diff --git a/.gitignore b/.gitignore index 29bb07d..e35c41e 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ node_modules .DS_Store .zed .vscode +taxi-trajectory/train.csv diff --git a/pages/guides/data-scientist/_meta.js b/pages/guides/data-scientist/_meta.js index 83c2273..13be606 100644 --- a/pages/guides/data-scientist/_meta.js +++ b/pages/guides/data-scientist/_meta.js @@ -6,4 +6,5 @@ export default { geopandas: "Geopandas", geoparquet: "Geoparquet", geocoding: "Geocoding", + "mobility-analytics": "Mobility Analytics", }; diff --git a/pages/guides/data-scientist/mobility-analytics.mdx b/pages/guides/data-scientist/mobility-analytics.mdx new file mode 100644 index 0000000..f04e7c7 --- /dev/null +++ b/pages/guides/data-scientist/mobility-analytics.mdx @@ -0,0 +1,130 @@ +Analyzing mobility data is a common task for data scientists. In this guide, we will use the [MovingPandas](https://movingpandas.org/) library to analyze mobility data. + +This is a reproduction of the carto's blog post here https://carto.com/blog/analyzing-mobility-hotspots-with-movingpandas using the geobase stack instead of the carto stack. + +```python +#! /usr/bin/env python + +import movingpandas as mpd +import pandas as pd +import h3 +import opendatasets as od +import os +import geopandas as gpd +from shapely.geometry import Point, Polygon +from datetime import datetime, timedelta +from p_tqdm import p_umap +from tqdm.notebook import tqdm +tqdm.pandas() + +input_file_path = 'taxi-trajectory/train.csv' +def get_porto_taxi_from_kaggle(): + if not os.path.exists(input_file_path): + od.download("https://www.kaggle.com/datasets/crailtap/taxi-trajectory") +get_porto_taxi_from_kaggle() +df = pd.read_csv(input_file_path, usecols=['TRIP_ID', 'TAXI_ID', 'TIMESTAMP', 'MISSING_DATA', 'POLYLINE']) + + +df.POLYLINE = df.POLYLINE.apply(eval) # Convert polyline string to list +df = df.query("MISSING_DATA == False") # Remove missing data records +df["geo_len"] = df["POLYLINE"].apply(lambda x: len(x)) +df.drop(df[df["geo_len"]==0].index, axis=0, inplace=True) +print(df.head()) + + +def unixtime_to_datetime(unix_time): + return datetime.fromtimestamp(unix_time) + +def compute_datetime(row): + unix_time = row['TIMESTAMP'] + offset = row['running_number'] * timedelta(seconds=15) + return unixtime_to_datetime(unix_time) + offset + +new_df = df.explode('POLYLINE') +new_df['geometry'] = new_df['POLYLINE'].apply(Point) +new_df['running_number'] = new_df.groupby('TRIP_ID').cumcount() +new_df['datetime'] = new_df.apply(compute_datetime, axis=1) +new_df.drop(columns=['POLYLINE', 'TIMESTAMP', 'running_number'], inplace=True) +new_df.head() + + + + +gdf = gpd.GeoDataFrame(new_df, crs=4326) +trajs = mpd.TrajectoryCollection(gdf, traj_id_col='TRIP_ID', obj_id_col='TAXI_ID', t='datetime') + + + + +cleaned = trajs.copy() +cleaned = mpd.OutlierCleaner(cleaned).clean(v_max=100, units=("km", "h")) +cleaned.add_speed(overwrite=True, units=('km', 'h')) + + + +# Add h3 indices at resolution 8 (you can adjust this resolution as needed) +gdf['h3'] = gdf.geometry.apply(lambda p: h3.geo_to_h3(p.y, p.x, 10)) + +h3cells = gdf['h3'].unique().tolist() + +polygonise = lambda hex_id: Polygon( + h3.h3_to_geo_boundary(hex_id, geo_json=True) +) + +all_polys = gpd.GeoSeries( + list(map(polygonise, h3cells)), index=h3cells, + name='geometry', crs="EPSG:4326" ) + +all_polys = all_polys.reset_index() + + + +def get_sub_traj(x): + results = [] + tmp = cleaned.clip(x[1]) + if len(tmp.trajectories)>0: + for jtraj in tmp.trajectories: + results.append([x[0],jtraj]) + return results + +my_values = list(all_polys.values) +res = p_umap(get_sub_traj, my_values) + +def flatten(l): + return [[item[0], item[1]] for sublist in l for item in sublist] + +tt = pd.DataFrame(flatten(res), columns=['index', 'traj']) +print(tt.head()) + + + +tt = tt.rename(columns={'index':'h3'}).reset_index() +tt['st_split'] = tt['traj'].apply(lambda x: mpd.TemporalSplitter(x).split(mode="hour").trajectories) +st_traj = tt.explode('st_split') +st_traj.dropna(inplace=True) + + + +def get_end_day_hour(traj): + try: + t = traj.get_end_time() + except: + print(traj) + return datetime(t.year, t.month, t.day, t.hour, 0, 0) + +st_traj['t'] = st_traj['st_split'].progress_apply(lambda x: get_end_day_hour(x)) +st_traj['duration'] = st_traj['st_split'].progress_apply(lambda x: x.get_duration()) + + + +agg_st_traj = st_traj.groupby(['h3','t'], as_index=False)['duration'].sum() +agg_st_traj['duration'] = agg_st_traj['duration'].apply(lambda x: x.total_seconds()) + +agg_st_traj['geometry'] = agg_st_traj['h3'].apply(lambda x: Polygon(h3.h3_to_geo_boundary(x, geo_json=True))) +print(agg_st_traj.head()) + +# write out the result to a geojson file +# convert the dataframe to a geodataframe +gdf = gpd.GeoDataFrame(agg_st_traj, crs=4326) +gdf.to_file('taxi-trajectory/agg_st_traj.geojson', driver='GeoJSON') +``` \ No newline at end of file diff --git a/pages/guides/data-scientist/mobility-analytics.py b/pages/guides/data-scientist/mobility-analytics.py new file mode 100644 index 0000000..8dc8813 --- /dev/null +++ b/pages/guides/data-scientist/mobility-analytics.py @@ -0,0 +1,195 @@ +#! /usr/bin/env python + +import movingpandas as mpd +import pandas as pd +import h3 +import opendatasets as od +import os +import geopandas as gpd +from shapely.geometry import Point, Polygon +from datetime import datetime, timedelta +from p_tqdm import p_umap +from tqdm.notebook import tqdm +tqdm.pandas() + +input_file_path = 'taxi-trajectory/train.csv' + + +def get_porto_taxi_from_kaggle(): + if not os.path.exists(input_file_path): + od.download("https://www.kaggle.com/datasets/crailtap/taxi-trajectory") + + +get_porto_taxi_from_kaggle() +# Read the raw data +df = pd.read_csv(input_file_path, nrows=10, usecols=[ + 'TRIP_ID', 'TAXI_ID', 'TIMESTAMP', 'MISSING_DATA', 'POLYLINE']) + +# Convert polyline string to list +df.POLYLINE = df.POLYLINE.apply(eval) + +# Remove missing data records +df = df.query("MISSING_DATA == False") + +# Calculate the length of the polyline +df["geo_len"] = df["POLYLINE"].apply(lambda x: len(x)) + +# Remove records with no polyline +df.drop(df[df["geo_len"] == 0].index, axis=0, inplace=True) + +# print logs of this check point of creating a data frame with the raw data plus the polyline length +print( + f"Created a data frame with the raw data plus the polyline length\n: {df.head()}") + + +def unixtime_to_datetime(unix_time): + return datetime.fromtimestamp(unix_time) + + +# Compute the datetime from the timestamp and the running number +def compute_datetime(row): + unix_time = row['TIMESTAMP'] + offset = row['running_number'] * timedelta(seconds=15) + return unixtime_to_datetime(unix_time) + offset + + +""" +The explode() function in pandas is used to transform lists within a DataFrame cell into separate rows. Let me explain with an example: +Let's say your original DataFrame df looks like this: + +``` +POLYLINE + TRIP_ID TAXI_ID POLYLINE +0 1 123 [[1,2], [3,4], [5,6]] +1 2 456 [[7,8], [9,10]] +``` + +If you apply the explode() function to the POLYLINE column, it will transform the list in each row into separate rows. The resulting DataFrame will look like this: +``` + TRIP_ID TAXI_ID POLYLINE +0 1 123 [1,2] +0 1 123 [3,4] +0 1 123 [5,6] +1 2 456 [7,8] +1 2 456 [9,10] +``` + +In the mobility analytics context: + +1. Each row in the original DataFrame represents one taxi trip +2. The 'POLYLINE' column contains a list of coordinate pairs representing the taxi's GPS points +3. explode() creates a new row for each coordinate pair while maintaining the other columns' values +4. This transformation is necessary to: + - Process each GPS point individually + - Create a proper trajectory object + - Perform spatial analysis on individual points + +It's similar to "unnesting" the GPS coordinates, making it easier to work with individual points in the trajectory. +""" +new_df = df.explode('POLYLINE') +new_df['geometry'] = new_df['POLYLINE'].apply(Point) +new_df['running_number'] = new_df.groupby('TRIP_ID').cumcount() +new_df['datetime'] = new_df.apply(compute_datetime, axis=1) +# print the first 5 rows of the new dataframe +print( + f"Interim df for creating trajectory collection using the geometry and datetime columns\n: {new_df.head()}") + +# : TRIP_ID TAXI_ID TIMESTAMP MISSING_DATA POLYLINE geo_len geometry running_number datetime +# 0 1372636858620000589 20000589 1372636858 False [-8.618643, 41.141412] 23 POINT (-8.618643 41.141412) 0 2013-07-01 02:00:58 +# 0 1372636858620000589 20000589 1372636858 False [-8.618499, 41.141376] 23 POINT (-8.618499 41.141376) 1 2013-07-01 02:01:13 +# 0 1372636858620000589 20000589 1372636858 False [-8.620326, 41.14251] 23 POINT (-8.620326 41.14251) 2 2013-07-01 02:01:28 + + +# drop the POLYLINE, TIMESTAMP, and running_number columns +new_df.drop(columns=['POLYLINE', 'TIMESTAMP', 'running_number'], inplace=True) + +# print the first 5 rows of the new dataframe +print( + f"Exploded the POLYLINE column into separate rows\n: {new_df.head()}") +# : TRIP_ID TAXI_ID MISSING_DATA geo_len geometry datetime +# 0 1372636858620000589 20000589 False 23 POINT (-8.618643 41.141412) 2013-07-01 02:00:58 +# 0 1372636858620000589 20000589 False 23 POINT (-8.618499 41.141376) 2013-07-01 02:01:13 + +# Creating trajectory collection from +gdf = gpd.GeoDataFrame(new_df, crs=4326) +trajs = mpd.TrajectoryCollection( + gdf, traj_id_col='TRIP_ID', obj_id_col='TAXI_ID', t='datetime') + + +cleaned = trajs.copy() +cleaned = mpd.OutlierCleaner(cleaned).clean(v_max=100, units=("km", "h")) +cleaned.add_speed(overwrite=True, units=('km', 'h')) + + +# Add h3 indices at resolution 8 (you can adjust this resolution as needed) +gdf['h3'] = gdf.geometry.apply(lambda p: h3.geo_to_h3(p.y, p.x, 10)) + +h3cells = gdf['h3'].unique().tolist() + + +def polygonise(hex_id): return Polygon( + h3.h3_to_geo_boundary(hex_id, geo_json=True) +) + + +all_polys = gpd.GeoSeries( + list(map(polygonise, h3cells)), index=h3cells, + name='geometry', crs="EPSG:4326") + +all_polys = all_polys.reset_index() + + +def get_sub_traj(x): + results = [] + tmp = cleaned.clip(x[1]) + if len(tmp.trajectories) > 0: + for jtraj in tmp.trajectories: + results.append([x[0], jtraj]) + return results + + +my_values = list(all_polys.values) +res = p_umap(get_sub_traj, my_values) + + +def flatten(l): + return [[item[0], item[1]] for sublist in l for item in sublist] + + +tt = pd.DataFrame(flatten(res), columns=['index', 'traj']) +print(tt.head()) + + +tt = tt.rename(columns={'index': 'h3'}).reset_index() +tt['st_split'] = tt['traj'].apply( + lambda x: mpd.TemporalSplitter(x).split(mode="hour").trajectories) +st_traj = tt.explode('st_split') +st_traj.dropna(inplace=True) + + +def get_end_day_hour(traj): + try: + t = traj.get_end_time() + except: + print(traj) + return datetime(t.year, t.month, t.day, t.hour, 0, 0) + + +st_traj['t'] = st_traj['st_split'].progress_apply( + lambda x: get_end_day_hour(x)) +st_traj['duration'] = st_traj['st_split'].progress_apply( + lambda x: x.get_duration()) + + +agg_st_traj = st_traj.groupby(['h3', 't'], as_index=False)['duration'].sum() +agg_st_traj['duration'] = agg_st_traj['duration'].apply( + lambda x: x.total_seconds()) + +agg_st_traj['geometry'] = agg_st_traj['h3'].apply( + lambda x: Polygon(h3.h3_to_geo_boundary(x, geo_json=True))) +print(agg_st_traj.head()) + +# write out the result to a geojson file +# convert the dataframe to a geodataframe +gdf = gpd.GeoDataFrame(agg_st_traj, crs=4326) +gdf.to_file('taxi-trajectory/agg_st_traj.geojson', driver='GeoJSON') diff --git 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