def route_animation(transport):
#calculate the shortest
G = ox.graph_from_place('Montpellier, France', network_type=transport)
start = (43.61032245, 3.8966295)
end = (43.61032245, 3.8966295)
start_node = ox.get_nearest_node(G, start)
end_node = ox.get_nearest_node(G, end)
route = nx.shortest_path(G, start_node, end_node)
#listing nodes and coordinates from the route
node_start = []
node_end = []
X_to = []
Y_to = []
X_from = []
Y_from = []
length = []
for u, v in zip(route[:-1], route[1:]):
node_start.append(u)
node_end.append(v)
length.append(round(G.edges[(u, v, 0)]['length']))
X_from.append(G.nodes[u]['x'])
Y_from.append(G.nodes[u]['y'])
X_to.append(G.nodes[v]['x'])
Y_to.append(G.nodes[v]['y'])
#create a data frame
df = pd.DataFrame(list(zip(node_start, node_end, X_from, Y_from, X_to, Y_to, length, travel_time)),
columns =["node_start", "node_end", "X_from", "Y_from", "X_to", "Y_to", "length", "travel_time"])
df.head()
start = df[df["node_start"] == start_node]
end = df[df["node_end"] == end_node]
#animate the route with Plotly Express
fig = px.scatter_mapbox(df, lon= "X_from", lat="Y_from", zoom=13, width=1000, height=800,
animation_frame=df.index)
fig.update_layout(mapbox_style="stamen-toner")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.data[0].marker = dict(size = 20, color="black")
fig.add_trace(px.scatter_mapbox(start, lon= "X_from", lat="Y_from").data[0])
fig.data[1].marker = dict(size = 15, color="red")
fig.add_trace(px.scatter_mapbox(end, lon= "X_from", lat="Y_from").data[0])
fig.data[2].marker = dict(size = 15, color="green")
fig.add_trace(px.line_mapbox(df, lon= "X_from", lat="Y_from").data[0])
fig.write_html("route_animation.html")
fig.show()
def get_map(df_geo):
# Map
fig_map = px.scatter_mapbox(df_geo,
lat="latitude",
lon="longitude",
animation_frame='date',
animation_group='school',
color="Category",
size="total",
color_discrete_sequence=px.colors.qualitative.Prism,
center={"lat": 34.221749,
"lon": -84.135526},
zoom=10,
# height=00,
# width=700,
hover_name='school',
hover_data=['school', 'total', 'F2F Students & Staff'])
fig_map.update_layout(mapbox_style="carto-positron")
fig_map.update_layout(margin={},
autosize=True,
title=dict(
text='Reported Positive Cases',
font=dict(
color='black',
size=26),
xanchor="center",
x=0.5))
# fig_map['layout']['sliders'][0]['y'] = 1.2
# fig_map['layout']['updatemenus'][0]['y'] = 1.2
return fig_map
def update_graph_live(n):
postgreSQL_select_Query = 'SELECT sensorid,time,lat,lon,signal from test WHERE ( unix_timestamp( ) - unix_timestamp( time ) ) < 10 GROUP BY CONCAT(sensorid, time) HAVING COUNT(*) = 1'
cursor.execute(postgreSQL_select_Query)
df = pd.read_sql_query(postgreSQL_select_Query, conn)
#df = pd.DataFrame(cars, columns = ['time', 'lat','lon','signal'])
#print(df)
#print('ploting')
# Create the graph with subplots
figure = px.scatter_mapbox(df,
lat='lat',
lon='lat',
color='lat',
color_discrete_map={
"Red": "red",
"Green": "green",
"Orange": "orange"
},
size_max=16,
zoom=9)
figure.update_layout(
uirevision=True,
mapbox={'center': {
'lon': -73.862614,
'lat': 40.799312
}})
print(df)
print('to plot')
return figure
def update_graph(xaxis_column_name):
if xaxis_column_name == 'Hour':
query = 'SELECT time, cam_ID, lat, lon, AVG(vehicles) as average_vehicles FROM traffic_cams WHERE time >= NOW() - INTERVAL 1 HOUR and mod(minute(time),5) = 0 GROUP BY CONCAT(cam_ID, time)'
if xaxis_column_name == 'Day':
query = 'SELECT time, cam_ID, lat, lon, AVG(vehicles) as average_vehicles FROM traffic_cams WHERE time >= NOW() - INTERVAL 24 HOUR and mod(minute(time),120) = 0 GROUP BY CONCAT(cam_ID, time)'
if xaxis_column_name == 'Week':
query = 'SELECT time, cam_ID, lat, lon, AVG(vehicles) as average_vehicles FROM traffic_cams WHERE time >= NOW() - INTERVAL 168 HOUR and mod(minute(time),840) = 0 GROUP BY CONCAT(cam_ID, time)'
df2 = pd.read_sql(query, con=engine)
df2['time_by_m'] = df2['time'].dt.floor('1Min')
df2 = df2.drop_duplicates(['time_by_m', 'cam_ID'])
df2['time_mark'] = df2['time_by_m'].astype(str)
df2.sort_values(by='time')
figure = px.scatter_mapbox(
df2,
lat='lat',
lon='lon',
color='average_vehicles',
size='average_vehicles',
color_continuous_scale=px.colors.sequential.Inferno,
size_max=16,
animation_frame='time_mark',
animation_group='cam_ID',
zoom=9)
return figure
def update_graph_live(n):
# build data frame for mapplot
df = pd.read_sql(
'SELECT * FROM traffic_cams WHERE ( unix_timestamp( ) - unix_timestamp( time ) ) < 15 GROUP BY cam_ID HAVING COUNT(*) = 1;',
con=engine)
# df = pd.read_sql('SELECT time, cam_ID, lat, lon, AVG(vehicles) as average_vehicles FROM traffic_cams WHERE ( unix_timestamp( ) - unix_timestamp( time ) ) < 15 GROUP BY CONCAT(cam_ID, time)', con=engine)
if len(df) < 10:
df = pd.read_sql(
'SELECT * FROM traffic_cams WHERE ( unix_timestamp( ) - unix_timestamp( time ) ) < 60 GROUP BY cam_ID HAVING COUNT(*) = 1;',
con=engine)
# start plot
#print(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
print(df)
print('ploting')
# Create the graph with subplots
figure = px.scatter_mapbox(
df,
lat='lat',
lon='lon',
color='vehicles',
size='vehicles',
color_continuous_scale=px.colors.sequential.thermal,
size_max=16,
zoom=11)
figure.update_layout(
uirevision=True,
mapbox={'center': {
'lon': -73.880274,
'lat': 40.836478
}})
return figure
def display_map(df):
st.subheader(" Displaying Point based map")
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(df, lat="Y", lon="X", color="METHOD", zoom=10)
return fig
def update_graph_live(n):
# build data frame for mapplot
df = pd.read_sql(
'SELECT time, cam_ID, lat, lon, cars, trucks, AVG(vehicles) as average_vehicles FROM traffic_cams WHERE ( unix_timestamp( ) - unix_timestamp( time ) ) < 10 GROUP BY CONCAT(cam_ID, time) HAVING COUNT(*) = 1',
con=engine)
#print(df)
#print('ploting')
# Create the graph with subplots
figure = px.scatter_mapbox(
df,
lat='lat',
lon='lon',
color='average_vehicles',
size='average_vehicles',
hover_data=['cars', 'trucks'],
color_continuous_scale=px.colors.sequential.Inferno,
size_max=16,
zoom=9)
figure.update_layout(
uirevision=True,
mapbox={'center': {
'lon': -73.862614,
'lat': 40.799312
}})
print(df)
print('to plot')
return figure
def display_mapc(df):
px.set_mapbox_access_token(
"pk.eyJ1IjoibXV5ZGlwYWxtYSIsImEiOiJjazk4eTI2ZmIwMjAzM2Vua3N0cHJhNXR6In0.y-fZg2ncJlXhVTAPO_9mmw")
fig = px.scatter_mapbox(df, lat='longitud', lon='latitud', color='cc', hover_data=['punto_vacunacion'], hover_name='punto_vacunacion',
center={'lon': df.loc[-1,'latitud'], 'lat': df.loc[-1,'longitud']}, zoom=13,labels={},width=800)
return fig
def update_map(clickData, x_axis_dimension, y_axis_dimension):
if clickData: # if the user has clicked the heatmap then
#query map data
spatial_data = GetSpatialBins(db_connection,
x_filter=clickData['points'][0]['x'],
y_filter=clickData['points'][0]['y'],
x_dimension=x_axis_dimension,
y_dimension=y_axis_dimension)
# build map plot
map_plot = px.scatter_mapbox(
spatial_data,
lat="LAT_BIN",
lon="LON_BIN",
color="COUNT",
size="COUNT",
color_continuous_scale=px.colors.sequential.Sunset,
range_color=(spatial_data.COUNT.min(), spatial_data.COUNT.max()),
size_max=15,
zoom=10)
# change setting to preserve map zoom and center on update
map_plot.update_layout(uirevision=True)
else: # If no data is selected then build a blank placeholder map to return
#TODO: There must be a better way to create a blank maps\
# single point dataframe for placeholder map
sample_data = pd.DataFrame(data={
'LAT': [42.96],
'LON': [-85.67],
'COUNT': [5]
})
# Build blank placeholder map
map_plot = px.scatter_mapbox(
sample_data,
lat='LAT',
lon='LON',
color='COUNT',
size='COUNT',
size_max=1,
color_continuous_scale=px.colors.sequential.Sunset,
zoom=10)
# change map style and padding settings
map_plot.update_layout(mapbox_style="carto-positron",
margin=dict(l=20, r=20, t=10, b=0))
return map_plot
def make_figure_box(size, feat):
fig = px.scatter_mapbox(
env,
size=size,
zoom=0.5,
lat="Latitude [degrees North]", lon="Longitude [degrees East]", color=feat, hover_name="Marine_provinces",
title=str(size) + " distribution and abundance (RPKG) on Tara Oceans.").for_each_trace(
lambda t: t.update(name=t.name.replace(str(feat) + "=", "")))
fig.update_layout(plot_bgcolor="#F9F9F9", paper_bgcolor="#F9F9F9", titlefont={
"size": 20})
fig.update_layout(autosize=True)
fig.update_yaxes(automargin=True)
fig.update_layout(mapbox_style="open-street-map")
return fig
def make_figure(size, color):
px.set_mapbox_access_token(
"pk.eyJ1IjoiYmFucXVldGt1bWEiLCJhIjoiY2p0YjZ4bGJ2MGlseTN5bzlxcnlsbW8xNCJ9.udbxOpc2gZQcUX4m1VIqBg"
)
return px.scatter_mapbox(GPS,
lat="緯度 (°)",
lon="経度 (°)",
color=color,
size=size,
size_max=15,
zoom=10,
color_continuous_scale=px.colors.cyclical.IceFire,
hover_name=size)
def mapbox(df):
"""Plot the locations from a df containing ['lat', 'lng', 'name'] in an
interactive window.
"""
zoom = autozoom(df) - 3
output = px.scatter_mapbox(df,
lat='lat',
lon='lng',
hover_name=['name', 'rating'],
zoom=zoom,
color='label',
width=600,
height=600)
return output
def update_graph_live(n):
# build data frame for mapplot
data = []
# read latest record from each consumer
for consumer in consumers:
#dummy poll
consumer.poll()
# go to end of the stream
consumer.seek_to_end()
for message in consumer:
value = message.value
data.append(value)
if len(data) > 0:
break
# build df for ploting
df = pd.DataFrame(data)
# log which cams are missing
#got_cams = df['cam_ID'].tolist()
#missed_cams = list(set(all_cams) - set(got_cams))
#missed_cams_sr = ' '.join([str(elem) for elem in missed_cams])
df.insert(1, 'missed_cams', ' ', True)
# save to database
df.to_sql(name='traffic_cams', con=engine, if_exists='append', index=False)
# start plot
print(len(data))
print('ploting')
# Create the graph with subplots
figure = px.scatter_mapbox(
df,
lat='lat',
lon='lon',
color='vehicles',
size='vehicles',
color_continuous_scale=px.colors.sequential.thermal,
size_max=15,
zoom=11)
figure.update_layout(
uirevision=True,
mapbox={'center': {
'lon': -73.884704,
'lat': 40.825355
}})
return figure
def display_map(df):
st.subheader(" Displaying Point based map")
px.set_mapbox_access_token(
"pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w"
)
fig = px.scatter_mapbox(df,
lat="lat",
lon="lon",
color="district",
size="confirmed",
color_continuous_scale=px.colors.cyclical.IceFire,
zoom=3,
width=900,
height=900)
return fig
def test_location_based():
test_coordinates = {
'latitude' : [36.1017316],
'longitude' : [-115.1891691],
}
user1 = pd.DataFrame(test_coordinates)
recommend_restaurants(top_res_lv, user1.longitude, user1.latitude)
# plot the locations of the restaurants and the user
fig = px.scatter_mapbox(recommend_restaurants(top_res_lv, user1.longitude, user1.latitude),
lat="latitude", lon="longitude",
zoom=10, width=1200, height=800,
hover_data= ['name', 'latitude', 'longitude'])
fig2 = fig.add_scattermapbox(
lat=user1["latitude"],lon= user1["longitude"]).update_traces(dict(mode='markers', marker = dict(size = 15)))
def update_map(criteria_value):
if criteria_value == 'None':
value = None
else:
value = str(criteria_value)
fig = px.scatter_mapbox(CSV,
lat="lat",
lon="lon",
title='Map of the offences',
color=value,
size="age",
hover_name='name',
hover_data=['birth date', 'block'],
zoom=9)
fig.update_layout(legend_orientation="h", legend=dict(x=-0.01, y=1.1))
return fig
def mapbox(df):
"""Plot the locations from a df containing ['lat', 'lng', 'name'] in an
interactive window.
"""
zoom = autozoom(df) - 3
output = px.scatter_mapbox(
df,
lat="lat",
lon="lng",
hover_name=["name", "rating"],
zoom=zoom,
color="label",
width=600,
height=600,
)
return output
def update_graph_live(n):
# build data frame for mapplot
data = []
#dummy poll
#consumer.poll(timeout_ms=0, max_records=40, update_offsets=True)
# go to end of the stream
#consumer.seek_to_end()
# build df for plot
max_time = 4
start_time = time.time()
for message in consumer:
value = message.value
data.append(value)
if (time.time() - start_time) > max_time:
break
#if len(data) > 14:
# break
# build df for ploting
df = pd.DataFrame(data)
# log which cams are missing
#got_cams = df['cam_ID'].tolist()
#missed_cams = list(set(all_cams) - set(got_cams))
#missed_cams_sr = ' '.join([str(elem) for elem in missed_cams])
#df.insert(1, 'missed_cams',missed_cams_sr, True)
#print(missed_cams)
# save to database
#df.to_sql(name='traffic_cams', con=engine, if_exists = 'append', index=False)
# start plot
print(len(data))
print('ploting')
# Create the graph with subplots
figure = px.scatter_mapbox(df,
lat='lat',
lon='lon',
color='vehicles',
size='vehicles',
color_continuous_scale=px.colors.sequential.thermal,
size_max=16,
zoom=11)
figure.update_layout(uirevision = True,
mapbox = {'center': {'lon':-73.880274, 'lat': 40.836478}})
return figure
def map_box_plot(df, locations):
"""
Generate a map box chart
"""
if len(locations) > 0:
df = df[df['Capture Location'].isin(locations)]
args = {
'lat': 'lat',
'lon': 'long',
'size': 'Annuli',
'color': 'Gender',
'size_max': 20,
'zoom': 10,
}
px.set_mapbox_access_token(environ['MAP_BOX'])
fig = go.Figure(px.scatter_mapbox(df, **args))
fig['layout']['height'] = 600
graph = dcc.Graph(id='map-box-chart', figure=fig)
return graph
def display_map(data, otherLst):
local = []
for index, row in data.iterrows():
if row['Resource Name'] in otherLst:
local.append(
[row['Resource Name'], row['Longitude'], row['Latitude']])
df = pd.DataFrame(local,
columns=['Resource Name', 'Longitude', 'Latitude'])
fig = px.scatter_mapbox(df,
lat=df['Latitude'],
lon=df['Longitude'],
hover_name=df["Resource Name"],
hover_data=[df["Longitude"], df["Latitude"]],
color_discrete_sequence=["fuchsia"],
zoom=3,
height=300)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
return fig
def kumapx():
px.set_mapbox_access_token(
"pk.eyJ1IjoiYmFucXVldGt1bWEiLCJhIjoiY2p0YjZ4bGJ2MGlseTN5bzlxcnlsbW8xNCJ9.udbxOpc2gZQcUX4m1VIqBg"
)
#mapboxのtokenを読み込む
return px.scatter_mapbox(
df,
lat="Latitude",
lon="Longtitude",
color="Wait Time(min)",
size="Wait Time(min)",
size_max=60,
zoom=10,
#animation_frame="DATETIME",
#animation_group="attractions",
color_continuous_scale=px.colors.cyclical.IceFire,
hover_name="attractions",
title="Disney's Animal Kingdom Theme Park Wait Time from {} to {}".
format(df["DATETIME"].min(), df["DATETIME"].max()))
def browse():
token = 'pk.eyJ1IjoibWF6YWJkdWwiLCJhIjoiY2trMWh1aXRsMHJhMjJxbzU0eTYzbTI5ZiJ9.DIP735c0I4CbxFHmTtNBCw'
df = pd.read_excel('personal-well-being-borough.xlsx',
sheet_name='Summary - Mean Scores',
skiprows=1)
columns = [1, 27, 37, 38]
df2 = df.iloc[list(range(0, 33)), columns] # select happiness
df2 = df2.drop([0, 1])
df2 = df2.reset_index(drop=True)
df2["2018/19.2"] = np.array(df2["2018/19.2"], dtype=np.float64)
ukavg = df.iloc[[50], [27]] #uk avg hapiness
ukavg = ukavg["2018/19.2"][50]
df2["%Difference from AVG"] = df2["2018/19.2"]
for i in range(len(df2["%Difference from AVG"])):
df2["%Difference from AVG"][i] = (
100 * (df2["%Difference from AVG"][i] - ukavg) / ukavg)
df2.columns = [
"Area", "Happiness", "Latitude", "Longitude", "%Difference from AVG"
]
fig = px.scatter_mapbox(df2,
lat=df2.Latitude,
lon=df2.Longitude,
size="Happiness",
hover_name="Area",
hover_data=["Happiness", "%Difference from AVG"],
color_discrete_sequence=["fuchsia"],
color="%Difference from AVG",
size_max=10,
zoom=9,
title="Happiness Rating per Borough")
fig.update_layout(mapbox_style="dark", mapbox_accesstoken=token)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
div = fig.to_html(full_html=False)
return render_template('browse.html', div_placeholder=div)
def topArtistPerCity():
totalDf = pd.DataFrame(columns=[
'Artist Name', 'Max Listeners', 'Location', 'lat', 'lng',
'artist Label'
])
maxCityDf = pd.DataFrame(
columns=['Artist Name', 'Max Listeners', 'Location'])
for item in myCursor3:
artistName = item['name']
artistArray = item['artist']['cities']
for row in artistArray:
artistListener = row['listeners']
artistLabel = row['streams']
artistCity = row['city']
lat = row['lat']
lng = row['lng']
totalDf.loc[len(totalDf)] = [
artistName, artistListener, artistCity, lat, lng, artistLabel
]
totalDf = totalDf.sort_values(['Location', 'Max Listeners'],
ascending=False) #.reset_index()
totalDf = totalDf.groupby(['Location']).max().reset_index() #.mean()
# print(totalDf)
for row in totalDf.iterrows():
print(row)
# px.set_mapbox_access_token(open(config.mapApiToken).read())
fig = px.scatter_mapbox(totalDf,
lat="lat",
lon="lng",
hover_name="Location",
hover_data=['artist Label'],
color_discrete_sequence=["fuchsia"],
zoom=3,
height=300)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
def display_map(df):
px.set_mapbox_access_token(
"pk.eyJ1IjoibXV5ZGlwYWxtYSIsImEiOiJjazk4eTI2ZmIwMjAzM2Vua3N0cHJhNXR6In0.y-fZg2ncJlXhVTAPO_9mmw")
fig = px.scatter_mapbox(df, lat='longitud', lon='latitud',hover_name='punto_vacunacion',hover_data=None,zoom=10)
return fig
def plot_cluster():
fig1 = px.scatter_mapbox(restaurant_lv, lat="latitude", lon="longitude", color="cluster", size='stars',
hover_data= ['name', 'latitude', 'longitude'], zoom=10, width=1200, height=800)
def display_map(df):
px.set_mapbox_access_token("pk.eyJ1Ijoic2hha2Fzb20iLCJhIjoiY2plMWg1NGFpMXZ5NjJxbjhlM2ttN3AwbiJ9.RtGYHmreKiyBfHuElgYq_w")
fig = px.scatter_mapbox(df, lat='latitude', lon='longitude', zoom=10)
return fig
# Recommended Stations:
df_recommeded = pd.DataFrame()
df_recommeded = df_alldata.loc[(df_alldata['STATION'] == '23 ST') |
(df_alldata['STATION'] == '34 ST-HERALD SQ') |
(df_alldata['STATION'] == '34 ST-PENN STA') |
(df_alldata['STATION'] == '42 ST-PORT AUTH') |
(df_alldata['STATION'] == '14 ST-UNION SQ') |
(df_alldata['STATION'] == '14 ST') |
(df_alldata['STATION'] == 'GRD CNTRL-42 ST') |
(df_alldata['STATION'] == '86 ST')]
# In[3]:
# [PPT SLIDE 5]: 2D map view (spatial distribution) of all station locations
fig = px.scatter_mapbox(df_alldata, lat='LAT', lon='LON', zoom=11)
fig.update_traces(marker=dict(size=10))
fig.update_layout(mapbox_style="stamen-toner")
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()
# In[4]:
# Subset of all stations with more than 500,000 total traffic over the dataset's time range:
df_stations_gt500k = df_alldata.take(
df_alldata.loc[df_alldata['TOT_IN_OUT'] >= 500000].index)
# [PPT SLIDE 6]: Map of these stations:
fig = px.scatter_mapbox(df_stations_gt500k,
lat="LAT",
lon="LON",
)
fatalities = pd.read_csv('Data/fatalities_geocoded.csv')
fatalities = fatalities.drop('Unnamed: 0', axis=1)
test = fatalities.groupby(
['state', 'race', 'location', 'latitude', 'longitude'])['name'].count()
test = test.reset_index()
test = test.rename({'name': 'count'}, axis=1)
test = test.sort_values('count', ascending=False)
fig = px.scatter_mapbox(test,
lat="latitude",
lon="longitude",
color="race",
size="count",
color_continuous_scale=px.colors.cyclical.IceFire,
size_max=20,
zoom=3,
hover_name='location',
opacity=.5)
########### Display the chart
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
server = flask.Flask(__name__)
app = dash.Dash(__name__,
server=server,
external_stylesheets=external_stylesheets)
app.title = "Police Shootings"
folium.CircleMarker([lat, lng], popup = label,
radius = 2, color = colors).add_to(sanfrans_map)
sanfrans_map
# In[54]:
# 컬러별로 보기가 어려운 관계로 다른 시각화 패키지 plotly를 활용해본다
import plotly_express as px
fig = px.scatter_mapbox(train_1000,
lat = train_1000["Y"],
lon = train_1000["X"],
color = train_1000["Category"],
hover_name = train_1000["PdDistrict"],
zoom = 10)
fig.update_layout(mapbox_style = "open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
# 시각화하며 얻은 사실
#
# 지도를 시각화한다고 해서 한눈에 특정지역에 특정범죄가 많이 발생하는것을 바로 파악하기는 어렵다는 사실이다.
# 이유는 상위권에 속한 범죄들이 보통 대부분의 지역에서도 상위권인 점(특정 범죄 제외)때문이다.
#
# 따라서 map을 통해서는 2가지를 파악해야 한다는 사실을 파악해볼 수 있었다
#
title=
'<b>Number of Zones per State from 4th May</b><br>Specific to Lockdown 3.0',
legend=legend,
barmode='stack')
fig.show()
# ### <a id='test'> Testing Labs in India</a>
# In[68]:
df_lab = pd.read_csv('dataset/ICMRTestingLabsWithCoords.csv')
fig = px.scatter_mapbox(df_lab,
lat="latitude",
lon="longitude",
mapbox_style='streets',
hover_name='lab',
hover_data=['city', 'state', 'pincode'],
zoom=2.5,
size_max=15,
title='COVID19 Testing Labs in India')
# In[34]:
def add_text(ax, fontsize=12):
for p in ax.patches:
x = p.get_bbox().get_points()[:, 0]
y = p.get_bbox().get_points()[1, 1]
ax.annotate('{}'.format(int(y)), (x.mean(), y),
ha='center',
va='bottom',
