About this dataset

Context

This list ranks the 1 cities in the Philadelphia County, PA by Non-Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Black Population: This column displays the rank of cities in the Philadelphia County, PA by their Non-Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Black Population: The Non-Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Philadelphia County Non-Hispanic Black Population: This tells us how much of the entire Philadelphia County, PA Non-Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

This statistic shows the quarterly average daily rate of hotels in Philadelphia in 2016 and 2017. In the first quarter of 2017, the average daily rate of hotels in Philadelphia in the United States was 169 U.S. dollars.

As included in this EnviroAtlas dataset, the community level domestic water use was calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Domestic water demand was calculated and applied using the Pennsylvania Department of Environmental Protection (PADEP) PWS Service Areas layer, population served per provider, and average domestic water use per provider. Within the EnviroAtlas study area, there are 108 service providers with 2016 estimates ranging from 26 to 323 GPD. In the absence of finer scale data, USGS Water Use Report county-level estimates were used for the study area extending into Delaware (80 GPD), Maryland (71 GPD), and New Jersey (80 GPD). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

About this dataset

Context

The dataset tabulates the Philadelphia population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Philadelphia across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Philadelphia was 1.55 million, a 1.04% decrease year-by-year from 2022. Previously, in 2022, Philadelphia population was 1.57 million, a decline of 1.43% compared to a population of 1.59 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Philadelphia increased by 36,868. In this period, the peak population was 1.6 million in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Philadelphia is shown in this column.
• Year on Year Change: This column displays the change in Philadelphia population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Philadelphia Population by Year. You can refer the same here

Graph and download economic data for Estimate of Median Household Income for Philadelphia County/city, PA (MHIPA42101A052NCEN) from 1989 to 2023 about Philadelphia County/City, PA; Philadelphia; PA; households; median; income; and USA.

Historical Dataset of Philadelphia High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1991-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1991-2023),American Indian Student Percentage Comparison Over Years (2000-2022),Asian Student Percentage Comparison Over Years (1999-2023),Hispanic Student Percentage Comparison Over Years (2002-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2000-2016),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

About this dataset

Context

This list ranks the 1 cities in the Philadelphia County, PA by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of cities in the Philadelphia County, PA by their Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Philadelphia County Other Race Population: This tells us how much of the entire Philadelphia County, PA Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Area Hydrography Shapefile contains the geometry and attributes of both perennial and intermittent area hydrography features, including ponds, lakes, oceans, swamps (up to the U.S. nautical three-mile limit), glaciers, and the area covered by large rivers, streams, and/or canals that are represented as double-line drainage. Single-line drainage water features can be found in the Linear Hydrography Shapefile (LINEARWATER.shp). Linear water features includes single-line drainage water features and artificial path features, where they exist, that run through double-line drainage features such as rivers, streams, and/or canals, and serve as a linear representation of these features.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2010 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

This dataset tracks annual two or more races student percentage from 2016 to 2023 for Overbrook High School vs. Pennsylvania and Philadelphia City School District

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).

Historical Dataset of Philadelphia Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1991-2023),Total Classroom Teachers Trends Over Years (1993-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1993-2023),American Indian Student Percentage Comparison Over Years (2002-2023),Asian Student Percentage Comparison Over Years (2003-2023),Hispanic Student Percentage Comparison Over Years (1999-2023),Black Student Percentage Comparison Over Years (1993-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2000-2016),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Topological Faces / Area Hydrography Relationship File (FACESAH.dbf) contains a record for each face / area hydrography feature relationship. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The face to which a record in the Topological Faces / Area Hydrography Relationship File (FACESAH.dbf) applies can be determined by linking to the Topological Faces Shapefile (FACES.shp) using the permanent topological face identifier (TFID) attribute. The area hydrography feature to which a record in the Topological Faces / Area Hydrography Relationship File (FACESAH.dbf) applies can be determined by linking to the Area Hydrography Shapefile (AREAWATER.shp) using the area hydrography identifier (HYDROID) attribute. A face may be part of multiple area water features. An area water feature may consist of multiple faces.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in New Philadelphia. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In New Philadelphia, the median household income for the households where the householder is White decreased by $6,439(12.25%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $52,550 in 2013 and $46,111 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in New Philadelphia, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in New Philadelphia.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Philadelphia median household income by race. You can refer the same here

Historical Dataset of The Sd Of Philadelphia Virtual Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2014-2023),Total Classroom Teachers Trends Over Years (2016-2023),Distribution of Students By Grade Trends,Asian Student Percentage Comparison Over Years (2014-2023),Hispanic Student Percentage Comparison Over Years (2014-2023),Black Student Percentage Comparison Over Years (2014-2023),White Student Percentage Comparison Over Years (2014-2023),Two or More Races Student Percentage Comparison Over Years (2014-2023),Diversity Score Comparison Over Years (2014-2023),Free Lunch Eligibility Comparison Over Years (2014-2023),Reading and Language Arts Proficiency Comparison Over Years (2014-2022),Math Proficiency Comparison Over Years (2014-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2014-2023),Graduation Rate Comparison Over Years (2014-2023)

Historical Dataset of Philadelphia Middle School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2005-2023),Total Classroom Teachers Trends Over Years (2005-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2005-2023),American Indian Student Percentage Comparison Over Years (2004-2023),Asian Student Percentage Comparison Over Years (2010-2023),Hispanic Student Percentage Comparison Over Years (2004-2023),Black Student Percentage Comparison Over Years (2005-2023),White Student Percentage Comparison Over Years (2005-2023),Two or More Races Student Percentage Comparison Over Years (2013-2014),Diversity Score Comparison Over Years (2005-2023),Free Lunch Eligibility Comparison Over Years (2005-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2006-2016),Reading and Language Arts Proficiency Comparison Over Years (2010-2017),Math Proficiency Comparison Over Years (2010-2017),Overall School Rank Trends Over Years (2010-2017)

The Family Exchanges Study Wave 1 (FESI) was conducted in 2008 by the Institute for Survey Research at Temple University. The original 634 "target" or core sample was recruited from African American and White respondents aged 40-60 living in Philadelphia and the surrounding counties--Bucks, Chester, Delaware, and Montgomery. To be eligible for the study, respondents had to have at least one living parent and one living offspring over 18 years of age. Temple University sought to recruit the parents, spouse, and up to three offspring over 18 years of age into the study. All target, parent, and spouse surveys were conducted by telephone. Offspring were given the option of completing the survey by telephone or web. A total of 337 parents, 511 offspring (with another 80 by web and 1 listed as other for a total of 592), and 197 spouses were successfully recruited into the first wave of the study. This collection includes four data files, one for each type of participant: target, spouse, parent, and offspring. For each of these participants, there are data related to relationships with other family members, perceptions of family members, and views on key social issues. Demographic information includes gender, marital status, education level, religion, age, race, ethnicity, and employment status.

Historical Dataset of Kipp Philadelphia Charter School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2005-2023),Total Classroom Teachers Trends Over Years (2005-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2005-2023),Hispanic Student Percentage Comparison Over Years (2005-2023),Black Student Percentage Comparison Over Years (2005-2023),White Student Percentage Comparison Over Years (2019-2022),Two or More Races Student Percentage Comparison Over Years (2011-2023),Diversity Score Comparison Over Years (2005-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2006-2016),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2014-2015)