qbtiles 0.6.0

Quadkey Bitmask Tiles — cloud-optimized tile index format

QBTiles

QBTiles (Quadkey Bitmask Tiles) — a spatial data format that encodes existence as a tree structure, reducing ID storage cost to zero.

Documentation | Live Demos | 한국어 문서

Install

# Python — build & write QBT files
pip install qbtiles

# TypeScript/JavaScript — read & query QBT files in the browser
npm install qbtiles

What It Does

Map tiles and spatial grids are inherently quadtrees. QBTiles encodes cell existence as 4-bit bitmasks in BFS order. The position of each entry is implied by the tree structure — no IDs, no coordinates stored.

Level 1:  [0100]          → only child 1 exists
Level 2:  [0001]          → only child 3 exists
Level 3:  [0010]          → only child 2 exists

Three Modes

Mode	Flags	Use Case	Access	Comparable to
Variable-entry	0x0	Tile archives (MVT, PNG)	Per tile	PMTiles
Fixed row	0x1	Raster grids	Per cell (Range Request)	COG (GeoTIFF)
Fixed columnar	0x3	Compressed grids	Whole file (gzip)	Parquet

Benchmarks

Variable-entry — Tile Index (vs PMTiles)

Dataset	Entries	PMTiles	QBTiles	Reduction
adm-korea	36K	80.9 KB	61.3 KB	-24.3%
Full OSM	160M	300.7 MB	235.2 MB	-21.8%

Fixed row — Raster Grid (WorldPop 51M cells, float32)

Format	Size	Ratio	Per-cell access
FlatGeobuf	6,001 MB	29.4x	per feature
GeoParquet	700 MB	3.4x	full download only
GeoTIFF (COG)	276 MB	1.4x	512×512 block
QBTiles	204 MB	1.0x	single cell

Fixed columnar — Compressed Grid (Korea 100m, 931K cells × 3 values)

Format	Size	Per cell
Parquet (gzip)	2.9 MB	coordinate scan
QBTiles columnar	1.7 MB	O(log N) quadkey search

Quick Start

Python — Build QBT Files

import qbtiles as qbt

# Mode 1: Tile archive — from a folder of z/x/y tiles (e.g., tiles/5/27/12.mvt)
qbt.build("korea_tiles.qbt", folder="tiles/")

# Mode 2: Columnar — coordinates + multiple value columns
# coords: list of (x, y) in the target CRS
# columns: dict of column_name → value list (same length as coords)
# cell_size: grid cell size in CRS units (meters for EPSG:5179)
# → zoom, origin, extent are auto-calculated from coords and cell_size
qbt.build("population.qbt.gz",
    coords=list(zip(df["x"], df["y"])),         # [(950000, 1950000), ...]
    columns={"total": totals, "male": males, "female": females},
    cell_size=100, crs=5179)                     # 100m grid, Korean CRS

# Mode 3: Fixed row — coordinates + single value array (for Range Request)
# values: flat list of numbers (one per cell)
# entry_size: bytes per cell (4 for float32)
qbt.build("global_pop.qbt",
    coords=list(zip(lons, lats)),                # [(-73.99, 40.75), ...]
    values=population,                           # [52.3, 41.2, ...]
    cell_size=1000, entry_size=4,                # 1km grid, 4 bytes/cell
    fields=[{"type": qbt.TYPE_FLOAT32, "name": "pop"}])

# GeoTIFF → QBTiles conversion (cell_size, CRS, extent auto-detected)
qbt.build("worldpop.qbt", geotiff="worldpop_2025.tif")

TypeScript — Read & Query

import { openQBT } from 'qbtiles';

// openQBT reads the header, detects the mode, and loads data automatically.

// Mode 1: Tile archive — serve MVT/PNG tiles from a single .qbt file
const tiles = await openQBT('korea_tiles.qbt');
const tile = await tiles.getTile(7, 109, 49);  // ArrayBuffer (gzip MVT) | null
tiles.addProtocol(maplibregl, 'qbt');           // one-line MapLibre integration

// Mode 3: Fixed row — per-cell Range Request on a remote file
const grid = await openQBT('https://cdn.example.com/global_pop.qbt');
const cells = await grid.query([126, 35, 128, 37]);  // [west, south, east, north]
// → Array<{ position: [lng, lat], value: number }>

// Mode 2: Columnar — downloads entire file, queries in memory
const pop = await openQBT('population.qbt.gz');
pop.columns!.get('total')!;                     // number[931495] — direct access
const result = await pop.query([126, 35, 128, 37]);
// → Array<{ position: [lng, lat], values: {total: 523, male: 261, female: 262} }>

File Format (v1)

[Header 128B+]  magic, version, flags, zoom, CRS, origin, extent,
                bitmask_length, values_offset, index_hash (SHA-256), field schema
[Bitmask]       gzip-compressed 4-bit nibbles in BFS order
[Values]        row: raw entry_size × leaf_count (Range-requestable)
                columnar: column-by-column (varint + fixed types)

Full spec: format-spec.md

API Reference

Python (pip install qbtiles) — Writer

Function	Description
build(output, ...)	Unified builder — auto-detects mode from folder / columns / values / geotiff
read_qbt_header(path_or_bytes)	Parse QBT header to dict
tile_to_quadkey_int64(z, x, y)	Tile coords → 64-bit quadkey

Low-level: build_quadtree(), serialize_bitmask(), write_qbt_variable(), write_qbt_fixed(), write_qbt_columnar()

TypeScript/JavaScript (npm install qbtiles) — Reader

Function / Class	Description
openQBT(url) → QBT	Unified loader — auto-detects mode from header flags
QBT.getTile(z, x, y)	Fetch tile data via Range Request (variable mode)
QBT.query(bbox)	Spatial query — all modes (Range Request or in-memory)
QBT.columns	Column values as Map<string, number[]> (columnar mode)
QBT.addProtocol(maplibregl)	One-line MapLibre custom protocol (variable mode)
QBT.toWGS84(x, y)	CRS conversion via proj4 (built-in for common EPSG codes)
registerCRS(epsg, proj4Def)	Register custom CRS definitions

Low-level: parseQBTHeader(), queryBbox(), mergeRanges(), fetchRanges(), readColumnarValues()

Live Demos

Tile Viewer — Variable-entry (0x0)

MVT vector tiles served via QBTiles index + Range Request. Administrative boundaries of South Korea.

Population Grid — Fixed columnar (0x3)

931K cells in 1.75 MB. Korea 100m population grid with 3 values per cell at 1.97 Byte/cell.

Range Request Comparison — Fixed row (0x1)

Split-screen comparison: QBTiles cell-level vs COG block-level Range Request on WorldPop 1km global population.

Related Work

• Sparse Voxel Octree (SVO) — Same bitmask principle in 3D (8-bit masks for octree)
• LOUDS — Succinct tree encoding via BFS bit sequences
• PMTiles — Hilbert curve tile indexing with varint delta encoding

License

MIT