Andhra Pradesh/Imports/PMGSY Facilities Import

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Goals

There are 42,474 geo-tagged facilities released under GODL License for Andhra Pradesh. PMGSY facilities information covers 17926 unique habitations. 9249 have more than one facility data. Plan is to import them and improve data quality in Andhra.

Import Data

Background

Source site: omms.nic.in

State wise CSV: https://github.com/pratapvardhan/rural-facilities-pmgsy

Data License: Government Open Data License - India (GODL).

ODbL compliance: Yes

Data

Data Statistics
S. No Category PMGSY Count OSM Count

pre import

Imported Count OSM Count

post import

Import Status Users Comments
1 Bank 2612 666 0 Not Started
2 High School - General 4822 1176 0 Not Started
3 Primary Health Centre 2076 0 Not Started
4 Panchayat Headquarter 12080 0 Not Started
5 Mandi (Notified) 873 0 Not Started
6 Collection Centre or Pack House 5709 0 Not Started
7 Agro Industry 3275 0 Not Started
8 Fuel Station 1410 1143 1040 2183 Completed ph4ni 216 already exist in OSM. Updated brands of 41 from PMGSY data. Rest are bad data.
9 Veterinary Hospital 2887 23 0 Not Started
10 High School - Girls 331 0 Not Started
11 Block Headquarter 499 0 Not Started
12 ITI 116 0 Not Started
13 Bedded Hospital 238 0 Not Started
14 Degree College 546 0 Not Started
15 Higher Secondary School 706 0 Not Started
16 Cold Storage 214 0 Not Started
17 Bus Stand 2661 896 0 Not Started
18 GRaM (Notified) 170 0 Not Started
19 Community Health Centre 219 0 Not Started
20 Warehouse 803 0 Not Started
21 Is Part of Rurban Cluster? 150 0 Not Started
22 Sugar Mills 77 0 Not Started
Total 42474 1040

OSM Data Files

To be prepared

Import Type

This will be a one-time import. There are not many attributes so there won't really be a need for Conflation.

Data Merge Workflow

GIF showing AP fuel stations before and after PMGSY import.
GIF of AP fuel stations before and after PMGSY import.

Team

  1. User:Ph4ni

Workflow

  • Import category and district wise. The dataset is using old district names.
  • Download relevant data from overpass. Do a nearest neighbor match between OSM data and PMGSY data. Delete overlaps in PMGSY data.
  • Convert the resultant CSV to OSM compatible format by using tags in columns.
  • Then convert that CSV file to .osm file using JOSM OpenData plugin.

See also

Posted in OSM AP Telegram group, yet to post in community forum.

User experiences

RMSI's experience with updates in Telangana, 6 Jan 2023