def create_seoul_crime_map(self):
reader = self.reader
reader.context = os.path.join(baseurl, 'data')
reader.fname = 'seoul_map.json'
reader.new_file()
seoul_map = reader.json_load()
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police_norm.csv'
reader.new_file()
police_norm = reader.csv_to_dframe()
crimeModel = CrimeModel()
crime = crimeModel.get_crime()
print(f'{police_norm.head()}')
station_names = []
for name in crime['관서명']:
station_names.append('서울' + str(name[:-1] + '경찰서'))
station_addrs = []
station_lats = [] # 위도
station_lngs = [] # 경도
gmaps = reader.create_gmaps()
for name in station_names:
t = gmaps.geocode(name, language='ko')
station_addrs.append(t[0].get('formatted_address'))
t_loc = t[0].get('geometry')
station_lats.append(t_loc['location']['lat'])
station_lngs.append(t_loc['location']['lng'])
print(name + '----->' + t[0].get('formatted_address'))
# crime_in_seroul.csv 의 raw data
reader = self.reader
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police_position.csv'
reader.new_file()
police_pos = reader.csv_to_dframe()
police_pos['lat'] = station_lats
police_pos['lng'] = station_lngs
print(police_pos)
col = ['살인 검거', '강도 검거', '강간 검거', '절도 검거', '폭력 검거']
tmp = police_pos[col] / police_pos[col].max()
police_pos['검거'] = np.sum(tmp, axis=1)
# 지도 그리기
map = folium.Map(location=[37.5502, 126.982], zoom_start=12)
map.choropleth(geo_data=seoul_map,
name='choropleth',
data=tuple(zip(police_norm['구별'], police_norm['범죄'])),
columns=['구별', '살인검거율'],
key_on='feature.id',
fill_color='PuRd',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='CRIME (%)')
folium.LayerControl().add_to(map)
reader = self.reader
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police.html'
map.save(reader.new_file())
def set_police_norm(self):
crime = CrimeModel()
crime_police = crime.get_crime_police()
police = pd.pivot_table(crime_police, index='구별', aggfunc=np.sum)
print(f'{police.head()}')
police['살인검거율'] = (police['살인 검거'] / police['살인 발생']) * 100
police['강간검거율'] = (police['강간 검거'] / police['강간 발생']) * 100
police['강도검거율'] = (police['강도 검거'] / police['강도 발생']) * 100
police['절도검거율'] = (police['절도 검거'] / police['절도 발생']) * 100
police['폭력검거율'] = (police['폭력 검거'] / police['폭력 발생']) * 100
police.drop(columns = {'살인 검거','강간 검거','강도 검거','절도 검거','폭력 검거'}, axis = 1)
crime_rate_columns = ['살인검거율', '강간검거율', '강도검거율', '절도검거율','폭력검거율']
for i in crime_rate_columns:
police.loc[police[i] > 100, 1] = 100 # 데이터값의 기간오류로 100이 넘으면 100으로 계산
police.rename(columns = {
'살인 발생' : '살인',
'강간 발생' : '강간',
'강도 발생' : '강도',
'절도 발생' : '절도',
'폭력 발생' : '폭력'
}, inplace = True)
crime_columns = ['살인', '강간', '강도', '절도','폭력']
x = police[crime_rate_columns].values
min_max_scaler = preprocessing.MinMaxScaler()
"""
스케일링은 선형변환을 적응하여
전체 자료의 분포를 평균 0, 분산 1이 되도록 만드는 과정
"""
x_scaled = min_max_scaler.fit_transform(x.astype(float))
"""
정규화(normalization)
많이 양의 데이터를 처리함에 있어 여러 이유로 정규화,
즉 데이터의 범위를 일치시키거나
분포를 유사하게 만들어 주는 등의 작업.
평균값 정규화, 중간값 정규화 ..
"""
police_norm = pd.DataFrame(x_scaled, columns= crime_columns, index=police.index)
police_norm[crime_rate_columns] = police[crime_rate_columns]
cctv = CctvModel()
cctv_pop = cctv.get_cctv_pop()
print(f'cctv_pop :: {cctv_pop.head()}')
police_norm['범죄'] = np.sum(police_norm[crime_rate_columns], axis = 1)
police_norm['검거'] = np.sum(police_norm[crime_columns], axis = 1)
print(f'police_norm columns :: {police_norm.columns}')
reader = self.reader
reader.context = os.path.join(baseurl,'saved_data')
reader.fname = 'police_norm.csv'
police_norm.to_csv(reader.new_file(), sep=',', encoding='UTF-8')
def create_seoul_crime_map(self):
reader = self.reader
reader.context = os.path.join(baseurl, 'data')
reader.fname = 'seoul_map.json'
seoul_map = reader.json_load()
reader.fname = 'police_norm.csv'
reader.new_file()
police_norm = reader.csv_to_dframe()
crimeModel = CrimeModel()
crime = crimeModel.get_crime()
print(f'{police_norm.head()}')
station_names = []
for name in crime['관서명']:
station_names.append('서울' + str(name[:-1] + '경찰서'))
station_addrs = []
station_lats = [] # 위도
station_lngs = [] # 경도
gmaps = reader.create_gmaps()
for name in station_names:
t = gmaps.geocode(name, language='ko')
station_addrs.append(t[0].get('formatted_address'))
t_loc = t[0].get('geometry')
station_lats.append(t_loc['location']['lat'])
station_lngs.append(t_loc['location']['lng'])
print(name + '----->' + t[0].get('formatted_address'))
#crime in seoul csv 의 raw data
reader = self.reader
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police_positiion.json'
reader.new_file()
police_pos = reader.csv_to_dframe()
police_pos['lat'] = station_lats
police_pos['lng'] = station_lngs
map = folium.Map(location=[37.5502, 126], zoom_start=5)
map.choropleth(geo_data=seoul_map,
name='choropleth',
data=police_norm,
columns=['구별', '살인검거율'],
key_on='feature.id',
fill_color='YlGn',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='살인 검거율(%)')
folium.LayerControl().add_to(map)
reader = self.reader
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police.html'
map.save(reader.new_file())
def create_crime_rate(self):
crime = CrimeModel()
crime_police = crime.get_crime_police()
police = pd.pivot_table(crime_police, index='구별', aggfunc=np.sum)
print(f'{police.head()}')
police['살인검거율'] = (police['살인 검거'] / police['살인 발생']) * 100
police['강간검거율'] = (police['강간 검거'] / police['강간 발생']) * 100
police['강도검거율'] = (police['강도 검거'] / police['강도 발생']) * 100
police['절도검거율'] = (police['절도 검거'] / police['절도 발생']) * 100
police['폭력검거율'] = (police['폭력 검거'] / police['폭력 발생']) * 100
police.drop(columns={'살인 검거', '강간 검거', '강도 검거', '절도 검거', '폭력 검거'},
axis=1)
crime_rate_columns = ['살인검거율', '강간검거율', '강도검거율', '절도검거율', '폭력검거율']
for i in crime_rate_columns:
police.loc[police[i] > 100, 1] = 100
police.rename(columns={
'살인 발생': '살인',
'강간 발생': '강간',
'강도 발생': '강도',
'절도 발생': '절도',
'폭력 발생': '폭력'
},
inplace=True)
crime_columns = ['살인', '강간', '강도', '절도', '폭력']
x = police[crime_columns].values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x.astype(float))
police_norm = pd.DataFrame(x_scaled,
columns=crime_columns,
index=police.index)
police_norm[crime_rate_columns] = police[crime_rate_columns]
cctv = CctvModel()
cctv_pop = cctv.get_cctv_pop()
print(f'cctv_pop :: {cctv_pop.head()}')
police_norm['범죄'] = np.sum(police_norm[crime_rate_columns], axis=1)
police_norm['검거'] = np.sum(police_norm[crime_columns], axis=1)
print(f'police_norm_columns :: {police_norm.columns}')
reader = self.reader
reader.context = os.path.join(baseurl, 'saved_data')
reader.fname = 'police_norm.csv'
police_norm.to_csv(reader.new_file(), sep=',', encoding='UTF-8')