This dataset represents the number of persons who successfully exit from homelessness in a given fiscal year in the Austin/Travis County Continuum of Care (CoC). This measure is comprised of Metric 7b1 and 7b2 from the HUD System Performance Measures. Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/xtip-he7k

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

description: This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply _ _ _shelter _ and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.; abstract: This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply _ _ _shelter _ and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.

Novel and comprehensive cross-sectional datasets were developed to document and measure city level homeless policies across issue area. The dataset is comprised of the 100 largest cities in the United States, including FIPS identifiers and matched Continuum of Care (CoC) level identifiers by CoC number associated with the city. The datasets include city-level homeless policies in the 100 largest cities across the issues of homeless plans; housing plans with mentions of homelessness; homeless outreach teams; and the role of sanitation in homeless policy. Each dataset includes sub-codes to evaluate the governance structure and policy goals of each type of city-level homeless policy. Primary data were collected in 2021 and 2022. Please see the attached publications for complete methodology and data collection procedures for each policy type.

Tempe relies on data to inform and support decision making for the city’s Homeless Solutions strategy. This comprehensive effort ensures that the city has the most up-to-date information to meet needs, identify emerging trends and create solutions. In this hub site, you’ll find data related to:Outreach and engagementReporting homeless encampmentsVerifying and resolving encampmentsAnnual Point-in-Time homeless countSite is Google Translate enabled. DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this page, please visit https://tempegov.hub.arcgis.com:/overview/edit.

This dataset provides information on individuals experiencing sheltered or unsheltered homelessness in the Austin/Travis County Continuum of Care (CoC) on a single night in January when the Point in Time (PIT) Count occurs. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. Unsheltered homelessness refers to individuals with a primary nighttime residence that is a public or private place not designed for or ordinarily used as a regular sleeping accommodation for human beings, including a car, park, abandoned building, bus or train station, airport, or camping ground on the night designated for the count. This measure overlaps, but is different from, the annual count of sheltered homelessness in HMIS (SD23 Measure EOA.E.1b). Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/hjiv-t2tm Last Updated December 2020 with data for 2020 PIT Count.

Analysis of ‘COVID-19 Deaths by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/60f5842f-a359-4b03-ad21-1bcfc3bf7fe6 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. Deaths are included on the date the individual died.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most date included is 5 days prior to today. All data update daily as more information becomes available.

B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate.

Data on the population characteristics of COVID-19 deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. It takes time to process this data. Because of this, data is lagged by 5 days and death totals for previous days may increase or decrease. More recent data is less reliable.

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020. Gender * The City collects information on gender identity using these guidelines.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation
* the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures.
These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives. * Facilities are mandated to report COVID-19 cases or deaths among their residents. The City follows up with these facilities to confirm.
* There may be differences between the City’s SNF data and the California Department of Public Health (CDPH) dashboard. The difference may be because the City and the State use dif

--- Original source retains full ownership of the source dataset ---

This dataset represents the number of persons who are experiencing homelessness for the first time in a fiscal year (October 1 - September 30) in the Austin/Travis County Continuum of Care (CoC). Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/wk3t-h5qe

This dataset provides information on individuals who exit homelessness to permanent housing destinations and then return to homelessness within 2 years from their exit in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/cutp-y8m4

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco po

This dataset provides information on individuals experiencing sheltered homelessness in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. This measure overlaps, but is different from, the Point in Time (PIT) Count (SD23 Measure EOA.E.1a), which is a snapshot of both sheltered and unsheltered homelessness on one night in January. Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/2ejn-hrh2

Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

Gender * The City collects information on gender identity using these guidelines.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation
* the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures.
These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Strategic Measure_Number of persons who have not been served by the community’s homeless system in the two years prior to entry into the homeless system’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/41c1755f-0106-4be5-b5b9-c680de3252dd on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset represents the number of persons who are experiencing homelessness for the first time in a fiscal year (October 1 - September 30) in the Austin/Travis County Continuum of Care (CoC).

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/wk3t-h5qe

--- Original source retains full ownership of the source dataset ---

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

To access the dataset that continues to refresh daily, navigate to this page: COVID-19 Deaths by Population Characteristics Over Time.   The dataset contains data on the following population characteristics that are no longer being reported publicly:

• Skilled Nursing Facility Occupancy
• Sexual orientation
• Comorbidities
• Homelessness
• Single room occupancy (SRO) tenancy
• Transmission Type

B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate.    Data on the population characteristics of COVID-19 deaths are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.      Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 deaths reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation    * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to Virtual Assistant information gathering starting December 2021. The California Department of Public Health, Virtual Assistant is only sent to adults who are 18+ years old. Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset will only update when any population characteristics are archived. Data for existing characteristic types will not change but new characteristic types may be added.   D. HOW TO USE THIS DATASET This dataset may include different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

E. CHANGE LOG

• 6/6/2023 - data on deaths by transmission type are no longer being updated. This data is currently through 6/1/2023 (as of 6/6/2023) and will not include any new data after this date.
• 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy are no longer being updated. This data is currently through 5/11/2023 (as of 5/16/2023) and will not include any new data after this date.
• 1/5/2023 - data on SNF deaths are no longer being updated. SNF data is currently through 12/31/2022 (as of 1/5/2023) and will not include any new data after this date.

Analysis of ‘Strategic Measure_Number of returns to homelessness’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2a3af1b3-b101-438c-b7b0-a62c2cf6c803 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset provides information on individuals who exit homelessness to permanent housing destinations and then return to homelessness within 2 years from their exit in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year.

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/cutp-y8m4

--- Original source retains full ownership of the source dataset ---

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. 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 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://bit.ly/rk5Tpc.

This dataset provides information on individuals experiencing sheltered homelessness in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. This measure overlaps, but is different from, the Point in Time (PIT) Count (SD23 Measure EOA.E.1a), which is a snapshot of both sheltered and unsheltered homelessness on one night in January.

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/2ejn-hrh2

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/