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Reported Crimes (ASR-RC-TBL-001)

Source: R/datasets_tps_arcgis_hub_wrappers.R
morie_datasets_tps_reported_crimes.Rd

Toronto PS ArcGIS Hub dataset wrapper. Thin dispatch to morie_datasets_tps_arcgis_hub_by_id() with the canonical hub item_id fe2e40a464e64cb3a0e69ac3ccd17dfa.

Usage

morie_datasets_tps_reported_crimes(
  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 WHERE filter passed to FeatureServer ?where= (default "1=1").

max_features

Optional cap on returned rows. Passed as resultRecordCount to 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). Default TRUE – 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 to tempfile().

Value

A data.frame / GeoJSON list / file path; see morie_datasets_tps_arcgis_hub_by_id() for per-format semantics.

Details

This dataset includes all reported crime offences by reported date aggregated by division.

Tags: ASR; TPS; Toronto Police; Annual Statistical Report; Reported Crimes

Examples

# \donttest{
df <- try(morie_datasets_tps_reported_crimes(max_features = 5L))
if (!inherits(df, "try-error")) head(df)
#>   INDEX_ REPORT_YEAR DIVISION            NEIGHBOURHOOD
#> 1      1        2014      D11     Bay-Cloverhill (169)
#> 2      2        2014      D11 Dovercourt Village (172)
#> 3      3        2014      D11 Dovercourt Village (172)
#> 4      4        2014      D11 Dovercourt Village (172)
#> 5      5        2014      D11 Dovercourt Village (172)
#>                    CATEGORY           SUBTYPE COUNT_ COUNT_CLEARED ObjectId
#> 1 Crimes Against the Person  Sexual Violation      1             1        1
#> 2   Crimes Against Property             Fraud      2             1        2
#> 3   Crimes Against Property             Other      2             0        3
#> 4   Crimes Against Property Theft Under $5000      1             1        4
#> 5 Crimes Against the Person           Assault      1             0        5
# }