Track geo-targeted SEO metrics daily by querying the search API with location parameters, storing position data over time, and comparing rankings across regions. Local SEO performance varies dramatically by geography, and national-level rank tracking misses these differences entirely. A daily geo-tracking pipeline captures map pack positions, organic rankings, and SERP feature presence for each target location, building the historical data needed to detect regional ranking changes early.
Prerequisites
- Python 3.8+ installed
- requests library installed
- A Scavio API key from scavio.dev
- Target keywords and locations defined
Walkthrough
Step 1: Define tracking targets
Configure the keywords and geographic locations to monitor daily.
Python
import os, requests, json, datetime
API_KEY = os.environ['SCAVIO_API_KEY']
TARGETS = [
{'keyword': 'plumber near me', 'locations': ['New York', 'Los Angeles', 'Chicago']},
{'keyword': 'best coffee shop', 'locations': ['San Francisco', 'Seattle', 'Portland']},
{'keyword': 'emergency dentist', 'locations': ['Houston', 'Phoenix', 'Dallas']},
]
HISTORY_FILE = 'geo_metrics_history.json'
print(f'Tracking {len(TARGETS)} keywords across {sum(len(t["locations"]) for t in TARGETS)} locations')Step 2: Query with geo parameters
Search each keyword with location-specific queries to capture regional rankings.
Python
def search_geo(keyword: str, location: str) -> dict:
query = f'{keyword} in {location}'
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': API_KEY},
json={'platform': 'google', 'query': query}, timeout=15)
data = resp.json()
return {
'keyword': keyword,
'location': location,
'date': datetime.date.today().isoformat(),
'organic_count': len(data.get('organic_results', [])),
'top_3': [{'title': r.get('title', ''), 'url': r.get('link', ''), 'position': i+1}
for i, r in enumerate(data.get('organic_results', [])[:3])],
'has_map_pack': 'local_results' in data or 'map_pack' in str(data.keys()),
'has_featured_snippet': 'featured_snippet' in data or 'answer_box' in data,
}
result = search_geo('plumber near me', 'New York')
print(f"{result['location']}: {result['organic_count']} results, map_pack={result['has_map_pack']}")Step 3: Run daily scan
Execute the scan across all keyword-location combinations and collect results.
Python
import time
def daily_scan(targets: list) -> list:
results = []
for target in targets:
for location in target['locations']:
result = search_geo(target['keyword'], location)
results.append(result)
print(f" {result['keyword']} / {result['location']}: {result['organic_count']} results")
time.sleep(0.3)
return results
today_results = daily_scan(TARGETS)
print(f'\nScanned {len(today_results)} keyword-location pairs')Step 4: Store and compare history
Save daily results and compare against previous days to detect ranking changes.
Python
def store_results(results: list):
history = []
try:
with open(HISTORY_FILE) as f:
history = json.load(f)
except FileNotFoundError:
pass
history.extend(results)
with open(HISTORY_FILE, 'w') as f:
json.dump(history, f, indent=2)
print(f'Stored {len(results)} results (total history: {len(history)})')
def compare_days(keyword: str, location: str) -> dict:
try:
with open(HISTORY_FILE) as f:
history = json.load(f)
except FileNotFoundError:
return {}
entries = [h for h in history if h['keyword'] == keyword and h['location'] == location]
entries.sort(key=lambda x: x['date'])
if len(entries) < 2:
return {'change': 'insufficient data'}
prev = entries[-2]
curr = entries[-1]
return {
'keyword': keyword,
'location': location,
'prev_date': prev['date'],
'curr_date': curr['date'],
'organic_change': curr['organic_count'] - prev['organic_count'],
'map_pack_change': curr['has_map_pack'] != prev['has_map_pack'],
}
store_results(today_results)Step 5: Generate geo report
Produce a summary report of ranking performance across all tracked locations.
Python
def geo_report(targets: list) -> str:
lines = [f'Geo SEO Report - {datetime.date.today().isoformat()}', '']
for target in targets:
lines.append(f'Keyword: {target["keyword"]}')
for location in target['locations']:
diff = compare_days(target['keyword'], location)
status = 'NEW' if diff.get('change') == 'insufficient data' else ''
if not status:
change = diff.get('organic_change', 0)
status = f'+{change}' if change > 0 else str(change) if change < 0 else 'stable'
lines.append(f' {location}: {status}')
lines.append('')
report = '\n'.join(lines)
print(report)
return report
geo_report(TARGETS)Python Example
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def geo_rank(keyword, location):
data = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': f'{keyword} in {location}'}).json()
top = data.get('organic_results', [])[:3]
return {'location': location, 'top': [r.get('title', '')[:50] for r in top]}
print(geo_rank('plumber near me', 'New York'))JavaScript Example
JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
async function geoRank(keyword, location) {
const r = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: H,
body: JSON.stringify({platform: 'google', query: `${keyword} in ${location}`})
});
const top = ((await r.json()).organic_results || []).slice(0, 3);
return {location, top: top.map(r => (r.title || '').slice(0, 50))};
}
geoRank('plumber near me', 'New York').then(console.log);Expected Output
JSON
A daily geo-targeted SERP tracking pipeline that monitors rankings across multiple locations, detects regional changes, and produces summary reports for local SEO optimization.