| import pandas as pd |
| import numpy as np |
| import overpy |
| from sklearn.cluster import KMeans,DBSCAN |
| from convex_hull import * |
| import config |
| from maplegend import * |
|
|
| import plotly.express as px |
| import folium |
| from folium import plugins |
|
|
|
|
| api = overpy.Overpass() |
|
|
| |
| def fetch_city_data(city_name): |
| |
| res = api.query(f"""[out:json]; |
| area[name='{city_name}'][boundary=administrative]->.searchArea; |
| (node["amenity"](area.searchArea); |
| way["amenity"](area.searchArea); |
| relation["amenity"](area.searchArea); |
| ); |
| (._; |
| >; |
| ); |
| out; |
| """) |
| return reduce(res) |
|
|
| |
| def reduce(res): |
| tags = [] |
| for i in res.nodes: |
| if len(i.tags) != 0: |
| i_tags = i.tags |
| i_tags['node_id'] = i.id |
| i_tags['lat'] = float(i.lat) |
| i_tags['lon'] = float(i.lon) |
| tags.append(i.tags) |
| df = pd.DataFrame(tags) |
| df = df[['node_id', 'lat', 'lon', 'name', 'amenity']] |
| df = df.dropna(subset=['node_id', 'lat', 'amenity']) |
| remove_amenity = [ |
| 'arts_centre', |
| "Ayurvedic Hospital", |
| "baby_hatch", |
| "bench", |
| "bicycle_parking", |
| "bicycle_rental", |
| "bicycle_repair_station", |
| "bureau_de_change", |
| "car_rental", |
| "car_wash", |
| "charging_station", |
| "fountain", |
| "grave_yard", |
| "House", |
| "language_school", |
| "loading_dock" |
| "meditation_centre", |
| "motorcycle_parking", |
| "orphanage", |
| "parking entrance" |
| "payment_terminal", |
| "photo_booth", |
| "post_depot", |
| "recycling", |
| "shelter", |
| "social_centre", |
| "social_facility", |
| "telephone", |
| "training", |
| "tuition", |
| "vending_machine", |
| "veterinary", |
| "waste_basket", |
| "waste_disposal", |
| "waste_transfer_station", |
| "water_point", |
| "weighbridge",] |
| for val in remove_amenity: |
| df = df[df.amenity != val] |
| df.name = np.where(df.name.isnull(), df.amenity, df.name) |
| return df |
|
|
| def locplot(df): |
| coords1=df[['lat','lon']].to_numpy() |
| map_osm = folium.Map(location=coords1[0]) |
| |
| for cords in coords1: |
| folium.CircleMarker(location=[cords[0], cords[1]],radius=2,weight=2).add_to(map_osm) |
| return map_osm |
|
|
| def cluster_models(data): |
| db_return = dbscan(data) |
| df =db_return[0] |
| n_cluster=db_return[1] |
| knn_return=Kmeans(df,n_cluster) |
| return cluster_coords(knn_return) |
|
|
| def dbscan(data): |
| x=data.copy() |
| coords = x[['lat', 'lon']].to_numpy() |
| dbsc = (DBSCAN(eps=config.epsilon,min_samples=config.min_samples, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))) |
| cluster_labels = dbsc.labels_ |
| num_clusters = len(set(cluster_labels)) |
| clusters = pd.Series([ coords[ cluster_labels == n ] for n in range (num_clusters) ]) |
| core_samples = np.zeros_like(cluster_labels, dtype='bool') |
| core_samples[dbsc.core_sample_indices_] = True |
| s = pd.Series(core_samples, name='bools') |
| df=[x[s.values],num_clusters,clusters] |
| return df |
| |
| def Kmeans(data, num_clusters): |
| x=data.copy() |
| coords1=x[['lat','lon']].to_numpy() |
| kmeans = KMeans(num_clusters, init = 'k-means++', random_state = config.random_state) |
| y_kmeans = kmeans.fit_predict(coords1) |
| km=[num_clusters,coords1,y_kmeans,data] |
| return km |
|
|
|
|
| def cluster_coords(Kn): |
| num_clusters=Kn[0] |
| coords1=Kn[1] |
| y_kmeans=Kn[2] |
| df=Kn[3] |
| most_significant = [] |
| least_significant = [] |
| for i in range(num_clusters): |
| if len(coords1[y_kmeans == i]) > 5: |
| if len(coords1[y_kmeans == i]) > 45: |
| most_significant.append(apply_convex_hull(coords1[y_kmeans == i])) |
| else: |
| least_significant.append(apply_convex_hull(coords1[y_kmeans == i])) |
| |
| return most_significant,least_significant,coords1 |
|
|
|
|
|
|
| def mapplot(most_significant,least_significant,coords1): |
| map_osm = folium.Map(location=coords1[0]) |
| |
| |
| folium.raster_layers.TileLayer('Open Street Map').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Terrain').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Toner').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Watercolor').add_to(map_osm) |
| folium.raster_layers.TileLayer('CartoDB Positron').add_to(map_osm) |
| folium.raster_layers.TileLayer('CartoDB Dark_Matter').add_to(map_osm) |
| |
| folium.LayerControl().add_to(map_osm) |
| minimap = plugins.MiniMap(toggle_display=True,position='bottomleft') |
| |
| map_osm.add_child(minimap) |
| |
| plugins.Fullscreen(position='topright').add_to(map_osm) |
| |
| for cords in coords1: |
| folium.CircleMarker(location=[cords[0], cords[1]],radius=1,color='blue').add_to(map_osm) |
| for i in range(len(least_significant)): |
| folium.Polygon(least_significant[i], |
| color="blue", |
| weight=2, |
| fill=True, |
| fill_color="yellow", |
| fill_opacity=0.4).add_to(map_osm) |
|
|
| for i in range(len(most_significant)): |
| folium.Polygon(most_significant[i], |
| color="black", |
| weight=2, |
| fill=True, |
| fill_color="red", |
| fill_opacity=0.4).add_to(map_osm) |
|
|
| |
| |
| macro=temp() |
|
|
| map_osm.add_child(macro) |
|
|
| return map_osm |
|
|
| def amenity_df(df): |
| |
| food_list = ['restaurant', 'fast_food', 'cafe', 'bar', 'ice_cream', 'fast_food','bar', 'food_court', 'club', 'drinking_water'] |
| market_list = ['marketplace', 'internet_cafe'] |
| bank_list = ['atm', 'bank', ] |
| toilets_list = ['toilets'] |
| education_list = ['school', 'college', 'university'] |
| hospital_list = ['pharmacy', 'hospital', 'clinic', 'dentist', 'nursing_home'] |
| parking_list = ['parking'] |
| entertainment_list = ['cinema', 'theatre', 'nightclub', 'cafe','coworking_space', 'studio', 'internet_cafe', |
| 'swimming_pool', 'library','pub'] |
| worship_list = ['place_of_worship'] |
| fuel_list = ['fuel', 'fire_station'] |
| others_list = ['post_box', 'community_centre', 'post_office', 'embassy', 'police', 'bus_station', 'public_building', |
| 'taxi'] |
| |
| amenity_list = ['food_list', 'market_list', 'bank_list', 'toilets_list', 'education_list', |
| 'hospital_list', 'parking_list', 'entertainment_list', 'worship_list', 'fuel_list', 'others_list'] |
| food,market,bank,toilets,education,hospital,parking,entertainment,worship,fuel,others = [],[],[],[],[],[],[],[],[],[],[] |
|
|
| for value in df.values: |
| if value[4] in food_list: food.append([value[1], value[2]]) |
| elif value[4] in market_list: market.append([value[1], value[2]]) |
| elif value[4] in bank_list: bank.append([value[1], value[2]]) |
| elif value[4] in toilets_list: toilets.append([value[1], value[2]]) |
| elif value[4] in education_list: education.append([value[1], value[2]]) |
| elif value[4] in hospital_list: hospital.append([value[1], value[2]]) |
| elif value[4] in parking_list: parking.append([value[1], value[2]]) |
| elif value[4] in entertainment_list: entertainment.append([value[1], value[2]]) |
| elif value[4] in worship_list: worship.append([value[1], value[2]]) |
| elif value[4] in fuel_list: fuel.append([value[1], value[2]]) |
| elif value[4] in others_list: others.append([value[1], value[2]]) |
| |
| amenities_list = [food,market,bank,toilets,education,hospital,parking,entertainment,worship,fuel,others] |
| amenities_str = ['Food','Market','Bank','Toilets','Education','Hospital','Parking','Entertainment','Worship','Fuel','Others'] |
| x=[] |
|
|
| for i,item in enumerate(amenities_list): |
| x.append(len(item)) |
|
|
| dx=pd.DataFrame(list(zip(amenities_str,amenities_list,x)),columns=['Amenity','lat_lon','Count']).sort_values(by=['Count'],ascending=False) |
|
|
| return dx |
|
|
| def barplot(dx): |
| dx_plot=dx[['Amenity','Count']] |
|
|
| fig = px.bar(dx_plot, x='Amenity', y='Count',color='Count',width=725,height=500) |
|
|
| return fig |
|
|
| def top5(dx,ilocation): |
|
|
| dx=dx.head(5) |
|
|
| amenity_name = dx.iloc[ilocation,0] |
| amenity_array = dx.iloc[ilocation,1] |
|
|
| amenities_df = pd.DataFrame(amenity_array, columns = ['lat', 'lon']) |
| coords12=amenities_df[['lat','lon']].to_numpy() |
|
|
| |
| if len(amenity_array) < 60: |
| n_clusters = 5 |
| else: |
| n_clusters = 20 |
|
|
| kmeans = KMeans(n_clusters, init = 'k-means++', random_state = 42) |
| y_kmeans = kmeans.fit_predict(amenity_array) |
|
|
|
|
| polygon = [] |
| amenity_array = np.array(amenity_array) |
| for i in range(n_clusters): |
| polygon.append(apply_convex_hull(amenity_array[y_kmeans == i])) |
|
|
| polygon = [i for i in polygon if i is not None] |
|
|
| map_osm = folium.Map(location=coords12[0]) |
| |
| |
| folium.raster_layers.TileLayer('Open Street Map').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Terrain').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Toner').add_to(map_osm) |
| folium.raster_layers.TileLayer('Stamen Watercolor').add_to(map_osm) |
| folium.raster_layers.TileLayer('CartoDB Positron').add_to(map_osm) |
| folium.raster_layers.TileLayer('CartoDB Dark_Matter').add_to(map_osm) |
| |
| folium.LayerControl().add_to(map_osm) |
| minimap = plugins.MiniMap(toggle_display=True) |
| |
| map_osm.add_child(minimap) |
| |
| plugins.Fullscreen(position='topright').add_to(map_osm) |
| |
| for cords in coords12: |
| folium.CircleMarker(location=[cords[0], cords[1]],radius=2,weight=1).add_to(map_osm) |
|
|
| for i in range(len(polygon)): |
| folium.Polygon(polygon[i], |
| color="blue", |
| weight=2, |
| fill=True, |
| fill_color="yellow", |
| fill_opacity=0.4).add_to(map_osm) |
|
|
| return map_osm |
|
|
|
|
|
|
