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Projects

Contribute to the Space/Time Directory and explore library materials with these interactive tools built on historic maps, our vast photography collections, and more!

Data

The table below lists datasets available in the NYC Space/Time Directory. Each Dataset consists of a Data Package descriptor, and two Newline Delimited JSON (NDJSON) files: one with all the dataset’s Objects , the other one with its Relations.

Each Dataset is available in a few different file formats (e.g. ZIP, NDJSON, GeoJSON), see the File Types table below for details.

For more information on working with data from the NYC Space/Time Directory, see the Data & Tools repository on GitHub.

File types

NDJSON Newline Delimited JSON: one JSON object per line. See GitHub for more information on using these files. A NYC Space/Time Directory Dataset can contain two NDJSON files: one with its Objects, and one with its Relations. ZIP Compressed archive of all files in the Dataset: a Data Package descriptor, Objects and Relations files, as well as derived GeoJSON and CSV files. GeoJSON This file contains all Objects in the Dataset that have geometries (either a point, line or polygon), converted to GeoJSON Features. Please note: the GeoJSON file does not contain Objects without geometries! For example, the GeoJSON file of the city-directories Dataset does not contain Objects that are not geocoded. GeoJSON (simplified) The simplified GeoJSON file contains the same data as the normal GeoJSON file, but the nested data property is flattened, and its fields converted to unnested properties. This makes using this file in QGIS and other tools a lot easier, but some structure may be lost. For details about this process, see GitHub. CSV The CSV file contains all Objects in the Dataset (with and without geometries), but the same flattening is applied as in the simplified GeoJSON file to convert the nested JSON structure of the Objects to tabular data.

Dataset types

Draft Draft datasets are currently being worked on; they are not finished, and things like field names might change. Crowdsourced Crowdsourced datasets are not static, new objects are added as new crowdsourced submissions come in. Inferred Inferred datasets are created by combining data from multiple datasets. For more information about combining datasets, see our tutorial on historical addresses.

Meetups

Historical Data & Maps at NYPL is a series of public workshops and talks which will highlight different parts of New York City’s history using data and maps from the NYC Space/Time Directory. In this series, we will focus on making new maps with old data using open source mapping tools, and learning how to use the Library’s open data sets and APIs to tell stories about New York City’s history by finding and combining materials from the NYPL’s Digital Collections.

Past and upcoming events:

Title Date

Tutorials

The following tutorials demonstrate how you can use tools and datasets from the NYC Space/Time Directory:

Code

The table below lists open source repositories made for the NYC Space/Time Directory that might be useful in other projects, too. More repositories can be found on the project’s GitHub page.

Repository Description Technology brick-by-brick Simple JSON API for small crowdsourcing apps used in different NYC Space/Time Directory projects Node.js + PostgreSQL Leaflet.GeotagPhoto Leaflet plugin for photo geotagging JavaScript + Leaflet spacetime-etl Extract/Transform/Load tool for NYC Space/Time Directory data Node.js spacetime-cli Command line tools for NYC Space/Time Directory data Node.js documentation Information about various parts of the NYC Space/Time Directory project, and how they work together dc-download Command-line tool for downloading images from Digital Collections Node.js maps-by-decade Maps by Decade shows New York City street maps, grouped by decade. JavaScript + React surveyor Web interface for crowdsourced geotagging of historical photos JavaScript + React interactive-architecture Interactive architecture diagrams with SVG JavaScript hocr-detect-columns Detects columns and connects indented lines in hOCR files Node.js nyc-street-normalizer Module to normalize New York City street and avenue names Node.js city-directory-entry-parser Module to parse lines from OCR’d New York City directories into separate fields, such as names, occupations, and addresses. Python building-inspector Crowdsourced extraction and correction of building footprints and addresses from historical maps Ruby on Rails nypl-warper Web interface for crowdsourced georectification of historical maps Ruby on Rails map-vectorizer Python tools which use computer vision to extract building outlines and other features from historical building atlases Python

Architecture

For in-depth information on all the parts, components and datasets that make up the NYC Space/Time Directory (and how they work together), see the project’s architecture page.

Articles

The following posts have been published about the NYC Space/Time Directory on NYPL’s blog:

Other websites and publications have also written stories about the project:

Tools & Experiments

Take a peek into the Space/Time Directory workshop! We’re sharing prototypes, proof of concepts, and visualizations the project as they’re made.

Related Resources

Browse related Library resources including digitized materials, data sets, APIs, and much more!