Neighborhoods regions. Overlapping areas assigned to each neighborhood individually creating overlaps in the data.-- Additional Information: Category: Boundary Purpose: Identifies full area of each neighborhood individually with ID number to assign contact information. Update Frequency: As Needed-- Metadata Link https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=54371

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.

The Choice Neighborhoods program is administered by the U.S. Department of Housing and Urban Development (HUD). It supports locally driven strategies to address struggling neighborhoods with distressed public or HUD-assisted housing through a comprehensive approach to neighborhood transformation.

This is a polygon data set of the Neighborhood Watch Group boundaries within City of Boise limits. A Neighborhood Watch Group is defined as a neighborhood surveillance program or group in which residents keep watch over one another's houses, patrol the streets, etc., in an attempt to prevent crime. When available Neighborhood Watch Group boundaries are derived from information provided from the Neighborhood Watch Group chairpersons. Where data was not provided, boundaries are estimated using best judgment from the Boise Police Department Neighborhood Watch Group Coordinator. The geographic data was developed in 2013 and is maintained by the Boise IT GIS. The data set is current to the date it was published.For more information about, please visit City of Boise Police Department or Energize Our Neighborhoods.

The My Neighborhood - Property application allows users to find property information for Baltimore County. This includes parcels and zoning information. Users have the ability to create a customized, printable map as well as a property information report. Users can search for a property and generate a report by entering in an address or 10-digit tax account id

The EDGE School Neighborhood Poverty Estimates rely on household economic data from the Census Bureau’s American Community Survey (ACS) and public school point locations developed by NCES to estimate the income-to-poverty ratio for neighborhoods around school buildings. Unlike neighborhood poverty estimates created from survey responses collected for predefined geographic areas like census tracts, Spatially Interpolated Demographic Estimates (SIDE) predict conditions at specific point locations based on the survey responses nearest to those locations. This approach allows SIDE estimates to extract new value from existing data sources to provide indicators of neighborhood conditions. The economic conditions of school neighborhoods may be different from the economic conditions in neighborhoods where students live. However, the economic condition of the neighborhood around a school may impact schools, just as the condition of neighborhood schools may impact local neighborhoods. The school neighborhood poverty estimates provide an additional indicator to help identify these local conditions.

All BPD data on Open Baltimore is preliminary data and subject to change. The information presented through Open Baltimore represents Part I victim based crime data. The data do not represent statistics submitted to the FBI's Uniform Crime Report (UCR); therefore any comparisons are strictly prohibited. For further clarification of UCR data, please visit http://www.fbi.gov/about-us/cjis/ucr/ucr. Please note that this data is preliminary and subject to change. Prior month data is likely to show changes when it is refreshed on a monthly basis. All data is geocoded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geocoded. Any attempt to match the approximate location of the incident to an exact address is strictly prohibited.

Dataset with the contact information for Housing Counseling Agencies, Neighborhood Advisory Committees, and Neighborhood Energy Centers. .

The Census Bureau does not recognize or release data for Boston neighborhoods. However, Census block groups can be aggregated to approximate Boston neighborhood boundaries to allow for reporting and visualization of Census data at the neighborhood level. Census block groups are created by the U.S. Census Bureau as statistical geographic subdivisions of a census tract defined for the tabulation and presentation of data from the decennial census and the American Community Survey. The 2020 Census block group boundary files for Boston can be found here. These block group-approximated neighborhood boundaries are used for work with Census data. Work that does not rely on Census data generally uses the Boston neighborhood boundaries found here.

This dataset provides information about the number of properties, residents, and average property values for Neighborhood Lane cross streets in Dandridge, TN.

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This data set contains DOT construction project information. The data is refreshed nightly from multiple data sources, therefore the data becomes stale rather quickly.

This dataset provides information about the number of properties, residents, and average property values for Neighborhood Road cross streets in Ringgold, GA.

Feature layer containing neighborhood association information for Sioux Falls, South Dakota.

The urban heat island effect — defined as the difference in temperature between the core of Louisville and its suburbs — contributes to heat-related illnesses and deaths and leads to higher air-conditioning bills for residents, according to a study released in April 2016. The urban core heat island effect in Louisville is rising at one of the fastest rates in the country. There are specific actions residents can take to help reduce the heat island effect. Here, residents can search to find the specific number to actions, such as the number of trees planted or cool roofs installed, recommended in their neighborhoods to address the urban heat island effect.

The columns represent the number of each action recommended per neighborhood to help reduce the urban heat island effect.

https://louisvilleky.gov/government/sustainability/urban-heat-island-pro...

Update Frequency: Update frequency: Datasets are refreshed every night to ensure the most current information is available. Even if there are no changes, the data will be updated nightly.

City of Milwaukee Neighborhood Improvement District (NID) polygons. https://city.milwaukee.gov/DCD

Boston Neighborhood Boundaries represent a combination of zoning neighborhood boundaries, zip code boundaries and 2010 census tract boundaries. These boundaries are used in the broad sense for visualization purposes, research analysis and planning studies. However these boundaries are not official neighborhood boundaries for the City of Boston. The BPDA is not responsible for any districts or boundaries within the City of Boston except for the districts we use for planning purposes.

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.The purpose of the study was to examine whether and how foreclosures affect neighborhood crime in five cities in the United States. Point-specific crime data was provide by the New York (New York) Police Department, the Chicago (Illinois) Police Department, the Miami (Florida) Police Department, the Philadelphia (Pennsylvania) Police Department, and the Atlanta (Georgia) Police Department. Researchers also created measures of violent and property crimes based on Uniform Crime Report (UCR) categories, and a measure of public order crime, which includes less serious offenses including loitering, prostitution, drug crimes, graffiti, and weapons offenses. Researchers obtained data on the number of foreclosure notices (Lis Pendens) filed, the number of Lis Pendens filed that do not become real estate owned (REO), and number of REO properties from court fillings, mortgage deeds and tax assessor's offices.

Check out the Division of Housing and Community Development website for more information about housing counseling.View metadata for key information about this dataset.Housing counseling agencies help families navigate the financial issues related to housing, and are funded in part by the City through the Division of Housing and Community Development. DHCD-funded services provided by these agencies include mortgage counseling, default and delinquency counseling, tenant support and housing consumer education. Through these services prospective homeowners can avoid predatory loans, a significant cause of foreclosure.For questions about this dataset, contact noelle.vought@phila.gov. For technical assistance, email maps@phila.gov.

Thank you for your interest in the Promise Neighborhoods Program! You can use the tools below to look at general information about the applications, and you can also find specific applications that you would like to explore in more detail. The data is sourced from supplemental forms as reported by applicants. The data from these forms may not be a full or accurate representation of the information provided in the formal application.