29:null 2a:["$","title",null,{"children":"02. Urban Data Formats and Sources | autarkjs | Observable"}] 2f:T40c,30:T53f, 31:T60c, 32:T768,

GeoJSON is a JavaScript Object Notation (JSON) based format that represents vector data using standardized JSON objects. It can describe a geometry, a feature, or a collection of features, and by default, it uses the World Geodetic System 1984 coordinate reference system.

According to the official specification, a GeoJSON object may represent a region of space as a Geometry, a spatially bounded entity as a Feature, or a list of features as a FeatureCollection. GeoJSON supports the following geometry types: Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection.

A Feature is defined by three main members: type, properties, and geometry. The type member identifies the object as a "Feature", the properties member contains descriptive attributes such as names or categories, and the geometry member defines the spatial shape of the feature, such as a Point, LineString, or Polygon.

A FeatureCollection groups multiple Feature objects. It contains a type member with the value "FeatureCollection" and a features member, which is an array of Feature objects. Unlike a Feature, a FeatureCollection does not directly store properties or geometry, since these belong to each individual Feature within the collection.

See below an example of GeoJSON data.

33:T823,

Well-Known Text (WKT) is a text-based format used to represent vector geometry in a standardized and human-readable way. It is widely used in geographic information systems to encode geometric objects as plain text. Unlike formats such as GeoJSON, WKT focuses solely on geometry representation and does not include descriptive attributes or feature properties.

According to its standard definition, a WKT geometry is written as a geometry type keyword followed by coordinate values enclosed in parentheses. The format supports geometry types such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection.

Each WKT object is therefore composed of two main parts: the geometry type and its coordinates. The geometry type identifies the kind of spatial object being represented, while the coordinates define its shape. For example, a POINT contains a single coordinate pair, a LINESTRING contains a sequence of connected coordinate pairs, and a POLYGON contains one exterior ring and, optionally, interior rings representing holes.

See below an example of WKT data.

34:T522,

GeoTIFF is a raster data format based on the Tagged Image File Format (TIFF) that is used to store georeferenced raster data. In addition to the pixel values of the image itself, it includes metadata that describes how the raster is positioned in geographic space, such as its coordinate reference system, spatial extent, and pixel resolution.

According to its specification, a GeoTIFF file combines the structure of a TIFF image with geographic metadata stored in dedicated tags. This allows the raster to be correctly located and interpreted by geographic information systems.

A GeoTIFF dataset is therefore composed of two main parts: the raster values and the georeferencing metadata. The raster values are arranged as a grid of pixels, where each pixel stores one or more values depending on the dataset. The georeferencing metadata defines how these pixels relate to real-world coordinates.

See below an example of GeoTIFF data.

35:T69e,

OpenStreetMap (OSM) is an open and collaboratively maintained geographic data source that provides vector data describing natural and built environments. Its data is created and updated by a global community of contributors and can be freely accessed, used, and shared. OSM is widely used in geographic information systems, urban studies, navigation applications, and spatial analysis.

OpenStreetMap represents elements through three main structures: nodes, ways, and relations. A node is a single geographic position, similar to a GeoJSON Point. A way is an ordered list of nodes and can represent linear or area features, making it comparable to a GeoJSON LineString or Polygon. A relation groups nodes, ways, or other relations to describe more complex entities. Each OSM element also includes tags, stored as key-value pairs, which describe its meaning and characteristics.

OpenStreetMap data is commonly distributed in formats such as OSM XML and PBF (Protocolbuffer Binary). OSM XML is a text-based format that stores nodes, ways, relations, and their tags in a structured and human-readable form, which makes it useful for inspection and small-scale processing. PBF stores the same information in a compact binary form.

See below an example of OpenStreetMap XML.

36:T748,function xmlView (xml) { function view(node) { if (node.nodeType === Node.TEXT_NODE) { const value = node.textContent.trim(); return value ? html`

${value}

` : null; } if (node.nodeType !== Node.ELEMENT_NODE) return null; const attrs = Array.from(node.attributes).map( a => html` ${a.name}="${a.value}"` ); const children = Array.from(node.childNodes).map(view).filter(Boolean); if (!children.length) { return html`

<${node.nodeName}${attrs} />

`; } return html`

<${node.nodeName}${attrs}>

${children}

</${node.nodeName}>

`; } return html`

${view(xml.documentElement)}

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\n This notebook was produced for the Urban Data Analytics with Autark tutorial, presented at SIBGRAPI 2026 by:\n

\n\n- João Lucas Rulff da Costa (FGV, [jlrulff@gmail.com](mailto:jlrulff@gmail.com))\n- Lucas Alexandre (UFF, [lucasalexandre@id.uff.br](mailto:lucasalexandre@id.uff.br))\n- Talisson Souza (UFF, [talissonsouza@id.uff.br](mailto:talissonsouza@id.uff.br))\n- Gustavo Moreira (UIC, [gmorei3@uic.edu](mailto:gmorei3@uic.edu))\n- Daniel Oliveira (UFF, [danielcmo@ic.uff.br](mailto:danielcmo@ic.uff.br))\n- Harish Doraiswamy (MSR, [harish.doraiswamy@microsoft.com](mailto:harish.doraiswamy@microsoft.com))\n- Fabio Miranda (UIC, [fabiom@uic.edu](mailto:fabiom@uic.edu))\n- Claudio Silva (NYU, [csilva@nyu.edu](mailto:csilva@nyu.edu))\n- Marcos Lage (UFF, [mlage@ic.uff.br](mailto:mlage@ic.uff.br))\n\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"## Table of contents\n\n\n\n

Vector data\n
1. Point, Multipoint
2. LineString, MultiLineString
3. Polygon, MultiPolygon
4. GeometryCollection
\n
Raster data
Urban data formats\n
1. The GeoJSON Format
2. Well-known text (WKT)
3. GeoTIFF
\n
Urban data sources\n
1. OpenStreetMap
2. Open data portals
\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"## 1. Vector data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":63,"value":"

\nUrban datasets that represent urban features such as parks, roads, and buildings are usually represented in vector format. Vector data uses coordinates and geometry (points, lines, polygons) to describe complex structures.\n\n

\n\nThe coordinates of a geometry depend on the definition of a coordinate system.\n\nUrban datasets either use a geodetic (i.e., defined over Earth's surface) or a projected system. When a geodetic system is used, the coordinates are either 2D (longitude, latitude) or 3D (longitude, latitude, elevation).\n\n

\n\nLet's assume a coordinate system with the origin at the top-left corner of the drawing area, where the first and second coordinates represent the horizontal and vertical directions, respectively. Assume the first coordinate grows\nfrom left to right and the second one from top to bottom.\n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":82,"value":"### 1.1. Point and MultiPoint","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":84,"value":"

\nA **Point** consists of a single spatial coordinate. For example, a point with coordinates (80, 40) would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":111,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"

\nSimilarlly, a MultiPoint is a geometry composed of two or more points. For example, a MultiPoint composed of two points, one with coordinates (80, 40) and the other with coordinates (40, 80) would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":86,"value":"### 1.2. LineString and MultiLineString","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":90,"value":"

\nA LineString is a geometry composed of a list of connected points. For example, a LineString composed of two points, one with coordinates (80, 40) and the other with coordinates (40, 80) would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":215,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":229,"value":"

\nA MultiLineString is a geometry composed of multiple LineStrings. For example, a MultiLineString composed of two lines, one with coordinates (80, 40) and (40, 80), and another with coordinates (20, 20) and (20, 60), would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":233,"value":" ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":93,"value":"### 1.3. Polygon and MultiPolygon","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":97,"value":"

\nA Polygon is a geometry composed of a closed sequence of connected points. For example, a Polygon with coordinates (20, 20), (80, 40), (40, 80), (20, 60), and (20, 20) would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":247,"value":" ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"

\nA MultiPolygon is a geometry composed of multiple polygons. For example, a MultiPolygon composed of two polygons, one with coordinates (20, 20), (80, 40), (40, 80), (20, 60), and (20, 20), and another with coordinates (85, 10), (120, 15) and (100, 40), would be illustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":256,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":99,"value":"### 1.4. GeometryCollection","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":101,"value":"

\nA GeometryCollection is a geometry composed of different geometry types. For example, a GeometryCollection composed of a LineString with coordinates (20, 20), (80, 40), and (40, 80), a Point with coordinates (20, 60), and a Polygon with coordinates (85, 10), (120, 15), (100, 40), and (85, 10), would be\nillustrated as:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":278,"value":"$2f","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":356,"value":"## 2. Raster data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":461,"value":"

\nThe raster format is a data representation model used to describe space as a regular grid of cells, usually called pixels. Each pixel stores a value representing a property of the area it covers, such as elevation, temperature, or land use. Unlike vector data, which represents features through geometries such as points, lines, and polygons, raster data represents geographic phenomena as a grid of values over space.\n

\n

\n\n

\nA raster dataset is defined by its spatial structure and its cell values. Its main components include the number of rows and columns, the size of each cell, the geographic location of the grid, and the coordinate system used to place it in space.\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":471,"value":"$30","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":477,"value":"

\nEach cell contains one or more values, depending on the type of raster. For example, a single-band raster may store elevation values, while a multi-band raster may store several spectral bands from satellite imagery.\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":485,"value":"$31","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":65,"value":"## 3. Urban data formats","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":67,"value":"### 3.1. GeoJSON","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":283,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":339,"value":"geojson = FileAttachment(\"ex.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":382,"value":"### 3.2. Well-known text (WKT)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":392,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":405,"value":"wkt = FileAttachment(\"ex@1.wkt\").text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":429,"value":"

\nWKT can represent the geometry but it cannot represent also associated properties. For this reason, when both geometry and attributes need to be stored, WKT is often combined with another tabular format such as CSV, where the attribute fields are stored in columns and the geometry is stored in a dedicated WKT column.\n

\n

\n\n

\nSee below an example of CSV data with the WKT notation used to store geometry information.\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":450,"value":"csv = FileAttachment(\"ex.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":439,"value":"viewof table = Inputs.table(csv)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":487,"value":"### 3.3. GeoTIFF","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":491,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":511,"value":" tiff = {\n const buffer = await FileAttachment(\"ex@1.tif\").arrayBuffer();\n const tiff = await GeoTIFF.fromArrayBuffer(buffer);\n const image = await tiff.getImage();\n\n return {\n width: image.getWidth(),\n height: image.getHeight(),\n bbox: image.getBoundingBox(),\n origin: image.getOrigin(),\n resolution: image.getResolution(),\n geoKeys: image.getGeoKeys()\n };\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"## 4. Urban data sources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":72,"value":"### 4.1. OpenStreetMap\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":541,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":566,"value":"{\n const text = await FileAttachment(\"ex.osm\").text();\n const xml = new DOMParser().parseFromString(text, \"application/xml\");\n\n return xmlView(xml);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":577,"value":"

\n OSM data can be fetched using the Overpass API. It allows users to request only the elements they need, such as roads, buildings, or points of interest\n within a given area. Results can be returned in formats such as XML or JSON.\n

\n

\n\n

\n Overpass has a query language, that can be used to specify elements of interest.\n

\n

\n\n

\n For example, the query below:\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":591,"value":" overpass = `\n [out:xml];\n node[\"amenity\"=\"cafe\"](40.0,-3.1,40.1,-3.0);\n out;\n `;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":608,"value":"Would produce the following result:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":612,"value":"{\n const response = await fetch(\"https://overpass-api.de/api/interpreter\", {\n method: \"POST\",\n body: overpass\n });\n \n const text = await response.text();\n const xml = new DOMParser().parseFromString(text, \"application/xml\");\n\n return xmlView(xml);\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":638,"value":"

\n OpenStreetMap data can also be obtained through prepared data extracts, such as those provided by Geofabrik. Instead of querying the database directly through Overpass\n API, these extracts offer downloadable snapshots of OSM data for predefined regions, such as countries, states, or cities.\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":642,"value":"### 4.2 Open data portals","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":647,"value":"

\n Urban datasets are often made available through open data portals maintained by cities and public agencies, such as NYC Open Data. These portals provide public access\n to a wide range of datasets describing the urban environment, including transportation, land use, buildings, public services, infrastructure, environment, and demographic indicators.\n

\n

\n\n

\n Depending on the dataset, the information may be vector or raster, and it is commonly distributed in formats such as CSV with WKT columns and GeoJSON. These portals are important sources for urban analysis because they provide\n structured, regularly updated, and geographically referenced data that can be reused in research, planning, and civic applications.\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":530,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":520,"value":"GeoTIFF = await import(\"https://esm.sh/geotiff@2.1.3?bundle\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":622,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"2","titling":"auto"}} 2e:"$2d:body"