How to join Thailand district data to administrative boundaries

Join by the district PCODE in the same reference release, rather than by a translated district name. Retain unmatched rows and the boundary version so a spelling difference or changed geography cannot silently disappear.

Procedure

  1. Choose province or district granularity before importing the table.
  2. Read identifiers as text, trim accidental whitespace and check uniqueness on both sides.
  3. Join by PCODE, report unmatched identifiers and inspect a sample boundary before publishing.

Interpretation checks

Input or conditionHow to interpret it
Name-only joinBangkok names can have multiple transliterations; a match is ambiguous.
Duplicate PCODECheck whether your table is a time series before allowing repeated geography.
Unmatched rowKeep it in an exception table; do not assign it to a nearby district.

Illustrative working example

Code illustrates a review procedure, not an observed result. Replace illustrative inputs with verified records; missing values deliberately remain unknown.

input = {"district_code": "TH1001", "value": 12}
# Keep district codes as strings; verify the code exists in
# the chosen boundary release before joining by identifier.
assert isinstance(input["district_code"], str)

Useful output and decision limits

A reproducible table of district identifiers, values and join exceptions. It supports regional reporting, not cadastral parcel ownership.

Datasets, coverage and source contracts

Related customer workflows