This dataset contains two measures designed to be used in tandem to characterize United States census tracts, originally developed for use in stratified analyses of the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network. The first measure is a 2010 tract-level community type categorization based on a modification of Rural-Urban Commuting Area (RUCA) Codes that incorporates census-designated urban areas and tract land area, with five categories: higher density urban, lower density urban, suburban/small town, rural, and undesignated (McAlexander, et al., 2022). The second measure is a neighborhood social and economic environment (NSEE) score, a community-type stratified z-score sum of 6 US census-derived variables, with sums scaled between 0 and 100, computed for the year 2000 and 2010. A tract with a higher NSEE z-score sum indicates more socioeconomic disadvantage compared to a tract with a lower z-score sum. Analysts should not compare NSEE scores across LEAD community types, as values have been computed and scaled within community type.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

"In this dataset, while calculating the Socio-Economic Status, the accessibility indexes of neighborhoods to education, culture and arts, transportation, health, and commercial activities, infrastructure adequacy, and the population density, household size, economic status, social aid status, public transportation usage, and health status of the citizens living in the neighborhoods were evaluated and a PCA analysis was conducted.

All evaluations were made through the data in the IBB(Istanbul Metropolitan Municipality) pool."

Data Creator and Owner: Istanbul Metropolitan Municipality

Check for extra details, license and datasource: https://data.ibb.gov.tr/dataset/2023-yili-istanbul-ses-skoru

This layer displays calculated 2025 Tree Equity Scores for the Suburban Official Plan Transect. The layer was produced using a modified version of the American Forests methodology, where a Tree Canopy Goal was created based on a Transect-level building density calculation. Tree Equity Scores were calculated at the census tract level for all Official Plan designated residential areas within the urban boundary. Canopy cover was calculated using the 2022 Canopy Cover Assessment. The Priority Index was calculated using data from 2021 Census, 2019-2020 Canadian Community Health Survey, and 2022 Landsat thermal band imagery. More information on the methodology used to create the data can be found on the City of Ottawa website. This layer was produced by Climate Change and Resiliency Services, Strategic Initiatives Department, to guide the selection of priority areas for tree planting under the Tree Planting Strategy. Accuracy: Data are complete and there are no known issues. The Tree Equity Score analysis was created using 2021 census data to provide information on socio-economic and demographic data. Census data was combined with urban heat and canopy cover data to produce a Tree Equity Score. The analysis was performed only for urban, residential areas of the census tracts. Results of the analysis have been used to identify and prioritize areas of in need of tree canopy cover. Attribution: This data is owned by Climate Change and Resiliency Services Citations: American Forests’ Methodology, 2025 Environics Analytics (EA) - 2025 Community Health, Statistics Canada 2021 Census, U.S. Geological SurveyAttributes: CTUID: Unique census tract identification numberCTNUM: Shortened census tract identification number NAME: Census tract nameTRANSECT: Transect that the census tract falls withinCANCVR: Canopy cover as a percentage of census tract areaCANGOAL: Transect-level goal canopy cover based on building densityCANGAP: Difference between the transect-level goal canopy cover and canopy cover as a percentage of census tract areaGAPSCORE: CANGAP normalized by the largest difference between goal and actual canopy coverPRIORITY INDEX: Index of seven equally weighted socio-economic, urban heat, and health factors for each census tractTREE EQUITY SCORE: Census tract Tree Equity Score calculated using the GAPSCORE and PRIORITY INDEXPRIORITY AREA: If the census tract has been identified as a Priority AreaNOTE: Information specific to the census tractTransects:Downtown CoreInner UrbanOuter UrbanSuburban Contact: Nick Stow, Program Manager, Natural Systems

Abstract

ADI: An index of socioeconomic status for communities. Dataset ingested directly from BigQuery.

Documentation

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.

The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.

Dataset source: https://help.broadstreet.io/article/adi/

This layer displays calculated 2025 Tree Equity Scores for the Suburban Official Plan Transect. The layer was produced using a modified version of the American Forests methodology, where a Tree Canopy Goal was created based on a Transect-level building density calculation. Tree Equity Scores were calculated at the census tract level for all Official Plan designated residential areas within the urban boundary. Canopy cover was calculated using the 2022 Canopy Cover Assessment. The Priority Index was calculated using data from 2021 Census, 2019-2020 Canadian Community Health Survey, and 2022 Landsat thermal band imagery. More information on the methodology used to create the data can be found on the City of Ottawa website. This layer was produced by Climate Change and Resiliency Services, Strategic Initiatives Department, to guide the selection of priority areas for tree planting under the Tree Planting Strategy. Accuracy: Data are complete and there are no known issues. The Tree Equity Score analysis was created using 2021 census data to provide information on socio-economic and demographic data. Census data was combined with urban heat and canopy cover data to produce a Tree Equity Score. The analysis was performed only for urban, residential areas of the census tracts. Results of the analysis have been used to identify and prioritize areas of in need of tree canopy cover. Attribution: This data is owned by Climate Change and Resiliency Services Citations: American Forests’ Methodology, 2025 Environics Analytics (EA) - 2025 Community Health, Statistics Canada 2021 Census, U.S. Geological SurveyAttributes: CTUID: Unique census tract identification numberCTNUM: Shortened census tract identification number NAME: Census tract nameTRANSECT: Transect that the census tract falls withinCANCVR: Canopy cover as a percentage of census tract areaCANGOAL: Transect-level goal canopy cover based on building densityCANGAP: Difference between the transect-level goal canopy cover and canopy cover as a percentage of census tract areaGAPSCORE: CANGAP normalized by the largest difference between goal and actual canopy coverPRIORITY INDEX: Index of seven equally weighted socio-economic, urban heat, and health factors for each census tractTREE EQUITY SCORE: Census tract Tree Equity Score calculated using the GAPSCORE and PRIORITY INDEXPRIORITY AREA: If the census tract has been identified as a Priority AreaNOTE: Information specific to the census tractTransects:Downtown CoreInner UrbanOuter UrbanSuburban Contact: Nick Stow, Program Manager, Natural Systems

Mixed effects regression models, for rural children, between neighborhood socioeconomic status (SES)-by-walkability interaction, and the main effects of walkability and SES without interaction.

Change was defined as the average concentration over the follow-up period: concentration at baseline such that a reduction in concentrations over time would have a negative change and increases in concentrations over time would be manifest as a positive change. Minimal adjustment included age, sex, and race/ethnicity. Moderately adjustment added control for education, a neighborhood socio-economic score (derived from census tract level data on education, occupation, median home values, and median household income), adiposity (1/height, 1/height2, weight, waist, and 1/hip), and pack-years at baseline as well as a time-varying smoking status. Main models further adjusted for HDL, total cholesterol, statin use, diabetes mellitus (using the 2003 ADA fasting criteria algorithm), systolic blood pressure, diastolic blood pressure, hypertensive diagnosis, and hypertensive medications. In sensitivity analyses, we tested an extended model that also included physical activity, alcohol use, second-hand smoke exposures, C-reactive protein, creatinine, fibrinogen, occupation, and neighborhood noise among a smaller subset of the population with complete information.

The dataset contains all variables of interest presented in the study. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant.

When using downloaded data, please cite corresponding paper and this repository:

Schwartz, Lindsay F., et al. “The Association of Neighborhood Characteristics and Frailty in Childhood Cancer Survivors: A Report from the St. Jude Lifetime Cohort Study.” Cancer Epidemiology, Biomarkers & Prevention, 2023, https://doi.org/10.1158/1055-9965.epi-22-1322.

Data dictionary:

part: Participation status

mstatus: Marital status

bmicat: BMI category

age: Age

agedx: Age at diagnosis

race2: Race

insure: Insurance

anyrt_prim: Any radiation therapy for primary diagnosis

anyrtdose: Any radiation dose

Cranialdose: Cranial radiation dose

chestdose: Chest radiation dose

pelvicdose: Pelvic radiation dose

therapy: Type of therapy

aa_class_dose_5: Cumulative Alkylating Agent: Classic (CED mg/m2) within 5 years of primary cancer diagnosis

anthra_cog_dose_5: Cumulative Anthracycline (DOXED per COG mg/m2) within 5 years of primary cancer diagnosis

cortico_dose_5: Cumulative Corticosteroid (PED mg/m2) within 5 years of primary cancer diagnosis

vinca_dose_5: Cumulative Vinca Alkyloids (mg/m2) within 5 years of primary cancer diagnosis

mtxtotal: Total methotrexate dose

platintotal: Total Platin dose

college: College education

income: Annual income

brainsurg_trt: SJLIFE Defined Brain Surgery (within 3mo pre -> 5yrs post treatment window)

amputation: Amputation

limbspare: Spared limb flag

laparotomy: Laparotomy

thoracic: Thoracic radiation

meetcdc: Meets CDC physical activity criteria

dietrank: Rank for HEI2015 TOTAL SCORE

smoke: Smoking status

pdep: Depression T Score ge 63

chest: Chest radiation flag

cranial: Cranial radiation flag

pelvic: Pelvic radiation flag

anthyn: Anthracycline flag

aayn: Alkylating agent flag

glucyn: Glucocorticoid flag

platyn: Platin agent flag

mtxyn: Methotrexate flag

vincyn: Vinca alkaloids flag

survyears: Survival years

predicted: Estimated Probability for Participation

exerciseQ: Quartile

ruca: Primary RUCA Code 2010

rucacat: RUCA cagegorization

fooddesert: LILATracts_halfAnd10

SESclass: Socio-Economical class

neighborhoodscore: Neighborhood socio-economic score

anycondition: Any condition with CTCAE grade 3 or higher

lowphy: Low physical activity (Based off CDC criteria)

platin: Platin agent flag

sex: Gender (1 Male, 0 Female)

survcat: Survival years grouping

status_fried: Frailty status as per Fried criteria

baddiet: Low diet score flag

lowexer: Low exercise access flag

lowses: Low socio-economic score flag

rural: Rurality flag from RUCA

urban: Urban area flag from RUCA

smokeyn: Smoking status

nocollege: No college education

bpl: Poverty status from American community survey

number: Serial number

Mixed effects regression models, for urban children, between neighborhood socioeconomic status (SES)-by-walkability interaction, and the main effects of walkability and SES without interaction.

!!PLEASE NOTE!! When downloading the data, please select "File Geodatabase" to preserve long field names. Shapefile will truncate field names to 10 characters.Version: CurrentThe Racial and Social Equity Index combines information on race, ethnicity, and related demographics with data on socioeconomic and health disadvantages to identify where priority populations make up relatively large proportions of neighborhood residents. Click here for a User Guide.See the layer in action in the Racial and Social Equity ViewerClick here for an 11x17 printable pdf version of the map.The Composite Index includes sub-indices of: Race, English Language Learners, and Origins Index ranks census tracts by an index of three measures weighted as follows: Persons of color (weight: 1.0) English language learner (weight: 0.5) Foreign born (weight: 0.5)Socioeconomic Disadvantage Index ranks census tracts by an index of two equally weighted measures:Income below 200% of poverty level Educational attainment less than a bachelor’s degreeHealth Disadvantage Index ranks census tracts by an index of seven equally weighted measures:No leisure-time physical activityDiagnosed diabetes ObesityMental health not good AsthmaLow life expectancy at birthDisabilityThe index does not reflect population densities, nor does it show variation within census tracts which can be important considerations at a local level.Sources are as indicated below.Produced by City of Seattle Office of Planning & Community Development. For more information on the indices, including guidance for use, contact Diana Canzoneri (diana.canzoneri@seattle.gov).Sources: 2017-2021 Five-Year American Community Survey Estimates, U.S. Census Bureau; 2020 Decennial Census, U.S. Census Bureau; estimates from the Centers for Disease Control’ Behavioral Risk Factor Surveillance System (BRFSS) published in the “The 500 Cities Project,”; Washington State Department of Health’s Washington Tracking Network (WTN);, and estimates from the Public Health – Seattle & King County (based on the Community Health Assessment Tool).Language is for population age 5 and older. Educational attainment is for the population age 25 and over.Life expectancy is life expectancy at birth.Other health measures based on percentages of the adult population.

Principal Component (PC) score coefficients to define the Socioeconomic Position (SEP) index derived at the block group level, Puerto Rico, 2000.

Mixed effects regression models, for urban adolescents, between neighborhood socioeconomic status (SES)-by-walkability interaction, and the main effects of walkability and SES without interaction.

Introduction: We investigated the mediating role of leisure activity engagement as marker of cognitive reserve in the relation between neighborhood socio-economic position (SEP) and cognitive decline over 6 years. Methods: The study analyzed longitudinal data from 897 older adults who participated in the two waves (2011 and 2017) of the Vivre-Leben-Vivere (VLV) survey in Switzerland (M = 74.33 years in the first wave). Trail Making Test parts A and B were administered in both waves. Leisure activity engagement was assessed during interviews. Neighborhood SEP was derived from the Swiss Neighborhood Index of Socio-Economic Position (Swiss-SEP), provided by the Swiss National Cohort (SNC). Results: Latent change score modeling revealed that 42.5% of the relationship between higher neighborhood SEP and smaller cognitive decline was mediated via a higher frequency of leisure activities in the first wave. Conclusion: Neighborhood SEP constitutes an important contextual factor potentially influencing the pathways of cognitive reserve accumulation and, therefore, should be taken into account to better understand their effects on cognitive decline in old age.

Living and housing status. Residential area and social structure. Economic basis. Foreigners in the neighborhood. Mobility. Political interest and voting behaviour.

Topics: 1. Residential area and social structure: type of residential house; city size (degree of urbanisation); residential area; description of the residential environment; one or two-family houses or apartment buildings in the residential environment; construction period of the residential environment; subjective assessment of the social class affiliation of the residents in the residential environment; satisfaction with the place of residence (scale); duration of residence at the place of residence.

1. Current housing and residential status: duration of residence; number of rooms; residential status; satisfaction with the flat, the immediate living environment and the environmental conditions in the living environment (scale); expected change in environmental problems in the living environment; importance of selected living conditions for personal well-being at the place of residence (e.g. job offers, infrastructure, residential area, schools, clean air, etc.).

2. Foreigners: foreigners in the residential environment; estimated proportion of foreigners in the residential environment; attitude towards the spatial separation of Germans and foreigners in a neighborhood; personal contacts with foreigners.

3. Mobility: intention to move; preference of moving (target area); assessment of the personal economic situation as well as the economic situation in the FRG and in the municipality of residence; expected change in unemployment figures.

4. Political interest and voting behaviour: Political interest at local level; eligibility to vote in the last Bundestag election; participation in the last federal election and voting behaviour (second vote); eligibility to vote in the last election to the Berlin House of Representatives; participation in the last election to the Berlin House of Representatives and voting behaviour (second vote).

Demography: sex: age (month of birth and year of birth); highest school leaving certificate or targeted school leaving certificate; age at school leaving certificate; vocational education and training certificate; employment; employment status; full-time or part-time employment; previous employment; previous and current employment position; marital status; self-assessment of class affiliation; religious denomination or religion; closeness to the church; frequency of church attendance; net income of the respondent; household size; number of children in the household and age of these children; number of persons in the household from 18 years of age; number of persons in the household who contribute to the household income; household net income; telephone connection in the household.

Interviewer rating: presence of other persons during the interview; intervention of persons present at the interview; willingness of the respondent to cooperate; reliability of the information; self-administered questionnaire together with the face-to-face interview or later; completion of the self-administered questionnaire alone or with assistance and type of return; respondent requested self-administered questionnaire.

Additionally coded was: federal state; administrative district; political community size (Boustedt); interview date; interview duration; interviewer ID; sex and age of the interviewer; weighting factor.

We utilized Twitter’s Streaming Application Programming Interface (API) to continuously collect a random 1% subset of publicly available geo-located. In total we have collected over 80 million tweets from over 600,000 Twitter users. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived national neighborhood variables and neighborhood demographic, economic, business, and health characteristics. Machine-labeled and manually-labeled tweets had a high level of accuracy: 78% for happiness, 83% for food and 85% for physical activity for dichotomized labels, with the following F-scores: 0.86 (food), (0.90 (exercise), and 0.54 (happiness).

1Based on a population data estimates from the Central Bureau of Statistics, the Netherlands.2Addresses/km2. An urbanized neighborhood was defined as 1500–2.500 addresses/km2.3Normalized score (−4–4) based on level of income, employment and educational level per postal code area of the neighborhood. A high socio-economic status was defined between −4 and 0.