Most people don't know the history of their street, neighborhood, or even city. The Pittsburgh Mapping and Historical Site Viewer provides a window into the past, allowing anyone to see how the city took shape over time. It shows how the city of 22,433 people in 1835 changes over time: how neighborhoods grow and expand, while others were planned but never built. Street names change over time, empty lots become buildings, and schools and churches open and close. The maps were made by cutting and georeferencing hand-drawn paper maps made over 100 years ago. Using historic maps, some more than 175 years old, this interactive map highlights sites in the National Registry of Historic Places among others. By browsing through the years at a location, you'll find a cemetery that became a school, the arrival of rail yards, and other indicators of how the city has evolved around changes in transportation, industry, and population. This map service includes a mosaic of several volumes of plat maps that were originally bound atlases. The mosaic was created using ArcGIS software from the scanned JPEG images, varying from 300-600 dpi. These images were obtained from the Historic Pittsburgh website (http://digital.library.pitt.edu/maps/hopkins.html) The images were georeferenced to WGS84 Web Mercator and the borders were clipped to create a contiguous map.This product is to be used for reference purposes only. The original historical paper maps were sometimes damaged or distorted to varying degrees due to age and use. There are spatial inaccuracies and some places where the footprints do not lineup perfectly or in some cases overlap.Read and see more about this project:KDKA News feedEsri BlogPittsburgh Post-Gazette articleUniversity of Pittsburgh

Overview

This dataset contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).

Preprocessing/Formatting

It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).

• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)

• A single street closure may span multiple segments of a street.

• The street closure permit refers to all the component line segments.

• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.

The roadway_id field is a unique GIS line segment representing the aforementioned segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.

The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.

City teams that use this data requested that each segment of each street closure permit be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows. Thus, the values in most fields of the three records are identical.

Each row has the fields segment_num and total_segments which detail the relationship of each record, and its corresponding permit, according to street segment. The above example produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.

The geometry field consists of string values of lat/long coordinates, which can be used to map the street segments.

All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.

Known Uses

These data are used by DOMI to track the status of street closures (and associated permits).

Further Documentation and Resources

An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits

Pittsburgh Department of Public Works Administrative Divisions