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added some plots and calculations

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This commit is contained in:
SoulKindred 2024-01-08 15:49:50 +01:00
parent beef5ad2c7
commit 4a39a7c265
3 changed files with 106 additions and 21 deletions
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29
analysis/calculations.py
View File

@ -1,8 +1,11 @@
import logging import logging
from shapely.geometry import Point, LineString from shapely.geometry import Point, LineString
from shapely import wkb from shapely import wkb
from shapely.geometry import shape
from db_connector import RemoteDB from db_connector import RemoteDB
import pandas as pd import pandas as pd
from pyproj import Proj, transform
from geopy.distance import geodesic
speedLimits = ["T0", "T20", "T30", "T50","T60", "T80", "T100"] speedLimits = ["T0", "T20", "T30", "T50","T60", "T80", "T100"]
@ -36,11 +39,11 @@ def get_data(db):
def process_data(sig_speed_df, accident_df): def process_data(sig_speed_df, accident_df):
result_df = pd.DataFrame(columns= ['TempoLim', 'Accidents_total']) result_df = pd.DataFrame(columns= ['TempoLim', 'Accidents_total', ])
for speed in speedLimits: for speed in speedLimits:
print("Checking for zone: " + speed) print("Checking for zone: " + speed)
filtered_df = sig_speed_df[sig_speed_df["temporegime_technical"].str.contains(speed, case=False, na=False)] filtered_df = sig_speed_df[sig_speed_df["temporegime_technical"].str.contains(speed, case=False, na=False)]
current_result = count_points_near_multilinestrings(accident_df, filtered_df, 0.000005) current_result = count_points_near_multilinestrings(accident_df, filtered_df, 0.00001)
result_df.loc[len(result_df)] = {'TempoLim': speed, 'Accidents_total': current_result} result_df.loc[len(result_df)] = {'TempoLim': speed, 'Accidents_total': current_result}
print("FINAL RESULT") print("FINAL RESULT")
print(result_df) print(result_df)
@ -57,11 +60,31 @@ def count_points_near_multilinestrings(points_df, multilinestrings_df, threshold
result_df = pd.DataFrame(result_counts) result_df = pd.DataFrame(result_counts)
return result_df['CountNear'].sum() return result_df['CountNear'].sum()
def calculate_sigspeed_length(db):
for speed in speedLimits:
get_data_sql = f"""
SELECT wkb_geometry, temporegime_technical
FROM signaled_speeds
WHERE temporegime_technical = '{speed}';
"""
result = db.execute_query(get_data_sql)
result_df = pd.DataFrame(result)
result_df['wkb_geometry'] = result_df['wkb_geometry'].apply(lambda x: wkb.loads(x, hex=True))
sigspeed_length = result_df['wkb_geometry'].apply(lambda x:get_accumulated_distance(x)).sum()
sigspeed_length = str(round(sigspeed_length * 1000, 2)) + " km"
print("Length for " + speed + ": " + sigspeed_length)
def get_accumulated_distance(coords_str):
polyline_geometry = shape(coords_str)
return polyline_geometry.length
if __name__ == "__main__": if __name__ == "__main__":
remote_db = RemoteDB() remote_db = RemoteDB()
try: try:
get_data(remote_db) #get_data(remote_db)
calculate_sigspeed_length(remote_db)
except Exception as e: except Exception as e:
print(f"Exception {e} in calculations.py") print(f"Exception {e} in calculations.py")
finally: finally:

5
analysis/html/index.html
View File

@ -19,12 +19,17 @@
iframe[src="heat_map_time.html"] { iframe[src="heat_map_time.html"] {
width: 100%; width: 100%;
height: 900px; height: 900px;
}
iframe[src="heat_map_toggle.html"] {
width: 100%;
height: 900px;
} }
</style> </style>
</head> </head>
<body> <body>
<iframe src="heat_map_time.html"></iframe> <iframe src="heat_map_time.html"></iframe>
<iframe src="heat_map_toggle.html"></iframe>0
<iframe src="acc_by_year.html"></iframe> <iframe src="acc_by_year.html"></iframe>
<iframe src="acc_by_weekday.html"></iframe> <iframe src="acc_by_weekday.html"></iframe>
<iframe src="acc_by_daytime.html"></iframe> <iframe src="acc_by_daytime.html"></iframe>

87
analysis/map.py
View File

@ -3,6 +3,7 @@ import geopandas as gpd
import colorsys import colorsys
import folium import folium
from folium import plugins from folium import plugins
from pyproj import CRS, Transformer
import logging import logging
from folium.plugins import HeatMap from folium.plugins import HeatMap
@ -23,7 +24,6 @@ formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(messag
stream_handler.setFormatter(formatter) stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler) logger.addHandler(stream_handler)
accidents_filepath = "../src/datasets/integrated/Accidents.geojson" accidents_filepath = "../src/datasets/integrated/Accidents.geojson"
signaled_speeds_filepath = "../src/datasets/integrated/signaled_speeds.geojson.geojson" signaled_speeds_filepath = "../src/datasets/integrated/signaled_speeds.geojson.geojson"
@ -46,7 +46,7 @@ gradient = {
0.9: 'red' 0.9: 'red'
} }
speedLimits = ["T0","T20","T30","T50","T60","T80","T100"] speedLimits = ["T0", "T20", "T30", "T50", "T60", "T80", "T100"]
color_dict = { color_dict = {
"T0": "red", "T0": "red",
"T20": "orange", "T20": "orange",
@ -60,7 +60,6 @@ color_dict = {
# Create Maps ========================================================================================================= # Create Maps =========================================================================================================
def create_heat_map_with_time(folium_map): def create_heat_map_with_time(folium_map):
# Process heat map data # Process heat map data
heat_view_data = get_view("heat") heat_view_data = get_view("heat")
heat_df = gpd.GeoDataFrame(heat_view_data, columns=['latitude', 'longitude', 'year']) heat_df = gpd.GeoDataFrame(heat_view_data, columns=['latitude', 'longitude', 'year'])
@ -68,7 +67,7 @@ def create_heat_map_with_time(folium_map):
assert not heat_df.empty, f" Heat Dataframe is empty: {heat_df.head(5)}" assert not heat_df.empty, f" Heat Dataframe is empty: {heat_df.head(5)}"
add_heat_map_time(heat_df, folium_map) add_heat_map_time(heat_df, folium_map)
logger.info(f"Heat map time added to time map.") logger.info(f"Heat map time added to time map.")
#interactive_map.save("test.html") # interactive_map.save("test.html")
add_signaled_speeds(folium_map) add_signaled_speeds(folium_map)
@ -76,7 +75,7 @@ def create_heat_map_with_time(folium_map):
add_bike_heat_map_time(folium_map) add_bike_heat_map_time(folium_map)
#Pedestrian Part # Pedestrian Part
add_pedestrian_heat_map_time(folium_map) add_pedestrian_heat_map_time(folium_map)
@ -84,7 +83,6 @@ def create_heat_map_with_time(folium_map):
def create_heat_map_toggle(folium_map): def create_heat_map_toggle(folium_map):
heat_view_data = get_view("heat") heat_view_data = get_view("heat")
heat_gdf = gpd.GeoDataFrame(heat_view_data, columns=['latitude', 'longitude', 'year']) heat_gdf = gpd.GeoDataFrame(heat_view_data, columns=['latitude', 'longitude', 'year'])
@ -102,13 +100,14 @@ def create_heat_map_toggle(folium_map):
# Layer Adding Methods ================================================================================================ # Layer Adding Methods ================================================================================================
def add_bike_heat_map_time(folium_map): def add_bike_heat_map_time(folium_map):
# Process heat map data # Process heat map data
bike_heat_view_data = get_view('bikeheat', 'latitude, longitude, year') bike_heat_view_data = get_view('bikeheat', 'latitude, longitude, year')
bike_heat_df = gpd.GeoDataFrame(bike_heat_view_data, columns=['latitude', 'longitude', 'year']) bike_heat_df = gpd.GeoDataFrame(bike_heat_view_data, columns=['latitude', 'longitude', 'year'])
assert not bike_heat_df.empty, f" Heat Dataframe is empty: {bike_heat_df.head(5)}" assert not bike_heat_df.empty, f" Heat Dataframe is empty: {bike_heat_df.head(5)}"
heat_data = [[[row['latitude'], row['longitude'], 0.1] for index, row in bike_heat_df[bike_heat_df['year'] == i].iterrows()] for heat_data = [
[[row['latitude'], row['longitude'], 0.1] for index, row in bike_heat_df[bike_heat_df['year'] == i].iterrows()]
for
i in range(2011, 2023)] i in range(2011, 2023)]
logger.debug(f"First element of heat data: {heat_data[0]}") logger.debug(f"First element of heat data: {heat_data[0]}")
index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022] index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
@ -131,20 +130,20 @@ def add_bike_heat_map_time(folium_map):
def add_pedestrian_heat_map_time(folium_map): def add_pedestrian_heat_map_time(folium_map):
# Process heat map data # Process heat map data
pedestrian_heat_view_data = get_view("pedestrianheat") pedestrian_heat_view_data = get_view("pedestrianheat")
heat_df = gpd.GeoDataFrame(pedestrian_heat_view_data, columns=['latitude', 'longitude', 'year']) heat_df = gpd.GeoDataFrame(pedestrian_heat_view_data, columns=['latitude', 'longitude', 'year'])
assert not heat_df.empty, f" Heat Dataframe is empty: {heat_df.head(5)}" assert not heat_df.empty, f" Heat Dataframe is empty: {heat_df.head(5)}"
heat_data = [[[row['latitude'], row['longitude'], 0.5] for index, row in heat_df[heat_df['year'] == i].iterrows()] for heat_data = [[[row['latitude'], row['longitude'], 0.5] for index, row in heat_df[heat_df['year'] == i].iterrows()]
for
i in range(2011, 2023)] i in range(2011, 2023)]
logger.debug(f"First element of PED heat data: {heat_data[0]}") logger.debug(f"First element of PED heat data: {heat_data[0]}")
index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022] index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
AccidentType = "Pedestrians: " AccidentType = "Pedestrians: "
index = [str(element) for element in index] index = [str(element) for element in index]
index = [AccidentType + element for element in index] index = [AccidentType + element for element in index]
#gradient = # gradient =
# plot heat map # plot heat map
gradient = generate_hue_gradient(0.2, 5) gradient = generate_hue_gradient(0.2, 5)
hm = plugins.HeatMapWithTime(heat_data, hm = plugins.HeatMapWithTime(heat_data,
@ -161,7 +160,8 @@ def add_pedestrian_heat_map_time(folium_map):
def add_heat_map_time(heat_df, folium_map): def add_heat_map_time(heat_df, folium_map):
heat_data = [[[row['latitude'], row['longitude'], 0.5] for index, row in heat_df[heat_df['year'] == i].iterrows()] for heat_data = [[[row['latitude'], row['longitude'], 0.5] for index, row in heat_df[heat_df['year'] == i].iterrows()]
for
i in range(2011, 2023)] i in range(2011, 2023)]
index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022] index = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
# create heat map # create heat map
@ -259,18 +259,73 @@ def generate_hue_gradient(hue, num_colors):
lightness = 0.1 + 0.8 * (i / (num_colors - 1)) lightness = 0.1 + 0.8 * (i / (num_colors - 1))
saturation = 0.1 + 0.8 * (i / (num_colors - 1)) saturation = 0.1 + 0.8 * (i / (num_colors - 1))
rgb = colorsys.hls_to_rgb(hue, lightness, saturation) rgb = colorsys.hls_to_rgb(hue, lightness, saturation)
gradient[i / (num_colors - 1)] = '#{:02x}{:02x}{:02x}'.format(int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255)) gradient[i / (num_colors - 1)] = '#{:02x}{:02x}{:02x}'.format(int(rgb[0] * 255), int(rgb[1] * 255),
int(rgb[2] * 255))
return gradient return gradient
def generate_contrasting_gradient(num_colors): def generate_contrasting_gradient(num_colors):
cmap = plt.get_cmap('viridis') # viridis is a map with contrasting colors cmap = plt.get_cmap('viridis') # viridis is a map with contrasting colors
gradient = {} gradient = {}
for i in range(num_colors): for i in range(num_colors):
rgba = cmap(i / (num_colors - 1)) rgba = cmap(i / (num_colors - 1))
gradient[i / (num_colors - 1)] = '#{:02x}{:02x}{:02x}'.format(int(rgba[0]*255), int(rgba[1]*255), int(rgba[2]*255)) gradient[i / (num_colors - 1)] = '#{:02x}{:02x}{:02x}'.format(int(rgba[0] * 255), int(rgba[1] * 255),
int(rgba[2] * 255))
return gradient return gradient
def add_miv_count_station_locations():
get_data_mic_sql = """
SELECT
zsid, ekoord, nkoord,
AVG(anzfahrzeuge) AS average_count
FROM
mivcount
GROUP BY
zsid, ekoord, nkoord
"""
remote_db = RemoteDB()
miv_result = remote_db.execute_query(get_data_mic_sql)
miv_df = pd.DataFrame(miv_result)
miv_df[['lon', 'lat']] = miv_df.apply(lambda row: convert_to_wgs84(row['ekoord'], row['nkoord']), axis=1)
miv_df['average_count'] = miv_df['average_count'].apply(lambda x: round(float(x)))
count_stations_layer = folium.FeatureGroup(name='Count-stations cars', show=False)
for index, row in miv_df.iterrows():
folium.Marker(location=[row['lat'], row['lon']], popup="avg. " + str(row['average_count']), show=False).add_to(count_stations_layer)
count_stations_layer.add_to(toggle_map)
remote_db.close()
def add_fb_count_station_locations():
get_data_mic_sql = """
SELECT DISTINCT
ost,
nord,
AVG(velo_total) as average_velo_count,
AVG(fuss_total) as average_fuss_count
FROM fbcount_copy
GROUP BY ost,nord;
"""
remote_db = RemoteDB()
FB_result = remote_db.execute_query(get_data_mic_sql)
FB_df = pd.DataFrame(FB_result)
FB_df[['ost', 'nord']] = FB_df.apply(lambda row: convert_to_wgs84(row['ost'], row['nord']), axis=1)
FB_df['average_velo_count'] = FB_df['average_velo_count'].apply(lambda x: round(float(x)))
FB_df['average_velo_count'] = FB_df['average_velo_count'].astype(str)
FB_df['average_fuss_count'] = FB_df['average_fuss_count'].apply(lambda x: round(float(x)))
FB_df['average_fuss_count'] = FB_df['average_fuss_count'].astype(str)
count_stations_layer = folium.FeatureGroup(name='Count-stations pedestrians and bicycles', show=False)
for index, row in FB_df.iterrows():
folium.Marker(location=[row['nord'], row['ost']], popup="Bicycle and pedestrian count station", show=False).add_to(count_stations_layer)
count_stations_layer.add_to(toggle_map)
remote_db.close()
def convert_to_wgs84(lon, lat):
swiss_crs = CRS.from_epsg(2056)
wgs84_crs = CRS.from_epsg(4326)
transformer = Transformer.from_crs(swiss_crs, wgs84_crs, always_xy=True)
lon, lat = transformer.transform(lon, lat)
return pd.Series({'lon': lon, 'lat': lat})
if __name__ == "__main__": if __name__ == "__main__":
time_map = folium.Map( time_map = folium.Map(
@ -293,11 +348,13 @@ if __name__ == "__main__":
tiles="cartodb positron" tiles="cartodb positron"
) )
add_miv_count_station_locations()
add_fb_count_station_locations()
#setup_views() #setup_views()
create_heat_map_with_time(time_map) create_heat_map_with_time(time_map)
create_heat_map_toggle(toggle_map) create_heat_map_toggle(toggle_map)
## Save Maps ============================================================================================ ## Save Maps ============================================================================================
save_map_as_html(toggle_map, "heat_map_toggle") save_map_as_html(toggle_map, "html/heat_map_toggle")
save_map_as_html(time_map, "html/heat_map_time") save_map_as_html(time_map, "html/heat_map_time")