I have Latitude ,longitude and timestamp of image. I want to cluster these image according to detect events. For instance I went to Paris for 4 days then london 3 days and so on. So I want to detect cluster of Paris data and after london.I want each event to be seperated. How can I use this Latitude ,longitude and timestamp together to make cluster? I have done with DBSCAN algorithm but could note merge the time in it.Look at my code if it helps to understand problem.
import pandas as pd, numpy as np, matplotlib.pyplot as plt, time
from sklearn.cluster import DBSCAN
from sklearn import metrics
from geopy.distance import great_circle
from shapely.geometry import MultiPoint
define the number of kilometers in one radian
kms_per_radian = 6371.0088
load the data set
df = pd.read_csv('data/summer-travel-gps-full.csv', encoding='utf-8')
df.head()
represent points consistently as (lat, lon)
coords = df.as_matrix(columns=['lat', 'lon'])
define epsilon as 1.5 kilometers, converted to radians for use by haversine
epsilon = 1.5 / kms_per_radian
start_time = time.time()
db = DBSCAN(eps=epsilon, min_samples=1, algorithm='ball_tree', metric='haversine').fit(np.radians(coords))
cluster_labels = db.labels_
get the number of clusters
num_clusters = len(set(cluster_labels))
all done, print the outcome
message = 'Clustered {:,} points down to {:,} clusters, for {:.1f}% compression in {:,.2f} seconds'
print(message.format(len(df), num_clusters, 100*(1 - float(num_clusters) / len(df)), time.time()-start_time))
print('Silhouette coefficient: {:0.03f}'.format(metrics.silhouette_score(coords, cluster_labels)))
turn the clusters in to a pandas series, where each element is a cluster of points
clusters = pd.Series([coords[cluster_labels==n] for n in range(num_clusters)])
def get_centermost_point(cluster):
centroid = (MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
centermost_point = min(cluster, key=lambda point: great_circle(point, centroid).m)
return tuple(centermost_point)
centermost_points = clusters.map(get_centermost_point)
unzip the list of centermost points (lat, lon) tuples into separate lat and lon lists
lats, lons = zip(*centermost_points)
from these lats/lons create a new df of one representative point for each cluster
rep_points = pd.DataFrame({'lon':lons, 'lat':lats})
rep_points.tail()
pull row from original data set where lat/lon match the lat/lon of each row of representative points
that way we get the full details like city, country, and date from the original dataframe
rs = rep_points.apply(lambda row: df[(df['lat']==row['lat']) & (df['lon']==row['lon'])].iloc[0], axis=1)
rs.to_csv('data/summer-travel-gps-dbscan.csv', encoding='utf-8')
rs.tail()
plot the final reduced set of coordinate points vs the original full set
fig, ax = plt.subplots(figsize=[10, 6])
rs_scatter = ax.scatter(rs['lon'], rs['lat'], c='#99cc99', edgecolor='None', alpha=0.7, s=120)
df_scatter = ax.scatter(df['lon'], df['lat'], c='k', alpha=0.9, s=3)
ax.set_title('Full data set vs DBSCAN reduced set')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.legend([df_scatter, rs_scatter], ['Full set', 'Reduced set'], loc='upper right')
plt.show()
