AndreaBozzo/ceres-open-data-index huggingface dataset:
Small Business Profiles for the Congressional Districts - 2021

The congressional district profiles are part of Advocacy’s state profile series, which provides user-friendly snapshots of national, state, and congressional district small business statistics.  The district profiles focus on the impact of small employers. Each profile provides the district’s total number of small employers and their industry breakout, plus the number of people employed and payroll expended. The profiles also show the total number of self-employed workers and a map showing how they are distributed in the district.

Tags: 2021, congressional-district, profiles, sba, small-business, state
Organization: Small Business Administration
All PCDDF, TOC, and Lipid Data

Sediment and Spider PCDDF Raw Data. 

This dataset is associated with the following publication:
Beaubien, G.B., D.P. Walters, D.M. White, R.R. Otter, K. Fritz, B. Crone, and M.A. Mills. Riparian Spiders: Sentinels of Polychlorinated Dibenzo-p-dioxin and Dibenzofuran-Contaminated Sediment.   ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(2): 414-420, (2023).

Tags: dioxins-and-furans, great-lakes-area-of-concern, remediation, sentinel
Organization: U.S. Environmental Protection Agency
State of Aging in Allegheny County Survey

For more than three decades UCSUR has documented the status of older adults in the County along multiple life domains.  Every decade we issue a comprehensive report on aging in Allegheny County and this report represents our most recent effort.  It documents important shifts in the demographic profile of the population in the last three decades, characterizes the current status of the elderly in multiple life domains, and looks ahead to the future of aging in the County.  This report is unique in that we examine not only those aged 65 and older, but also the next generation old persons, the Baby Boomers.  Collaborators on this project include the Allegheny County Area Agency on Aging, the United Way of Allegheny County, and the Aging Institute of UPMC Senior Services and the University of Pittsburgh.

 The purpose of this report is to provide a comprehensive analysis of aging in Allegheny County.  To this end, we integrate survey data collected from a representative sample of older county residents with secondary data available from Federal, State, and County agencies to characterize older individuals on multiple dimensions, including demographic change and population projections, income, work and retirement, neighborhoods and housing, health, senior service use, transportation, volunteering, happiness and life satisfaction, among others.  Since baby boomers represent the future of aging in the County we include data for those aged 55-64 as well as those aged 65 and older.

Tags: aging, allegheny-county, gerontology, older-adults, survey
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Pittsburgh American Community Survey 2014 - Miscellaneous Data

Miscellaneous Reports from the 2014 American Community Survey Report

Tags: acs, age, american-community-survey, census, community, demographics, education, employment, health-insurance, healthcare, housing, income, mortgage, population, public-assistance, race, rent, retirement, salary, survey, wage
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Pittsburgh Neighborhood Atlas, 1977

This compilation includes five historical datasets that are part of the University of Pittsburgh Library collection. The datasets were transcribed from The Pittsburgh Neighborhood Atlas, published in 1977. The atlas was prepared by the Pittsburgh Neighborhood Alliance.  The information provides an insight into the neighborhoods conditions and the direction in which they were moving at the time of preparation. Much of the material describing neighborhood characteristics came from figures compiled for smaller areas: voting districts or census blocks.
The five datasets in this collection provide data about overall neighborhood satisfaction and satisfaction with public services, based on a city-wide citizen survey. Also included are statistics about public assistance, the crime rate and the changes in real estate and mortgage loans transactions.

Tags: 1977, archives, crime, demographics, historic-pittsburgh, mortgage-loans, neighborhood, pittsburgh, problems, public-assistance, real-estate, satisfaction
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Grow Pittsburgh Food Gardens

Food growing locations registered with Grow Pittsburgh. Data includes community gardens, community farms, schoolyard gardens, or urban farms. This data is included in Grow Pittsburgh's Grower's Map

Tags: civic-vitality, environment, farms, food, gardens, health, healthy-food, land-use, properties, property, school-gardens, sustainability, vacant-land
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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, commuters, commuting, downtown, oakland, parking, pittsburgh, sustainability, transportation, walking
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Pittsburgh American Community Survey Census Data 2014 - Sex by Occupation

Occupation describes the kind of work a person does on the job. Occupation data were derived from answers to questions 45 and 46 in the 2015 American Community Survey (ACS). Question 45 asks: “What kind of work was this person doing?” Question 46 asks: “What were this person’s most important activities or duties?”

These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person’s job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.

These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development, and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.

Tags: acs, american-community-survey, census, demographics, education, employment, language, occupation, poverty, public-assistance, race, retirement, salaries, salary, schools, sex, snap, social-security, survey, vacancy, welfare
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
A Community Profile of Pittsburgh Neighborhoods, 1974

These data include four historical datasets that were transcribed from the Community Profiles of Pittsburgh reports, which were published in 1974. The Community Profiles were prepared for 71 Pittsburgh neighborhoods by the City of Pittsburgh's Department of City Planning and include demographic information, housing, socio-economic conditions, and community facilities. 

The four datasets included here are data on building permits issued in 1972, arrests for major crimes in 1972, public assistance given in 1972, and community facilities serving and located in Pittsburgh neighborhoods.

Tags: archives, arrests, building-permits, community-facilities, crimes, historic-pittsburgh, history, neighborhood-indicators, parks, public-assistance, recreation
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Landslides

From [NASA's Cooperative Open Online Landslide Repository (COOLR)](https://science.gsfc.nasa.gov/600/citizen-science/landslides/index.html). 

The Cooperative Open Online Landslide Repository (COOLR) is a worldwide database of landslide events. It currently includes NASA’s Global Landslide Catalog (GLC) and landslide events contributed by citizen scientists. In a future release of COOLR, collated landslide inventories will be added by REST API or manually.

Tags: environment, federal, geography, landslide, nasa
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
Autonomous Vehicle Survey of Bicyclists and Pedestrians in Pittsburgh

In Pittsburgh, Autonomous Vehicle (AV) companies have been testing autonomous vehicles since September 2016. However, the tech is new, and there have been some high-profile behavior that we believe warrants a larger conversation. So in early 2017, we set out to design a survey to see both how BikePGH donor-members, and Pittsburgh residents at large, feel about about sharing the road with AVs as a bicyclist and/or as a pedestrian. Our survey asked participants how they feel about being a fellow road user with AVs, either walking or biking. We also wanted to collect stories about people’s experiences interacting with this nascent technology. We are unaware of any public surveys about people’s feelings or understanding of this new technology.
We hope that our results will help add to the body of data and help the public and politicians understand the complexity of possible futures that different economic models AV technology can bring to our cities and towncenters.

We conducted our 2017 survey in two parts. First, we launched the survey exclusively to donor-members, yielding 321 responses (out of 2,900) via email. Once we closed the survey, we launched it again, but allowed the general public to take it. Through promoting it on our website, social media channels, and a few news articles, we yielded 798 responses (mostly from people in the Pittsburgh region), for a combined total of 1,119 responses.

Regarding the 2019 survey: In total, 795 people responded. BikePGH solicited responses from their blog, website, and email list. There were also a few local news articles about the survey. While many questions were kept similar to the 2017 survey, BikePGH wanted to dig a bit deeper into regulations as well as demographics this time around.

The 2019 follow up survey also aims to see how the landscape has changed, and how specifically, Pittsburghers on bike and on foot feel about sharing the road with AVs so that we’re all better prepared to deal with this new reality and help make sure that it is introduced as safely as humanly possible.

Tags: accident, argo, auto, automobile, autonomous-vehicle, av, avs, bicycle, bicyclists, bike, bike-lane, bikepgh, car, crash, crosswalk, ford, pedestrian, pittsburgh, public-safety, robot, robots, safety, self-driving-car, share-the-road, sidewalk, streets, survey, taxi, tech, technology, transportation, uber, waymo
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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, park-and-ride, park-n-ride, pnr, region, regional, spc, transportation
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: age, bicycle-counts, bike-count, downtown, gender, ped-counts, pedestrian-counts, pspl, public-life, public-space, stationary-activity
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
2010 Census Blocks with Geographic Codes Southwestern PA

This file can be used as a tool to append geographic codes to geocoded point data. The file was developed by Pitt's Center for Social and Urban Research and provides the county, census tract, county subarea/municipality, Allegheny County Council District, PA House and Senate District numbers, school district, and Zip codes. Also included from the City of Pittsburgh: neighborhoods, wards, City Council election districts, and City administrative boundaries, including Permits Licenses and Inspection administrative zones, Public Works administrative zones, Fire districts, and Police zones. The file contains data from Allegheny, Armstrong, Beaver, Butler, Fayette, Greene, Indiana, Lawrence, Washington, and Westmoreland Counties in Pennsylvania.

Tags: council, county, county-subarea, districts, gis-census, house, mapping, municipality, neighborhoods, school-districts, senate, tracts, wards, zip-codes
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: attendance, boundary, equity, feeder, feeder-patterns, schools, students
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: disability, disabled, enrollment, gifted, iep, individualized-education-plan, individualized-education-program, pittsburgh-public-schools, pps, special-education, student
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: Allegheny County / City of Pittsburgh / Western PA Regional Data Center
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: affordable-housing, block-groups, foreclosures, housing, housing-markets, market-value-analysis, mva, neighborhoods, properties, property, reinvestment-fund, rental-subsidy, sales-prices, the-reinvestment-fund, trf, ura, urban-redevelopment-authority, vacancy
Organization: Allegheny County / City of Pittsburgh / Western PA Regional Data Center