The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. NOTE: This map has been updated as of 1/16/24 to use a newer version of the data layer which contains more cities than it previously did. As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. Map opens in Sacramento, CA. Use bookmarks or the search bar to get to other cities.Cities included in this mapAlabama: Birmingham, Mobile, MontgomeryArizona: PhoenixArkansas: Arkadelphia, Batesville, Camden, Conway, El Dorado, Fort Smith, Little Rock, Russellville, TexarkanaCalifornia: Fresno, Los Angeles, Oakland, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: Boulder, Colorado Springs, Denver, Fort Collins, Fort Morgan, Grand Junction, Greeley, Longmont, PuebloConnecticut: Bridgeport and Fairfield; Hartford; New Britain; New Haven; Stamford, Darien, and New Canaan; WaterburyFlorida: Crestview, Daytona Beach, DeFuniak Springs, DeLand, Jacksonville, Miami, New Smyrna, Orlando, Pensacola, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Columbus, Macon, SavannahIowa: Boone, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Dubuque, Sioux City, WaterlooIllinois: Aurora, Chicago, Decatur, East St. Louis, Joliet, Peoria, Rockford, SpringfieldIndiana: Evansville, Fort Wayne, Indianapolis, Lake County Gary, Muncie, South Bend, Terre HauteKansas: Atchison, Greater Kansas City, Junction City, Topeka, WichitaKentucky: Covington, Lexington, LouisvilleLouisiana: New Orleans, ShreveportMaine: Augusta, Boothbay, Portland, Sanford, WatervilleMaryland: BaltimoreMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Fall River, Fitchburg, Haverhill, Holyoke Chicopee, Lawrence, Lexington, Lowell, Lynn, Malden, Medford, Melrose, Milton, Needham, New Bedford, Newton, Pittsfield, Quincy, Revere, Salem, Saugus, Somerville, Springfield, Waltham, Watertown, Winchester, Winthrop, WorcesterMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Austin, Duluth, Mankato, Minneapolis, Rochester, Staples, St. Cloud, St. PaulMississippi: JacksonMissouri: Cape Girardeau, Carthage, Greater Kansas City, Joplin, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Elizabeth City, Fayetteville, Goldsboro, Greensboro, Hendersonville, High Point, New Bern, Rocky Mount, Statesville, Winston-SalemNorth Dakota: Fargo, Grand Forks, Minot, WillistonNebraska: Lincoln, OmahaNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen County, Camden, Essex County, Monmouth, Passaic County, Perth Amboy, Trenton, Union CountyNew York: Albany, Binghamton/Johnson City, Bronx, Brooklyn, Buffalo, Elmira, Jamestown, Lower Westchester County, Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Schenectady, Staten Island, Syracuse, Troy, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorain, Portsmouth, Springfield, Toledo, Warren, YoungstownOklahoma: Ada, Alva, Enid, Miami Ottawa County, Muskogee, Norman, Oklahoma City, South McAlester, TulsaOregon: PortlandPennsylvania: Allentown, Altoona, Bethlehem, Chester, Erie, Harrisburg, Johnstown, Lancaster, McKeesport, New Castle, Philadelphia, Pittsburgh, Wilkes-Barre, YorkRhode Island: Pawtucket & Central Falls, Providence, WoonsocketSouth Carolina: Aiken, Charleston, Columbia, Greater Anderson, Greater Greensville, Orangeburg, Rock Hill, Spartanburg, SumterSouth Dakota: Aberdeen, Huron, Milbank, Mitchell, Rapid City, Sioux Falls, Vermillion, WatertownTennessee: Chattanooga, Elizabethton, Erwin, Greenville, Johnson City, Knoxville, Memphis, NashvilleTexas: Amarillo, Austin, Beaumont, Dallas, El Paso, Forth Worth, Galveston, Houston, Port Arthur, San Antonio, Waco, Wichita FallsUtah: Ogden, Salt Lake CityVirginia: Bristol, Danville, Harrisonburg, Lynchburg, Newport News, Norfolk, Petersburg, Phoebus, Richmond, Roanoke, StauntonVermont: Bennington, Brattleboro, Burlington, Montpelier, Newport City, Poultney, Rutland, Springfield, St. Albans, St. Johnsbury, WindsorWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Madison, Milwaukee County, Oshkosh, RacineWest Virginia: Charleston, Huntington, WheelingAn example of a map produced by the HOLC of Philadelphia:

This dataset reflects reported incidents of crime that have occurred in the City of Chicago over the past year, minus the most recent seven days of data. 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. Any use of the information for commercial purposes is strictly prohibited. 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.

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

There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia:

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 27.1 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 11.6 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 home to many corporations such as Monsanto, Arch Coal, and Emerson Electric. The economy of St. Louis is centered around business and healthcare, and in addition is home to 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. In spite of high crime levels, the GDP of the St. Louis metropolitan area has been increasing since 2001.

In 2023, around 3,640.56 violent crimes per 100,000 residents were reported in Oakland, California. This made Oakland the most dangerous city in the United States in that year. Four categories of violent crimes were used: murder and non-negligent manslaughter; forcible rape; robbery; and aggravated assault. Only cities with a population of at least 200,000 were considered.

The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department, created these 41 neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. These neighborhoods are not codified in Planning Code nor Administrative Code, although this map is referenced in Planning Code Section 415 as the “American Community Survey Neighborhood Profile Boundaries Map."

This dataset is produced by assigning Census tracts to neighborhoods based on existing neighborhood definitions used by Planning and MOHCD. A qualitative assessment is made to identify the appropriate neighborhood for a given tract based on understanding of population distribution and significant landmarks. Once all tracts have been assigned a neighborhood, the tracts are dissolved to produce this dataset, Analysis Neighborhoods. It's companion dataset of all Census tracts assigned a neighborhood is available here: https://data.sfgov.org/d/bwbp-wk3r

Saint Paul has had a formal structure of neighborhood organizations to engage residents and collaborate with city government since 1975--one of the first in the nation. These organizations are known as district councils because they are resident groups that engage and represent the people living in one of the city's 17 planning districts. Each district council is a 501(c)(3) non-profit with a voluntary board of directors composed of members elected by their neighbors. The district council structure was formed as part of the development of the city's Citizen Participation Program, now known as the Community Engagement Program. The purpose of this program is to create opportunities for residents to learn about what is happening in their neighborhoods and collaborate with one another and city government to maintain and improve the quality of life in neighborhoods. The program includes funding allocated to each district council on a formula basis, technical assistance from city planners and other city staff regarding issues that are important to the neighborhood and non-profit management assistance from the city's Community Engagement Coordinator.District councils each are involved in work to improve the physical, social and economic structures in their neighborhood. The activity common to all district councils is the development of a district plan (sometimes referred to as a neighborhood plan) that is reviewed by the Planning Commission, City Council and the Metropolitan Council--the region's metropolitan planning organization--before being adopted as part of the city's Comprehensive Plan. The Comprehensive Plan is a key tool used by the city to guide law-making and budgeting. District plans are an opportunity for residents to influence how those laws and budgets impact their neighborhoods. Along with this critical planning work, district councils may also be involved in:Reviewing community development proposalsAdvocating for park and recreation center improvementsCoordinating community gardens and neighborhood beautification projectsPromoting environmental action through volunteering and advocacyOrganizing block clubs and working with the police department and other city agencies to improve public safetyDistrict councils rely on community-building activities and events as the basis for convening residents to become involved in their neighborhood. These include neighborhood forums, festivals, parades and block parties.A commitment to equity is foundational to successful community engagement. In 2017, the district councils proposed a change to the Community Engagement Program's Innovation Fund that was adopted in 2018. The fund is now divided equally among the 17 district councils to promote equitable practices and neighborhood outcomes. The goals of this program are:District council staff and volunteers more accurately reflect the communities they serve.District councils review and adopt policies and practices that intentionally create space for residents who are currently under-represented.District councils pursue systemic work that reflects the needs and priorities of residents who have been historically under-represented.Additionally, district council staff are required to participate in a peer support/best practices network composed of district councils or similar grassroots, place-based organizations in the region.

In 2020, Memphis, TN-MS-AR reported 1,358.8 violent crimes per 100,000 inhabitants, the most out of any metro area in the United States. Monroe, LA followed closely behind, with a violent crime rate of 1,308.5 crimes per 100,000 inhabitants.

Select Aggregate Planimetric CAD Vectors from set of 2001-2019 Community Profile AutoCAD files - Initial release version Oct 26, 2023This is an AGOL-hosted Feature Service - hence text point labels only display horizontallySee these related Services (circa Oct 2023):DCRA Community Profile Availability and Map Sheet Outlines with PDF Maps as downloadable attachmentsDCRA Community Profile Aerial Imagery (Community Hi-Res Scale)DCRA Community Profile Aerial Imagery (Community General Area Low-Res Scale)State of Alaska Maxar RGB Satellite ImageryHigh Level Documentation as a PowerPoint Presentation in PDF form

Web app displaying 2021-YTD NIBRS crime data. Data is included for all areas that the St. Louis County Police Department and the St. Louis County Park Rangers patrol. Additionally, data is included for the following police departments in St. Louis County: Ballwin, Bel Nor, Bel Ridge, Bella Villa, Bellefontaine Neighbors, Breckenridge Hills, Brentwood, Bridgeton, Country Club Hills, Chesterfield, Clayton, Crestwood, Creve Coeur, Des Peres, Ellisville, Eureka, Frontenac, Hillsdale, Kirkwood, Ladue, Lakeshire, Moline Acres, Maplewood, Normandy, Olivette, Overland, Pagedale, Riverview, Richmond Heights, Rock Hill, Saint Louis County, Shrewsbury, St. John, Sunset Hills, Town & Country, Velda City, Webster Groves, and Woodson Terrace.The data in this map should not be compared to previous UCR Part 1 crime data provided by the department. NIBRS data is counted differently, and comparisons made between the data will not be accurate.

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.

In 2023, the Balearic Islands region had the highest crime rate in Spain. Catalonia followed with a rate of 64.1 crimes per 1,000 inhabitants. Extremadura was the autonomous community with the lowest crime rate at 33.5.

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER Please read and understand the following information.

This dataset contains a record of incidents that the Austin Police Department responded to and wrote a report. Please note one incident may have several offenses associated with it, but this dataset only depicts the highest level offense of that incident. Data is from 2003 to present. This dataset is updated weekly. Understanding the following conditions will allow you to get the most out of the data provided. Due to the methodological differences in data collection, different data sources may produce different results. This database is updated weekly, and a similar or same search done on different dates can produce different results. Comparisons should not be made between numbers generated with this database to any other official police reports. Data provided represents only calls for police service where a report was written. Totals in the database may vary considerably from official totals following investigation and final categorization. Therefore, the data should not be used for comparisons with Uniform Crime Report statistics. The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided. Pursuant to section 552.301 (c) of the Government Code, the City of Austin has designated certain addresses to receive requests for public information sent by electronic mail. For requests seeking public records held by the Austin Police Department, please submit by utilizing the following link: https://apd-austintx.govqa.us/WEBAPP/_rs/(S(0auyup1oiorznxkwim1a1vpj))/supporthome.aspx

This 2013 dataset includes information at the block group-level for the 5-county Austin metro area. Economic, educational, housing, mobility, and environmental indicators are calculated for each block group to provide a comprehensive opportunity index score. This score reflects "opportunity" in the area, defined as a situation or condition that places individuals in a position to be more likely to succeed or excel. This data was collected and calculated by the Kirwan Institute, with collaboration from Green Doors and various community partners, and is compiled in "Geography of Opportunity in Austin" (http://www.greendoors.org/programs/docs/Geography-of-Opportunity-Austin-2013.pdf#page=43). The data can be viewed in an interactive map form here: http://www.arcgis.com/home/webmap/viewer.html?webmap=5db08646b03547abab85aec0a3592fb7. The data is also available in shapefile format for use in ESRI GIS mapping applications here: https://data.austintexas.gov/Neighborhood/Kirwin-Opportunity-Map/3ns6-m3cy.

The Walk Score(tm) is calculated by mapping out the distance to amenities in nine different categories (grocery stores, restaurants, shopping, coffee shops, banks, parks, schools, book stores/libraries, and entertainment) and are weighted according to importance. The distance to a location, counts, and weights determine a base score of an address, which is then normalized to a score from 0 to 100. More information on Walk Score can be found at http://www.walkscore.com/. Source: Walk Score Years Available: 2011, 2017

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq The City of Austin’s Community Tree Priority Map (formerly Planting Prioritization) serves as a decision support tool to determine where to focus forestry activities in Austin, Texas. This map shows U.S. Census tracts (2010) containing tabular data related to community forestry priorities determined by the Community Tree Preservation Division’s Urban Forest Program. Prioritization is determined through the priority score. This score combines nine measures normalized and summarized into four broad categories. The score is aggregated at the neighborhood (U.S. Census tract) level. Scores can range from 0 to 100 with higher scores meaning a higher need for community forestry activities to achieve more equitable canopy distribution. Finally, the priority level provides a categorical representation of the data for a simplified view. Priority Score = ( Σ Natural Environment + Σ Social Vulnerability + Σ Community Investment + Σ Health & Well-Being ) / 4 This map was updated in 2020. Minor updates are made as-needed with a review and data update scheduled for 2025 (every 5 years). Ultimately, this map is used to aggregate Urban Forest Grant/Portal projects and tree planting/distribution data to assess program performance. This dataset is intended to be downloaded as a GIS Shapefile but may also be viewed in Excel. It's also available in ArcGIS Online at https://austin.maps.arcgis.com/home/item.html?id=7d7c5260e60c4f8ab811d2c5fda6c40f

This statistic shows the crime severity index value of metropolitan areas in Canada in 2023. As of 2023, the crime severity index in Saskatoon, Saskatchewan, stood at 116.31.

Pittsburgh Neighborhoods

This part of DS 781 presents data for folds for the geologic and geomorphic map of the Offshore of Pacifica map area, California. The vector data file is included in "Folds_OffshorePacifica.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshorePacifica/data_catalog_OffshorePacifica.html. These data accompany the pamphlet and map sheets of Edwards, B.D., Phillips, E.L., Dartnell, P., Greene, H.G., Bretz, C.K., Kvitek, R.G., Hartwell, S.R., Johnson, S.Y., Cochrane, G.R., Dieter, B.E., Sliter, R.W., Ross, S.L., Golden, N.E., Watt, J.T., Chin, J.L., Erdey, M.D., Krigsman, L.M., Manson, M.W., and Endris, C.A. (S.A. Cochran and B.D. Edwards, eds.), 2014, California State Waters Map Series—Offshore of Pacifica, California: U.S. Geological Survey Open-File Report 2014–1260, pamphlet 38 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20141260. Folds in the Offshore of Pacific map area were primarily mapped by interpretation of seismic reflection profile data from US ...