In 2022, the New Orleans-Metairie, LA metro area recorded the highest homicide rate of U.S. cities with a population over 250,000, at **** homicides per 100,000 residents, followed by the Memphis, TN-MS-AR metro area. However, homicide data was not recorded in all U.S. metro areas, meaning that there may be some cities with a higher homicide rate. St. Louis St. Louis, which had a murder and nonnegligent manslaughter rate of **** in 2022, is the second-largest city by population in Missouri. It is home to many famous treasures, such as the St. Louis Cardinals baseball team, Washington University in St. Louis, the Saint Louis Zoo, and the renowned Gateway Arch. It is also home to many corporations, such as Monsanto, Arch Coal, and Emerson Electric. The economy of St. Louis is centered around business and healthcare, and boasts ten Fortune 500 companies. Crime in St. Louis Despite all of this, St. Louis suffers from high levels of crime and violence. As of 2023, it was listed as the seventh most dangerous city in the world as a result of their extremely high murder rate. Not only does St. Louis have one of the highest homicide rates in the United States, it also reports one of the highest numbers of violent crimes. Despite high crime levels, the GDP of the St. Louis metropolitan area has been increasing since 2001.

The Part 1 crime rate captures incidents of homicide, rape, aggravated assault, robbery, burglary, larceny, and auto theft that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore City Police Department Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

In 2023, the Balearic Islands region had the highest crime rate in Spain. Catalonia followed with a rate of **** crimes per 1,000 inhabitants. Extremadura was the autonomous community with the lowest crime rate at ****.

Toronto Neighbourhoods Boundary File includes Crime Data by Neighbourhood. Counts are available at the offence and/or victim level for Assault, Auto Theft, Bike Theft, Break and Enter, Robbery, Theft Over, Homicide, Shootings and Theft from Motor Vehicle. Data also includes crime rates per 100,000 people by neighbourhood based on each year's Projected Population by Environics Analytics.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario..In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

The property crime rate measures the number of Part 1 crimes identified as being property-based (burglary and auto theft) that are reported to the Police Department. These incidents are per 1,000 residents in the neighborhood to allow for comparison across areas. Source: Baltimore Police Department Years Availabile: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

Recorded crime figures for CSP areas. Number of offences for the last two years, percentage change, and rates per 1,000 population for the latest year.

This statistic shows the rate of property crimes in Canada in 2023, by metro area. There were roughly 4,362.09 reported property crimes per 100,000 residents in Canada's Edmonton, Alberta metropolitan area in 2023.

With a crime rate of 132.4 per 1,000 people Cleveland, in North East England, had the highest crime rate of all the police force areas in England and Wales in 2023/24. High crime rates are evident in other areas of northern England, such as West Yorkshire and Greater Manchester at 121.7 and 117.7 respectively. In the English capital, London, the crime rate was 105.1 per 1,000 people. The lowest crime rate in England was in the relatively rural areas of Wiltshire in South West England, as well as North Yorkshire. Overall crime on the in England and Wales The number of crimes in England and Wales reached approximately 6.74 million in 2022/23, falling slightly to 6.66 million in 2023/24. Overall crime has been rising steadily across England and Wales for almost a decade, even when adjusted for population rises. In 2022/23, for example, the crime rate in England and Wales was 93.6, the highest since 2006/07. When compared with the rest of the United Kingdom, England and Wales is something of an outlier, as crime rates for Scotland and Northern Ireland have not followed the same trajectory of rising crime. Additionally, there has been a sharp increase in violent crimes and sexual offences since the mid-2010s in England and Wales. While theft offences have generally been falling, the number of shoplifting offences reached a peak of 440,000 in 2023/24. Troubled justice system under pressure Alongside rising crime figures, many indicators also signal that the justice system is getting pushed to breaking point. The percentage of crimes that are solved in England and Wales was just 5.7 percent in 2023, with sexual offences having a clearance rate of just 3.6 percent. Crimes are also taking far longer than usual to pass through the justice system. In 2023, it took an average of 676 days for a crown court case to reach a conclusion from the time of the offence. This is most likely related to the large backlog of cases in crown courts, which reached over 62,200 in 2023. Furthermore, prisons in England and Wales are dangerously overcrowded, with just 1,458 spare prison places available as of June 2024.

The City of Tempe promotes safe and secure communities by providing individuals the opportunity to successfully complete a diversion or probation program with Community Supervision. Community Supervision aims to connect individuals with misdemeanor offenses to appropriate education, treatment, and/or resources to increase the likelihood of successful completion and remaining crime free while in the program. Increasing successful completion and safety rates furthers the City priority of Safe and Secure Communities. Participants in Community Supervision programs are connected with evidenced based treatment programs to address domestic violence, substance abuse, anger management, and other antisocial behaviors. Participants are also connected with personal improvement opportunities (parenting classes, financial counseling classes, support groups, etc.) which research has identified as protective factors against continued criminal activity and/or recidivism. By connecting individuals with both evidenced based interventions and known protective factors, participants will develop the skills and abilities to improve their quality of life and prevent further criminal behavior, as well as remain positive and contributing members of the community. This measure tracks successful completion rates and for individuals who have completed a Deferred Prosecution or Misdemeanor Supervised Probation program with the City of Tempe and also tracks individuals who remain crime free while participating with Community Supervision. The performance measure dashboard is availabe at 1.17 Community Supervision Success Rate.Additional InformationSource: City of Tempe Community Health and Human Services Open Caseware Case Management System; Arizona Prosecuting Attorneys' Advisory Council (APAAC) Annual Report of Deferred Prosecution ProgramsContact: Dianna KalandrosContact E-Mail: Dianna_Kalandros@tempe.govData Source Type: Excel; TablePreparation Method: Manual reporting Publish Frequency: AnnualPublish Method: ManualData Dictionary (pending)

The rate of people who considered their neighborhood unsafe in Chiapas increased by 1.5 thousand people per 100,000 inhabitants (+4.8 percent) compared to the previous year. In total, the rate amounted to 32.73 thousand people per 100,000 inhabitants in 2024. For more insights about the rate of people who considered their neighborhood unsafe consider different countries: In 2024, in comparison to Chiapas, the rate in Mexico City as well as in Michoacán de Ocampo was forecast to be higher.

In 2014 and 2015, The LA County Enterprise GIS team under the Geographic Information Officer worked with the Unincorporated Area Deputies and Field Deputies of each Board Office to establish names that reflect the desires of residents. CSAs differ from the more informal Community geographies because:They are focused on broad statistics and reporting, not mapping of communities.They represent board approved names assigned to Census block groups and city boundaries.They cover the entire unincorporated County (no gaps).There are not overlapping areas. Additionally, CSAs use the following naming conventions:All names are assumed to begin with Unincorporated (e.g. Unincorporated El Camino Village) which will not be part of the CSA Name (so the name of the Statistical Area would be El Camino Village).Names will not contain “Island.” Beginning each name with Unincorporated will distinguish an area from any surrounding cities. There may be one or more exceptions for certain small areas (e.g. Bandini Islands)A forward slash implies an undetermined boundary between two areas within a statistical geography (e.g. Westfield/Academy Hills or View Park/Windsor Hills)Certain established names may include hyphens (e.g. Florence-Firestone)Aliases may be defined in parentheses (e.g. Unincorporated Long Beach (Bonner/Carson Park))The original set of names were derived from community names used in the 2011 Redistricting process, chosen with the assistance of the Board of Supervisors.Updates: 2023 December: CSA data updated to include "Unincorporated Charter Oak" (south of 10 Freeway) into "Unincorporated Covina".2023 June: CSA data was updated to include "Kinneloa Mesa" community, which was a part of Unincorporated East Pasadena.2023 January: Updated layer schema to include feature type (“FEAT_TYPE”) field, which can be one of land, water, breakwater, or pier (consistent with the City Boundaries layer).2022 December: CSA data was updated to incorporate the “Tesoro Del Valle” annexation to the city of Santa Clarita. Unincorporated Valencia is now completely annexed to the City of Santa Clarita. In addition to land area, this data also includes other feature types such as piers, breakwater and water area. 2022 September: CSA data was updated to match with city boundaries along shoreline/coastal area and minor boundary adjusted in some other areas.

This hosted feature layer has been published in RI State Plane Feet NAD 83.The RI Neighborhood Stabilization Program (NSP) Mapping analysis was performed to assist the Office of Housing and Community Development in identifying target areas with both a Foreclosure Rate (Block Group Level) >=6.5% and a Subprime Loan percentage rate >= 1.4% (Zip Code Level). Based on these criteria the following communities were identified as containing such target areas: Central Falls, Cranston, Cumberland, East Providence, Johnston, North Providence, Pawtucket, Providence, Warwick, West Warwick, and Woonsocket. Federal funding, under the Housing and Economic Recovery Act of 2008 (HERA), Neighborhood Stabilization Program (NSP), totaling $19.6 will be expended in these NSP Target Areas to assist in the rehabilitation and redevelopment of abandoned and foreclosed homes, stabilizing communities.The State of Rhode Island distributes funds allocated, giving priority emphasis and consideration to those areas with the greatest need, including those areas with - 1) Highest percentage of home foreclosures; 2) Highest percentage of homes financed by subprime mortgage loans; and 3) Anticipated increases in rate of foreclosure. The RI Office of Housing and Community Development, with the assistance of Rhode Island Housing, utilized the following sources to meet the above requirements. 1) U.S. Department of Housing & Urban Development (HUD) developed foreclosure data to assist grantees in identification of Target Areas. The State utilized HUD's predictive foreclosure rates to identify those areas which are likely to face a significant rise in the rate of home foreclosures. HUD's methodology factored in Home Mortgage Disclosure Act, income, unemployment, and other information in its calculation. The results were analyzed and revealed a high level of consistency with other needs data available. 2) The State obtained subprime mortgage loan information from the Federal Reserve Bank of Boston. Though the data does not include all mortgages, and was only available at the zip code level rather than Census Tract, findings were generally consistent with other need categories. This data was joined to the Foreclosure dataset in order to select areas with both a Foreclosure Rate >=6.5% and a Subprime Loan Rate >=1.4%. 3) The State also obtained, from the Warren Group, actual local foreclosure transaction records. The Warren Group is a source for real estate and banking news and transaction data throughout New England. This entity has analyzed local deed records in assembling information presented. The data set was normalized due to potential limitations. An analysis revealed a high level of consistency with HUD-predictive foreclosure rates.

In 2020, Hot Springs, Arkansas had the highest burglary rate in the United States, with 1,202.9 cases of burglary per 100,000 of its inhabitants. Lake Charles, Louisiana had the second highest burglary rate, at 1,065.7 cases per 100,000 inhabitants.

A dashboard used by government agencies to monitor key performance indicators (KPIs) and communicate progress made on strategic outcomes with the general public and other interested stakeholders.

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widespread COVID-19 is in San Francisco and it helps public health officials determine if we are testing enough given the number of people who are testing positive. When there are fewer than 20 positives tests for a given neighborhood and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for neighborhood by the total number of residents who live in that neighborhood (included in the dataset), then multiply by 10,000. When there are fewer than 20 total tests for a given neighborhood and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

E. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing cleaning efforts.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our testing data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

This dataset tracks annual graduation rate from 2021 to 2022 for Neighborhood House Charter School District vs. Massachusetts

The percent of persons between the ages of 16 and 64 that are in the labor force (and are looking for work) but are not currently working. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021

Infants learn and develop within an ecological context that includes family, peers, and broader built and social environments. This development relies on proximal processes—reciprocal interactions between infants and the people and environments around them that help them understand their world. Most research examining predictors of proximal processes like parent-child interaction and parenting has focused on elements within the home and family. However, factors like the neighborhood built environment may also exhibit an influence, and may be particularly critical in infancy, as socioeconomic disparities in cognition and language emerge early in life. Moreover, influence from the built environment could independently exacerbate these disparities, as research indicates that neighborhood impacts may be especially relevant for families living in neighborhoods that have experienced disinvestment and therefore have been under-resourced. The current study examines these questions by determining the association of neighborhood vacancy rate and observed physical disorder—indicators of poverty, residential stability, and long-term structural discrimination—with parental cognitive stimulation among predominantly Black/African-American families in Flint, Michigan. Flint is particularly salient for this study because vacancy rates and disinvestment vary widely across the city, driven by its long-time status as a city struggling economically. Regression analyses controlling for caregiver education, mental health, and social support indicated that vacancy rate and physical disorder negatively predicted parental cognitive stimulation. Moreover, there were significant interactions between the built environment and social support, indicating that, particularly for parent-child shared reading, vacancy rate and physical disorder predicted reduced shared reading only when parents had limited social support. These results have important implications for public policy around vacant property demolition and neighborhood reinvestment programs, as they indicate that the neighborhood built environment is associated with parenting behaviors that have important impacts on infants’ learning and development.

Graph and download economic data for Unemployment Rate in Skagway-Hoonah-Angoon Census Area, AK (LAUCN022320000000003A) from 1990 to 2009 about Skagway-Hoonah-Angoon Census Area, AK; AK; unemployment; rate; and USA.

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