Data Export¶

Flock You supports exporting detection data via the web interface for analysis, mapping, or sharing.

Export Formats¶

Format	Extension	Best For
CSV	`.csv`	Spreadsheets, data analysis
KML	`.kml`	Google Earth, mapping

How to Export¶

Via Web Interface¶

Open the dashboard at http://localhost:5000
Click the Export button
Select format (CSV or KML)
Download the file

Via API¶

# Export as CSV
curl http://localhost:5000/api/export/csv -o detections.csv

# Export as KML
curl http://localhost:5000/api/export/kml -o detections.kml

CSV Format¶

Comma-separated values compatible with Excel, Google Sheets, and data analysis tools.

Columns¶

Column	Type	Description
`timestamp`	ISO 8601	Detection time
`type`	String	Device type (e.g., "Flock Safety")
`mac`	String	Device MAC address
`ssid`	String	WiFi network name
`rssi`	Integer	Signal strength (dBm)
`method`	String	Detection method
`latitude`	Float	GPS latitude (if available)
`longitude`	Float	GPS longitude (if available)

Example¶

timestamp,type,mac,ssid,rssi,method,latitude,longitude
2024-01-15T14:30:00Z,Flock Safety,3C:71:BF:12:34:56,FLOCK-S3-1234,-62,wifi_ssid,37.7749,-122.4194
2024-01-15T14:35:00Z,Raven,AA:BB:CC:DD:EE:FF,,-65,ble_uuid,37.7751,-122.4189

KML Format¶

Keyhole Markup Language for geographic visualization.

Structure¶

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
  <Document>
    <name>Flock You Detections</name>

    <Placemark>
      <name>Flock Safety</name>
      <description>
        MAC: 3C:71:BF:12:34:56
        SSID: FLOCK-S3-1234
        RSSI: -62 dBm
        Method: wifi_ssid
      </description>
      <TimeStamp>
        <when>2024-01-15T14:30:00Z</when>
      </TimeStamp>
      <Point>
        <coordinates>-122.4194,37.7749,0</coordinates>
      </Point>
    </Placemark>

  </Document>
</kml>

Compatible Applications¶

KML files can be imported into:

Google Earth
Google Maps (My Maps)
QGIS
ArcGIS
GPS Visualizer

Data Analysis¶

Opening in Excel¶

Export as CSV
Open Excel and select File > Open
Choose the CSV file
Data will populate in columns

Opening in Python¶

import pandas as pd

# Load CSV
df = pd.read_csv('detections.csv')

# Filter by type
flock_detections = df[df['type'] == 'Flock Safety']

# Count by type
print(df['type'].value_counts())

Visualizing on Map¶

import folium
import pandas as pd

df = pd.read_csv('detections.csv')

# Create map centered on detections
m = folium.Map(
    location=[df['latitude'].mean(), df['longitude'].mean()],
    zoom_start=12
)

# Add markers
for _, row in df.iterrows():
    if pd.notna(row['latitude']):
        folium.Marker(
            [row['latitude'], row['longitude']],
            popup=f"{row['type']}<br>RSSI: {row['rssi']} dBm",
            icon=folium.Icon(color='red' if 'Flock' in row['type'] else 'blue')
        ).add_to(m)

m.save('detections_map.html')

Adding GPS Data¶

The web interface can capture GPS coordinates if running on a device with location services:

Enable location in browser when prompted
Detections will include lat/long coordinates
Export will include geographic data

For mobile use, consider running the web server on a laptop while driving.

Privacy Notice¶

Location Data

Exported files may contain GPS coordinates revealing where you detected surveillance devices. Consider privacy implications before sharing.

Recommendations:

Only share with trusted parties
Consider removing or obfuscating exact locations
Don't post raw exports publicly