A script for plotting Foursquare/Swarm check-in data.

plot_check_ins.py

# Adapted from: https://stackoverflow.com/a/77261943/1316276.

import matplotlib

matplotlib.use("Agg")

import geodatasets
import geopandas as gpd
import glob
import json
import matplotlib.pyplot as plt
import os
import pandas as pd
import requests

from datetime import datetime, timedelta
from json import JSONDecodeError
from shapely.geometry import Point


def get_checkins(foursquare_path):
    api_key_path = f"{foursquare_path}/fsq_api_key.txt"
    with open(api_key_path) as f:
        api_key = f.read().strip()

    headers = {"accept": "application/json", "Authorization": api_key}
    url = "https://api.foursquare.com/v3/places/{fsq_id}?fields=geocodes"

    rows = []
    checkin_jsons = glob.glob(f"{foursquare_path}/checkins*.json")
    for checkin_json in checkin_jsons:
        with open(checkin_json) as f:
            checkins = json.load(f)

        for checkin in checkins["items"]:
            try:
                fsq_id = checkin["venue"]["id"]
            except KeyError:
                continue

            response = requests.get(url.format(fsq_id=fsq_id), headers=headers)
            try:
                locations = response.json()
                rows.append(
                    {
                        "name": checkin["venue"]["name"],
                        "fsq_id": fsq_id,
                        "date": checkin["createdAt"],
                        "lat": locations["geocodes"]["main"]["latitude"],
                        "lon": locations["geocodes"]["main"]["longitude"],
                    }
                )
            except JSONDecodeError:
                continue
            except KeyError:
                continue

    df = pd.DataFrame(rows)
    df.to_csv(f"{foursquare_path}/all_checkins.csv", index=False)


def save_maps(foursquare_path):
    df = pd.read_csv(f"{foursquare_path}/all_checkins.csv")
    geometry = [Point(xy) for xy in zip(df["lon"], df["lat"])]
    gdf = gpd.GeoDataFrame(df, geometry=geometry)
    gdf.set_crs(epsg=4326, inplace=True)

    world = gpd.read_file(geodatasets.get_path("naturalearth.land"))

    output_dir = "maps"
    os.makedirs(output_dir)
    cur_date = datetime.strptime(df["date"].min().split()[0], "%Y-%m-%d")
    end_date = datetime.strptime(df["date"].max().split()[0], "%Y-%m-%d")
    days = (end_date - cur_date).days
    for day in range(days + 1):
        plt.close("all")
        ax = world.plot(figsize=(10, 6), color="lightgray", edgecolor="black")

        cur_date_start = cur_date.strftime("%Y-%m-%d 00:00:00.000000")
        prev_checkins = gdf[gdf["date"] < cur_date_start]
        if len(prev_checkins) > 0:
            prev_checkins.plot(ax=ax, color="blue", markersize=20)

        cur_date_end = cur_date.strftime("%Y-%m-%d 24:00:00.000000")
        today_checkins = gdf[
            (cur_date_start < gdf["date"]) & (gdf["date"] < cur_date_end)
        ]
        if len(today_checkins) > 0:
            today_checkins.plot(ax=ax, color="red", markersize=40)

        checkins = gdf[gdf["date"] < cur_date_end]
        n_checkins = len(checkins)
        n_places = len(checkins["fsq_id"].unique())

        cur_date_str = cur_date_start.split()[0]
        ax.set_title(f"{cur_date_str}  Check-ins: {n_checkins}  Places: {n_places}")
        ax.axis("off")
        ax.set_aspect("equal")
        plt.savefig(f"{output_dir}/{cur_date_str}.png", dpi=300, bbox_inches="tight")

        cur_date += timedelta(days=1)


def main():
    foursquare_path = "/home/airalcorn2/Documents/Foursquare"
    get_checkins(foursquare_path)
    save_maps(foursquare_path)
    # To create a video of the saved images, in the maps directory, run:
    # ffmpeg -framerate 14 -pattern_type glob -i 'maps/*.png' -c:v libx264 out.mp4


if __name__ == "__main__":
    main()