import geopandas as gpd
gpda = gpd.read_file('arquivo.json', driver='GeoJSON')
import fiona
gpd.io.file.fiona.drvsupport.supported_drivers['KML'] = 'rw'
df = gpd.read_file('arquivo.kml', driver='KML')
dg=df['geometry']
env = gpda['geometry']
env.contains(dg)#The indices of the two GeoSeries are different.
import geopandas as gpd
import matplotlib.pyplot as plt
gdf_geometrias_gsp = gpd.read_file('SP_Municipios_2020.shp') #arquivo de referencia
gdf_geometrias_gsp.head
shape2=['geometry'] #nome da coluna
data_2= shape[shape['NM_MUN']=='São Paulo'] #dados do arquiv de referencia
data_2.plot()
filename='dados_capital_sp.json'
print(data_2['geometry'])
data_2.to_file(filename, driver ="GeoJSON")
df_roubo=pd.read_excel('Celular.xlsx')
df_roubo.dropna(subset=['LATITUDE','LONGITUDE'])
df_roubo[['LATITUDE', 'LONGITUDE']]
df_roubo['geometry']=None
type(df_roubo)
for index, row in df_roubo.iterrows():
df_roubo.loc[index,'geometry'] = Point(row.LONGITUDE, row.LATITUDE) #esse põem pontos
gdf_roubos= gpd.GeoDataFrame(df_roubo, geometry='geometry')
gdf_roubos.plot()
gpd_capital_spp = gpd.read_file('dados_capital_sp.json', driver='GeoJSON')
import matplotlib.pyplot as plt
polygon_sp= gpd_capital_spp.iloc[0].geometry
gdf_roubos_capital = gdf_roubos['geometry'].intersection(polygon_sp)
gdf_roubos_capital.plot()
gdf_roubos.plot()
gpd_capital_sp.plot()
import folium
fmap = folium.Map()
fig.ax = plt.subplots(figsize=(10,10))
gdf_roubos_capital.plot(ax=ax)
gpd_capital_sp.plot(ax=ax, facecolor='None', edgecolor='Black')
polygon_sp.plot(ax=ax, facecolor='None', edgecolor='Black')
import webbrowser
webbrowser.open("fmap.html")
gdf_roubos_capital.to_file('mapa1.json', driver ="GeoJSON")
gdf_roubos.dropna(subset=["LATITUDE", "LONGITUDE"], inplace=True)
gdf_roubo_capital = gpd.read_file('mapa1.json', driver='GeoJSON')
gdf_roubos_capital_slice = gdf_roubo_capital.iloc[:100]# só 100 primeiros
gdf_roubos_slice = gdf_roubo.iloc[:100]
gdf_roubos_capital_slice.crs = "EPSG:29101"
medialat = gdf_roubos['LATITUDE'].mean()
medialong = gdf_roubos['LONGITUDE'].mean()
fmap = folium.Map(location=[medialat, medialong])
feat_geojson=folium.features.GeoJson(gdf_roubos_capital_slice)
fmap.add_child(feat_geojson) #juntando os dados
fmap.save("fmap1.html")
webbrowser.open("fmap1.html")
from folium.plugins import FastMarkerCluster
fmap = folium.Map(location=[medialat, medialong])
mc=FastMarkerCluster=FastMarkerCluster(gdf_roubos[['LATITUDE','LONGITUDE']])
fmap.add_child(mc)
fmap.save("fmap2.html")
webbrowser.open("fmap2.html")
gdf_geometrias_gsp#limites dos municipios
limites=folium.features.GeoJson(gdf_geometrias_gsp, style_function=lambda feature:{'color': 'black', 'weight': '2', 'fillOpacity': '0.0'} )
fmap.add_child(limites)
from folium.plugins import HeatMap
fmap = folium.Map(location=[medialat, medialong], title ='cartodbpositron')
HeatMap = HeatMap (gdf_roubos[['LATITUDE','LONGITUDE']])
fmap.add_child(HeatMap)
fmap.save("fmap3.html")
webbrowser.open("fmap3.html")
for index, municipio in gdf_geometrias_gsp.iterrows():
qtd_roubos = len(gdf_roubos[gdf_roubos.intersects(municipio.geometry)])
gdf_geometrias_gsp.loc[index,'qtd_roubos']=qtd_roubos
media_latitude_gsp = gdf_roubos['LATITUDE'].mean()
media_longitude_gsp=gdf_roubos['LONGITUDE'].mean()
fmap = folium.Map(locations=[media_latitude_gsp, media_longitude_gsp],titles='cartodbpositron')
for _, municipios in gdf_geometrias_gsp.iterrows():
municipio_geojson = folium.features.GeoJson(municipio.geometry, style_function = lambda feature:{'color':'blue', 'weinght': '2', 'fillOpacity':0.1})
popup=folium.Popup("""Municipios:{} Roubo registrados em Novembro: {}""".format(municipio.NM_MUN, str(int(municipio.qtd_roubos)))) #'Series' object has no attribute
popup.add_to(municipio_geojson)
municipio_geojson.add_to(fmap)
fmap.save("fmap4.html")
webbrowser.open("fmap4.html")
