AndreaBozzo/ceres-open-data-index huggingface dataset:
Smart Streetlight RFI Responses

##Overview

This guide to the City of Pittsburgh Smart Streetlight Request for Information (RFI) was created to provide clarity and transparency to the ongoing Smart Streetlight project in the City of Pittsburgh. 

Pittsburgh has approximately 40,000 City-owned and operated streetlights, the vast majority of which are traditional high-pressure sodium HID luminaires controlled by simple photocell units. Due to high energy and maintenance costs, the City is developing a plan to replace the bulk of this system with remotely managed LED luminaires, with the goal being a potential energy savings of 60-80%. These savings can finance the conversion program through an Energy Savings Performance Contract. The City has previously piloted this approach in 2012, upgrading 4,500 luminaires through a State-funded energy savings program.  

The City recognizes that updated streetlights can offer a ready-made physical and communications platform for the sensors, controls and backhaul that power a Smart City. In the case of a Smart Streetlight deployment however, the problem is not pre-defined, so it is unclear what the City may actually need. This is why the City chose to release an RFI instead of the more common Request for Proposals (RFP), in order to understand the state of the industry, perform a market analysis and provide a learning experience for City personnel.  This RFI was designed to help the City understand the industry's perspective on these emerging technologies while providing a more efficient and equitable lighting system. 

The City of Pittsburgh Office of Management and Budget released the Smart Streetlights RFI on March 2nd, 2017 and closed it on April 10th, 2017.  The City received twenty-five responses from different businesses who offered a wide variety of services, products and concepts. All of the responses, as well as the RFI itself and the resulting questions and answers, are available to view.

##"Smart Cities"

The term "Smart City" refers to a range of technologies designed to sense and control various real-world phenomena, and software design and development projects to collect, present and administer those technologies' data and functions. 

##LEDs

LEDs have a higher efficiency in their conversion of electricity to light. LEDs also last much longer than sodium lamps. They also allow for fine-tuned control to maximize energy savings and performance. 

Tags: led, procurement, request for information, rfi, smart, smart cities, streetlight
Organization: City of Pittsburgh
Allegheny County Commercial Vehicle Inspections

This dataset lists the locations and results of all commercial vehicle inspections performed by the Allegheny County Police Motor Carrier Safety Assistance Program (MCSAP). The goal of the MCSAP is to reduce CMV-involved crashes, fatalities, and injuries through consistent, uniform, and effective CMV safety programs.

https://www.fmcsa.dot.gov/grants/mcsap-basic-incentive-grant/motor-carrier-safety-assistance-program-mcsap-grant


Tags: Traffic count, crash, police, public safety
Organization: Allegheny County
Westmoreland County Crash Data

Contains locations and information about every crash incident reported to the police in Westmoreland County from 2011 to 2015. Fields include injury severity, fatalities, information about the vehicles involved, location information, and factors that may have contributed to the crash. Data is provided by PennDOT and is subject to PennDOT's data privacy restrictions, which are noted in the metadata information section below.

**This data is historical only, but new data can be found on PennDOT's Crash Download Map tool linked in the Resources section.

Organization: Allegheny County
Summer Meal Sites

Information pertaining to Summer Meal Sites, as collected by Citiparks in the City of Pittsburgh Department of Parks and Recreation. This dataset includes the address of each feeding site, the dates where meals start and stop being served, the days they operate, availability of hot and cold meals, availability of ovens and/or coolers and the number of meals served by time of day.

Tags: cold, feeding, food, hot, meals, site, summer
Organization: City of Pittsburgh
Regional Park-n-Ride Facilities

The Southwestern Pennsylvania Commission maintains an inventory of the region’s park-n-ride facilities that contains detailed information for each of the more than 100 park-n-ride facilities located in the 10-county SPC region. The inventory includes facility location, number of parking spaces, ownership information, any applicable parking fees, links to area public transit service provider(s), the individual transit routes serving the facility, and listings of on-site and nearby amenities. Data about each park-n-ride facility is updated through regular field visits and coordination with the region's transit operators and PennDOT. Field data for about one-half of the region’s park-n-ride facilities is collected annually as part of the regular update process.

Tags: Facilities, PNR, Park and Ride, Park-and-Ride, Park-n-Ride, Region, Regional, SPC, Transportation
Organization: Southwestern Pennsylvania Commission
Pittsburgh Public Schools Enrollment by Individualized Education Plan (IEP)

This dataset captures students attending a Pittsburgh Public School that are classified as either gifted or having some other type of Individualized Education Plan (IEP). 

According to the [Pennsylvania Department of Education](https://www.education.pa.gov/K-12/Special%20Education/FAQContact/Pages/SpecialEdFAQ.aspx), an IEP is ​"the written plan for the education of a student who has a disability or is gifted. The IEP is based on the individual student's needs and describes the special help the student will receive in school. " 

The data categories students as having either a gifted IEP, a non-gifted IEP, or no IEP.  This data includes only students enrolled in a Pittsburgh Public School. Students enrolled in a charter, private, or parochial school are excluded from this dataset.

When analyzing this data, it's important to note that IEP evaluations may not be conducted until students reach the second or third grades. Please refer to the table showing IEP's by grade to see how data varies by grade level of the student.

Neighborhoods were sometimes combined with other adjacent neighborhoods to enable reporting and overcome the District's restriction on reporting totals for any group with fewer than 11 cases.  Neighborhood data is reported for K-12 students.

Data was extracted from the Pittsburgh Public Schools data system in January, 2021. It captures the school where the student was enrolled on October 1st. The neighborhood school the student feeds into based on their address as of the beginning of the 2020-21 school year.



Tags: IEP, Individualized education plan, disability, disabled, enrollment, gifted, individualized education program, pittsburgh public schools, pps, special education, student
Organization: Pittsburgh Public Schools
Pittsburgh Public Schools Feeder Pattern Attendance Boundaries

This data shows the attendance boundaries used  to assign students to feeder pattern schools based on their place of residence. These boundaries were adopted for the 2012-13 school year by the Pittsburgh Public Schools.  The boundaries were drawn to align with major roads, neighborhood boundaries, and natural features. Efforts were also made to enable all students within an elementary school to move to the same middle school, and allow all students in a middle school to transition to the same high school.

Tags: Schools, attendance, boundary, equity, feeder, feeder patterns, students
Organization: Pittsburgh Public Schools
Pittsburgh Public Schools Enrollment by Neighborhood, School, and Feeder Pattern

This dataset includes enrollment data for Pittsburgh Public Schools. Data is presented by school, feeder pattern / attendance boundary, and by neighborhood. 
A table also includes data on the number of students attending schools by neighborhood.  Data includes preschool students through 12th grade.

This data can be very useful in understanding neighborhood-level enrollment patterns, student demographics by neighborhood and school, and can also be used to inform school-community partnerships.

Students attending charter, private and parochial schools are not included in this data.  Only students enrolled in a Pittsburgh Public School are captured.

Totals with fewer than 11 students have been redacted to adhere to School District privacy policies. 

Data was extracted from the Pittsburgh Public Schools data system in January, 2021. It captures the school where the student was enrolled on October 1st. The neighborhood school the student feeds into based on their address as of the beginning of the 2020-21 school year.

Tags: attendance, education, enrollment, feeder, pittsburgh public schools, pps, school, student
Organization: Pittsburgh Public Schools
Housing Market Value Analysis - Urban Redevelopment Authority

In late 2016, the URA, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for the City of Pittsburgh. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional neighborhood boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. 

Pittsburgh’s 2016 MVA utilized data that helps to define the local real estate market between July, 2013 and June, 2016:

•	Median Sales Price

•	Variance of Sales Price

•	Percent Households Owner Occupied

•	Density of Residential Housing Units

•	Percent Rental with Subsidy

•	Foreclosures as a Percent of Sales

•	Permits as a Percent of Housing Units

•	Percent of Housing Units Built Before 1940

•	Percent of Properties with Assessed Condition “Poor” or worse

•	Vacant Housing Units as a Percentage of Habitable Units

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

During the research process, staff from the URA and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market. 

Tags: MVA, Market Value Analysis, Reinvestment Fund, TRF, The Reinvestment Fund, affordable housing, block groups, foreclosures, housing, housing markets, neighborhoods, properties, property, reinvestment fund, rental subsidy, sales prices, ura, urban redevelopment authority, vacancy
Organization: Urban Redevelopment Authority of Pittsburgh
Make My Trip Count 2015

The Make My Trip Count (MMTC) commuter survey, conducted in September and October 2015 by GBA, the Pittsburgh 2030 District, and 10 other regional transportation stakeholders, asked respondents a series of questions related to zip code of residence, destination location, and mode(s) for making the commute to and from work or school. More than 20,000 responses were successfully completed and analyzed for measured performance against local modeled commuter patterns.

Tags: Alternative Transportation, Bike, Bus, Commuting, Downtown, Oakland, PIttsburgh, Parking, Sustainability, Transportation, Walking, commuters
Organization: Green Building Alliance
Kingsley House Membership and Occupation Data 1920/1926

This data set was prepared by Adrianna Taraboletti, a student enrolled in the University of Pittsburgh’s Masters in Library and Information Science program. These datasets are taken from the annual reports for the Kingsley Association from 1920 to 1926. The Kingsley House was part of the greater Kingsley Association, which was established in 1893. The purpose of the Kingsley Association was to provide services to working-class families of Pittsburgh. For more information on the Kingsley Association and the Kingsley House, see the readme file included here.

These datasets transcribe the percentages of members’ occupations and percentages of members of the Kingsley House based on age and gender for each year, with the exception of the years 1921 and 1924 when no data was published. 

There are four datasets included are Registered Member of the Kingsley House by Age Groups Percentages 1920-1923, Registered Member of the Kingsley House by Age Groups Percentages 1925-1926, Occupation Percentages 1920- 1923, and Occupation Percentages 1925-1926.

This data was published by the Kingsley Association and obtained through Historic Pittsburgh, which is hosted by the University of Pittsburgh Library System.


Tags: archives, city of pittsburgh, historic pittsburgh, history, kingsley association, membership, occupation, social settlement, social settlements
Organization: University of Pittsburgh
True Time API Link (real time arrival info)

This link will take you to the site where you can create an account to access Pittsburgh Regional Transit's real time arrival information. To request access to Pittsburgh Regional Transit's real-time API, login or create an account at http://truetime.portauthority.org. Once you log in, go to "My Account", under "Developer API", click the link to request access.

Tags: API, Arrival, LRT, PAAC, Port Authority, Real Time, True Time, bus, light rail, subway, transportation
Organization: Pittsburgh Regional Transit
Envision Downtown: Public Space Public Life Survey

Pedestrian and Bicycle counts, Stationary Activity Counts, and Age and Gender counts for 16 locations in Downtown Pittsburgh. Data was collected over a weekday (7am - 8pm) and weekend day (7am - 8pm).

Tags: PSPL, age, bicycle counts, bike count, downtown, gender, ped counts, pedestrian counts, public life, public space, stationary activity
Organization: Envision Downtown
Data Protected Bike Lanes, Oct 2019

On Wednesday, October 16, BikePGH, in collaboration with the How’s My Driving app, rallied nearly 70 volunteers to disperse along select Pittsburgh bike lanes to count the number of times vehicles were parking, standing, or loading in the bike-only lanes.

Dubbed the “Data Protected Bike Lane Project,” volunteers logged 123 violations, all captured during the morning, lunch and evening rush. Using the How’s My Driving app, a new tool that allows anyone to quickly report dangerous driving behavior, these volunteers covered Forbes Ave (between Bigelow and Morewood), Penn Ave (Stanwix to 11th), Allegheny Circle, and Third Ave (Stanwix to Smithfield).

More: https://www.bikepgh.org/2019/10/29/bike-lanes-blocked-for-up-to-50-of-the-time-according-to-our-data-protected-bike-lane-project/

Tags: bicycle, bike, bike infrastructure, bike lane, bike lanes, bikelane, bikepgh, enforcement, parking, transportation
Organization: BikePGH
Allegheny County Census Tracts 2016

This is an Allegheny County extract of the 2016 US Census Tracts downloaded from [https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html](https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html).

Tags: 2016, _orphaned_etl, arcgis, boundaries, census, sd, service definition, tracts
Organization: Allegheny County
Butler County Crash Data

Contains locations and information about every crash incident reported to the police in Butler County from 2011 to 2015. Fields include injury severity, fatalities, information about the vehicles involved, location information, and factors that may have contributed to the crash. Data is provided by PennDOT and is subject to PennDOT's data privacy restrictions, which are noted in the metadata information section below.

**This data is historical only, but new data can be found on PennDOT's Crash Download Map tool linked in the Resources section.

Organization: Allegheny County
Beaver County Crash Data

Contains locations and information about every crash incident reported to the police in Beaver County from 2011 to 2015. Fields include injury severity, fatalities, information about the vehicles involved, location information, and factors that may have contributed to the crash. Data is provided by PennDOT and is subject to PennDOT's data privacy restrictions, which are noted in the metadata information section below.

**This data is historical only, but new data can be found on PennDOT's Crash Download Map tool linked in the Resources section.


Organization: Allegheny County
A+ Schools Report to the Community

This report consolidates information from multiple data sources including PPS, PDE and Pittsburgh charter schools. Data is obtained through downloads from the web or through data requests. Raw data used to generate the reports will be made available as the files are processed. 

Tags: achievement data, charter schools, chronic absenteeism, education, graduation rate, pde, per pupil spending, pittsburgh public schools, pps, proficiency, promise readiness, teachers
Organization: A+ Schools
Allegheny County Zip Code Boundaries

<div>This dataset demarcates the zip code boundaries that lie within Allegheny County.</div><div><br /></div><div>If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal <a href='http://www.wprdc.org' target='_blank'>(http://www.wprdc.org)</a>, this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (<a href='http://openac.alcogis.opendata.arcgis.com/' target='_blank'>http://openac.alcogis.opendata.arcgis.com/</a>). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.</div><div><br /></div><div><b>Category</b>:  Civic Vitality and Governance</div><div><b><br /></b></div><div><b>Organization</b>:  Allegheny County</div><div><br /></div><div><b>Department:</b>  Geographic Information Systems Group; Department of Administrative Services</div><div><br /></div><div><b>Temporal Coverage:</b>  current</div><div><br /></div><div><b>Data Notes: </b></div><div><br /></div><div><b>Coordinate System:</b>  Pennsylvania State Plane South Zone 3702; U.S. Survey Foot</div><div><br /></div><div><b>Development Notes:</b>  none</div><div><br /></div><div><b>Other:</b> none</div><div><br /></div><div><b>Related Document(s):</b>  Data Dictionary (none)</div><div><br /></div><div><b>Frequency - Data Change:</b>  As needed</div><div><br /></div><div><b>Frequency - Publishing:</b>  As needed</div><div><b><br /></b></div><div><b>Data Steward Name:</b>  Eli Thomas</div><div><br /></div><div><b>Data Steward Email:</b> gishelp@alleghenycounty.us</div>

Tags: _etl, landmarks, street aliases, street dictionary, tables, zip codes
Organization: Allegheny County
Allegheny County 2000/2010 Census Tract Relationships

The Allegheny County 2000-2010 Census Tract Relationship File shows how 2010 Census tracts in Allegheny County, Pennsylvania relate to the 2000 Census tracts. Each record (row) consists of one unique relationship between a 2000 Census tract/2010 Census tract spatial set where a spatial set is the unique area shared by the record’s 2000 and 2010 tracts. Changes in tracts involve area, land area, population, and/or housing unit counts. Specifically, each record specifies the area, land area, population, and housing unit counts that were transferred to the record’s 2010 tract (TRACT10) from the record’s 2000 tract. The 2000 area (AREA00), land area (AREALAND00), population (POP00), and housing unit count (HU00) for the record is the standardized 2010 value for the record’s 2000 tract (TRACT00). These are the values to use when comparing 2010 population and housing unit counts to those values in the 2000 Census for a particular tract.

The relationship file provides the information necessary to conduct a decennial analysis between the 2000 and 2010 Censuses for a particular tract. Steps to use this file for a decennial analysis follow:

1.	Obtain the population and/or housing unit count from the 2000 Census for the tract of interest.

2.	Find a record in the relationship file where the tract of interest appears in the TRACT00 field. 

3.	Compare the population (POP00) and housing unit count (HU00) from the relationship file record to the 2000 Census population and housing unit count found in step 1.

This data was prepared by Lisa Over as a course project for LIS 2970 Open Government Data in the Master of Library Science Program at the University of Pittsburgh.

Tags: allegheny-county, allegheny-county-gis, census, council of government
Organization: University of Pittsburgh
Washington County Crash Data

Contains locations and information about every crash incident reported to the police in Washington County from 2011 to 2015. Fields include injury severity, fatalities, information about the vehicles involved, location information, and factors that may have contributed to the crash. Data is provided by PennDOT and is subject to PennDOT's data privacy restrictions, which are noted in the metadata information section below.

**This data is historical only, but new data can be found on PennDOT's Crash Download Map tool linked in the Resources section.

Organization: Allegheny County
2020 Census Redistricting Data Extracts (PL 94-171)

Chris Briem of the University of Pittsburgh Center for Social and Urban Research has been producing extracts from the Census Bureau's PL 94-171 redistricting file.

On August 12th, the U.S. Census Bureau released the first detailed data from the 2020 Decennial Census of Population and Housing. Before this, the only data that has been released from the 2020 Census has been total population counts by state needed for the reapportionment of congressional seats. The data just released is known as PL 94-171 data and includes the final census enumeration of the population by race and ethnicity for counties, municipalities, and smaller levels of geography.

PL 94-171 data is used in the redrawing of the boundaries for federal, state, and local legislative districts, a process known as redistricting. This data includes housing unit counts, occupancy status for housing units, population totals, and population by race Hispanic/Latino origin, voting-age population (age 18+), and group quarters counts. The Census Bureau will be releasing additional data, including more detailed population and household statistics from the 2020 Census in the future. 

Tags: 2010, 2020, Block, Census 2020, Census Bureau, County Subarea, Demographics, Municipality, Neighborhood, PL 94-171, Population, Redistricting
Organization: University of Pittsburgh