def DistRank_F(address):
zip_code = [
'20001', '20002', '20003', '20004', '20005', '20006', '20007', '20008',
'20009', '20010', '20011', '20012', '20015', '20016', '20017', '20018',
'20019', '20020', '20024', '20032', '20036', '20037', '22201', '22202',
'22203', '22204', '22205', '22206', '22207', '22209', '22211', '22213',
'22214', '22301', '22302', '22304', '22305', '22311', '22314'
]
# three types of isochrone
types = ["driving", "cycling", "walking"]
# time of isochrone
times = [10, 30, 60]
# add isochrone area to dataframe
df = pd.DataFrame(zip_code, columns={"Zip Code"})
from geopy.geocoders import Nominatim
# create a random name of agent so that the service will not time out
agent = "distance" + str(random.randint(0, 100))
geolocator = Nominatim(user_agent=agent)
location = geolocator.geocode(address)
# loc is the location of address
loc = (location.longitude, location.latitude)
# zi_p is the zip code of address
zi_p = (str.split(location.address, ",")[-2]).lstrip()
for i in range(len(types)):
ty_pe = types[i]
for j in range(len(times)):
time = times[j]
name = 'Ranking of '+str(types[i]) + \
" "+str(times[j])+" "+"of Family"
li_st = []
for k in range(len(zip_code)):
li_st.append(distance(loc, zip_code[k], ty_pe, time))
df[name] = li_st
df[name] = ranking(df[name])
df.loc[df["Zip Code"] == zi_p, name] = 5
name_col = str(ty_pe.title()) + " Area of Family"
cols = []
for m in df.columns:
if ((ty_pe in str.split(m, " "))
and ("Family" in str.split(m, " "))):
cols.append(m)
df[name_col] = df[cols].apply(lambda x: x.sum(), axis=1)
df[name_col] = ranking(df[name_col])
return df
def safety_consideration(safety, data):
safety_list = ["Property | Theft", "Violent | Sex Abuse", "Violent | Homicide", "Property | Burglary", "Property | Arson",
"Violent | Assault with Dangerous Weapon", "Violent | Robbery"]
not_selected = list(set(safety_list) ^ set(safety))
data["Ranking of Crime"] = ranking(
(data[safety]*0.2).apply(sum, axis=1)+(data[not_selected]*0.1).apply(sum, axis=1))
return data