Standards, Metadata & Geospatial Policy

Metadata is data about the data - the label that tells you what a dataset is and whether it fits your purpose. The international standard for it is ISO 19115, part of the ISO 19100 family that governs geographic information. It defines the fields a proper record carries: title and abstract, the CRS, lineage (where the data came from and what was done to it), positional and attribute accuracy, the date and currency, the responsible contact, and the usage licence.

The test that matters is fitness for purpose. A land-use layer accurate to fifty metres is perfect for a regional overview and useless for a plot-boundary dispute - and only the metadata tells you which situation you are in. A companion standard, ISO 19110 (feature cataloguing), documents what each attribute and code actually means, so that use = 2 is not a guess. Writing metadata feels like a chore precisely because its benefit lands on the next person - who is usually you, six months later, staring at final_landuse.shp.

If metadata says what a dataset is, OGC standards - from the Open Geospatial Consortium - govern how it moves between systems. They are the reason a layer published by one organisation opens in anyone's GIS without a file being emailed. A handful you will meet constantly: WMS serves a rendered map image; WFS serves the actual vector features, queryable and editable; WMTS serves pre-rendered tiles for fast display; and OGC API - Features is the modern, JSON-based successor to WFS. For storage and exchange there is the GeoPackage container and the WKT/WKT2 text encoding that writes geometries and CRS definitions in plain characters - the same WKT2 your .prj file speaks.

The practical upshot for a designer: when a state portal or Bhuvan publishes a WMS or WFS endpoint, you connect to it rather than downloading and re-downloading. Your map reads the authoritative source live, and everyone is looking at the same data. Standards are what turn a pile of incompatible files into an interoperable commons.

For a long time, working with fine-scale spatial data in India meant navigating restrictions and prior approvals. That has changed in deliberate steps, and you should know the three landmarks by name.

NDSAP - the National Data Sharing and Accessibility Policy of 2012 - established that non-sensitive government data should be shared openly; it is the policy behind data.gov.in and the Government Open Data License - India (GODL) under which those datasets are released. Then, on 15 February 2021, the Department of Science & Technology issued the Guidelines for acquiring and producing Geospatial Data and Geospatial Data Services including Maps, which liberalised the field markedly - Indian entities no longer need prior approval to collect, generate or process most geospatial data, moving to a self-certification model. Building on that, the National Geospatial Policy 2022, also from the Department of Science & Technology, set out a framework and goals for the country's geospatial ecosystem. The direction across all three is one way: more open, more Indian-collected, and increasingly GIS-ready - the same current that drives the AMRUT and NUIS master-plan programmes.

"Accurate" is a loaded word, and standards break it into parts you should keep separate. Positional accuracy asks how close a feature's coordinates are to its true location on the ground. Attribute accuracy asks whether the facts in the table are correct - is that polygon really a school? Completeness asks what is missing. Currency asks how old it is. Logical consistency asks whether the data contradicts itself - overlapping parcels, gaps between adjoining wards. A dataset can be superb on one axis and hopeless on another, and a single sentence of metadata about each is worth more than a vague claim of "high quality." Recording these is not bureaucracy; it is the difference between a map that informs a decision and one that quietly misleads it.

A map carries authority - people believe it more readily than a paragraph making the same claim - and that authority is a responsibility. Three habits keep it honest. State your uncertainty: a suitability map drawn from fifty-metre data should not be presented as if it resolved individual plots. Respect privacy: fine-grained location data can re-identify people, so aggregate to a level that protects individuals, especially with anything drawn from movement, property or household data. Watch the frame: the choice of boundaries, colours and class breaks can make the same numbers tell opposite stories - the map-maker's decisions are never neutral. Under India's open-data settlement you increasingly can access rich spatial data; the ethical question is not whether you may, but whether your map tells the truth about how much it really knows.