The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.

This dataset contains county-level totals for the years 2002-2014 for eight types of crime: murder, rape, robbery, aggravated assault, burglary, larceny, motor vehicle theft, and arson. These crimes are classed as Part I criminal offenses by the United States Federal Bureau of Investigations (FBI) in their Uniform Crime Reporting (UCR) program. Each record in the dataset represents the total of each type of criminal offense reported in (or, in the case of missing data, attributed to) the county in a given year.A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38649/versions/V1

This project examined physical incivilities (disorder), social strengths and vulnerabilities, and police reports in a declining first-ring suburb of Salt Lake City. Physical and social conditions were assessed on residential face blocks surrounding a new subdivision that was built as a revitalization effort. Data were collected before and after the completion of the new subdivision to assess the effects of the subdivision and of more proximal social and physical conditions on residents' blocks in order to understand important revitalization outcomes of crime, fear, and housing satisfaction and conditions. The study also highlighted place attachment of residents as a psychological strength that deserved greater attention. The research site consisted of a neighborhood located on the near west side of Salt Lake City that had been experiencing gradual decline. The neighborhood surrounded a new 84-unit single family detached housing subdivision, which was built in 1995 with money from a HUD demonstration grant. The study began in 1993 with a systematic observational assessment of crime and fear-related physical features on 59 blocks of the older neighborhood surrounding the planned housing site and 8 sampled addresses on each block, followed by interviews with surrounding block residents during 1994-1995, interviews with residents in the newly built housing in 1997, and interviews and physical condition assessments on the surrounding blocks in 1998-1999. Police crime report and city building permit data for the periods during and immediately following both waves of data collection were obtained and matched to sample addresses. Variables in Parts 1 and 2, Environmental and Survey Data for Older Subdivision, focus on distance of respondent's home to the subdivision, psychological proximity to the subdivision, if new housing was in the respondent's neighborhood, nonresidential properties on the block, physical incivilities, self-reported past victimization, fear of crime, place attachment, collective efficacy (neighboring, participation, social control, sense of community), rating of neighborhood qualities, whether block neighbors had improved property, community confidence, perceived block crime problems, observed conditions, self-reported home repairs and improvements, building permits, and home satisfaction. Demographic variables for Parts 1 and 2 include income, home ownership, ethnicity, religion, gender, age, marital status, if the resident lived in a house, household size, number of children in the household, and length of residence. Variables in Part 3, Environmental and Survey Data for Intervention Site, include neighborhood qualities and convenience, whether the respondent's children would attend a local school, and variables similar to those in Parts 1 and 2. Demographic variables in Part 3 specify the year the respondent moved in, number of children in the household, race and ethnicity, marital status, religion, sex, and income in 1996.

The Neighborhood Summit is an annual conference open to all neighborhood leaders and potential partners, which showcases community building success stories and celebrates neighborhoods across the community.

The intent of the Site Plan Review procedure is to provide present and future residents and users of land in the COUNTY a means whereby orderly and harmonious DEVELOPMENT is ensured in the COUNTY. Site Plan Reviews require additional consideration to ensure that the USES permitted are established and operated in a manner that is compatible with existing and planned land USES in the NEIGHBORHOOD. The regulation of Site Plan Reviews is designed to protect and promote the health, safety, convenience and general welfare of the present and future residents of the COUNTY. Developed from the approved land use application and permitting process.

IntroductionThe goal of these studies was to investigate the reliability and validity of virtual systematic social observation (virtual SSO) using Google Street View in a Swedish neighborhood context.MethodsThis was accomplished in two studies. Study 1 focused on interrater reliability and construct validity, comparing ratings conducted in-person to those done using Google Street View, across 24 study sites within four postal code areas. Study 2 focused on criterion validity of virtual SSO in terms of neighborhoods with low versus high income levels, including 133 study sites within 22 postal code areas in a large Swedish city. In both studies, assessment of the neighborhood context was conducted at each study site, using a protocol adapted to a Swedish context.ResultsScales for Physical Decay, Neighborhood Dangerousness, and Physical Disorder were found to be reliable, with adequate interrater reliability, high consistency across methods, and high internal consistency. In Study 2, significantly higher levels of observed Physical Decay, Neighborhood Dangerousness, and signs of garbage or litter were observed in postal codes areas (site data was aggregated to postal code level) with lower as compared to higher income levels.DiscussionWe concluded that the scales within the virtual SSO with Google Street View protocol that were developed in this series of studies represents a reliable and valid measure of several key neighborhood contextual features. Implications for understanding the complex person-context interactions central to many theories of positive development among youth were discussed in relation to the study findings.

These are the results of the online expert survey conducted to assess the relative importance of our neighbourhood quality indicators. This survey was part of the data collection of the research project, titled "Towards resilient and liveable neighbourhoods post Covid-19: evaluating neighbourhood quality in Sydney (AUS) and Newcastle upon Tyne (UK)", funded by Urban Studies Foundation (USF) Pandemics and Cities Programme ((# G215014). The study aimed to propose an objective, comprehensive assessment tool that can be used to assess NQ and tested its feasibility through a pilot study in multiple neighbourhoods in Newcastle upon Tyne, UK and Sydney, Australia.The online survey was distributed globally to experts in urban design and planning through the project team’s networks to objectively assess the relative importance of developed environmental indicators and measures for defining neighbourhood quality in a post-pandemic context. A hybrid TOPSIS-EM approach was applied to the survey data to determine criteria weights for ranking the case study neighbourhoods in both cities.

Context

The data was taken from http://tomslee.net/airbnb-data-collection-get-the-data. The data was collected from the public Airbnb web site and the code was used is available on https://github.com/tomslee/airbnb-data-collection.

Content

room_id: A unique number identifying an Airbnb listing. The listing has a URL on the Airbnb web site of http://airbnb.com/rooms/room_id
host_id: A unique number identifying an Airbnb host. The host’s page has a URL on the Airbnb web site of http://airbnb.com/users/show/host_id
room_type: One of “Entire home/apt”, “Private room”, or “Shared room”
borough: A subregion of the city or search area for which the survey is carried out. The borough is taken from a shapefile of the city that is obtained independently of the Airbnb web site. For some cities, there is no borough information; for others the borough may be a number. If you have better shapefiles for a city of interest, please send them to me.
neighborhood: As with borough: a subregion of the city or search area for which the survey is carried out. For cities that have both, a neighbourhood is smaller than a borough. For some cities there is no neighbourhood information.
reviews: The number of reviews that a listing has received. Airbnb has said that 70% of visits end up with a review, so the number of reviews can be used to estimate the number of visits. Note that such an estimate will not be reliable for an individual listing (especially as reviews occasionally vanish from the site), but over a city as a whole it should be a useful metric of traffic.
overall_satisfaction: The average rating (out of five) that the listing has received from those visitors who left a review.
accommodates: The number of guests a listing can accommodate.
bedrooms: The number of bedrooms a listing offers.
price: The price (in $US) for a night stay. In early surveys, there may be some values that were recorded by month.
minstay: The minimum stay for a visit, as posted by the host.
latitude and longitude: The latitude and longitude of the listing as posted on the Airbnb site: this may be off by a few hundred metres. I do not have a way to track individual listing locations with
last_modified: the date and time that the values were read from the Airbnb web site.

This dataset includes the data used to develop Maps 8 and 9 for the Connect SoCal 2024 Equity Analysis Technical Report, adopted on April 4, 2024. The dataset includes two fields with information about gentrification during two time periods (2000-2010 and 2010-2019) in the SCAG region based on ACS data. In this dataset, gentrification is defined as: (1) tract median household income in the bottom 40 percent of the countywide income distribution at the beginning of the period, (2) increase in college-educated people (as the percentage of population aged 25 years and older at the beginning of the period) in the top 25 percent of the countywide distribution, (3) no less than 100 people aged 25 years at the beginning of the period, and (4) over 50 percent of the tract land area within a census defined urbanized area. The dataset also includes a field with information about areas with a high number of eviction filings between 2010 and 2018 in the SCAG region with data from the Eviction Lab. In this dataset, "high eviction filings" is defined as an average annual eviction filing rate over three. This dataset was prepared to share more information from the maps in Connect SoCal 2024 Equity Analysis Technical Report. For more details on the methodology, please see the methodology section(s) of the Equity Analysis Technical Report: https://scag.ca.gov/sites/main/files/file-attachments/23-2987-tr-equity-analysis-final-040424.pdf?1712261887 For more details about SCAG's models, or to request model data, please see SCAG's website: https://scag.ca.gov/data-services-requests.

This dataset includes publicly available data published primarily by the Pennsylvania Department of Education and the Pennsylvania Office of Safe Schools. The dataset was created by combining several publications by the Pennsylvania Department of Education, including the 2017 School Fast Fact database, 2016-2017 Academic Performance database, and the 2017 Keystone Score database. The dataset includes institutional (school-wide) variables for every public high school in Pennslyvania (n = 407 ). The data includes information surrounding each institution's socio-economic status, racial composition, academic performance, and type of and total use of exclusionary discipline (in-school suspension, out-of-school suspension, and expulsion) for the school year 2016-2017. The dataset also includes neighborhood information for each school location. This data was collected from AreaVibes, a website known for its ability to guide individuals in their search for ideal residential areas in the United States and Canada. AreaVibes deploys a unique algorithm that evaluates multiple different data points for each location, including amenities, cost of living, crime rates, employment, housing, schools, and user ratings. This dataset deployed AreaVibes to input the physical addresses of each high school in order to retrieve the livability score for the surrounding neighborhoods of these educational institutions. Furthermore, the website was instrumental in collecting neighborhood crime scores, offering valuable insights into the levels of criminal activity within specific geographic zones. The crime score takes into account both violent crime and property crime. However, higher weights are given to violent crimes (65%) than property crime (35%) as they are more severe. Data for calculation by Areavibes is derived from FBI Uniform Crime Report.School discipline is crucial for ensuring safety, well-being, and academic success. However, the continued use of exclusionary discipline practices, such as suspension and expulsion, has raised concerns due to their ineffectiveness and harmful effects on students. Despite compelling evidence against these practices, many educational institutions persist in relying on them. This persistence has led to a troubling reality—a racial and socioeconomic discipline gap in schools. This data is used to explore the evident racial and socioeconomic disparities within high school discipline frameworks, shedding light on the complex web of factors that contribute to these disparities and exploring potential solutions. Drawing from social disorganization theory, the data explores the interplay between neighborhood and school characteristics, emphasizing the importance of considering the social context of schools.

The Detroit Neighborhood Health Study (DNHS) is a prospective, representative longitudinal cohort study of predominantly African American adults living in Detroit, Michigan. The main purpose of the study was to determine the predictive effects of ecological stressors, such as income distribution and residential segregation, on the development of post-traumatic stress disorder (PTSD), substance use, and other psychological and behavioral outcomes. An additional purpose was to study the interrelationships between ecological stressors, exposure to potentially traumatic events (PTEs), PTSD, substance use, and immune function. The study team hypothesized that exposure to ecological stressors would influence the risk of PTE exposure, PTSD, substance use, other psychological outcomes, and the relationships between these factors. The current collection includes data from all 5 waves of the study. Cohort participants were initially recruited in 2008 with a dual-frame probability design, using telephone numbers obtained from the U.S. Postal Service Delivery Sequence Files as well as a listed-assisted random-digit-dial frame. Individuals without listed landlines or telephones and individuals with only a cell phone listed were invited to participate through a postal mail effort. Participants completed a 40 minute, structured telephone interview annually between 2008-2012 to assess perceptions of participants' neighborhoods, mental and physical health status, social support, exposure to traumatic events, and alcohol and tobacco use. In addition, the study team completed a structured assessment of Detroit's 54 neighborhoods in order to describe the characteristics of respondents' neighborhoods. The assessment included information about the quality of housing exteriors; presence of graffiti, abandoned cars, alcohol and tobacco advertisements, and security warning signs; presence of vacant buildings; and street and traffic noise levels. All survey participants were offered the opportunity to provide a blood specimen (venipuncture, blood spot, or saliva) for immune and inflammatory marker testing as well as genetic testing of DNA. Participants received an additional $25USD if they elected to give a sample. Informed consent was obtained at the beginning of each interview and again at specimen collection. However, these specimens are not included as part of this data collection. For more information about the study, please visit the Detroit Neighborhood Health Study website. Genotypic data from DNHS are available on the NIH database of Genotypes and Phenotypes (dbGaP).

The Promise Neighborhoods Fund website depicts open grantmaking information on the applicants received, grantees awarded and project locations. The purpose of Promise Neighborhoods is to improve significantly the educational and developmental outcomes of children in our most distressed communities, and to transform those communities by-- (1) Supporting efforts to improve child outcomes and ensure that data on those outcomes are communicated and analyzed on an ongoing basis by leaders and members of the community; (2) Identifying and increasing the capacity of eligible entities that are focused on achieving results and building a college-going culture in the neighborhood; (3) Building a complete continuum of cradle-through-college-to-career solutions (continuum of solutions), which has both academic programs and family and community supports, with a strong school or schools at the center. Academic programs must include (a) High-quality early learning programs designed to improve outcomes in multiple domains of early learning; (b) programs, policies, and personnel for children in kindergarten through the 12th grade that are linked to improved academic outcomes; and (c) programs that prepare students for college and career success. Family and community supports must include programs to improve student health, safety, community stability, family and community engagement, and student access to 21st century learning tools. The continuum of solutions also must be linked and integrated seamlessly so there are common outcomes, a focus on similar milestones, support during transitional time periods, and no time or resource gaps that create obstacles for students in making academic progress. The continuum also must be based on the best available evidence including, where available, strong or moderate evidence, and include programs, policies, practices, services, systems, and supports that result in improving educational and developmental outcomes for children from cradle through college to career; (4) Integrating programs and breaking down agency "silos" so that solutions are implemented effectively and efficiently across agencies; (5) Supporting the efforts of eligible entities, working with local governments, to build the infrastructure of policies, practices, systems, and resources needed to sustain and "scale up" proven, effective solutions across the broader region beyond the initial neighborhood; and (6) Learning about the overall impact of Promise Neighborhoods and about the relationship between particular strategies in Promise Neighborhoods and student outcomes, including a rigorous evaluation of the program.

What you get:

Upvote! The database contains +40,000 records on US Gross Rent & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 325,272 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to upvote. Upvote right now, please. Enjoy!

Get the full free database with coupon code: FreeDatabase, See directions at the bottom of the description... And make sure to upvote :) coupon ends at 2:00 pm 8-23-2017

Gross Rent & Geographic Statistics:

• Mean Gross Rent (double)
• Median Gross Rent (double)
• Standard Deviation of Gross Rent (double)
• Number of Samples (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The data set originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 325,272 location for Free Database Coupon Code:

Don't settle. Go big and win big. Optimize your potential**. Access all gross rent records and more on a scale roughly equivalent to a neighborhood, see link below:

• Website: Golden Oak Research make sure to upvote

A small startup with big dreams, giving the every day, up and coming data scientist professional grade data at affordable prices It's what we do.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population composition of the renter-occupied housing units by Neighborhood Statistical Areas E02 and E06 in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

This data set consists of several sub-corpora used for the analysis of the discursive construction of 'neighborhood' in Brooklyn, New York. It comprises orthographic transcriptions of 200 spoken interviews (BK_SpokenRA), the written contents of 20 Brooklyn neighborhood organization websites (BK_OrgaWeb), five years of press releases from the Brooklyn Borough president published between January 2014 and March 2019 (BK_BBHPR), and online restaurant reviews from Yelp.com collected between October 2018 and July 2019 (BK_Yelp).

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show age, type, vacancy rates, and owner/renter tenure of housing units by Neighborhood Planning Unit in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show household income numbers and ranges by Neighborhood Statistical Areas in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here). Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Note:- Only publicly available data can be worked upon

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This subsample of the national crime surveys consists of data on personal and household victimization for persons aged 12 and older in 26 major United States cities in the period 1972-1975. The National Crime Surveys were designed by the Bureau of Justice Statistics to meet three primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the numbers and types of crimes not reported to police, and (3) to provide uniform measures of selected types of crimes in order to permit reliable comparisons over time and between areas. The surveys provide measures of victimization on the basis of six crimes (including attempts): rape, robbery, assault, burglary, larceny, and motor vehicle theft. The total National Crime Survey employed two distinct samples: a National Sample, and a Cities Sample. The cities sample consists of information about victimization in 26 major United States cities. The data collection was conducted by the United States Census Bureau, initial processing of the data and documentation was performed by the Data Use and Access Laboratories (DUALabs), and subsequent processing was performed by the ICPSR under grants from the Bureau of Justice Statistics (BJS). This Cities Attitude Sub-Sample study also includes information on personal attitudes and perceptions of crime and the police, the fear of crime, and the effect of this fear on behavioral patterns such as choice of shopping areas and places of entertainment. Data are provided on reasons for respondents' choice of neighborhood, and feelings about neighborhood, crime, personal safety, and the local police. Also specified are date, type, place, and nature of the incidents, injuries suffered, hospital treatment and medical expenses incurred, offender's personal profile, relationship of offender to victim, property stolen and value, items recovered and value, insurance coverage, and police report and reasons if incident was not reported to the police. Demographic items cover age, sex, marital status, race, ethnicity, education, employment, family income, and previous residence and reasons for migrating. This subsample is a one-half random sample of the Complete Sample, NATIONAL CRIME SURVEYS: CITIES, 1972-1975 (ICPSR 7658), in which an attitude questionnaire was administered. The subsample contains data from the same 26 cities that were used in the Complete Sample.