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Map and scores
What a Location Pillars score is
Location Pillars is a pan-India livability score on the MyStreetBiz map. Ten pillars have sourced data. Four stay null until a real source exists. Missing observations are excluded and reweighted, never guessed.
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A second score, not a property valuation
Location Pillars answers “how does this place score on sourced livability signals?” It is shown on the map and in Real Estate Terminal. It is not a warehouse score, a legal title search, a Circle Rate, a registered sale, or an appraisal of a specific building.
MyStreetBiz does not publish a table of typical pillar scores for Indian cities. Those would be guesses. Open a point on the map to see that point’s sourced breakdown.
Sourced pillars
Today these pillars have scoring logic backed by public spatial data (OpenStreetMap and, where sampled, OpenAQ and terrain): Commute & Connectivity; Proximity to Amenities; Social Infrastructure; Mobility & Transport; Environment & Recreation; Air Quality; Water & Blue Space; Flood & Terrain Exposure; Traffic Flow & Arterial Roads; Walkability & Pedestrian Access.
A pillar can still be null at a point if that area has not been sampled yet. Null means “not observed here”, not zero.
Null, not a fake zero
When a pillar has no credible free spatial source for India, MyStreetBiz does not invent a proxy. Police-station distance is not “safety”. The four source-blocked pillars stay listed with the data they would need:
- Safety & Security — needs geocoded crime statistics — not available as free/public data for India at point-level granularity.
- Governance & Digitalization — needs an e-governance/digital-infrastructure index — not available as free/public spatial data.
- Economy & Culture — needs local economic indicators (GDP/employment by area) — not available as free/public spatial data at this granularity.
- Water & Power — needs utility reliability data (water supply hours, power outage frequency) — not available as free/public spatial data.
How missing pillars affect the number you see
Null pillars are excluded and their weight is redistributed across pillars that did return a value. Confidence widens as more pillars are missing. That is why two nearby points can show different intervals even when the headline score looks similar.