def main(argv=None):
global DATA_FILE
# Process commandline arguments
# If we were just going to process one tile then here would be the place to start
# Return a file list from process_arguments, adjust OS_GRID_SIZE, return os_grid_cell
DATA_FILE, os_grid_cell, name = process_arguments(argv)
# Report on datafiles
zf = zipfile.ZipFile(DATA_FILE, 'r')
datafiles = zf.namelist()
print("Data zip file: {}".format(DATA_FILE))
print("Found {} datafiles".format(len(datafiles)))
xorg, yorg = tile_origin(os_grid_cell)
# Calculate bounding box
lat_ll, lng_ll = OSGB36toWGS84(xorg, yorg) # lower left
lat_ur, lng_ur = OSGB36toWGS84(xorg + OS_GRID_SIZE, yorg + OS_GRID_SIZE)
bb = "[{}, {}], [{}, {}]".format(lat_ll, lng_ll, lat_ur, lng_ur)
print("Bounding box: {}".format(bb))
# Write bounding box to data_dict
write_to_data_dict(os_grid_cell, bb, name)
# Iterate over datafiles
filelist = [x for x in range(len(datafiles))]
# Assume all tiles in a dataset have the same ncols, nrows, cellsize and NODATA_value
metadata = get_header_info(datafiles[0])
# Output_data_size is OS_GRID_SIZE/cellsize
output_data_size = OS_GRID_SIZE / metadata["cellsize"]
bigdata = np.zeros((output_data_size, output_data_size), dtype=np.float)
for idx in filelist:
# Get data
metadata = get_header_info(datafiles[idx])
data = get_image(datafiles[idx], metadata["ncols"], metadata["nrows"])
data[data == -9999] = 0.0
# Calculate x,y offset
xoffset, yoffset = calculate_offsets(metadata, output_data_size, xorg, yorg)
width = metadata["ncols"]
height = metadata["nrows"]
# Write into array
bigdata[yoffset - height:yoffset, xoffset:xoffset + width] = data
# Show the data
plot_image(bigdata)
# Export the data to an image
filename = "images/" + os_grid_cell
matplotlib.image.imsave(filename, bigdata, cmap=plt.gray())
def convert_postcodes() -> List[str]:
fieldnames = [
'Postcode', 'Positional_quality_indicator', 'Eastings', 'Northings',
'Country_code', 'NHS_regional_HA_code', 'NHS_HA_code',
'Admin_county_code', 'Admin_district_code', 'Admin_ward_code'
]
sys.stdout.write('Finding CSV files...\n')
csvs = []
for root, dirs, files in os.walk(os.path.join('download', 'CSV')):
csvs.extend(os.path.join(root, f) for f in files if f.endswith('csv'))
num = len(csvs)
sys.stdout.write('Converting %d CSV files...\n' % num)
for idx, file_path in enumerate(csvs):
locations = []
sys.stdout.write('{0:.0%} ({1}/{2}) {3}\n'.format(
(idx + 1) / num, idx + 1, num, file_path,
))
# Read the OSGB36 file
with open(file_path, 'r') as f:
reader = csv.DictReader(f, fieldnames=fieldnames)
for row in reader:
lat, lng = OSGB36toWGS84(
int(row['Eastings']),
int(row['Northings'])
)
locations.append((row['Postcode'], lng, lat))
# Write the WGD84 file with the same name
with open(os.path.join('download', 'latlng', file_path), 'w') as f:
writer = csv.DictWriter(f, fieldnames=['lat', 'lng', 'postcode'])
for postcode, lng, lat in locations:
writer.writerow({'postcode': postcode, 'lng': lng, 'lat': lat})
return csvs
def overlay_point_map(self, data_frame, basemap):
"""plots point map over existing basemap"""
data_frame = data_frame.fillna('None')
for index, row in data_frame.iterrows():
size = self.dict_size[row.ActionBased]
html="<small> \
<h3> Incident #: " + str(row.IncidentNumber) + "</h3> \
<p> \
<b>Action Based: </b>" + str(row.ActionBased) + "<br> \
<b>Action Parent: </b>" + str(row.ActionParent) + "<br> \
<b>Action: </b>" + str(row.Action) + "<br> \
<b>Motive: </b>" + str(row.Motive) + "<br> \
<b>BriefDescriptionOfFire: </b><i>" + \
str(row.BriefDescriptionOfFire) +"</i><br> \
<b>FurtherInformation: </b><i>" + \
str(row.FurtherInformation) + "</i><br> \
<b>PropertyType: </b><i>" + \
str(row.PropertyType) +"</i><br> \
<b>Date and time of call: </b>" + str(row.DDDateTimeOfCall) + "<br> \
</p> \
</small>"
iframe = folium.element.IFrame(html=html, width=450, height=200)
popup = folium.Popup(iframe, max_width=500)
folium.CircleMarker(
location=list(
OSGB36toWGS84(row.easting / 10, row.northing / 10)),
radius=size,
popup=popup,
color='#00ffffff', #none
fill_color='#ff3300' #red
).add_to(basemap)
return basemap
def get_lat_long_coords(data_frame):
"""returns a long and lat from easting and northing"""
coords_list = []
for index, row in data_frame.iterrows():
coords_list.append(
list(OSGB36toWGS84(row.easting / 10, row.northing / 10)))
coords = pd.DataFrame(coords_list, columns=['long', 'lat'])
return coords
print('################################')
print('Working Base :',Base, ' using ', sys.platform)
print('################################')
################################################################
sFileNameIn=Base+'/01-Vermeulen/00-RawData/public-toilets.csv'
sFileNameOut=Base+'/01-Vermeulen/06-Report/01-EDS/02-Python/colchester_toilets_clusters.html'
# Load map centred on Colchester
uk = folium.Map(location=[51.8860942,0.8336077], zoom_start=10, control_scale=True)
# Load locally stored colchester public toilets data
os.chdir('C:/VKHCG/01-Vermeulen/00-RawData')
toilets = pd.read_csv("public-toilets.csv")
t=0
for each in toilets.iterrows():
datalist=list(OSGB36toWGS84(each[1]['Geo X'],each[1]['Geo Y']))
t+=1
if t==1:
data=[datalist]
else:
data.append(datalist)
# create a marker cluster called "Public toilet cluster"
marker_cluster = folium.plugins.FastMarkerCluster(data).add_to(uk)
#add a marker for each toilet, add it to the cluster, not the map
for each in toilets.iterrows():
print(list([each[1]['Geo X'],each[1]['Geo Y']]))
print(list(OSGB36toWGS84(each[1]['Geo X'],each[1]['Geo Y'])))
folium.Marker(list(OSGB36toWGS84(each[1]['Geo X'],each[1]['Geo Y'])), popup=each[1]['Street Address']).add_to(marker_cluster)
def __lat_lng(self):
if self.easting is not None and self.northing is not None:
return OSGB36toWGS84(self.easting, self.northing)
else:
return None, None
def accidentmap():
data_attendent_18 = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/anuj_db") \
.option("dbtable", "attendent_18") \
.option("driver", "org.postgresql.Driver") \
.option("user", "anuj_db") \
.option("password", "anuj_db") \
.load()
data_attendent_17 = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/anuj_db") \
.option("dbtable", "attendent_17") \
.option("driver", "org.postgresql.Driver") \
.option("user", "anuj_db") \
.option("password", "anuj_db") \
.load()
data_attendent_16 = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/anuj_db") \
.option("dbtable", "attendent_16") \
.option("driver", "org.postgresql.Driver") \
.option("user", "anuj_db") \
.option("password", "anuj_db") \
.load()
data_attendent_15 = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/anuj_db") \
.option("dbtable", "attendent_15") \
.option("driver", "org.postgresql.Driver") \
.option("user", "anuj_db") \
.option("password", "anuj_db") \
.load()
data_attendent_14 = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/anuj_db") \
.option("dbtable", "attendent_14") \
.option("driver", "org.postgresql.Driver") \
.option("user", "anuj_db") \
.option("password", "anuj_db") \
.load()
"""""""""""""""""""""""""""""""""""""""""""""""
LATITUDE LONGITUDE CONVERSION 2018
"""""""""""""""""""""""""""""""""""""""""""""""
fatal_df = data_attendent_18.filter(data_attendent_18.severitynum == 1)
east_18 = fatal_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_18 = fatal_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_fatal = fatal_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_fatal = fatal_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_fatal = fatal_df.select("day").rdd.flatMap(lambda x: x).collect()
time_fatal = fatal_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_fatal = fatal_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_fatal = fatal_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_fatal = []
long_list_fatal = []
for i in range(len(east_18)):
lat, long = OSGB36toWGS84(east_18[i], north_18[i])
lat_list_fatal.append(lat)
long_list_fatal.append(long)
serious_df = data_attendent_18.filter(data_attendent_18.severitynum == 2)
east_18 = serious_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_18 = serious_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_serious = serious_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_serious = serious_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_serious = serious_df.select("day").rdd.flatMap(lambda x: x).collect()
time_serious = serious_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_serious = serious_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_serious = serious_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_serious = []
long_list_serious = []
for i in range(len(east_18)):
lat, long = OSGB36toWGS84(east_18[i], north_18[i])
lat_list_serious.append(lat)
long_list_serious.append(long)
"""""""""""""""""""""""""""""""""""""""""""""""
CLUSTER MAP 2018
"""""""""""""""""""""""""""""""""""""""""""""""
mean_lat = statistics.mean(lat_list_serious)
mean_long = statistics.mean(long_list_serious)
acc_map_18 = folium.Map(location=[mean_lat, mean_long], zoom_start=10)
marker_cluster = MarkerCluster().add_to(acc_map_18)
for k in range(len(lat_list_fatal)):
location = lat_list_fatal[k], long_list_fatal[k]
popup = '<b>FATAL COLLISION, ' + str(casualty_fatal[k]) + ' KILLED</b> <br> <br>On ' + date_fatal[k] + ', ' + \
day_fatal[k] + ' at ' + time_fatal[k] + ', an accident occured at ' + loc_fatal[k] + ' in ' + \
borough_fatal[k] + ' killing ' + str(casualty_fatal[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='red')).add_to(marker_cluster)
for k in range(len(lat_list_serious)):
location = lat_list_serious[k], long_list_serious[k]
popup = '<b>SERIOUS COLLISION, ' + str(casualty_serious[k]) + ' INJURED</b> <br> <br>On ' + date_serious[
k] + ', ' + \
day_serious[k] + ' at ' + time_serious[k] + ', an accident occured at ' + loc_serious[k] + ' in ' + \
borough_serious[k] + ' severely injuring ' + str(casualty_serious[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='blue')).add_to(marker_cluster)
acc_map_18.save('assets/map_18.html')
"""""""""""""""""""""""""""""""""""""""""""""""
LATITUDE LONGITUDE CONVERSION 2017
"""""""""""""""""""""""""""""""""""""""""""""""
fatal_df = data_attendent_17.filter(data_attendent_17.severitynum == 1)
east_17 = fatal_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_17 = fatal_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_fatal = fatal_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_fatal = fatal_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_fatal = fatal_df.select("day").rdd.flatMap(lambda x: x).collect()
time_fatal = fatal_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_fatal = fatal_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_fatal = fatal_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_fatal = []
long_list_fatal = []
for i in range(len(east_17)):
lat, long = OSGB36toWGS84(east_17[i], north_17[i])
lat_list_fatal.append(lat)
long_list_fatal.append(long)
serious_df = data_attendent_17.filter(data_attendent_17.severitynum == 2)
east_17 = serious_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_17 = serious_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_serious = serious_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_serious = serious_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_serious = serious_df.select("day").rdd.flatMap(lambda x: x).collect()
time_serious = serious_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_serious = serious_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_serious = serious_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_serious = []
long_list_serious = []
for i in range(len(east_17)):
lat, long = OSGB36toWGS84(east_17[i], north_17[i])
lat_list_serious.append(lat)
long_list_serious.append(long)
"""""""""""""""""""""""""""""""""""""""""""""""
CLUSTER MAP 2017
"""""""""""""""""""""""""""""""""""""""""""""""
mean_lat = statistics.mean(lat_list_serious)
mean_long = statistics.mean(long_list_serious)
acc_map_17 = folium.Map(location=[mean_lat, mean_long], zoom_start=10)
marker_cluster = MarkerCluster().add_to(acc_map_17)
for k in range(len(lat_list_fatal)):
location = lat_list_fatal[k], long_list_fatal[k]
popup = '<b>FATAL COLLISION, ' + str(casualty_fatal[k]) + ' KILLED</b> <br> <br>On ' + date_fatal[k] + ', ' + \
day_fatal[k] + ' at ' + time_fatal[k] + ', an accident occured at ' + loc_fatal[k] + ' in ' + \
borough_fatal[k] + ' killing ' + str(casualty_fatal[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='red')).add_to(marker_cluster)
for k in range(len(lat_list_serious)):
location = lat_list_serious[k], long_list_serious[k]
popup = '<b>SERIOUS COLLISION, ' + str(casualty_serious[k]) + ' INJURED</b> <br> <br>On ' + date_serious[
k] + ', ' + \
day_serious[k] + ' at ' + time_serious[k] + ', an accident occured at ' + loc_serious[k] + ' in ' + \
borough_serious[k] + ' severely injuring ' + str(casualty_serious[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='blue')).add_to(marker_cluster)
acc_map_17.save('assets/map_17.html')
"""""""""""""""""""""""""""""""""""""""""""""""
LATITUDE LONGITUDE CONVERSION 2016
"""""""""""""""""""""""""""""""""""""""""""""""
fatal_df = data_attendent_16.filter(data_attendent_16.severitynum == 1)
east_16 = fatal_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_16 = fatal_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_fatal = fatal_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_fatal = fatal_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_fatal = fatal_df.select("day").rdd.flatMap(lambda x: x).collect()
time_fatal = fatal_df.select("time").rdd.flatMap(lambda x: x).collect()
loc_fatal = fatal_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_fatal = fatal_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_fatal = []
long_list_fatal = []
for i in range(len(east_16)):
lat, long = OSGB36toWGS84(east_16[i], north_16[i])
lat_list_fatal.append(lat)
long_list_fatal.append(long)
serious_df = data_attendent_16.filter(data_attendent_16.severitynum == 2)
east_16 = serious_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_16 = serious_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_serious = serious_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_serious = serious_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_serious = serious_df.select("day").rdd.flatMap(lambda x: x).collect()
time_serious = serious_df.select("time").rdd.flatMap(lambda x: x).collect()
loc_serious = serious_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_serious = serious_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_serious = []
long_list_serious = []
for i in range(len(east_16)):
lat, long = OSGB36toWGS84(east_16[i], north_16[i])
lat_list_serious.append(lat)
long_list_serious.append(long)
"""""""""""""""""""""""""""""""""""""""""""""""
CLUSTER MAP 2016
"""""""""""""""""""""""""""""""""""""""""""""""
mean_lat = statistics.mean(lat_list_serious)
mean_long = statistics.mean(long_list_serious)
acc_map_16 = folium.Map(location=[mean_lat, mean_long], zoom_start=10)
marker_cluster = MarkerCluster().add_to(acc_map_16)
for k in range(len(lat_list_fatal)):
location = lat_list_fatal[k], long_list_fatal[k]
popup = '<b>FATAL COLLISION, ' + str(casualty_fatal[k]) + ' KILLED</b> <br> <br>On ' + date_fatal[k] + ', ' + \
day_fatal[k] + ' at ' + time_fatal[k] + ', an accident occured at ' + loc_fatal[k] + ' in ' + \
borough_fatal[k] + ' killing ' + str(casualty_fatal[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='red')).add_to(marker_cluster)
for k in range(len(lat_list_serious)):
location = lat_list_serious[k], long_list_serious[k]
popup = '<b>SERIOUS COLLISION, ' + str(casualty_serious[k]) + ' INJURED</b> <br> <br>On ' + date_serious[
k] + ', ' + \
day_serious[k] + ' at ' + time_serious[k] + ', an accident occured at ' + loc_serious[k] + ' in ' + \
borough_serious[k] + ' severely injuring ' + str(casualty_serious[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='blue')).add_to(marker_cluster)
acc_map_16.save('assets/map_16.html')
"""""""""""""""""""""""""""""""""""""""""""""""
LATITUDE LONGITUDE CONVERSION 2015
"""""""""""""""""""""""""""""""""""""""""""""""
fatal_df = data_attendent_15.filter(data_attendent_15.severitynum == 1)
east_15 = fatal_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_15 = fatal_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_fatal = fatal_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_fatal = fatal_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_fatal = fatal_df.select("day").rdd.flatMap(lambda x: x).collect()
time_fatal = fatal_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_fatal = fatal_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_fatal = fatal_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_fatal = []
long_list_fatal = []
for i in range(len(east_15)):
lat, long = OSGB36toWGS84(east_15[i], north_15[i])
lat_list_fatal.append(lat)
long_list_fatal.append(long)
serious_df = data_attendent_15.filter(data_attendent_15.severitynum == 2)
east_15 = serious_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_15 = serious_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_serious = serious_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_serious = serious_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_serious = serious_df.select("day").rdd.flatMap(lambda x: x).collect()
time_serious = serious_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_serious = serious_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_serious = serious_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_serious = []
long_list_serious = []
for i in range(len(east_15)):
lat, long = OSGB36toWGS84(east_15[i], north_15[i])
lat_list_serious.append(lat)
long_list_serious.append(long)
"""""""""""""""""""""""""""""""""""""""""""""""
CLUSTER MAP 2015
"""""""""""""""""""""""""""""""""""""""""""""""
mean_lat = statistics.mean(lat_list_serious)
mean_long = statistics.mean(long_list_serious)
acc_map_15 = folium.Map(location=[mean_lat, mean_long], zoom_start=10)
marker_cluster = MarkerCluster().add_to(acc_map_15)
for k in range(len(lat_list_fatal)):
location = lat_list_fatal[k], long_list_fatal[k]
popup = '<b>FATAL COLLISION, ' + str(casualty_fatal[k]) + ' KILLED</b> <br> <br>On ' + date_fatal[k] + ', ' + \
day_fatal[k] + ' at ' + time_fatal[k] + ', an accident occured at ' + loc_fatal[k] + ' in ' + \
borough_fatal[k] + ' killing ' + str(casualty_fatal[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='red')).add_to(marker_cluster)
for k in range(len(lat_list_serious)):
location = lat_list_serious[k], long_list_serious[k]
popup = '<b>SERIOUS COLLISION, ' + str(casualty_serious[k]) + ' INJURED</b> <br> <br>On ' + date_serious[
k] + ', ' + \
day_serious[k] + ' at ' + time_serious[k] + ', an accident occured at ' + loc_serious[k] + ' in ' + \
borough_serious[k] + ' severely injuring ' + str(casualty_serious[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='blue')).add_to(marker_cluster)
acc_map_15.save('assets/map_15.html')
"""""""""""""""""""""""""""""""""""""""""""""""
LATITUDE LONGITUDE CONVERSION 2014
"""""""""""""""""""""""""""""""""""""""""""""""
fatal_df = data_attendent_14.filter(data_attendent_14.severitynum == 1)
east_14 = fatal_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_14 = fatal_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_fatal = fatal_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_fatal = fatal_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_fatal = fatal_df.select("day").rdd.flatMap(lambda x: x).collect()
time_fatal = fatal_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_fatal = fatal_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_fatal = fatal_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_fatal = []
long_list_fatal = []
for i in range(len(east_14)):
lat, long = OSGB36toWGS84(east_14[i], north_14[i])
lat_list_fatal.append(lat)
long_list_fatal.append(long)
serious_df = data_attendent_14.filter(data_attendent_14.severitynum == 2)
east_14 = serious_df.select("easting").rdd.flatMap(lambda x: x).collect()
north_14 = serious_df.select("northing").rdd.flatMap(lambda x: x).collect()
casualty_serious = serious_df.select("numofcasualties").rdd.flatMap(lambda x: x).collect()
date_serious = serious_df.select("accidentdate").rdd.flatMap(lambda x: x).collect()
day_serious = serious_df.select("day").rdd.flatMap(lambda x: x).collect()
time_serious = serious_df.select("time_new").rdd.flatMap(lambda x: x).collect()
loc_serious = serious_df.select("location").rdd.flatMap(lambda x: x).collect()
borough_serious = serious_df.select("borough").rdd.flatMap(lambda x: x).collect()
lat_list_serious = []
long_list_serious = []
for i in range(len(east_14)):
lat, long = OSGB36toWGS84(east_14[i], north_14[i])
lat_list_serious.append(lat)
long_list_serious.append(long)
"""""""""""""""""""""""""""""""""""""""""""""""
CLUSTER MAP 2014
"""""""""""""""""""""""""""""""""""""""""""""""
mean_lat = statistics.mean(lat_list_serious)
mean_long = statistics.mean(long_list_serious)
acc_map_14 = folium.Map(location=[mean_lat, mean_long], zoom_start=10)
marker_cluster = MarkerCluster().add_to(acc_map_14)
for k in range(len(lat_list_fatal)):
location = lat_list_fatal[k], long_list_fatal[k]
popup = '<b>FATAL COLLISION, ' + str(casualty_fatal[k]) + ' KILLED</b> <br> <br>On ' + date_fatal[k] + ', ' + \
day_fatal[k] + ' at ' + time_fatal[k] + ', an accident occured at ' + loc_fatal[k] + ' in ' + \
borough_fatal[k] + ' killing ' + str(casualty_fatal[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='red')).add_to(marker_cluster)
for k in range(len(lat_list_serious)):
location = lat_list_serious[k], long_list_serious[k]
popup = '<b>SERIOUS COLLISION, ' + str(casualty_serious[k]) + ' INJURED</b> <br> <br>On ' + date_serious[
k] + ', ' + \
day_serious[k] + ' at ' + time_serious[k] + ', an accident occured at ' + loc_serious[k] + ' in ' + \
borough_serious[k] + ' severely injuring ' + str(casualty_serious[k])
folium.Marker(
location=location,
clustered_marker=True,
popup=folium.Popup(popup, max_width=300, min_width=300),
icon=folium.Icon(color='blue')).add_to(marker_cluster)
acc_map_14.save('assets/map_14.html')
from bng_to_latlon import OSGB36toWGS84 print(OSGB36toWGS84(534627,181456)) from latlon_to_bng import WGS84toOSGB36 print(WGS84toOSGB36(51.51603,-0.06216 ))
import pandas as pd
from bng_to_latlon import OSGB36toWGS84
import webbrowser
################################################################
if sys.platform == 'linux':
Base = os.path.expanduser('~') + '/VKHCG'
else:
Base = 'C:/VKHCG'
print('################################')
print('Working Base :', Base, ' using ', sys.platform)
print('################################')
################################################################
sFileNameIn = Base + '/01-Vermeulen/00-RawData/public-toilets.csv'
sFileNameOut = Base + '/01-Vermeulen/06-Report/01-EDS/02-Python/colchester_toilets.html'
# Load map centred on Colchester
uk = folium.Map(location=[51.8860942, 0.8336077], zoom_start=10)
# Load locally stored colchester public toilets data
toilets = pd.read_csv(sFileNameIn)
#add a marker for each toilet
for each in toilets.iterrows():
print(list([each[1]['Geo X'], each[1]['Geo Y']]))
print(list(OSGB36toWGS84(each[1]['Geo X'], each[1]['Geo Y'])))
folium.Marker(list(OSGB36toWGS84(each[1]['Geo X'],each[1]['Geo Y'])), \
popup=each[1]['Street Address']).add_to(uk)
# Save map
print('Create HTML page')
uk.save(sFileNameOut)
webbrowser.open('file://' + os.path.realpath(sFileNameOut))