def compute_toc_tiers_from_metrolink_stations(
stations: geopandas.GeoDataFrame,
clip: geopandas.GeoDataFrame,
cushion: float = DEFAULT_CUSHION,
) -> geopandas.GeoDataFrame:
"""
Compute TOC Tiers from Metrolink stations, clipped to a boundary.
Parameters
==========
stations: geopandas.GeoDataFrame
The Metrolink stations data frame.
clip: geopandas.GeoDataFrame
The boundary to clip the toc tiers to (probably the City of Los Angeles)
"""
stations = stations.to_crs(f"EPSG:{SOCAL_FEET}")
stations = stations.assign(
tier_4=geopandas.GeoSeries([shapely.geometry.GeometryCollection()] *
len(stations),
crs=f"EPSG:{SOCAL_FEET}"),
tier_3=stations.geometry.buffer(750.0 * cushion),
tier_2=stations.geometry.buffer(1500.0 * cushion),
tier_1=stations.geometry.buffer(2640.0 * cushion),
)
stations = stations.assign(
tier_1=stations.set_geometry("tier_1").to_crs(f"EPSG:{WGS84}").tier_1,
tier_2=stations.set_geometry("tier_2").to_crs(f"EPSG:{WGS84}").tier_2,
tier_3=stations.set_geometry("tier_3").to_crs(f"EPSG:{WGS84}").tier_3,
tier_4=stations.set_geometry("tier_4").to_crs(f"EPSG:{WGS84}").tier_4,
).to_crs(f"EPSG:{WGS84}")
stations = stations[stations.set_geometry("tier_1").intersects(
clip.iloc[0].geometry)]
stations["mode"] = "metrolink"
return stations[[
"Name",
"ext_id",
"description",
"geometry",
"tier_1",
"tier_2",
"tier_3",
"tier_4",
"mode",
]].rename(columns={
"ext_id": "station_id",
"Name": "name"
})
def extract_dublin_local_authorities(
geometries: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
"""Extract Dublin Local Authorities from Ireland Small Area Geometries Data.
Args:
geometries (gpd.GeoDataFrame): Ireland Small Area Geometries
Returns:
gpd.GeoDataFrame: Dublin Small Area Geometries
"""
return geometries.assign(
COUNTYNAME=lambda x: x["COUNTYNAME"].apply(unidecode)
)[lambda x: np.isin(
x["COUNTYNAME"],
["Dun Laoghaire-Rathdown", "Fingal", "South Dublin", "Dublin City"],
)]
isf_wo = isf_wo.drop(['srchAssetID', 'gpscoordinateX', 'gpscoordinateY', 'initialproblemID', \
'resolveddatetime', 'entereddate', 'finalresolutionID'], axis = 1)
isf_wo_inv = pd.merge(isf_wo, inv, how='left', on = 'inventoryID')
isf_wo_inv = isf_wo_inv.drop(['gpscoordinateX', 'gpscoordinateY'], axis = 1)
# Setting up data into geopandas
geometry = [Point(xy) for xy in zip(isf_wo_inv['gpsX'], isf_wo_inv['gpsY'])]
gLights2 = GeoDataFrame(isf_wo_inv, geometry=geometry)
gLights2 = gLights2.drop_duplicates(subset = ['WoID'])
geometry = [Point(xy) for xy in zip(NCR2['X'], NCR2['Y'])]
gNCR2 = GeoDataFrame(NCR2, geometry=geometry)
BUFFER = .000625 # 1/4th of a city block in radius of Maryland coordinates.
#BUFFER = .00125 # 1/2 of a city block in radius of Maryland coordinates.
gLights_Buff2 = gLights2.assign(geometry = lambda x: x.geometry.buffer(BUFFER))
# Overwrites geometry variable with a buffer centered at the point of interest. A.k.a. applies the function geometry(x) to gNCR and saves it as geometry.
Matched_NLights = gpd.sjoin(gLights_Buff2, gNCR2, 'left')
Matched_NLights['Crime_LO_intime'] = [0]*len(Matched_NLights) # Counter to be used
Matched_NLights = Matched_NLights.dropna(subset = ['WoCompleted'])
Matched_NLights = Matched_NLights.dropna(subset = ['REPORT_DAT'])
Matched_NLights = Matched_NLights.reset_index(drop=True)
# Flagging possible lights that influenced crime:
def flag(z):
bar = progressbar.ProgressBar(maxval=len(z), widgets=[progressbar.ETA(), ' ', progressbar.Percentage()])
bar.start()
for i in range(len(z)):