Bicycle Thefts Open Data
Source:R/datasets_tps_arcgis_hub_wrappers.R
morie_datasets_tps_bicycle_thefts.RdToronto PS ArcGIS Hub dataset wrapper. Thin dispatch to
morie_datasets_tps_arcgis_hub_by_id() with the canonical
hub item_id a89d10d5e28444ceb0c8d1d4c0ee39cc.
Usage
morie_datasets_tps_bicycle_thefts(
format = "json",
where = "1=1",
max_features = NULL,
layer_idx = 0L,
offline = TRUE,
dest = NULL
)Arguments
- format
One of
"json"(default),"geojson","csv","shapefile","fgdb".- where
SoQL-style
WHEREfilter passed to FeatureServer?where=(default"1=1").- max_features
Optional cap on returned rows. Passed as
resultRecordCountto FeatureServer; ignored for binary formats.- layer_idx
Integer index of the FeatureServer layer to pull (default
0L, the first layer).- offline
Logical; if
TRUE, the hub_id is resolved via the included catalog (no network needed for the resolution step). DefaultTRUE– you can run this against the 71 catalog entries without network. Live data fetches always hit the network regardless of this argument; "offline" here only affects the hub_id -> FeatureServer URL lookup.- dest
Optional path for binary downloads (
format %in% c("shapefile", "fgdb")); defaults totempfile().
Value
A data.frame / GeoJSON list / file path; see
morie_datasets_tps_arcgis_hub_by_id() for per-format semantics.
Details
Bicycle Theft occurrences by reported date.
Tags: Bike; Bicycle; Thefts; Crime; Toronto; TPS; Toronto Police
Examples
# \donttest{
df <- try(morie_datasets_tps_bicycle_thefts(max_features = 5L))
if (!inherits(df, "try-error")) head(df)
#> OBJECTID EVENT_UNIQUE_ID OCC_DATE OCC_YEAR OCC_MONTH OCC_DOW OCC_DAY
#> 1 1 GO-20141261431 1.388552e+12 2014 January Wednesday 1
#> 2 2 GO-20141263784 1.388552e+12 2014 January Wednesday 1
#> 3 3 GO-20141263544 1.388034e+12 2013 December Thursday 26
#> 4 4 GO-20149000090 1.388552e+12 2014 January Wednesday 1
#> 5 5 GO-20141266048 1.388380e+12 2013 December Monday 30
#> OCC_DOY OCC_HOUR REPORT_DATE REPORT_YEAR REPORT_MONTH REPORT_DOW REPORT_DAY
#> 1 1 7 1.388552e+12 2014 January Wednesday 1
#> 2 1 18 1.388552e+12 2014 January Wednesday 1
#> 3 360 19 1.388552e+12 2014 January Wednesday 1
#> 4 1 12 1.388639e+12 2014 January Thursday 2
#> 5 364 17 1.388639e+12 2014 January Thursday 2
#> REPORT_DOY REPORT_HOUR DIVISION
#> 1 1 7 D14
#> 2 1 18 D14
#> 3 1 17 D14
#> 4 2 20 D52
#> 5 2 8 D53
#> LOCATION_TYPE
#> 1 Apartment (Rooming House, Condo)
#> 2 Single Home, House (Attach Garage, Cottage, Mobile)
#> 3 Other Commercial / Corporate Places (For Profit, Warehouse, Corp. Bldg
#> 4 Apartment (Rooming House, Condo)
#> 5 Streets, Roads, Highways (Bicycle Path, Private Road)
#> PREMISES_TYPE BIKE_MAKE BIKE_MODEL BIKE_TYPE BIKE_SPEED BIKE_COLOUR
#> 1 Apartment SUPERCYCLE <NA> MT 10 <NA>
#> 2 House TREK SOHO S RG 1 BLK
#> 3 Commercial FELT F59 RC 21 SILRED
#> 4 Apartment GI TCX2 (2010) OT 9 BLU
#> 5 Outside KHS VITAMIN A OT 24 WHI
#> BIKE_COST STATUS PRIMARY_OFFENCE HOOD_158 NEIGHBOURHOOD_158
#> 1 NA STOLEN THEFT UNDER 085 South Parkdale (85)
#> 2 NA RECOVERED PROPERTY - FOUND 080 Palmerston-Little Italy (80)
#> 3 1300 STOLEN B&E 165 Harbourfront-CityPlace (165)
#> 4 1019 STOLEN THEFT UNDER 165 Harbourfront-CityPlace (165)
#> 5 500 STOLEN THEFT UNDER 039 Bedford Park-Nortown (39)
#> HOOD_140 NEIGHBOURHOOD_140 LONG_WGS84 LAT_WGS84
#> 1 085 South Parkdale (85) -79.44365 43.63766
#> 2 080 Palmerston-Little Italy (80) -79.41465 43.66052
#> 3 077 Waterfront Communities-The Island (77) -79.39564 43.64002
#> 4 077 Waterfront Communities-The Island (77) -79.39376 43.64277
#> 5 039 Bedford Park-Nortown (39) -79.42957 43.71842
# }