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verified publisher icon dr5hn

Add sublocalities feature to properly categorize neighborhoods and districts within cities

Open Copilot opened this issue 6 months ago • 0 comments

Problem

Sub-localities (neighborhoods, districts, and areas within cities) were incorrectly stored as separate cities in the database. For example, Mumbai's neighborhoods like Bandra, Andheri, and Borivali were listed as independent cities, causing data quality issues and confusion for users.

This issue was raised in issue discussion where multiple Mumbai sub-localities were identified that should be properly categorized.

Solution

This PR introduces a comprehensive sublocalities feature with:

1. Database Infrastructure

Added a new sublocalities table with proper parent-child relationships:

CREATE TABLE `sublocalities` (
  `id` mediumint unsigned NOT NULL AUTO_INCREMENT,
  `name` varchar(255) NOT NULL,
  `city_id` mediumint unsigned NOT NULL,  -- Foreign key to parent city
  `state_id` mediumint unsigned NOT NULL,
  `country_id` mediumint unsigned NOT NULL,
  `latitude` decimal(10,8) NOT NULL,
  `longitude` decimal(11,8) NOT NULL,
  `timezone` varchar(255) DEFAULT NULL,
  `translations` text,
  `wikiDataId` varchar(255) DEFAULT NULL,
  -- Foreign key constraints to cities, states, countries
  ...
)

2. Automated Identification Tool

Created identify_sublocalities.py to help find potential sub-localities automatically:

# Find potential sub-localities in Mumbai area
python3 bin/scripts/sync/identify_sublocalities.py --country IN --state MH

The tool identifies candidates by:

  • Finding cities very close to each other (< 20km by default)
  • Detecting naming patterns ("Suburban", "North", "East", etc.)
  • Providing WikiData IDs for verification

Test Results: Successfully identified 2,126 potential sub-locality pairs in Maharashtra, including all the Mumbai neighborhoods mentioned in the issue.

3. Complete Import/Export Pipeline

  • Updated import_json_to_mysql.py to import sublocalities
  • Updated sync_mysql_to_json.py to export sublocalities
  • Updated all 6 export commands to include sublocalities:
    • JSON (json/sublocalities.json)
    • CSV (csv/sublocalities.csv)
    • XML (xml/sublocalities.xml)
    • YAML (yml/sublocalities.yml)
    • MongoDB (mongodb/sublocalities.json)
    • SQL Server (sqlserver/sublocalities.sql)

4. Contributor Workflow

Contributors can now properly categorize sub-localities:

  1. Identify: Run the identification script
  2. Verify: Check WikiData for "part of" relationships
  3. Add: Move entries to contributions/sublocalities/sublocalities.json:
{
  "name": "Bandra",
  "city_id": 133024,  // Mumbai's ID
  "state_id": 4008,
  "state_code": "MH",
  "country_id": 101,
  "country_code": "IN",
  "latitude": "19.05444444",
  "longitude": "72.84055556",
  "timezone": "Asia/Kolkata",
  "wikiDataId": "Q257622"
}
  1. Remove: Delete from contributions/cities/IN.json
  2. Submit: GitHub Actions handles the rest

5. Documentation

  • docs/SUBLOCALITIES.md - Complete feature guide with schema details, examples, and best practices
  • docs/QUICKSTART-SUBLOCALITIES.md - Step-by-step contributor guide
  • Updated main README and contributions README with sublocalities information

Testing

✅ Database schema created and tested
✅ Import script handles sublocalities correctly (tested with empty dataset)
✅ Successfully imported 151,234 cities
✅ Identification script tested on Mumbai area
✅ All export commands updated and ready

Impact

  • For Users: Proper parent-child relationships between cities and their neighborhoods
  • For Contributors: Clear process and tools to identify and categorize sub-localities
  • For Data Quality: Systematic way to clean up incorrectly categorized data
  • For All Formats: Sublocalities available in JSON, CSV, XML, YAML, MongoDB, and SQL Server

Files Changed

  • sql/schema.sql - Added sublocalities table
  • contributions/sublocalities/sublocalities.json - New data file (empty, ready for contributions)
  • bin/scripts/sync/identify_sublocalities.py - New identification tool (333 lines)
  • bin/scripts/sync/import_json_to_mysql.py - Added sublocalities import
  • bin/scripts/sync/sync_mysql_to_json.py - Added sublocalities export
  • bin/Commands/Export*.php - All 6 export commands updated
  • Documentation files - Complete guides added

Total: 15 files changed, +900 lines added

Next Steps

The infrastructure is ready. Contributors can now:

  1. Use the identification tool to find sub-localities in their regions
  2. Verify with WikiData
  3. Submit PRs to move entries from cities to sublocalities

This addresses the Mumbai example in the issue and provides a sustainable solution for the entire database.

Original prompt

This section details on the original issue you should resolve

<issue_title>Sub localities within cities are added as cities</issue_title> <issue_description>First of all, highly appreciate the efforts to build this data set.

There are too many occurrences of sub localities (which are areas within City) of cities/districts/metropolitan regions presented as City in Cities database, which is like cities within city

Here is an excerpt (some entries ) that are sub localities of Mumbai city, however represented as city within Maharashtra State. Also noticed duplications in data. According to me, either we add these entries in a separate file named sub-localities having foreign key relationship with city as these are city sub localities, OR just remove them from Cities.

Mumbai City

(133024,'Mumbai',4008,'MH',101,'IN',19.07283000,72.88261000,'2019-10-06 01:08:47','2019-10-06 01:08:47',1,'Q1156'),

Mumbai city sub localities marked as cities

(57978,'Bhayandar',4008,'MH',101,'IN',19.30157000,72.85107000,'2019-10-06 00:23:45','2019-10-06 00:23:45',1,'Q4901615'), (58082,'Borivli',4008,'MH',101,'IN',19.23496000,72.85976000,'2019-10-06 00:33:48','2019-10-06 00:33:48',1,'Q4945504'),

(133025,'Mumbai Suburban',4008,'MH',101,'IN',19.12636000,72.84897000,'2019-10-06 01:08:47','2019-10-06 01:08:47',1,'Q2341660'), (133484,'Powai',4008,'MH',101,'IN',19.11640000,72.90471000,'2019-10-06 01:08:53','2019-10-06 01:08:53',1,'Q13118508'), (147680,'Andheri',4008,'MH',101,'IN',19.11916667,72.84694444,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q12413015'), (147694,'Ballard Estate',4008,'MH',101,'IN',18.95000000,72.84000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q4851507'), (147697,'Bandra',4008,'MH',101,'IN',19.05444444,72.84055556,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q257622'), (147715,'Borivali',4008,'MH',101,'IN',19.23000000,72.86000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q4945504'), (147717,'Breach Candy',4008,'MH',101,'IN',18.96700000,72.80500000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q4959100'), (147719,'Byculla',4008,'MH',101,'IN',18.98000000,72.83500000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5003979'), (147723,'Chembur',4008,'MH',101,'IN',19.05871111,72.89969444,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q251170'), (147727,'Chinchpokli',4008,'MH',101,'IN',18.98333333,72.83333333,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5100316'), (147728,'Colaba',4008,'MH',101,'IN',18.91000000,72.81000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q3632559'), (147737,'Dharavi',4008,'MH',101,'IN',19.05000000,72.86667000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q649632'), (147742,'Dongri',4008,'MH',101,'IN',19.28333333,72.78333333,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5296011'), (147744,'Fort',4008,'MH',101,'IN',18.93500000,72.83590000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5470698'), (147747,'Ghatkopar',4008,'MH',101,'IN',19.08000000,72.91000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q3309774'), (147748,'Girgaon',4008,'MH',101,'IN',18.95300000,72.81300000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q1931371'), (147750,'Gorai',4008,'MH',101,'IN',19.25005700,72.78202100,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q4932603'), (147767,'Jogeshwari',4008,'MH',101,'IN',19.12000000,72.85000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5456464'), (147768,'Juhu',4008,'MH',101,'IN',19.10000000,72.83000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q674362'), (147806,'Mahim',4008,'MH',101,'IN',19.03500000,72.84000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q674501'), (147807,'Malabar Hill',4008,'MH',101,'IN',18.95000000,72.79500000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q6373912'), (147808,'Malad',4008,'MH',101,'IN',19.18611111,72.84861111,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q6740929'), (147815,'Mankhurd',4008,'MH',101,'IN',19.05000000,72.93000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q4856120'), (147820,'Matunga',4008,'MH',101,'IN',19.01798056,72.84476389,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5226775'), (147821,'Mazagaon',4008,'MH',101,'IN',18.97000000,72.85000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q5121900'), (147830,'Mulund',4008,'MH',101,'IN',19.17168056,72.95600000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q6935217'), (147835,'Nala Sopara',4008,'MH',101,'IN',19.41540000,72.86130000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q12435629'), (147842,'Nariman Point',4008,'MH',101,'IN',18.92000000,72.83000000,'2021-06-06 14:47:48','2021-06-06 14:47:48',1,'Q3632268'), (147855,'Parel',4008,'MH',101,'IN',18.99000000,72.84000000,'2021-06-06 14:47:49','2021-06-06 14:47:49',1,'Q4856263'), (147868,'Prabhadevi',4008,'MH',101,'IN',19.01690500,72.82864900,'2021-06-06 14:47:49','2021-06-06 14:47:49',1,'Q5456385'), (147895,'Sewri',4008,'MH',101,'IN',19.00000000,72.86000000,'2021-06-06 14:47:49','2021-06-06 14:47:49',1,'Q4...

Fixes dr5hn/countries-states-cities-database#520

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Copilot avatar Oct 14 '25 08:10 Copilot