21Hkv6eFZhWQwDBBdsuNThX8IUJhKQzlV7em01NTDSXolh3Y+e4vfLPv8tRKju1eNbpuR7vukmwEap9LQigtNPeR4dPvYLg5LNTec3/nYDaYkYlyY2Juhs94SEBp2b5WKQpDWFXa5RCLfIrX9ReJCd2BD6OnDpuev2AAtExr2acWskIqtdmbEz7RUYuiSBJB2OALkcNFQSIGGhhUnIIaGiyPCQBXWTTT4qTtnGcWCiSE9mXkHVGmAlqk0bNuKax+OOpQY1jujNc2rtcT7XF9inJ43/EsM0DKhYWN65YSuvqxwVJujzuzEcKGXa3TXJoBpQsMGVDXvcJK0DB7LDM3WtzPnJi5aXQA4R6Nf3+WlF5r5zQ2V9DOeJE5A9AryzXYzBDk9DcXigo0dgXmEhqLiIHo0N2wxLgRpMhjdQaj4GdViOjdcdDJi9Exafi+AeUxPFJXZDRcSw+biQtSbgIgLL9c7oyrEW1jFR0KAKBkX4pkU+13Anqg0FJV5z6rrm6vtJoZu77evPFO8yh/dsxmgZioN5eQnhgInw1piRJAVgM15Z+0TaEtmYth0mQGAvRIaKiIxAFAzPQ3lTC+d2HofQzTcQBqAfVBpKOf+/r6eofTV67sn3989+/MiB6/nzwRgQULDCq5vrnrdjoWbH9+8vnv53tPn337T/fvjm0WP3OytJQBaYPVEaXHTguubqzBPUX5i4u9/uofd3ZNfuy+/fvrB0gdXZgDYK5WGdYSRdVhyKOWnv0Kl4cXdq+UOqswAsG9Cw2pWbXH45OG7Xde9f/VgsSPqfwTYPdMTK8vcWHpZn336xcsXd88edD93N8+XO6zEALBvllxWIeSG7S65VGYAaIHpiSpseriVGAAaodJQi80VG9K2R58igBboaWAO97EEaJDpiVpsaAy2tBKgTUJDXbYyHm8o4gCwFKGhImEk3kpuAKA1QkNd6r+Cl2kAmiU0cAKrKwFaJjRwGokBoFlCAwCQxeZO1RneiqKSHZ/MTQA0zuZO1emNyuVvZxWMNjxKDAAtExr4n2FWEBQACISGqq1VZhAUABjSCFm7ShoaAEBooE+ZAYBRpidaNL2rY5gTER0A6LHksmqxp2H2JMWhfBDe93j8+M8/bn+xtBKAUULDBoSh/dTcEOPCxFs8DA3TzwegZaYnNuD+/n56GUXIB7FI0L1NGNPD/2hiAIBDVBo2IMwajP7X6LYKvTQwfeTRx30qABhSaajdRAHgUPNB/pA/7GxQbwDgEJWGDRjtadCuCEBhKg0bkNYD0uggMQBQks2dNiONCI9uH0sMABQmNGzP9NZMAHAhQsMmKTMAUJ5GyC3JX0sJAItTadgeiQGAVQgNAEAW0xMAQBaVBgAgi9AAAGQRGgCALEIDAJBFaAAAsggNAEAWoQEAyCI0AABZhAYAIIvQAABkERoAgCxCAwCQRWgAALIIDQBAFqEBAMgiNAAAWYQGACDLf5s1qUZaWuO+AAAAAElFTkSuQmCC)
Plotting data by neighbourhoods
Neighbourhoods shapefile
https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#a45bd45a-ede8-730e-1abc-93105b2c439f
http://opendata.toronto.ca/gcc/neighbourhoods_planning_areas_wgs84.zip
Owner: Social Development, Finance & Administration
Currency: June 2014
Neighbourhoods (WGS84)
Shapefile: NEIGHBORHOODS_WGS84
NEIGHBORHOODS_WGS84_readme.txt:
NEIGHBORHOODS_WGS84_readme
* Column name (Description)
* AREA_S_CD = AREA_SHORT_CODE
* AREA_NAME = AREA_NAME
Input shapefile
nbds.sh <- readOGR("C:/Users/14165/Desktop/ArcGIS/SHAPEFILES/neighbourhoods_planning_areas_wgs84", "NEIGHBORHOODS_WGS84")
OGR data source with driver: ESRI Shapefile
Source: "C:\Users\14165\Desktop\ArcGIS\SHAPEFILES\neighbourhoods_planning_areas_wgs84", layer: "NEIGHBORHOODS_WGS84"
with 140 features
It has 2 fields
Add “id” column
nbds.sh@data$id <- as.integer(nbds.sh@data$AREA_S_CD)
Make centroids of each neighbourhood, for placing labels when plotting
nbds.sh.centroids <- as.data.frame(gCentroid(nbds.sh, byid = TRUE))
Add “id” column
nbds.sh.centroids$id <- nbds.sh@data$id
Shapefile processing
nbds.sh.points = fortify(nbds.sh, region = "id")
nbds.sh.df = join(nbds.sh.points, nbds.sh@data, by = "id")
Merge neighbourhood shapefile and Ontario Works dataframe
nbds.sh.ow_2013 <- merge(nbds.sh.df, ow_nbds_2013_monthly, by = "id")
Make graphics object
p.nbds_2013 <- ggplot() +
geom_polygon(data = nbds.sh.ow_2013,
aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH),
color = "black", size = 0.2) +
coord_map() +
scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) +
theme_nothing(legend = TRUE) +
labs(title="Mean monthly number of Ontario Works cases by neighbourhood in Toronto in 2013") +
geom_text(aes(x=x,y=y, group=NULL, label=id), data = nbds.sh.centroids, size = 2)
Plot graphics object
p.nbds_2013+guides(fill = guide_legend(reverse = TRUE))
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)
Time series
Avril Coghlan, Using R for Time Series Analysis
Vincent Zoonekynd, Time series
TESS Ontario Works data manipulation
Group by month and create time series:
ow_cases_months <- tess_dt[,.(MNTH, CASES, NEW_CASES, EXITS)]
ow_cases_monthly <- ow_cases_months[,.(CASES=sum(CASES), NEW_CASES=sum(NEW_CASES), EXITS=sum(EXITS)),by=.(MNTH)]
ow_cases_monthly$MNTH <- NULL
head(ow_cases_monthly)
ow_ts <- ts(ow_cases_monthly, start = c(2004,1), frequency = 12)
head(ow_ts)
CASES NEW_CASES EXITS
Jan 2004 70169 3597 3788
Feb 2004 70327 3407 3977
Mar 2004 70929 5042 4430
Apr 2004 70040 5044 3917
May 2004 69999 4567 4146
Jun 2004 69976 4806 4318
TESS Ontario Works plots
Plot time series
options(scipen=999) #do not use scientific notation
plot(ow_ts)
![](data:image/png;base64,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)
Make time series of CASES
CASES <- ts(ow_cases_monthly$CASES, start = c(2004,1), frequency = 12)
plot(CASES)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAAVFBMVEUAAAAAADoAAGYAOpAAZrY6AAA6ADo6AGY6ZmY6kNtmAABmtrZmtv+QOgCQOmaQZgCQ2/+2ZgC2Zma2/7a2///bkDrb////tmb/25D//7b//9v///+I9N65AAAACXBIWXMAAA7DAAAOwwHHb6hkAAARV0lEQVR4nO2dDXejuhFAtbvJa5Mm7aZdPyfx//+fNWBjbBAgmBEz6N5zNiG2w8DoriI0fIQTgFPC1hsAsBTkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtafJ+v4ean3+UNgdgPknyHsJLs3C8LgBsR4q83++tsodffytsDEAKKfJ+vb5dF48MHGBz6HnBLYlj3kvXy5gXDJA22/D12sw20O+CAZjnBbcgL7iFIgW4hSIFuIWpMnALRQpwCz0vuIUiBbiFIgW4hXlecAvyglsoUoBbhIsUAWA1GvLOmCpjFAKrUZF3RpECeWE19LzgFhV5ZxQpkBdWoyPvdJECeWE1SvLmXh2USGZ502c5AGIoyTtZpEBeWI3WAdtkkSJldQBDMFUGbqFIAdMYbS16XpjG6PE1RQqYpih5KVLsimC0vShSwCTIq7o60CScbDYYRQqY5CavrWajSAFTtN6ej9vqQzcrFX6mymCKq7yX7621m7chRQqIczdW6DfZ1o1Izwtxpga6G7ciRQqI04wQRtpq22akSAFxwuQk2abtyDwvRAkz6hNbNiTyQhTkzbI60KAZNEw01YYtibwQpSlIzPnUNijN894utGSe1y9Tcw3dj22BTs/7/T51e0jkdUCZ8p7tfZJcHWzBVkPK2Wht4DG8jb6PvPYpVt7MqwMF5rfRRq2JvBADefOsDhRIaKNtmhN5IQby5lkdrKd3bURKG23SnsgLNf0TyJKaCHlhO/oFCeTNtDpYycClPmlNtEWDIi9UIK9GXMhCePh+Qt5sq4N1hNGlxFVkA3nhNKhsagshL2xDd7RwuRkO8uZaHazjsTnC5NU/k6vIAPLCYGuk34osf5MiLwi1BvLCFiCvTlzQR6gxsrcp8haP2J12s9+xN7O8PHvYGqItkbld6XnLRrodkBeyId4OORsWeYtGvhmQF/Kg0ArIC3nQaIWMLYu8BaPSCMgLOdBphHxNi7zlotQGyAv6IK92XFBDqw2ytS3yFotaEyAvaKPXBLkaF3mLRbEJMp2fg7zFotoEWc4dRN5SydICuv4ib6lkagHkBXnMHVUprhp594W9+SzFVSPvnsh5vY6BKTnk3Q1WrzTTWzPy7gazF/mqrThtC77fRx+bjbwbYvf2CmorTtqCQ3hpFo7XhVWrA0nyp37zM4BSNuD7vVX28Ovv1asDSTbIvNYYW0Xer9f2qdnHyMABebdik8zrHCPS85bGRpl3I+95zHvpehnzWmOzxG95mX1a7K/XZrYh0u8i72Ygb8a4IMqGeZcPjbxlgbzTUKSwyaZpFw9OkaIkts26D3mZKjPKxlmXDk+RoiA2T/pWB070vO4xkHPZQhtFinIwkXPJjaBIUQw2Uu5A3tyrg2mMpFxwM5C3GIyk3L68FCnMYSbjchtCkaIUzGTcuLxMlRnETsbFtiRzkYLHt26FoYTblpee1wJh5KdtkdoWihR7JbQ5Du0XI9iWlyLF5oTTNcmhKsrayrfQ1jDPu1Pa7rZZsJVv5IURwvWbyUTblpcixTaEh+9W8yyzWRQpdsR1bGtskDCAYXmZKtuEq7gOcmtYXq6k2ILekMEyIhtJz7sXfGXUrrwUKTIS7r55wbC8FCmycT1G23o7UpHYYOZ5nRO8HKM9gLwQOl9dYVjea5HiLfYBh+k2id88Cmy5jryff/0+H7X9/HP6iJXY/CbdFI7TaFXey1TZx/nrx9P61UEUx2kUmJbWLFJUc7wUKTRxncX2hM2Va5D84OnW8z41Y4e1q4MIvpPYHmsu3g2lIsWP383A9+uVYYMezpMYOv8Wr0D2gzXHEMKPEXe9590E3nN4O1V+xQpkP7jJ6orEfQ7b8e7dOZzzdwt5ndBPmP8Udg7W2rshpJQLkdcJe5T3YRdCSBwDI68TeldH7DGD19537sdnr/fy/fP5pT4eq+YTVrDH1GvS/2O65wzO3LdUeT+f3yp335qF5ew59Rr0pkN3nUAleetyb33GQqzwKxwXasqSd+beJcr79fpSFdAqb2OF35TVwVx6BzI7T+Cs3UuW9+1iMPLmpDeXv/v8zbl8f4m8zXA3dmllyupgJr2j8ALyN72LS8a89ZkLzdhhMQUkX5J2Av/+hV0jL+/xx++v16rPjZ5mnrQ6mEd78mu4/3nXTO5k8jzvsb4k+PN51aChkOyL0R0vrDqVxRXy8gpRRPbF6M0zlJG+qb1EXg88ngNQSPaE5Z1xF7KU1cE8SioNd9CQt7nKB3mzUWyyJnYceR1QbLKQ1z/lJmt8z5HXAeUmC3ndU3CyRidWkNcBRSdrRN/M8vLs4SWUnSxBeW/60fPmovBkRXefCpsDCk8W8jqm+FzFEpAsL1cPZ6f4XEnJy9XD+Sk+V1LycvVwfshVJANcPWwfciUlL1cPZ4dcCcrL1cN5IVeRFHD1sH3IlZC8XD2cH3IVyQFXD9uHXEnJKwQNMh9yFWG5vN//ZrYhD+QqwlJ5D5xVlg1yFWGRvJcHVWWJC6QqRrq8zSm9L7niAqmKkTzPexb36VJjyxIXSFWMZXfMQd6MkKoYyUUKet7ckKoYCw7YPhjzZoVUxVg021Ads43PNny/T1ykSYvMhlTFWDrPexyd5z1ce+ZjrIumRWZDqmKsKA//Nypvc2OHmtiJk7TIbEhVDJVzG2bcxZcWmQ2pipEsb3Meb30NcRR6XklIVYxUeZuTeW/fhzmES9fLmHc9pCpGcoXt6uz4VO/1tlBRw2mR2ZCqGInyzhgQCMctHjIVZVl5uIKrh/NApqIsuW9Dw2FMXooUYpCpKIvumNMsjQwbKFLIQaaiLLpX2d3CAEyVCUKmoiy4ergW8zB2ag5FCkHIVJT0ClsznB294wg9ryBkKorOpe8UKeQgU1GU7ttAkWItobcAj3DTEZuENkNkKgry2gR5Z6AkL0WKdYRTmyIyFUXrgI0ixRpC+4VMjaAiL1Nl6+hqS6LiZL6Sgse3zgF550HPa4+7WTISFYcihTnC3SKJikORwhzhbplExWGe1xr3meHgYATktUYY/RE6UKQwBomZD0UKY5CY+TBVZgvykgC3e7IFeUmAntcW5CUBihSmIC0pUKQwBWlJgXleU5CWFJDXFKQlBSV5P0J4qgsVsTuT0EpDkJUklA7YzmPdj+reDtEbodJMQ5CVJPSmyo7184KYKkuBrCShV6RoyhMUKRIgKWnQ8xqCpKShPObt1NpWrK4USEoazDYYgqSkwTyvHchJIshrB3KSCPLagZwkgrxmICWpIK8ZSEkqyGsGUpIK8pqBlKSCvFYgI8kgrxXISDLIawUykgzyWoGMJIO8RiAh6SCvEUhIOshrBBKSDvIagYSkg7w2IB8LQF4bkI8FIK8NyMcCkNcG5GMByGsC0rEE5DUB6VgC8pqAdCwhs7w8e3gY0rEEel4TkI4lIK8FyMYikNcCZGMRyGsBsrGIYuU1dchoaVscUai8lbqh89PWm7NteK+UKW/obEJjbrgsb7c5kEyJ8l4V7SpcvVh3x1tsGPIuo0B5Q3ch9F6foa/0xiPvMsqT917Xoe2Y2jbx3hl5l1G4vMObMb5xEeVXgLzLKE7eEP1hxuvX92S3HncXUra8cz/VPZkoDLyfY5OgB/JOf6wd495mh5HXAqXJO39/OzWMgVcFtx93l4K8kx8d/BXkNcCu5R2Y00qJG+6+Ta97Gbi7mD3LGwbOWhCTtzmKW7BJ7e9OrB4m2bm8j5HSwk5PLCT6G+62SX7eojB2LO9QzyYtb9oqQ+df/bN8vaMo9i/v3QljiWFnFSTmr/NRXoVKc1EoyVs/NPvMzz8iq1vC3VTX5YSx1KgS8t78vA4W7uaQE7cIOujIewgvzcLxurBqdYvoynsdQSyQd8avjI+KL2GDxhRx4ajI+/3eKnv49ffq1aXSuzHE8mDzOuuxz4TLFi3eBIiiIu/X69t18RgZOOg1pqgnM0ca8Q8hrSJ763mlu7iZq4t+DHkV0RrzXrre3GNe8dWulBd3NVGabfh6bQaekX7Xj7wrAyOvJvua593QlftayOWQEXdV2ZW8m7rSn95gjkGZXRUpjMkLyuynSLF5PxcGlkCTvUyVbW1uvQ29BVBlJ0UKG7pQAM6LzZ53yQk0BkDevJgsUiw5gcYEofMV1DFZpEg9d9GMLcibFYvzvKmz+4ZkCe0X0MegvMkCGJIFeXNisEiRKoApV6gJZ8RakWLBFeGmZEHejNiaKhu+x9I4tlxB3oxkLlIkPL519ucSNiwD6Vd5wlJs9bxDH5zw15oqFurUpWCySPH42XA6RW+uZM4Vcxu0X0wWKfqfvtwypO+vPVXsbdFuMTjPm/RrqFIwzuTt/R7yFozBIkXKZuBuyVgrUiRuB/KWjPmpsvFfRN6S8XclRYgsQ3G463mRF654KFI8/ur98/ygWHwUKSK/j7xl422e97YCzt8qHrfycgoMKM02RIcLS1anuQ5wjJK8/3yOHKgtWR3AIEryvpwO4UlqdQCDqMlbTZON+Iu8sBpFeU+1v9kfqALFoCuvzOoABkFecIvjeV4oHeQFtyAvuAV5wS2byQuwmo3k3S4IMYjhNggxiOE2CDGI4TYIMYjhNggxiOE2CDGI4TYIMYjhNggxiOE2CDGIAeAG5AW3IC+4BXnBLcgLbkFecAvygluQF9yCvOAW5AW3IC+4BXnBLcgLbtGQt37Ia31bs2MIP37fLZz5mLy/+soYn89jd2GViXE4v/U29svLY5x34B9/Hl7RinF7Sy9GhUib91CQ9/v9vPH1TaiP54Xq323hVO2cwI6MxTie1//1ut7esRiHakHA3oEY1Q0N6xvIdpOmFOP2ll6MCpE276Mg7+dz1aiHn3+ahw5+PJ3ahVP9NCyBHRmJ0Swcos/2lonxdLrukHCM9u7H3aRpxWjfUoxxkmrzPmpj3vP/vnaPujk6/PqP2I4Mxvj8S+JP7XgMMXn7MaoHNB5FxRqJ0b6lG0Oyzbuoyftx1ejYWTg3yV+/5cY/gzGOP//3KjOOi8cQGzb0Y5xOt0zdXlGK0b6lGkO0zbtoyVs94LX5H33+2i7UfwvFdmQ4xqH6Y9V0jWox5A6mejHqF6pGv3tFKUb7lmYM0Ta/Q0ne43Xs/tDo1QO3pXYkEuPwQ7DHiu1H1Vl9Tj3Xa1GM+hVxeSMx2rc0Y0i2+T068jb/mft/busFoR2JxWhGic3YSyuG5Hj0Icbp+k1y2BCL0b6lGEOyzR9QkffQJKR/oHO43MNSQKxojCZxIodt8RhyveJjjOoH6QO2aIz2LcUYgm3+iIa8h8uGDk6VCf0vjMf4eq3ekeix4jGaJlKJcbquWGyqbCRG+5ZmjAo3Pe9tLDhYpBDZkbEY1RiryaFeDKkx70CM07XRpYoUIzGkhu2j+1HhRt7L34nao041VbQ8PBrjKFPyHI3xoRej8yddZEZjJEbnLbUYNW7kBcgD8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgryqfL1ebvD583+vCvf4LBzkVUf4CS/QgrzqIK8WyKtOI291z+vP539VDyqqntBZ3z9c6OmTxYK86nTlrZ8TWT0K6/I4LLG7OxcJ8qrTlffldP3y9vVaeSv2nLUSQV51uvK+ndovzb3vZZ5aVCjIq05MXr2n5JQC8qoz2vPCCpBXnYi8X1Qt1oK86kTkrWcbTh/0v8tBXnVi8tbzvEw2rAB5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuOX/61bDqgUAJT0AAAAASUVORK5CYII=)
Make time series of NEW_CASES
NEW_CASES <- ts(ow_cases_monthly$NEW_CASES, start = c(2004,1), frequency = 12)
plot(NEW_CASES)
![](data:image/png;base64,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)
Decomposing time series into trend, seasonal, and remainder terms
Now we use stats::decompose and stats::stl to separate time series as a sum of trend, seasonal, and remainder terms.
CASES_components <- decompose(CASES)
autoplot(CASES_components)
![](data:image/png;base64,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)
NEW_CASES_components <- decompose(NEW_CASES)
autoplot(NEW_CASES_components)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAABYlBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYZGT8ZGWIZP4EZYp8aGhozMzM6AAA6ADo6OgA6OmY6ZmY6ZpA6ZrY6kLY6kNs/GWI/P4E/gb1NTU1NTW5NTY5NbqtNjshiGRliGT9iGWJin9lmAABmOgBmOmZmZmZmkJBmkNtmtttmtv9uTU1uTW5uTY5ubo5ubqtuq+SBPxmBPz+BvdmOTU2OTW6OTY6Obk2ObquOyP+QOgCQZgCQZjqQZmaQkLaQtpCQttuQ27aQ2/+fYhmf2Z+f2dmrbk2rbm6rbo6rjk2ryKur5OSr5P+2ZgC2Zjq2kDq2tpC2ttu229u22/+2/9u2//+9gT+92Z+92dm/v7/Ijk3I///Zn2LZvYHZ2Z/Z2b3Z2dnbkDrbkGbbtmbbtpDb27bb29vb/7bb/9vb///kq27k///r6+v/tmb/yI7/25D/27b/5Kv//7b//8j//9v//+T///+Xw60uAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2di3/dtnXH4ST1Yqvqc26tZLUdZ48mWWvH2Tp5j7bJnHSrs3bN4jiyItlLtzWubFeOxf9/fF4SJECeAwLEAe/vfGzpipc//gDwe88FQRJUGQKRaKjYBUAgXAPwIpINwItINgAvItkAvIhkA/Aikg3Ai0g2AC8i2QgH7yNVxnc+8bjNF7e/8VX27C8/rV5Q4j+UOm9a88Xt851XtO2yjA2OrkqEJYLDq9Rb/rZZAvDoJTpDT3L/86Y3DPBObpdjbHIEvJ4jILz5rs532X/vnns/xIZp8cT20dHhpW2XY4xYIELDW/BzKf/5pzebHsSXu+qVEudi0Sv5ome7l77cPfc32ZNd9b2vsufXLz3pr5G9+Celzv24zF5/uJ0n00tVGmvez//67bdVvokmmjc+ztdtiPvy26pepSjB70pkm1fadquCP79+vlvqwqRZQS+wvlZb1nZ5/vF4pF763abI3/lEWw3hGuHhrfqTu0UHosjBTzYvmkXPdr+bvzz3893yK/759eIvfY2Sm6L/ocPbf3+TZTdvdOB9tFmlfPXKm+c7r7TtPtsttlPk7LbUmQ5vt8D6Wm1Z2+Uvbr+8q3KDbju0qyGcIzy82ccvffridpFkfr9bYJLvuS/zZJzvvR8X+BQs5K++zMHId+03vnp+PT/CevEbdV5b4/vFfu/1Tdv381f5UdmjpnfbvtF2G17cfqlM8sWn49wvsj/dVt1Xve0WKVIrdVujCt5ugXtrdcra1rn48qk23SzbrIZwj2Xgzb9o60X1q6zZcR+fe7989fx6Tkyxf8sXtahd4+W//l3xWoOsfT8n89P6i17ftNbn/d/f/vOuKrC5VK/TvtIP2B7lwmJjbanbGm2MNwXurbUpa7u8Kp6+7mY1hHssA++TeuCh4LCmqX7xpFh0qSHv4wLekrxHm1Wf1F//6nuf9OFt3q9y5ebIqyNs4a2+sHN4q0XFyu0rHd6iQMV7banbGm0Q3BS4v1ZT1nZ5Uzxt3WY1hHss0+edC2/2+zfL3qs7vHlv5Lv/8p//d50Cb/Ej/8Q5wtuUdQLeZrVg7b8FscxowwbZzNhtuKTBW+3pbrehFPzPP6q3rN2GHrzDbkNVgraz0HYLile9cd5H5355/XzWKXVboyG8/bWasrbLW3h76xarcZsV0cYC47zlLs8PjfJjo/Ig6P3s2fXu4VYfXvXn/QO2J/mi7MVvakoflQfr+gGbBq/pgC3H9qtinKowyA+aXtQHbPUrbbvFyn9V6NpS1zWqV9AL3FurU9a2zg287bLNasHafwtioTNs9fflpebVW9pQ2SUN3pd36+/T/lBYTekjw1CZBm/7Rne0QdXbqEpQDpVtXunbLVYuUWxL3dTo0hDe3lqbsrbLW3i7y5rVEM4RGt5z9THJs7yH93I5Jv/lbj04/6w9SaH3eYvB/0+6a1Qj+t+veqPZ8zfV+T9UGbI9SaHB2wrbA7byvMUviq5s0dt85b/KlZtXve1uRt3aUhfRrNCDt7dWU9Z2eQfedt12NYRriLuqbDPghUBMBOBFJBuAF5FsAF5EsiEOXgSCGoAXkWwAXkSyAXgRyQbgRSQbgBeRbMyG96ElTuyRjbznSbEOC5GFEmABeBOwEFkoARaANwELkYUSYAF4E7AQWSgBFsHhnbvdbQscOfMDmVeIhfLikV69XQSAV5gF4KULAK8wC8BLFwBeYRaAly4AvMIslIne2IUSagF4hVkAXroA8AqzALx0AQ3ex3tF7Je/X3+Qff3e3l/8MWt+AV6fCsBLF9Az79Mc1IP94tXZhznGP2p+AV6vCsBLF5Dh/frv7mZnH90tX/79g+z0bx/UvwCvT4UyDjesv95OAjK8RY7N+wlF5+H0p38sWK5/5e99Mw8bvJMZHdENhfPD/JhqspLS03fK7Ft0IPK/61/1Csi8XhTIvAwBFd6nm0Ozg/1B5gW83hTKeJpi/fV2ElDhPbixebWPPm84C8DLEBDhrQ7VivR79qsHZx/eqEYbbmC0AfBGtCDCW3cPHu/tvXY3wzhvOAvAyxAQ4Z0OwOtFAXgZAsArywLwMgSAV5YF4GUIAK8sC8DLEABeWRbqxETv+uvtJAC8siwAL0MQHN65292yUBnufmcHMq8MC2RehgDwyrIAvAwB4JVlAXgZghDw3tsp4tU7gJevALwMQQB4j67eOrz48N5FZF4HBeBlCELAe+1O9Q/wshWq83OOR2r1dhMEgPf43Vv5vy9+AHj5CsDLERDhNd7zbrskMuf2cGfnMroNfAXg5QiI8Jruecet7/4tAC9HQIPXeM+77TagqreLPu+UQpWhvxUMXuP8fX4t/Cv8wGu859186/vRlZ0qLtwv/x7P6JV3GYQV1xRNhfWKK+1XALtkQm2CsO7ou8Z73m23vjc5l9xtsKQbPcoclWJ60KOpRae23eyrDMtO+i3FKJRqf09opTWt+ryOkXLT4C2jf8+7r1vfOw1sme2oWqzktTBboOpaqN5Sw6u6OVTHwzypw0ihNgLz8LFBYSgJUUAuFC28w0vr83LPsG1KpypGy4bWe4NNbl4BvGUtjD1d/dWmDdpvnDZ9UsnS4B1X9aphSSJ2ASEiwWu859126zv3DNtg1ykt63SWxzhwcd+JBmVVr2zwjhq86L5Z11t1fli/oGriN7Rr2KrOerpoCO9kxZOB13zPu2Wcl3uGbaR0vZ6EyurdM0lUs0LWX2DqYBqVm79dd6K2+1W3Xz/c4ii8Tb03azQf7bYh6hzdrjKgvbv1TUJvv+H6TTrdR04I3ulo4WWeYRttJKVnAKUxaKVYbVJTOyp10ph18nm3h9mskHH3ojIWUBPWPfZmwXAnTsDb9eilxG5Hq7NOC69ekI6H0vN3NuinTdR7nfAyz7BNsTFUVMvNKaOVtKmpm2lOtJfdz0D9VnbS34vjBVRZj9hOktUSXvs5NOzEXgepH10F5avBlKqNf2gWbS5Xg7fHy2Qs2LRiLGLByxttmKzzQNGvZvNbaV+ltaALU/d7U/vCbZbWX7gajZqBwbwjGGy++2Vu6MsY6mCqoEOW03tH1rxtsuh90k+69dZSdd+S3dGY+M417M7+BtOH90TpjFgE2jeheW3VFqrfSe2m57pl67c6tRhs3nxkZaqFlqRJitHQUnW/PzCh6OZsvd6qy1RvS9P9K//HhN7h3ZxhIw6VcdilHRxZBIRunKbort8b99j0AzbHRt1egV6w8UL1VwwBL62JTT0TpX9r6NsZHhyweoAU3BeHN48Piu4udajMC7zTX4YnlE+6rjA27jB/DwY0GBb6du3biD++PQWvqZvU38RQIQ9e/cKcKd1y59zZM+YbT6urQEVWnZ8Sw9B67YLNhRrlq+5y1V17eBmHn/r6HCq7WWbe+sKckQ8O5ZiW91EcFzh/feob6S31lOT0YySKgm0xT9EvWnuSWxuk66yqH4v0ziyOf9XQyhQA3pzevMt7kXDAZu4nzqjNuIANrzEMO5EZdngpvR93C7+KbHNEN3irqovec1a9azo673bh1zYWA17GaMP0ubK+ghfSrm2wK1KE10huEf1Rls056824zfBor/ubWKbI8DJD6k70oEgSXmtYR1ksp5b0XE0tE+CVYjF29L3meldhHBEHvKlYbDe8xqoD3lQsthxeFwER3tO3i3vY8NT3gBaAly2gwVvc7VPcx4anvoezALxsAQ3epwWjB/t46ntAC8DLFhC7DVX2xVPfAwbmRHeOyWYr7ljDU99DWoxcZbXqersLqPB+/V7z5Gw89T2QBeDlCojwnr6937zEU98DWQBeroAGb80unvoe0gLwcgU0eIvx3eJQDU99D2gBeLkCYrdhOgDvXAXg5QoArxgLwMsVAF4xFoCXKwC8ciwIkyrMtfCoEGABeOVY2G8rWXe9nQXB4Z273S2K5GYvlxLIvPEtkHmZAsArxwLwMgWAV44F4GUKAK8cC8DLFABeQRaAlycAvIIsAC9PAHgFWQBengDwCrIAvDwB4E3AQmShBFgA3gQsRBZKgIU3eBGIWAF4EckG4EUkG4AXkWwAXkSyAXgRyQbgRSQbgBeRbOAkhWALkYUSYAF4E7AQWSgBFoA3AQuRhRJgIRZe8vPSRxXrsBBZqMAWahN2DeBNwEJkoULD+3kdgDdtC5GFArz0yrgp1mEhslCA114BUvWV9Q8pFiILlYKFCHg52+gWgjT3kbYOWyHFQmShYlu08DI35BLeMy/l065/dg2KJCxEFiq2hYjMa7deQQv7sRBZqNgWgDcNi3mFMs7qm0S9Ae+4woPFlGIBi7XCO6ZIDd7ubjDPEt5TaCuRdqJEC5GFim7hH97Tt4sHWoV6lFV6LezFQmSholt4h7d4WOvpO3fPPtyvHiK47/chgum18LSFyEKlYOEd3qcFquEe35peC09biCxUChZB+rztE7PbB2d/M48G1qMrO1W8eqf8m7rdsgzK+Jok4CsWsRBZqBQsQpykKB44XDyCOKe2/lW/4T3zEj7tvc+uQZGChchCRbcIkHm/fu9GftjWz7yAd46FyEJFtwgx2lA8/X2qz3uv222wWyfSwlMKwOtsMabwDm/Fbtl1KEcbbhhHG46u3jq8+PDexZVkXsC7Dngf7xWxPzHOe3TtTvUP8HqyALzmYME7Fi28x+/eyv998QMHeNsKBCNraYtphbbSauqdKLwPc24Pd3YuO3QbUmthggXgdbNI7NoG9k7U0aC0cAQLkYVKwEIEvPQtqG4xKMP7moCvWMYidKEWsIjStLHupDhkDpUp82e3/1EcKAafXV3RFZgUQiwmFGqiUOpkoOBayGzaOJn36MobvG4DpYXzfZj/Y1RfF1BaOJLFqKJAd7xQfXjZFiKbNh689SDZJLxNy1J2or7mdPV1gVFBtbApPFmMKfprTsPLt5DYtCfR4H34wWUevNOfXdVTTLaw6v+eYWFRUC2simmy+i/4FpNNS7FYvGnLBbG6DdQ+b1MLcgvrCnsL9wVGBdXCrCBbWBV0i7F6Uyzs9SZZLN201YLA8M6+k8LewuZdQt2JBsFQQbYwKgYCU6HUyZiCbjGj3uNN68XCd9PWS8LCO/9OiqbIzBY2tZfnFu4KpuA9sRZqFF6GhWu9JTTtqEU8eK1XlR3fzHsNF+4TRhvUSa+FtSpr+96gsFVfGQRDha2F3SyMCjX4abMIVu/YTcu3aJaEhdd8J0XBbnHAdq+md3QT9UC1aougst7r/gX2HYX2vk2hOv8tFmYFz8KoGNbLZhGs3pGb1t0i8EkK250U1VAZ6aqy4aew/9lVZsXIZ1cNBFPpYajgWVgVbfKZsghS7/hNO8Niscyrw6tnXrt1tdt7xe7v9n71a8VAYFF4sRhX6PCaLSyFssArpd5Rm1Z+n3ey+mpEYW5hS3sNFXSLcUV2Yi3U5petUAvUO9WmDT7aYL6Tgj7aMNnC/YJPt7C1vfy18FAwBe+YReh6R2xagoVdIX6c96RbaWL1Le01SpZRQbeYhnd0J44Uimgxp96pNm2s08N5d/fezs4bDHitaXG6+r0WDmExriDAO9dipN6TFqk2bazTw1dv5f94twFZ9oldYWnhIBbjigUsRuo9KUi1aeNdVZbnXsALeKctxMH78N7On906bLoNNLXSB7gJxVG6gK+QZeFaKIpAcr2tFgk9k6L/UaQkFGZa7CsYFqOKBSxG6s3IvGk1rYh72OzWnqoPeIMVahvhdZnuKc0WXsgC8Fo26B9ep+me0mzhhSwAr2WDAeB1me5JsXeiLqC0sLMFHV5nFF3rTYE3yaaN9EwKp+me0mzhhSwAr2WDHHiJz6Rwme5Jr76twPpX1XjtB4ITZ4tRxQIW8+qdZtMm9UyKJFt4IYtY9V4VvHX2HX0mRT+o21XdF5TSaAK+giVwK9QCFoELFbFpYz2T4vjmzoXPrt5yy7zli7BpkW8xpljAIlq9Izat18x7sLf3I9ozKY5vXv7ih/cPSXdSGIJafU1AamF3CzK8YSyi1Tti00Z6JsXRtTs5vA4zoztWH/CmbLEYvLRnUlSZl3YPm4/qbzO86X8+FoOX9kwKzj1sPqoPeINarAXesZg7VOa/+uuxiAZvxHoD3pVYAF7LBgPAy7mHzVybwC3MtxhRLGBhr/cCFpHqLeIeNv6WFLc01IHxGRZOhVqHRaRCRbqTwuUetlmfXWTekBbblXn1e9js1ito4YUsAK9lgwHgnXfAlhpZq4Y3Xr0B70osAK9lg4BXvgXgtWyQCW9xBfrUGbaZ8J4MJr9zq75XC7tiAQt7vRewiFTvEPA+3tvPpu6kALyeLQCvZYM8eE9/8rP9qavK5sNrLa+/nci1sCsWsIhY72hN6x/es49+nafbIHdSdEKxO+BsxUos2Icq6dTb/0mKxzeKvsLUnRSLZ96oaTFm5g1vEa3e3u+kyHPtmSnzAt6wFoDXskFO5q2u570hrs8LeCMUKjV4s2qobOJOCsDr2wLwWjboAG/ocV7rAa6/FuZbsI+6PVqMDDKFt4hU70TPsCVG1srhjVVvwLsSC8Br2SDglW+xlfBuwqZaAN6520UgpkJU5vWnWIeFyEIJsAC8CViILJQAC2/wIhCxAvAikg3Ai0g2AC8i2QC8iGQD8CKSDcCLSDYwzivYQmShBFgA3gQsRBZKgAXgTcBCZKEEWADeBCxEFkqABeBNwGLk1hpvFhLrDXhXYFFM/cHjdx31BrwrsFCFAvAO3we88i1KeFn0rqPe0eGdu11Es29wHskWyLxRLUbv1VInyLzm9wFvdIsaXBvAqlEw6E2i3rMFgDe2hUasCWDAa30f8Ea1MCTbPr+A1/o+4I1oYevoqt5rwGt+Xy68hm/QFFt4RDFyjNa+1cLLoFd2vX0JxMJb7r7+3kqxhe2KURZV9zfgNb8vFN56P/WSb4otbFeMs1h9ehXgHXlfJrzK8IpQG46FL4WzxSSKbb+JhvvQghEpNq1MePXRI3ptGBbeFOHgHXpsEbxeJ9orZ5Uu5vV//cHSk0u35ZfWwlxBtSvc4SVfGyms3g4Kr1OcFk8PzA72G45DPkTQUBO2wlUQ0qIgz2ngq+tBuzZSVL2dFD7hLZ8eePZR+eyf0A9UMVVFcRVsC1cFVVBXweWUw+Cc3CTAgurtqPAIb/X0wLyfsLcX/iGCxuA/m05WqN7vmUcZBcDOylnOC4XHhwhWTw88fedukX1DP0TQ9llU4tIDXaA6L+Zm3s2mjCm4XJa1K3RXql+bEre0pvWXeeunB5ZxsB/6IYL2CnFv5RIDr+q+zHpLZnn0AG5Gh3WwTQvoFuwy+VD4g7d+emANb4Q+b6NI9EZE/VoFr/CW2xqMKxEK1aN+vfBm1RBD0VE4+9WD0A8RHFMwk69AeB1u6glGlpI7Cukd3iIDv3Y3/EMERxUJ3ojYu8SRXYvQ43eBLZwUyT7KalyR3O0w/XMuGbv3HvjMSXALB0UUeI+u7FTx6p1A8HL2vAR4h7dGhJ9DhCfonPYLZcFWxMq8H1zOf9y7GCrznjDwFQCvYTCLaxGcLIGjkJHgPbp2Z/Mz2J0UhLNMcy38CHwMqS5A1tQ1MB4skoD3+GaZeS/cDwlvWb/p1o7OiamE0QtlVQg6nIjVbTi+mXd5LwY7YNOqONHckTkxF08uvIIOJ1Y62tCr5PgVnz4sXAW2ggmGV84lw1sBb1lRK8ARObF/pkTDS6Z3pfAedofK5m6XGu6XWQUJUYXhhYyie7yqbDo6ow1X3lg4824+rYMMnLVvmMJHoYyC0fQlO/OeEJPvOjNvM0i2PLxlpXuXm4wOARlI9sHJxGGPeHhJx23rhLc6SREL3hPbZYEEGX+w01imNdzmEH0UMlbmvRL49PB0uOfRKlfzBH1vroLv4V8gbhRya0YbvFtw8O2fOOEq+B4hBJZRyNAW9gC87hZ0fPXOIlvB91jSYqQZVgrv8c2dC59dvZU0vE5TJBAlsuvdC2ud1gnv8c3LX/zw/mH4axtCCPgstgIhw/u+LeKc/4k3VJbDG/iqslAC5yEjxmGe/HrrEeUKjaiZd4GrykIIhkNGE1A6DLClUG89Ilwb5/8eNv3mNds9bOVVZTW7qcNbhInNdgaEBWoRHd4IVyV7n6tMn6RswbnKnBXeLLoJtvPbp4VPhf8z7+EteoYe4S3nKtMnbLDN2xD39HBgC+vlE/4sfCj8WwwYSgfeaq4yfaoc21xlx+/e6sA7ldERicTC15p5n6tMn6TMNleZgNPDK7OQUai5j1iIlXnrucpsmVeHd5V93qgWUgo1b36oWPDWc5Vx+ryrGOcVYiGnUB18k4E3q4bK9EnKzHOVbSYdWcc4rwgLSYXizOXnaFH5xBnnXfVoQxQLWYVynaU+iTNs6PPGV4S1UO381aEsAO9aLAQWin/HCeANLhBpIbJQ7Im+AW9ogUgLkYViT/QNeEMLRFqILFQhCDjLMOBdiYXIQpWCcJeCAt6VWIgsVC0INTUf4F2JhchCbQRhZvoWAe/c7SLkR4hp4iLNVYbMG1+xuIX3+SpEZF5flXFTrMNCZKH6As8zBY3OhthsEPDKtxBZqKHA+xxtUwLAm4CFyEIZBL5nxwS8K7AQWSijwO+8xIB3BRYiC2UWeH1ACOBdgYXIQtkE9kMswBtYINJCZKFGBEGfmNB9H/DKtxBZqAmBAWDAG1gg0kJkoQiCHsCx4H26t/f6g+ou4vz3xFxlgDe6Qo7FnPuN/cBb3OFe3Ch8sF/8tY1zlUW1EFkosmBzoixityEH+OyjcoqRiXkbAG98hUALLw9a6r1PhjdPsnk/YW+vP3FO1p+rTI/J7SK2JwI9pnRylsi3X7ubnb5zN8uz78RcZci88RWCLRgZ2F/mbTg92MdcZQtbiCzUHAtPT56hw1sfreW/0Odd2EJkoQRY0ODtdBTOfvVgdK4ywCtBsR0WxMz7eG8v7/M2v0zjvAhErFh43msEwl8AXkSyAXgRyQbgRSQbgBeRbMyeGR2BiBWzn4CJQMQKj0/AxEmK+IrtsCDCy3kCph6zPlIIBCE8PgETmTe+YjssaPDiCZhRLUQWSoAFDV7WEzAB7xyF8WLB2IUSakHsNtCfgAl45ykAL13Agpf2BEzAO0cBeOkCMrxTAXj9KAAvXQB4hVkAXroA8AqzALx0AeAVZgF46YLg8M7d7rYFLu/jBzKvEAtkXrogALxH1+4AXmcF4KULAsB7/O4twOusALx0QQB4j67sFPHqHcDroAC8dEEAeNHnnaMAvHQB4BVmAXjpAiK8p28Xc5sSr204vrlz4bOrtwCviwLw0gU0eIvLdk/fuTt2D1uX3ctf/PD+4YX7gNdBAXjpAhq8TwtG+9ND2q7nPbp2J4e3GTDzWZkkW5ipALx0AbHbUGVf2j1sVea9V2feye0itMAZNn4Qrue9Qb2HLe/z7uzU7CLzMhXIvHQBFd6v37uR4R62BSwAL11AhPf07WJadNzDFt4C8NIFNHgrdkn3sNXn13CGzVEBeOkCGrzV3cP7xHHeDy7nP+5dROZ1UQBeuoDYbZgObajsYXttmc/KJNnCTAXgpQsCwHt8s8y8OEnhpAC8dEEAeKuhsos4YHNSAF66IAS8GG2YoQC8dEFweOdud9sCZ9j44RPeQwyVuSuQeemCAPAeXXkD3YYTC4aTFoCXLggBL27ALIME72AlwEsXBIC3OkkBeLcVXrdvHBdBAHjn34C5XPUZCluh3GqhbCsp00LAa37fP7zzh8rShVdZXg8U6sTCKQveyYbSVrCuPWbBF/jZe2oTI0WYAW+w+XmN5VVjCj8WTgorHwHg7b9Bh9e4WZNiEP6+cUb9Byt/XkcYeM33sB1d+9cw3QYWvM0WPLawH3ibWkzCq71hgncUx0l4J5O7GrwYE3Q3nwS84a7nte5RU29RmRQDeEd3e7/pjXt5FF7TjrPC2/9vXWGzFTK8/XoP4LUSRoLXWj1blejdln4Ehpf2HDaFQLjEBl57zIDXeg/bPR9n2IwpYrTPZ/qIWo+mrJ7dnDXZIdW6MpPZ19qXsabhMV/zHyaBrdWMPROSfyMwfusZC9jvLk3GYplXh/fo6q3Di7MvRie1cEB4rQIJ8FoLNOo7Jpg+nHCHt1lgO+w028Xp8x5du1P9mwVvt9Y0xXx4Jy36WzHCa7W0wtu8yeJz9M0A8JoEdHjZFsFHG8zPYTt+91b+74sfLH1hTgR4JwX0negX3tgnL8XDax3nzbk93Nm5PK/b4EcRAF6WAPC6WrSHZSNFmAGvFj6HyvwpYlt4gnfF9Xa2OAkDL67n3cS2wruMRQB4j65iWn++AvDyBSHgxfW8DgrAyxcEgPfhve71vHO3u02B+9fcwiu86PO6KZB5eYIA8KLP66oAvDxBCHjR53VUAF6eIAC8ep/XZ2WSbGGGAvDyBAHgxUMEXRWAlycIAK8ePiuTZAszFICXJyDCy3oOG+B1VABenoAGL+s5bHiIoKsC8PIENHhZz2HDQwRdFYCXJyB2GzLGc9j0hwhObhexCZxhcwuPz2HTHyLo85OYZHpgKJB5eYJpeA/29n7Eew4bHiLoqAC8PAEx8+I5bEtYAF6egAYv4zlsOD3srgC8PAENXtZz2I7fxYU5bgrAyxMQuw3T0cm8OD3sqAC8PEEAeNHndVUAXp4A8AqyALw8AeAVZAF4eYLg8M7d7jYFzrC5BTKvAAtkXp4A8AqyALw8AeAVZAF4eQLAK8gC8PIEgFeQBeDlCQCvIAvAyxMAXkEWgJcnIMNb3LGGGzDjWIgslAALMryP9/Yz4g2YgDe6YjssqPCe/uRn+7gYPZKFyEIJsCDCe/bRr/M8S7sBU4+pjI5AzI0JeB/fKDoJYzdgOsQ33aXbZbGSagSzGL8BM0+yZyOZ1zFSbq9FLVZSjQjwFlHdBnRjrM/rECm316IWK6lGJHizaqhs7AZMh0i5vRa1WEk14sI7Ns6LQMQKXAeNSCb4qrUAAAJlSURBVDYALyLZALyIZAPwIpKNheA1zrBeHw2GtDj7cO+1GSPSFIt84eszxg3HPbJqUNLvIbLRol4Y0sLv/s6Wgtc4w3pWXfUT1OKgOj0Y0KJY+NgfWbpHlj0tPhn6pVBBLOqFIS0yr/u7iGXgNc6wXl/1E9Ki+OUvjBbl+UZ/NrpHdvDav+c/Z58WmraoF4a08Lu/i1iuzzs8v1xd9RPS4vSn/+az22Cy8Jx5dY+s+sKdfUJ+2qJeGNTC+/5eDN7hDOv1VT8hLYopWosWDGjh/5RNxyOrdvvsS6GmLeqFQS187+/F4DXPsO61MpOTuIexyDuKT30esXU9sjCZ12BRLwxp4Xt/LzjaMJxhvb7qJ6TF1//gda+bLHxnRc0jq3a73z6v0aKZRzyghef9nS0Fr3GG9czr0InZ4sBnt8Fo4Tnz6h5ZtdtnXwo1bRGC3Z5FluhQmXmGda+VsU7i7g8ss8XTPZ/HhD2PEOO8RotmYUCLLFF4EYgAAXgRyQbgRSQbgBeRbABeRLIBeBHJBuCNEC9uqyrOP/vW+7ELk3AA3kgBbOcH4I0UgHd+AN5IUcGb/3z2rZ/vKnXpWf7jrapH8dKnsQuXSADeSNHCu/uNr7JHqvjx0qcvbp/Pskf5awQhAG+k6MCbJ9zqx7fef1Jk3efX34pdujQC8EaKTrfh/fqv/MejahTiUuzSpRGAN1JY4EWPgRGAN1KY4X1yDmMQ9AC8kcIM74vbeeoFwcQAvJHCDG85VAZ2iQF4EckG4EUkG4AXkWwAXkSyAXgRyQbgRSQbgBeRbABeRLIBeBHJBuBFJBv/DyhC75vCXJNqAAAAAElFTkSuQmCC)
CASES.stl <- stl(CASES, s.window = "periodic")
autoplot(CASES.stl)
![](data:image/png;base64,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)
NEW_CASES.stl <- stl(NEW_CASES, s.window = "periodic")
autoplot(NEW_CASES.stl)
![](data:image/png;base64,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)
Holt-Winters exponential smoothing
CASES.HW <- HoltWinters(CASES)
CASES.HW
Holt-Winters exponential smoothing with trend and additive seasonal component.
Call:
HoltWinters(x = CASES)
Smoothing parameters:
alpha: 0.9535295
beta : 0.1484578
gamma: 1
Coefficients:
[,1]
a 84371.18862
b -445.73297
s1 -1132.80119
s2 -1618.51347
s3 -369.32564
s4 -3.67365
s5 626.57812
s6 466.48438
s7 853.77241
s8 638.32040
s9 352.15093
s10 473.25854
s11 298.98513
s12 -793.18862
plot(CASES.HW)
![](data:image/png;base64,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)
CASES.HW.forecast <- forecast(CASES.HW, h = 24)
plot(CASES.HW.forecast)
![](data:image/png;base64,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)
ARIMA
CASES.arima <- auto.arima(CASES)
CASES.arima
Series: CASES
ARIMA(0,2,1)(0,0,2)[12]
Coefficients:
ma1 sma1 sma2
-0.8806 0.1925 0.2403
s.e. 0.0497 0.0852 0.0827
sigma^2 estimated as 825250: log likelihood=-1053.56
AIC=2115.11 AICc=2115.44 BIC=2126.52
op <- par(mfrow=c(2,1), mar=c(2,4,1,2)+.1)
acf(CASES, main="")
pacf(CASES, main="")
par(op)
![](data:image/png;base64,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)
CASES.forecast <- forecast(CASES, level = c(95), h = 22)
plot(CASES.forecast)
![](data:image/png;base64,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)
CASES.arima.forecast <- forecast(CASES.arima, level = c(95), h = 22)
plot(CASES.arima.forecast)
![](data:image/png;base64,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)
Ontario Monthly Social Assistance Caseloads 1969-2018
SA <- fread("historical_sa_recipients_dataset_q2_2018_19.csv")
SA$Beneficiaries <- NULL
Make time series
SA_ts <- ts(SA$Cases, start = c(1969,1), frequency = 12)
plot(SA_ts)
![](data:image/png;base64,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)
SA_ts_1997 <- window(SA_ts, c(1997,1))
plot(SA_ts_1997)
![](data:image/png;base64,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)
SA_ts_1997.components <- decompose(SA_ts_1997)
autoplot(SA_ts_1997.components)
![](data:image/png;base64,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)
SA_ts_1997.stl <- stl(SA_ts_1997, s.window = "periodic")
autoplot(SA_ts_1997.stl)
![](data:image/png;base64,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)
Maytree Social Assistance Summaries
Maytree_ON <- fread("ON.csv")
str(Maytree_ON)
Classes ‘data.table’ and 'data.frame': 25 obs. of 7 variables:
$ Year : chr "1997-98" "1998-99" "1999-00" "2000-01" ...
$ Ontario Works Cases : int 362334 310493 262439 215618 196596 195137 192096 191723 198377 199242 ...
$ Ontario Works Beneficiaries: int 796109 690608 577620 469494 419493 404067 389754 380670 386801 383068 ...
$ ODSP Cases : int 185479 189392 189536 191885 192048 194140 200087 205880 212058 221718 ...
$ ODSP Beneficiaries : int 261737 268159 268286 271144 270558 271740 278393 285231 292622 305202 ...
$ Total Cases : int 547813 499884 451975 407503 388644 389277 392183 397603 410435 420960 ...
$ Total Beneficiaries : int 1057846 958767 845907 740637 690051 675807 668148 665901 679423 688270 ...
- attr(*, ".internal.selfref")=<externalptr>
Maytree_OW.ts <- ts(Maytree_ON$`Ontario Works Cases`, start = 1997, frequency = 1)
Maytree_ODSP.ts <- ts(Maytree_ON$`ODSP Cases`, start = 1997, frequency = 1)
plot(Maytree_OW.ts)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAAV1BMVEUAAAAAADoAAGYAOpAAZrY6AAA6ADo6AGY6Ojo6OpA6kNtmAABmADpmtrZmtv+QOgCQOjqQ2/+2ZgC2/7a2///bkDrb2//b////tmb/25D//7b//9v///+Fl/ljAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAQgklEQVR4nO3dAXvaOBaFYadJZnaS2WbKlgkk/v+/c7ENNGmwsSXdi8719z5PO9kWSxacVYUu2E0LiGpufQJAKsILWYQXsggvZBFeyCK8kEV4IYvwQhbhhSzCC1mEF7IIL2QRXsgivJBFeCGL8EIW4YUswgtZhBeyCC9kEV7IIryQRXghi/BCFuGFLMILWYQXsggvZBFeyCK8kEV4IYvwQhbhhSzCC1mEF7IIL2QRXsgivJBFeCGrcHgbINutwlu2OawR4YUswgtZRuF9fxkWJd9+FmkOuMAmvNvmafhhd/ohqzngEpPwvr+cI7u9f81uDrjIJLxvz99PP+5GFg6EF9mYeSHLas17nHpZ88KO0W7D2/Ow2zAy7xJeFFDXPi+RxgKEF7IqK1KQXsxXWZGC8GK+yrbKCC/mq6xIQXgxX2UzL+nFfLUVKQgvZqutSEF4MVtd+7yEFwvUFl7Si9kqK1IQXsxXWZGC8GK+2rbKCC9mq6xIsbAjrFp1My/hxVy1FSkIL2arrUixuCesV3X7vIQXczmHd84F/ggv5qmuSEF4MVd1RYqlXWG96tsqI7yYqb4iBeHFTMy8kFVfkWJpX1itCosUhBfzVFikILyYp8rwkl7MYbTb0K10d+n3pCC8mMEuvP0+w4dNsyXNEV7MYBbeY2zTroxOeDGDWXj3j314E+9JQXpxXZ0zL+HFDEbh7fZ4H9rTW7flzRFeXGe1VXbI792P8QIb4UW+Ovd5SS9mILyQZRneXdMtHdKaI7y4yii8m6Z52v/1mlqkILyYwSa8m/vXdtPPusm3byW9uMbumxT7P7rwJt84m/DiGsMP5rz/2zLzwpDRNylO821qkWJph1gjozds22GbYdeMvF8jvMhX6z4v4cVVhBey6g0v6cUVhBeyCC9kEV7Iqji8pBfTCC9kEV7IIryQVXN4SS8mEV7IIryQVXV4SS+mEF7IIryQRXghq+7wkl5MILyQRXghi/BCVuXhJb0YR3ghi/BCVu3hJb0YRXghi/BCFuGFLKMQvb80vcR7D6d3jBUxusTp6RZWo/eyIrzIZhLe95dzZJMvLp3UMVbFJLwfbqOSfFn/pI6xKvXPvKQXI6zWvMept8Cal/BihNFuw3Dr7KYZmXcJLwqofp+X8GKMQHhJLy6rv0hBeDEiIbzdvdW2E8vZtmyRgvBiREJ4N/ev+8eHdvMw+tiyW2WkF5ctD29XgehuDjhWfmgLFymWPhirkRbezSGS2/HwMvPCQ8qy4eHt+f717Xl82VC2SEF4cVnSG7bm7sf7y0R2yxYpFj8aK6Gwz0t4cVHCmvfv/obuU2/YSvab8GisRHp4J96wFS5SLH84VmFpeDfN2chbsU7ZIsXyh2MV0mfeCaW3yggvLqn/mxRpj8cKpIfXsUiR8HisgMA3KZIejxUQ+CZF0uOxAgvDewjl9ysPLNxv8gEIb3l4JyfUq62cLT4ysUfElbJsOFYgpqbg0kUKwouv0te8m4kKW/EiRdIRCC4tvLvpiddgq4zw4ouE8G6nS8OtSZGC8OKLlM82TCe3NZp5SS9+s3i34WpyO+WLFGmHILSlM2+3Zpj8DsWgeJEi7RCElrDm3c3Lb6F+sw5BaGm7DfvHjELFsn4zj0FgNvu8BkWKxGMQWHqF7W7iM+kWRQrCi9+kfbbhypLXZquM9OKzxeGdmnBPTIoUqQchLJPP8zLzwsPy8A4r3um9BpMiRfJRCGpxeI/vxbbTRWKLIkXyUQhqaXh3pzXv/jHrKxWEF9kWhvfD9fU2WVUKwotsi3cbru4j/LKb2AxOjCHpxS9G4d00zdP+r9ePj0/st8hhCMlm2dD93aafdYtulRFefGTyhq2fb/d/dI8sWqTIOA4BmWyVDR9Zf/+3LT7zEl78YlSkOM23o9+8ILzIZnS5p+2wutiNfsc4OYSkFydZ4X3/Z8andDL7LXYgwiG8kEV4IUsuvKQXJ4QXsggvZOmFl/TiiPBCFuGFLKMKm2lzpBe9lPC+PTf3r5tZl4vM77fwsQgk5UJ7dz+2968zL3aa3W/hYxFIyqfKnvoPOk7e9b1cv4WPRSDLw9t90rwL7/XvsBXpt/jBCCN95r3Ft4dLHIwwkte827xbYRJeZEvcbZi+wmnJfg2ORhCK+7yEFz3CC1mKRYr8wxGCYpEi/3CEoFikyD8cIUgWKQgvOpJFigLHIwDJIkWB4xGAZpGC8KJV3ect0QDkpa15Hfs1awDy0nYbrjK593DZBiAv5Q3b9T0ym3sPl24B4lJm3ubKrGp2B8zCLUCcyRs2q3sPF24B4hJm3r+HTbKJ1QMzLzykh3fqsw1W9x4u3QSkLQ3vpjmb2jEzuvdw6SYgLX3mderXtAlIk62wFWoDwpK2yh6mHtizL1IUaiOKX6u5FT0rKTPv9sqC16dIQXh/+ZTYZjVBTlw2TOfXZauM8J6NPxGx45u85t1OLApcihSlGpE3HdDI8U0L7+6Q3O+HCXZkWnWaeQlv5+qTEDe+CeHt9nCHSI4W2VyKFIS3nZnMqPFN2W2Y8SUKjyJFsVaEzX0CYsZXeZ939eFdEsmI8U1cNkx/JLJkvw6tiFoax3jxTQjv5v51+9DuH6e+UOFSpCjXjKKEoUeLb8qa96nddVfMmbhug0+RolwzehJzGCu+ad9h2//5s/81wmurbL3hTR93pPimfXu4+2TZRHi9ihRrDW9eAONUjRPWvN2n0DdPU8sGv5l3lekt8C2UvCZq+eBEylbZ5qHbcZjabHAqUhRsR0eZ0CS18tvHfW6eYaN9XqcixQrDW2zAC2I3/Rm1232CTbtIUbIhDUWHOyNwC2LpH+CF4T3t36YWKcp/0nRd4S092snXoYp17ZSlM++sTzb4FSlWFl6DwY7ks/rgdlK2yq58jaJ1LFKsK7w2Y/0aU4XgdpLWvJsrSwbHrbI1pddupB/CKjHlHqV/DWjiow1+RYqiLVXOdKB9YpWC20nebdg/3vhyT+Vbqpv1OMWC20kLb/d+rI4iRdmmKraOUS6TEt7hG2yT3IoUZZuq1yoGudTy8G6aGRcdKdeva1PVWsMYl1sa3m3eFyiW9+vcVp3ijzCJSYVtuC/xzqVIUbitKoUfYCKTzzb04e33GUbvvkJ454s+vmRm4T3G1n6rLPyLG3x4GczCe/yCpn2RonRjtQk9uDwRZt7Qr2/kseUyCm/3hu6hPb11y2zuqsAvcOCh5bP6MPohv3c/xgtshV+UsC9x2IEVIf9NCoPW6hF1XIUQ3ooFHVYxRuHdnj7+MHa3NsJ7XcxRFWQT3u1hvfv23L1j8wlvyNc54pjKMgnv8Hne/sLphDdZxDGVZbRVNhSFu+tJEt5EAYdUmuHM23bX1nEKb8CXOt6IijNa8x4jO35VKMJ7RbgBGTDbbRgWDu8vhDdNuAEZiLHPG/C1DjcgA1HCG+3FDjYcG4S3TsGGY4PwVinWaKyECW+s1zvUYMwQ3hpFGoshwlujSGMxFCe8kV7xQEOxRHgrFGcktghvheKMxBbhrU+YgVgLFN4wL3qUcZgjvPWJMg5zhLc6QYbhIFJ4g7zsMUbhgfDWJsQgfBDe2oQYhA/CW5sQg/ARKrwRXvgAQ3BDeCsTYAhuCG9d9EfgKFZ49V97+QF4IrxVUT9/X4S3Kurn7ytYeNVfffHTd0Z4a6J99u4Ib020z94d4a2I9MnfgFF4T/codrn3sE/DDpTP/RZswrs93cJq9F5WhPcC5XO/BZPwni8u7XQHTKeWreme+Y2YhPfDvd5d7j3s1LI13TO/kXgzr2wGVM/7dqzWvMep13/Na9y2IdHTviGj3Ybh1tlNMzLvGr9SmjHQPOtbirbPa9+4FcmTvq2Q4ZUMguI531i4IoVL8wb0zvj2whUpnNovTu6EKxBwq8yl/eLkTrgC8YoUXh2UJXa6dYg686rFQetsKxGxSOHXRTFK51qPkEUKty6KUTrXesTc5/XrowydM61K5PDqZELmROsStEjh2EkJMidal6hFCs9esomcZnXibpV5dpNJ4yzrE7ZI4dxPDoVzrFLwmVciGArnWKXARQrnjlI11Z9htQIXKdx7SkF0M4Te53Xvaimim8U5vM1Zkebm9enX1TJEN1PoIoVzX81HMx7tcEqxxS5S2Hc2mtdr+SW6+aJvldn2Nh3QiQmYabeE6EUKy97mJPBifoluGeuYeVuDN4mzm/u6oCh6HisWvkjxqdMlb6iuNbW046QDMSV+kWLsBHJCnHJY3xfRLWkFRYppCQFOTiDRLWv14e0sCjAJrMYaihSzzFxDEN2KrKJIMdu1ABPdqqxkq2yBT0Xe5vPf3OqccNE6ihTpCu6uoTRmXshaVZECsay2SAF97PNCFuGFLKPwbg9Lhn7ZuxXfbUDFjN6w3f04LHsfWsILQ4ZbZe8vh7drhBdmTIsUm/tXwgsztkWKzQPhhRmjNe8xsm/PY58ra4BsJuE9l9jeX0Y/FGnEe0qnP4H+VP6dD/Fk059AowZCPNn0J9CogRBPNv0JNGogxJNNfwKNGgjxZNOfQKMGQjzZ9CfQqIEQTzb9CTRqIMSTTX8CjQIeCC9kEV7IIryQRXghi/BCFuGFLMILWYQXsggvZBFeyCK8kEV4IavS8PZ3bumvErFrmrsfl3/w6W+4rOuDeX9tu//z529/4tGf0/gunkGmOsP7/nIY3rZ7RneHH7pfF35w6m//R+kkXeyvS1F/PQGn8Z378xnfxTPIVWd494/d9SG2334OF+nZPLRff3Dqb/T2G2X76yekrien8Z37cxrfpTPIVmd4B4f/f57H/PUHp/7abeF/US/3190poY+R0/jO/XmNb+RPstQc3s0hQ/2/abtLPzj1127+c1qsWfbXnv7jNL5zf47ju/QnWSoOb3fXlmFxdPj96w9O/b09dzfg2Bi8up/66/+ge0Wdxnfuz3F8F/4kT73h3Z1W907hHenv+JflZ8LP/Z07cRrfub/jX7qM78ufZKo2vMPdsvyWDWP9DX87rNQM+2tP/3EaX9t+6sRjfBfOIFOt4T3e5tjtDdtof8NfF99P+r2/7n9YvmEb7W/gML5LZ5Cp0vD+upaqy1bZRH/Dk116JvzSX3vqxGl85/68xnfxDDLVGd794+kNhE+RYqq//nku/IbmQn/t+V9Xn/Gd+/MZ3+UzyFRneLfDFbK7IW5PxcSvPzj1t2mapvCK8FJ/5+nPaXzn/lzGd/kMMtUZXmAGwgtZhBeyCC9kEV7IIryQRXghi/BCFuGFLMILWYQXsggvZBFeyCK8kEV4IYvwQhbhhSzCC1mEF7IIL2QRXsgivJBFeCGL8EIW4YUswgtZhBeyCK+p4T5RB9/+91z6CrggvOYs7hWFDuE1R3itEF5zQ3jfDsuG/eN/D8uIp/3jcE3RrdVteNaC8Jr7GN7+NpDdzd26X5+uuYzlCK+5j+F9ak+/fX977nJrcsO1tSC85j6G93t7/m24tH352/CsCOE1Nxbe4yYa4U1GeM1NzrzIQHjNjYT3japFLsJrbiS8/W5Du2H+TUd4zY2Ft9/nZbMhA+GFLMILWYQXsggvZBFeyCK8kEV4IYvwQhbhhSzCC1mEF7IIL2QRXsgivJBFeCGL8EIW4YUswgtZhBeyCC9kEV7IIryQRXghi/BC1v8B7MFkGrI3W04AAAAASUVORK5CYII=)
plot(Maytree_ODSP.ts)
![](data:image/png;base64,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)
plot(Maytree_ODSP.ts+Maytree_OW.ts)
![](data:image/png;base64,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)
Statistics Canada Table 11-10-0239-01
T1110023901 <- fread("1110023901-noSymbol.csv")
str(T1110023901)
Classes ‘data.table’ and 'data.frame': 42 obs. of 3 variables:
$ Reference period : int 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 ...
$ Number of persons : int 1830 2182 2266 2281 2300 2500 2512 2760 2604 2636 ...
$ Number with income: int 52 81 54 93 76 91 112 126 127 111 ...
- attr(*, ".internal.selfref")=<externalptr>
T1110023901.ts <- ts(T1110023901$`Number of persons`, start = 1976, frequency = 1)
plot(T1110023901.ts, ylab = "Number of persons", main = "Persons 16 years and older in Toronto with social assistance income" )
![](data:image/png;base64,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)
T1110023901.ts_1997 <- window(T1110023901.ts, c(1997,1))
plot(T1110023901.ts_1997)
![](data:image/png;base64,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)
op <- par(mfrow=c(2,1), mar=c(2,4,1,2)+.1)
acf(T1110023901.ts_1997, main="")
pacf(T1110023901.ts_1997, main="")
par(op)
![](data:image/png;base64,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)
T1110023901.ts_1997.forecast <- forecast(T1110023901.ts_1997, level = c(95), h = 12)
plot(T1110023901.ts_1997.forecast)
![](data:image/png;base64,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)
T1110023901.ts_1997.arima <- auto.arima(T1110023901.ts_1997)
T1110023901.ts_1997.arima
Series: T1110023901.ts_1997
ARIMA(0,1,1) with drift
Coefficients:
ma1 drift
-0.6140 97.5973
s.e. 0.3226 10.4978
sigma^2 estimated as 11017: log likelihood=-120.63
AIC=247.27 AICc=248.77 BIC=250.25
T1110023901.ts_1997.arima.forecast <- forecast(T1110023901.ts_1997.arima, level = c(95), h = 5)
plot(T1110023901.ts_1997.arima.forecast)
![](data:image/png;base64,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)
---
title: "Toronto Employment and Social Services Ontario Works Dataset"
output: 
  html_notebook: 
    toc: yes
---

# Author and date

* Jordan Bell
* July 1, 2019
* <https://jordanbell2357.github.io/TESS_OW.nb.html>

# Sources and descriptions of datasets

This R notebook explores the Ontario Works (social assistance for people without income) case numbers in the
dataset **Head of Household** from Toronto Employment and Social Services.
We compute mean  monthly rates for a given year (2013, the latest year with data for all months),
and plot this data as maps of Toronto divided into wards (44 wards) and neighbourhoods (140 neighbourhoods).


<https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/community-services/#15580d71-31ca-010e-3895-6b77b49d966f>


>This dataset contains the Head of Household trending data from 2004 to present. It provides information on households by neighbourhood with reference to the Ontario Works social assistance program. The information in this dataset will inform the delivery of services by providing trending information on social assistance.
>
>Currency: March 2015

<http://opendata.toronto.ca/employment.social/head.household/opendata_tess_ow.zip>

Children, Community and Social Services, Ontario, [Social Assistance Caseloads](https://www.ontario.ca/data/social-assistance-caseloads)

> Total numbers of social assistance recipients and beneficiaries each month since January 1969.

<https://files.ontario.ca/opendata/historical_sa_recipients_dataset_q2_2018_19.xlsx>

Maytree, [Social Assistance Summaries](https://maytree.com/social-assistance-summaries/ontario/):

> The Social Assistance Summaries series tracks the number of recipients of social assistance (welfare payments) in each province and territory.

<https://maytree.com/wp-content/uploads/ON.xlsx>

[Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas](https://www150.statcan.gc.ca/t1/tbl1/en/cv!recreate.action?pid=1110023901&selectedNodeIds=1D17,4D14,5D1,5D2&checkedLevels=1D1,2D1&refPeriods=19760101,20170101&dimensionLayouts=layout3,layout3,layout3,layout3,layout2,layout3&vectorDisplay=false)

> Table: 11-10-0239-01 (formerly CANSIM 206-0052)

"1110023901-noSymbol.csv"

"Number of persons" = number of persons whose age is $\geq 16$. 

"Number with income" = number of persons with income from social assistance


# Reading datasets and grouping observations

```{r}
library(data.table)
library(plyr) #join
library(ggplot2) #fortify, ggplot
library(scales) #scale_fill_distiller
library(sp) #used by rgdal
library(rgdal) #readOGR
library(ggmap) #theme_nothing
library(rgeos) #gCentroids
library(forecast) #auto.arima, forecast
```
Read .csv containing monthly TESS Ontario works cases for January 2004 to November 2014.
The size of the csv file is 2.68 GB.

```{r}
CSVFILE <- "opendata_tess_ow.csv"
tess_dt <- fread(CSVFILE) #data.table
```
Structure of datatable:
```{r}
str(tess_dt)
```

Select columns from tess_dt
```{r}
ow_cases <- tess_dt[,.(YEAR_NUM, MNTH, WARD_SCODE, CENSUS_NEIGH_SCODE, CASES)]
```

Drop rows where at least one of CENSUS_NEIGH_SCODE and WARD_SCODE is missing
```{r}
ow_cases_dropna <- na.omit(ow_cases, cols=c("CENSUS_NEIGH_SCODE", "WARD_SCODE"))
```
Calculate mean number of cases in 2010 by month and ward
```{r}
ow_wards_2010 <- ow_cases_dropna[YEAR_NUM==2010,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2010_months <- ow_wards_2010[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2010_monthly <- ow_wards_2010_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
```
Add "id" column
```{r}
ow_wards_2010_monthly$id <- ow_wards_2010_monthly$WARD_SCODE
```
Calculate mean number of cases in 2013 by month and ward
```{r}
ow_wards_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2013_months <- ow_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2013_monthly <- ow_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
```
Add "id" column
```{r}
ow_wards_2013_monthly$id <- ow_wards_2013_monthly$WARD_SCODE
```

# Plotting data by wards

**City Wards**

<https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#29b6fadf-0bd6-2af9-4a8c-8c41da285ad7>

> Owner: City Clerk's Office
>
> Currency: August 2018

**44 Ward Model - May 2010 (WGS84 - Latitude / Longitude)**

<http://opendata.toronto.ca/gcc/wards_may2010_wgs84.zip>

Shapefile: icitw_wgs84 

readme.txt:

> Wards: There are a total of 44 electoral wards in the City of Toronto  
> * GEO_ID = unique geographic identifier    
> * NAME = Name of the Ward with corresponding ward number    
> * SCODE_NAME = Ward Number    
> * LCODE_NAME = Ward Number and the community council area it is in (N,S, E or W)    
> * TYPE_DESC = Ward    
> * TYPE_CODE = City Ward    

We shall input wards shapefile, create centroids of wards, and process shapefile as dataframe. First
we input the shapefile
```{r}
wards.sh <- readOGR("C:/Users/14165/Desktop/ArcGIS/SHAPEFILES/May2010_WGS84", "icitw_wgs84")
```
Add "id" column
```{r}
wards.sh@data$id <- as.integer(wards.sh@data$SCODE_NAME)
```
Make centroids of each ward, for placing labels when plotting
```{r}
wards.sh.centroids  <- as.data.frame(gCentroid(wards.sh, byid = TRUE))
```
Add "id" column
```{r}
wards.sh.centroids$id <- wards.sh@data$id
```
Shapefile processing
```{r}
wards.sh.points = fortify(wards.sh, region="id")
wards.sh.df = join(wards.sh.points, wards.sh@data, by="id")
```
Merge ward shapefile and ow_wards_2010_monthly dataframe
```{r}
wards.sh.ow_2010 <- merge(wards.sh.df, ow_wards_2010_monthly, by = "id")
```
Make graphics object for wards.sh.ow_2010
```{r}
p.wards_2010 <- ggplot() + 
  geom_polygon(data = wards.sh.ow_2010, 
               aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH),
               color = "black", size = 0.25) +  
  coord_map() + 
  scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) + 
  theme_nothing(legend = TRUE) + 
  labs(title="Mean monthly number of Ontario Works cases by ward in Toronto in 2010") + 
  geom_text(aes(x=x, y=y, group=NULL, label=id), data = wards.sh.centroids, size = 2)
```
Plot graphics object
```{r}
p.wards_2010 + guides(fill = guide_legend(reverse = TRUE)) #ggplot2
```
Calculate mean number of cases in 2013 by month and ward
```{r}
ow_wards_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2013_months <- ow_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]

ow_wards_2013_monthly <- ow_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)] 
```
Add "id" column
```{r}
ow_wards_2013_monthly$id <- ow_wards_2013_monthly$WARD_SCODE
```
Merge ward shapefile and ow_wards_2013_monthly dataframe
```{r}
wards.sh.ow_2013 <- merge(wards.sh.df, ow_wards_2013_monthly, by = "id")
```
Make graphics object
```{r}
p.wards_2013 <- ggplot() + 
  geom_polygon(data = wards.sh.ow_2013, 
               aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH), 
               color = "black", size = 0.25) + 
  coord_map() + 
  scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) + 
  theme_nothing(legend = TRUE) + 
  labs(title="Mean monthly number of Ontario Works cases by ward in Toronto in 2013") + 
  geom_text(aes(x=x,y=y, group=NULL, label=id), data = wards.sh.centroids, size=2)
```
Plot graphics object
```{r}
p.wards_2013 + guides(fill = guide_legend(reverse = TRUE)) #ggplot2
```
Calculate mean number of cases in 2013 by month and neighbourhood
```{r}
ow_nbds_2013 <- ow_cases_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, CENSUS_NEIGH_SCODE)]

ow_nbds_2013_months <- ow_nbds_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, CENSUS_NEIGH_SCODE)]

ow_nbds_2013_monthly <- ow_nbds_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(CENSUS_NEIGH_SCODE)]
```
Add "id" column
```{r}
ow_nbds_2013_monthly$id <- ow_nbds_2013_monthly$CENSUS_NEIGH_SCODE
```
Doing finer aggregation: mean monthly cases who are single (without dependents)
```{r}
ow_singles <- tess_dt[FAMILY_TYP_NM=="Singles",.(YEAR_NUM, MNTH, WARD_SCODE, CENSUS_NEIGH_SCODE, CASES)]

ow_singles_dropna <- na.omit(ow_singles, cols=c("CENSUS_NEIGH_SCODE", "WARD_SCODE"))

ow_singles_wards_2013 <- ow_singles_dropna[YEAR_NUM==2013,.(TOTAL_CASES=sum(CASES)),by=.(MNTH, WARD_SCODE)]

ow_singles_wards_2013_months <- ow_singles_wards_2013[,.(MONTHLY_CASES=sum(TOTAL_CASES)),by=.(MNTH, WARD_SCODE)]

ow_singles_wards_2013_monthly <- ow_singles_wards_2013_months[,.(MEAN_CASES_PER_MONTH=mean(MONTHLY_CASES)),by=.(WARD_SCODE)]
```
Add "id" column
```{r}
ow_singles_wards_2013_monthly$id <- ow_singles_wards_2013_monthly$WARD_SCODE
```
Merge ward shapefile and Ontario Works dataframe
```{r}
wards.sh.ow_singles_2013 <- merge(wards.sh.df, ow_singles_wards_2013_monthly, by = "id")
```
We shall plot wards.sh.ow_singles_2013. Make graphics object
```{r}
p.wards_singles_2013 <- ggplot() +
  geom_polygon(data = wards.sh.ow_singles_2013, 
               aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH), 
               color = "black", size = 0.25) + 
  coord_map() + 
  scale_fill_distiller(name="Cases", palette = "PuRd", trans = "reverse", breaks = pretty_breaks(n = 8)) + 
  theme_nothing(legend = TRUE) + 
  labs(title="Mean monthly number of Ontario Works cases (singles) by ward in Toronto in 2013") + 
  geom_text(aes(x=x,y=y, group=NULL, label=id), data = wards.sh.centroids, size = 2)
```
Plot graphics object
```{r}
p.wards_singles_2013 + guides(fill = guide_legend(reverse = TRUE))
```

# Plotting data by neighbourhoods

**Neighbourhoods shapefile**

<https://www.toronto.ca/city-government/data-research-maps/open-data/open-data-catalogue/#a45bd45a-ede8-730e-1abc-93105b2c439f>

<http://opendata.toronto.ca/gcc/neighbourhoods_planning_areas_wgs84.zip>

> Owner: Social Development, Finance & Administration
>
> Currency: June 2014

**Neighbourhoods (WGS84)**

Shapefile: NEIGHBORHOODS_WGS84

NEIGHBORHOODS_WGS84_readme.txt:

> NEIGHBORHOODS_WGS84_readme  
> * Column name  (Description)    
> * AREA_S_CD = AREA_SHORT_CODE    
> * AREA_NAME = AREA_NAME    

Input shapefile
```{r}
nbds.sh <- readOGR("C:/Users/14165/Desktop/ArcGIS/SHAPEFILES/neighbourhoods_planning_areas_wgs84", "NEIGHBORHOODS_WGS84")
```
Add "id" column
```{r}
nbds.sh@data$id <- as.integer(nbds.sh@data$AREA_S_CD)
```
Make centroids of each neighbourhood, for placing labels when plotting
```{r}
nbds.sh.centroids  <- as.data.frame(gCentroid(nbds.sh, byid = TRUE))
```
Add "id" column
```{r}
nbds.sh.centroids$id <- nbds.sh@data$id
```
Shapefile processing
```{r}
nbds.sh.points = fortify(nbds.sh, region = "id")
nbds.sh.df = join(nbds.sh.points, nbds.sh@data, by = "id")
```
Merge neighbourhood shapefile and Ontario Works dataframe
```{r}
nbds.sh.ow_2013 <- merge(nbds.sh.df, ow_nbds_2013_monthly, by = "id")
```
Make graphics object
```{r}
p.nbds_2013 <- ggplot() +
  geom_polygon(data = nbds.sh.ow_2013, 
               aes(x = long, y = lat, group = group, fill = MEAN_CASES_PER_MONTH), 
               color = "black", size = 0.2) + 
  coord_map() + 
  scale_fill_distiller(name="Cases", palette = "YlOrBr", trans = "reverse", breaks = pretty_breaks(n = 8)) + 
  theme_nothing(legend = TRUE) + 
  labs(title="Mean monthly number of Ontario Works cases by neighbourhood in Toronto in 2013") + 
  geom_text(aes(x=x,y=y, group=NULL, label=id), data = nbds.sh.centroids, size = 2)
```
Plot graphics object
```{r}
p.nbds_2013+guides(fill = guide_legend(reverse = TRUE))
```



# Time series

Avril Coghlan, [Using R for Time Series Analysis](https://a-little-book-of-r-for-time-series.readthedocs.io/en/latest/src/timeseries.html)

Vincent Zoonekynd, [Time series](http://zoonek2.free.fr/UNIX/48_R/15.html)



## TESS Ontario Works data manipulation

Group by month and create time series:
```{r}
ow_cases_months <- tess_dt[,.(MNTH, CASES, NEW_CASES, EXITS)]

ow_cases_monthly <- ow_cases_months[,.(CASES=sum(CASES), NEW_CASES=sum(NEW_CASES), EXITS=sum(EXITS)),by=.(MNTH)]

ow_cases_monthly$MNTH <- NULL

head(ow_cases_monthly)
```

```{r}
ow_ts <- ts(ow_cases_monthly, start = c(2004,1), frequency = 12)

head(ow_ts)
```

## TESS Ontario Works plots

Plot time series
```{r}
options(scipen=999) #do not use scientific notation

plot(ow_ts)
```

Make time series of CASES

```{r}
CASES <- ts(ow_cases_monthly$CASES, start = c(2004,1), frequency = 12)

plot(CASES)
```

Make time series of NEW_CASES

```{r}
NEW_CASES <- ts(ow_cases_monthly$NEW_CASES, start = c(2004,1), frequency = 12)

plot(NEW_CASES)
```


## Decomposing time series into trend, seasonal, and remainder terms

Now we use stats::decompose and stats::stl to separate time series as a sum of 
trend, seasonal, and remainder terms.

```{r}
CASES_components <- decompose(CASES)
```

```{r}
autoplot(CASES_components)
```

```{r}
NEW_CASES_components <- decompose(NEW_CASES)
```

```{r}
autoplot(NEW_CASES_components)
```

```{r}
CASES.stl <- stl(CASES, s.window = "periodic")
```

```{r}
autoplot(CASES.stl)
```

```{r}
NEW_CASES.stl <- stl(NEW_CASES, s.window = "periodic")
```

```{r}
autoplot(NEW_CASES.stl)
```


## Holt-Winters exponential smoothing

```{r}
CASES.HW <- HoltWinters(CASES)

CASES.HW
```

```{r}
plot(CASES.HW)
```

```{r}
CASES.HW.forecast <- forecast(CASES.HW, h = 24)

plot(CASES.HW.forecast)
```


## ARIMA

```{r}
CASES.arima <- auto.arima(CASES)
```

```{r}
CASES.arima
```


```{r}
op <- par(mfrow=c(2,1), mar=c(2,4,1,2)+.1)
acf(CASES,  main="")
pacf(CASES, main="")
par(op)
```


```{r}
CASES.forecast <- forecast(CASES, level = c(95), h = 22)

plot(CASES.forecast)
```

```{r}
CASES.arima.forecast <- forecast(CASES.arima, level = c(95), h = 22)

plot(CASES.arima.forecast)
```



## Ontario Monthly Social Assistance Caseloads 1969-2018

```{r}
SA <- fread("historical_sa_recipients_dataset_q2_2018_19.csv")

SA$Beneficiaries <- NULL
```

Make time series

```{r}
SA_ts <- ts(SA$Cases, start = c(1969,1), frequency = 12)
```


```{r}
plot(SA_ts)
```


```{r}
SA_ts_1997 <- window(SA_ts, c(1997,1))

plot(SA_ts_1997)
```

```{r}
SA_ts_1997.components <- decompose(SA_ts_1997)

autoplot(SA_ts_1997.components)
```


```{r}
SA_ts_1997.stl <- stl(SA_ts_1997, s.window = "periodic")

autoplot(SA_ts_1997.stl)
```


## Maytree Social Assistance Summaries

```{r}
Maytree_ON <- fread("ON.csv")
```

```{r}
str(Maytree_ON)
```


```{r}
Maytree_OW.ts <- ts(Maytree_ON$`Ontario Works Cases`, start = 1997, frequency = 1)

Maytree_ODSP.ts <- ts(Maytree_ON$`ODSP Cases`, start = 1997, frequency = 1)
```

```{r}
plot(Maytree_OW.ts)
```

```{r}
plot(Maytree_ODSP.ts)
```

```{r}
plot(Maytree_ODSP.ts+Maytree_OW.ts)
```

## Statistics Canada Table 11-10-0239-01

```{r}
T1110023901 <- fread("1110023901-noSymbol.csv")
```

```{r}
str(T1110023901)
```


```{r}
T1110023901.ts <- ts(T1110023901$`Number of persons`, start = 1976, frequency = 1)

plot(T1110023901.ts, ylab = "Number of persons", main = "Persons 16 years and older in Toronto with social assistance income" )
```

```{r}
T1110023901.ts_1997 <- window(T1110023901.ts, c(1997,1))

plot(T1110023901.ts_1997)
```


```{r}
op <- par(mfrow=c(2,1), mar=c(2,4,1,2)+.1)
acf(T1110023901.ts_1997,  main="")
pacf(T1110023901.ts_1997, main="")
par(op)
```

```{r}
T1110023901.ts_1997.forecast <- forecast(T1110023901.ts_1997, level = c(95), h = 12)

plot(T1110023901.ts_1997.forecast)
```


```{r}
T1110023901.ts_1997.arima <- auto.arima(T1110023901.ts_1997)

T1110023901.ts_1997.arima
```

```{r}
T1110023901.ts_1997.arima.forecast <- forecast(T1110023901.ts_1997.arima, level = c(95), h = 5)

plot(T1110023901.ts_1997.arima.forecast)
```