These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

The sample SAS and Stata code provided here is intended for use with certain datasets in the National Neighborhood Data Archive (NaNDA). NaNDA (https://www.openicpsr.org/openicpsr/nanda) contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk was previously available on the UDS Mapper website, which is no longer active. An archived copy of the ZIP-code-to-ZCTA crosswalk file has been included here. Sample SAS and Stata code are provided for merging the UDS mapper crosswalk with NaNDA datasets.

The Los Angeles Index of Neighborhood Change is a tool that allows users to explore the extent to which Los Angeles Zip Codes have undergone demographic change from 2000 to 2014. Created in 2015/2016, the data comes from 2000, 2005, 2013, and 2014. Please read details about each measure for exact years.Index scores are an aggregate of six demographic measures indicative of gentrification. The measures are standardized and combined using weights that reflect the proportion of each measure that is statistically significant.Measure 1: Percent change in low/high IRS filer ratio. For the purposes of this measure, High Income = >$75K Adjust Gross Income tax filer and Low Income = <$25k filers who also received an earned income tax credit. Years Compared for Measure 1: 2005 and 2013 | Source: IRS Income Tax Return DataMeasure 2: Change in percent of residents 25 years or older with Bachelor's Degrees or HigherMeasure 3: Change in percent of White, non-Hispanic/Latino residentsMeasure 4: Percent change in median household income (2000 income is adjusted to 2014 dollars)Measure 5: % Change in median gross rent (2000 rent is adjusted to 2013/2014 dollars)Measure 6: Percent change in average household size Year Compared for Measures 2-5: 2000 and 2014, Measure 6: 2013Sources: Decennial Census, 2000 | American Community Survey (5-Year Estimate, 2009-2013; 2010; 2014)Date Updated: December 13, 2016Refresh Rate: Never - Historical data

During the COVID-19 pandemic, certain occupations and industries were deemed "essential", and typically included individuals who worked in healthcare, food service, public transportation, etc. However, early on in the pandemic, while these workers faced disproportionately higher risks, they often did not receive adequate personal protective equipment (PPE), were unable to work from home, and were limited in their ability to take other precautions to safeguard their health (Chen et al., 2021). As a result, previous studies have documented higher rates of infection, hospitalization, and death among essential workers compared to their non-essential worker counterparts (Selden & Berdahl, 2021; Wei et al., 2022). This dataset provides users with information on the number and proportion of essential workers in census tracts or ZIP Code tabulation areas (ZCTAs) in the United States over the 2016-2020 period.

Reference: https://www.zillow.com/research/zhvi-methodology/

Official Background

In setting out to create a new home price index, a major problem Zillow sought to overcome in existing indices was their inability to deal with the changing composition of properties sold in one time period versus another time period. Both a median sale price index and a repeat sales index are vulnerable to such biases (see the analysis here for an example of how influential the bias can be). For example, if expensive homes sell at a disproportionately higher rate than less expensive homes in one time period, a median sale price index will characterize this market as experiencing price appreciation relative to the prior period of time even if the true value of homes is unchanged between the two periods.

The ideal home price index would be based off sale prices for the same set of homes in each time period so there was never an issue of the sales mix being different across periods. This approach of using a constant basket of goods is widely used, common examples being a commodity price index and a consumer price index. Unfortunately, unlike commodities and consumer goods, for which we can observe prices in all time periods, we can’t observe prices on the same set of homes in all time periods because not all homes are sold in every time period.

The innovation that Zillow developed in 2005 was a way of approximating this ideal home price index by leveraging the valuations Zillow creates on all homes (called Zestimates). Instead of actual sale prices on every home, the index is created from estimated sale prices on every home. While there is some estimation error associated with each estimated sale price (which we report here), this error is just as likely to be above the actual sale price of a home as below (in statistical terms, this is referred to as minimal systematic error). Because of this fact, the distribution of actual sale prices for homes sold in a given time period looks very similar to the distribution of estimated sale prices for this same set of homes. But, importantly, Zillow has estimated sale prices not just for the homes that sold, but for all homes even if they didn’t sell in that time period. From this data, a comprehensive and robust benchmark of home value trends can be computed which is immune to the changing mix of properties that sell in different periods of time (see Dorsey et al. (2010) for another recent discussion of this approach).

For an in-depth comparison of the Zillow Home Value Index to the Case Shiller Home Price Index, please refer to the Zillow Home Value Index Comparison to Case-Shiller

Each Zillow Home Value Index (ZHVI) is a time series tracking the monthly median home value in a particular geographical region. In general, each ZHVI time series begins in April 1996. We generate the ZHVI at seven geographic levels: neighborhood, ZIP code, city, congressional district, county, metropolitan area, state and the nation.

Underlying Data

Estimated sale prices (Zestimates) are computed based on proprietary statistical and machine learning models. These models begin the estimation process by subdividing all of the homes in United States into micro-regions, or subsets of homes either near one another or similar in physical attributes to one another. Within each micro-region, the models observe recent sale transactions and learn the relative contribution of various home attributes in predicting the sale price. These home attributes include physical facts about the home and land, prior sale transactions, tax assessment information and geographic location. Based on the patterns learned, these models can then estimate sale prices on homes that have not yet sold.

The sale transactions from which the models learn patterns include all full-value, arms-length sales that are not foreclosure resales. The purpose of the Zestimate is to give consumers an indication of the fair value of a home under the assumption that it is sold as a conventional, non-foreclosure sale. Similarly, the purpose of the Zillow Home Value Index is to give consumers insight into the home value trends for homes that are not being sold out of foreclosure status. Zillow research indicates that homes sold as foreclosures have typical discounts relative to non-foreclosure sales of between 20 and 40 percent, depending on the foreclosure saturation of the market. This is not to say that the Zestimate is not influenced by foreclosure resales. Zestimates are, in fact, influenced by foreclosure sales, but the pathway of this influence is through the downward pressure foreclosure sales put on non-foreclosure sale prices. It is the price signal observed in the latter that we are attempting to measure and, in turn, predict with the Zestimate.

Market Segments Within each region, we calculate the ZHVI for various subsets of homes (or mar...

“Complete streets” is a design concept for primarily urban streets and intersections (existing and/or new) intended to encourage active transportation by bicyclists and pedestrians by making streets safer, convenient, and attractive for active transportation; motorized transportation and parking are also accommodated in the design concept. The social and economic performance indicators included in the social LCA (SLCA) framework that was used in this project provide a great deal of insight into specific and different potential benefits of a given complete streets project. The SLCA framework is based on five categories of concerns and 17 performance measures or indicators. The indicators were tested in the project and evaluated for final recommendations for use in future studies. The results are compared with the existing streets that were configured to be vehicle-centric. The case studies were solicited in more and less advantaged neighborhoods so that the equity aspects of the framework could also be evaluated. Use of the CalEnviroScreen tool from the California Environmental Protection Agency was also investigated to assess the exposure of neighborhoods and their vulnerability to environmental impacts in conjunction with the performance indicators when evaluating the potential benefits for disadvantaged neighborhoods. The document presents the data that was used for the SLCA, environmental LCA, and CalEnviroScreen Tool. Methods The three case studies that are evaluated include projects that are in an urban, a suburban, and a suburban/rural area. These projects are:

San Fernando complete street project located in an advantaged neighborhood of San Jose, CA (urban) Franklin Boulevard complete street project located in a disadvantaged neighborhood of Sacramento, CA (suburban) Kentucky Avenue complete street project located in Woodland, CA (suburban/rural)

The San Fernando Street and Kentucky Avenue complete street projects were already built at the time of evaluation, therefore this study includes calculations for social performance indicators before and after the projects' completion. Franklin Boulevard complete street project assessment, on the other hand, is based on proposed design documents as this street has not been built yet. The social performance measures that are selected to be used in this report are presented in Table 1. All performance measures description, methodologies, and data resources are explained in the final report for each case study. Table 1. Social performance measures selected for use in the proposed complete streets LCA framework.

Selected Category

Selected Performance Measures

Accessibility

Access to Community Destinations

Access to school

Jobs

Access to Jobs

Job Creation

Mobility/Connectivity

Active Transportation to Local and Regional Transit Connectivity Index*

Connectivity Index

Bike/Pedestrian Delay

Level of Service (Auto)

Safety/Public Health

Level of Service (Bicycle Level of Service)

Level of Service (Pedestrian level of Service)

Level of Service (Bicycle Level of Stress)

Crashes

Physical Activity and Health

Vehicle Miles Traveled (VMT) Impacts

Pedestrian Miles Traveled (PMT)

Bicycle Miles Traveled (BMT)

Livability

Green Land Consumption

Street Trees

The main challenges in the SLCA study were data collection and the inherent context-specific and qualitative nature of social aspects of each complete street project. The evaluations of performance measures have been interpreted and discussed for each case study separately. All the performance measures were quantified for the three case studies using project-specific data. Assumptions, modeling approaches, and performance measures descriptions and methodologies are explained and available in the final report for each case study. The references to the data sources are also cited in the final report. Data that were used to quantify indicator results are presented in this data summary report. There is no restriction to publishing data, except for the schools’ surveys; however, the results of the survey are published in the final report. Datasets The following part of this document contains a data summary for each case study. Each case study, Social LCA for San Fernando St., Franklin Blvd., and Kentucky Ave., has its own directory containing excel files that are described in the following paragraphs. The summary of percentile rankings for environmental and public health burdens and populations for neighborhoods near complete streets from CalEnviroScore is presented in Table 2. The data for environmental LCA for all three case studies are also available as an excel file in this dataset. San Fernando St. Complete Street Case Study

The “San Fernando Street history of complete streets projects.xlsx” excel file compares the elements of the complete street considered in this case study. The access to destinations data for San Fernando Street is shown in the “San Fernando-List of destinations.xlsx” excel file. The “San Fernando-Access to jobs-Non-government Office Workers.xlsx” and “San Fernando-Access to jobs-State Workers.xlsx” excel files represent the access to jobs data for San Fernando Street for the non-governmental office workers and state workers, respectively. The Urban, Transit, Bicycle, and Pedestrian Level of Services (LOS) data for San Fernando Street are shown in “San Fernando-Urban LOS.xlsx” and “San Fernando-Transit LOS, BLOS, PLOS.xlsx” excel files. The “San Fernando- Crashes.csv” file, includes crashes data, and belongs to the complete list of San Jose streets and different modes of transportation between 2014 and 2019.

Social LCA Franklin Blvd. Complete Street Case Study

The access to destinations data for Franklin Blvd. is shown in the “Franklin-List of Destinations 2019 and 2015.xlsx” excel file. The “Franklin-Access to jobs-Non-government Office Workers.xlsx” and “Franklin-Access to jobs-State Workers.xlsx” excel files represent the access to jobs data for Franklin Blvd. for the non-governmental office workers and state workers, respectively. The Urban, Transit, Bicycle, and Pedestrian Level of Services (LOS) data for Franklin Blvd. are shown in “Franklin-Urban LOS.xlsx” and “Franklin-Transit LOS, BLOS, PLOS.xlsx” excel files.

Social LCA Kentucky Ave. Complete Street Case Study

The access to destinations data for Kentucky Ave. is shown in the “Kentucky-List of Destinations.xlsx” excel file. The “Kentucky-Access to jobs.xlsx” excel file represents all the access to jobs data for Kentucky Ave. The Urban, Transit, Bicycle, and Pedestrian Level of Services (LOS) data for Kentucky Ave. are shown in “Kentucky -Transit LOS, BLOS, PLOS.xlsx” excel file.

CalEnviroScreen Data Table 2. Summary of percentile rankings for environmental and public health burdens and populations for neighborhoods near complete streets from CalEnviroScore.

Complete Street

Zip code

Neighborhood ID number

0.5 or 2.0 mile distance

CalEnviroScore percentile (%)

Population

San Fernando St.

95110

6085500300

0.5

55-60

3140

95110

6085500800

0.5

60-65

2600

95112

6085501000

0.5

70-75

4769

95192

6085500901

0.5

55-60

3723

95112

6085501200

0.5

60-65

4186

95128

6085502102

2

55-60

7469

95126

6085502002

2

70-75

4887

95128

6085502001

2

50-55

5022

95126

6085500500

2

30-35

5275

95050

6085505203

2

55-60

4809

95110

6085505100

2

65-70

3027

95110

6085500300

2

55-60

3140

95126

6085500400

2

40-45

2369

95126

6085500600

2

35-40

4586

95126

6085501900

2

50-55

4641

95125

6085502302

2

10-15

2826

95125

6085502301

2

30-35

3245

95126

6085502201

2

25-30

6260

95125

6085503121

2

70-75

4499

95112

6085503122

2

85-90

3449

95112

6085503112

2

70-75

4025

95122

6085503105

2

90-95

2484

95122

6085503117

2

75-80

3120

95122

6085503110

2

80-85

4618

95116

6085501501

2

75-80

4278

95116

6085501402

2

60-65

2947

95116

6085501401

2

75-80

3295

95112

6085501200

2

60-65

4186

95131

6085504318

2

85-90

5265

95133

6085503601

2

85-90

2992

95133

6085504319

2

70-75

6936

95133

6085503709

2

65-70

5088

95116

6085503707

2

50-55

5462

95116

6085503602

2

80-85

4741

95116

6085503710

2

70-75

3599

95116

6085503711

2

60-65

4763

95122

6085503105

2

90-95

2488

95110

6085500300

2

55-60

3140

95112

6085501101

2

55-60

4074

95112

6085501000

2

70-75

4769

95110

6085500800

2

60-65

2600

95116

6085501502

2

70-75

4549

95110

6085503113

2

75-80

4760

95112

6085503112

2

70-75

4025

Franklin Blvd.

95822

6067003501

0.5

50-55

2629

95820

6067003600

0.5

65-70

2826

95818

6067002500

0.5

10-15

1587

95820

6067003700

0.5

75-80

4219

95822

6067003501

2

50-55

2629

95818

6067002400

2

30-35

4387

65818

6067002200

2

90-95

4004

95818

6067002300

2

35-40

3156

95818

6067002000

2

85-90

2376

95816

6067001500

2

30-35

4329

95817

6067001800

2

70-75

4686

95817

6067001700

2

55-60

4794

95822

6067003502

2

50-55

2916

95822

6067004100

2

65-70

5015

95823

6067004903

2

70-75

6740

95823

6067004701

2

85-90

3303

95823

6067004702

2

90-95

4945

95824

6067004601

2

75-80

7614

95824

6067003202

2

60-65

5052

95820

6067004401

2

75-80

4122

95820

6067002900

2

55-60

4499

95819

6067001600

2

20-25

5421

Kentucky

Perceived neighborhood disorder by HIV virologic suppression (

Demographic and psychosocial characteristics by degree of perceived neighborhood disorder.

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

The City Survey asks residents to indicate their usage and satisfaction with city services and infrastructure like libraries, Muni, public safety, and street cleanliness. The City Survey was conducted every year from 1996 to 2004, and biennially from 2005 onward. The City Survey was not conducted in 2019 due to the COVID-19 pandemic, and resumed in 2023. Survey methodology was changed in 2015 from a mail to a phone survey, and expanded to include in-person and online options in 2023. Comparisons to previous years should be interpreted with caution. Results should be weighted using the column "weight" in order to adjust for demographic differences between the City Survey sample and San Francisco's population. Please note that survey results were originally reported as unweighted until 1997. From 1997 onward, all City Survey results were reweighted with the exception of data from 2011. For ease of use, the column "weight" has been coded with a value of one for these years. A code book is also attached to this dataset under About > Attachments. Neighborhood and Zip Code data have been hidden from this data set and are only available upon special request to citysurvey@sfgov.org. For more information regarding San Francisco City Survey 1996-2023 Database, please visit the City Survey website at https://sf.gov/citysurvey or contact the San Francisco Controller's Office at citysurvey@sfgov.org.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to 3/2/2020 when testing began.

Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time.

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).

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website.

Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongoing data quality efforts result in improved mapping on a rolling basis.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by geography are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 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 case and death data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 2/23/2022 - the New Cases Map dashboard began pulling from this dataset. To access Cases by Geography Over Time, please refer to this dataset.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

This dataset consists of gun violence within Jefferson county that may fall within LMPDs radar, including non-fatal shootings, homicides, as well as shot-spotter data. The mapping data points where there are victims have been obfuscated to maintain privacy, while still being accurate enough to be placed in its correct boundaries, particularly around, neighborhoods, ZIP Codes, Council districts, and police divisions. The data also excludes any victim information that could be used to identify any individual. this data is used to make the public aware of what is going on in their communities. The data consists of only criminal incidents, excluding any cases that are deemed non-criminal.Field NameField DescriptionCase numberPolice report number. For ShotSpotter detections, it is the ShotSpotter ID.DateTimeDate and time in which the original incident occurred. Time is rounded down.AddressAddress rounded down to the one hundred block of where the initial incident occured. Unless it is an intersection.NeighborhoodNeighborhood in which the original incident occurred.Council DistrictCouncil district in which the original incident occurred.LatitudeLatitude coordinate used to map the incidentLongitudeLongitude coordinate used to map the incidentZIP CodeZIP Code in which the original incident occurred.Crime Typea distinction between incidents, whether it is a non-fatal shooting, homicide, or a ShotSpotter detection.CauseUsed to differentiate on the cause of death for homicide victims.SexGender of the victim of the initial incident.RaceRace/Ethnicity of the victim in a given incident.Age GroupCategorized age groups used to anonymize victim information.Division NamePolice division or department where the initial incident occurred.Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities, unless LMPD becomes involved in smaller agency incident.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Contact:Ivan Benitez, Ph.D.Gun Violence Data FellowOffice for Safe and Healthy Neighborhoodsivan.benitez@louisvilleky.gov

Context

This dataset contains every property sale from 2003 - 2019 in every borough in New York City.

Columns

-Borough: The name of the borough in which the property is located. -Neighborhood: Department of Finance assessors determine the neighborhood name in the course of valuing properties. The common name of the neighborhood is generally the same as the name Finance designates. However, there may be slight differences in neighborhood boundary lines and some sub-neighborhoods may not be included. -Building Class Category: This is a field that we are including so that users of the Rolling Sales Files can easily identify similar properties by broad usage (e.g. One Family Homes) without looking up individual Building Classes. Files are sorted by Borough, Neighborhood, Building Class Category, Block and Lot. -Tax Class at Present: Every property in the city is assigned to one of four tax classes (Classes 1, 2, 3, and 4), based on the use of the property. Class 1: Includes most residential property of up to three units (such as one-, two-, and three-family homes and small stores or offices with one or two attached apartments), vacant land that is zoned for residential use, and most condominiums that are not more than three stories. Class 2: Includes all other property that is primarily residential, such as cooperatives and condominiums. Class 3: Includes property with equipment owned by a gas, telephone or electric company. Class 4: Includes all other properties not included in class 1,2, and 3, such as offices, factories, warehouses, garage buildings, etc. Glossary of Terms for Property Sales Files -Block: A Tax Block is a sub-division of the borough on which real properties are located. The Department of Finance uses a Borough-Block-Lot classification to label all real property in the City. “Whereas” addresses describe the street location of a property, the block and lot distinguishes one unit of real property from another, such as the different condominiums in a single building. Also, block and lots are not subject to name changes based on which side of the parcel the building puts its entrance on. -Lot: A Tax Lot is a subdivision of a Tax Block and represents the property unique location. -Easement: An easement is a right, such as a right of way, which allows an entity to make limited use of another’s real property. For example: MTA railroad tracks that run across a portion of another property. -Building Class at Present: The Building Classification is used to describe a property’s constructive use. The first position of the Building Class is a letter that is used to describe a general class of properties (for example “A” signifies one-family homes, “O” signifies office buildings. “R” signifies condominiums). The second position, a number, adds more specific information about the property’s use or construction style (using our previous examples “A0” is a Cape Cod style one family home, “O4” is a tower type office building and “R5” is a commercial condominium unit). The term Building Class used by the Department of Finance is interchangeable with the term Building Code used by the Department of Buildings. See NYC Building Classifications. -Address: The street address of the property as listed on the Sales File. Coop sales include the apartment number in the address field. -Zip Code: The property’s postal code -Residential Units: The number of residential units at the listed property. -Commercial Units: The number of commercial units at the listed property. -Total Units: The total number of units at the listed property. -Land Square Feet: The land area of the property listed in square feet. -Gross Square Feet: The total area of all the floors of a building as measured from the exterior surfaces of the outside walls of the building, including the land area and space within any building or structure on the property. -Year Built: Year the structure on the property was built. -Building Class at Time of Sale: The Building Classification is used to describe a property’s constructive use. The first position of the Building Class is a letter that is used to describe a general class of properties (for example “A” signifies one-family homes, “O” signifies office buildings. “R” signifies condominiums). The second position, a number, adds more specific information about the property’s use or construction style (using our previous examples “A0” is a Cape Cod style one family home, “O4” is a tower type office building and “R5” is a commercial condominium unit). The term Building Class as used by the Department of Finance is interchangeable with the term Building Code as used by the Department of Buildings. -Sales Price: Price paid for the property. -Sale Date: Date the property sold. $0 Sales Price: A $0 sale indicates that there was a transfer of ownership without a cash consideration. Th...

Summary results from NYC Community Health Survey 2010-2016: adults ages 18 years and older Source: NYC Community Health Survey (CHS) 2010-16. The Community Health Survey (CHS) includes self-reported data from adults, years 18 and older. CHS has included adults with landline phones since 2002 and, starting in 2009, also has included adults who can be reached by cell-phone. Starting in 2011, CHS weighting methods were updated to use Census 2010 and additional demographic characteristics (http://www1.nyc.gov/assets/doh/downloads/pdf/epi/epiresearch-chsmethods.pdf ).

Data are age-adjusted to the US 2000 Standard Population.
Data prepared by Bureau of Epidemiology Services, New York City Department of Health and Mental Hygiene

The New York City Community Health Survey (CHS) is a telephone survey conducted annually by the DOHMH, Division of Epidemiology, Bureau of Epidemiology Services. CHS provides robust data on the health of New Yorkers, including neighborhood, borough, and citywide estimates on a broad range of chronic diseases and behavioral risk factors. The data are analyzed and disseminated to influence health program decisions, and increase the understanding of the relationship between health behavior and health status. For more information see EpiQuery, https://a816-healthpsi.nyc.gov/epiquery/CHS/CHSXIndex.html

"TARGET POPULATION The target population of the CHS includes non-institutionalized adults aged 18 and older who live in a household with a landline telephone in New York City (the five borough area). Starting in 2009, adults living in households with only cell phones have also been included in the survey.

HEALTH TOPICS Most years the CHS includes approximately 125 questions, covering the following health topics: general health status and mental health, health care access, cardiovascular health, diabetes, asthma, immunizations, nutrition and physical activity, smoking, HIV, sexual behavior, alcohol consumption, cancer screening and other health topics. A core group of demographics variables are included every year to facilitate weighting and comparisons among different groups of New Yorkers.

SAMPLING The CHS uses a stratified random sample to produce neighborhood and citywide estimates. Neighborhoods are defined using the United Hospital Fund's (UHF) neighborhood designation, which assigns neighborhood based on the ZIP code of the respondent. New ZIP codes have been added since the UHF's were originally defined. There are 42 UHF neighborhoods in NYC. However, to avoid small sample sizes for CHS estimates, UHF estimates are generally collapsed into 34 UHFs/groups.

Starting in 2009, a second sample consisting of cell-only households with New York City exchanges was added. This design is non-overlapping because in the cell-only sample, adults living in households with landline telephones were screened out.

A computer-assisted telephone interviewing (CATI) system is used to collect the survey data. The CHS sampling frame was constructed with a list of telephone numbers provided by a commercial vendor. Upon agreement to participate in the survey, one adult is randomly selected from the household to complete the interview.

Interviewing is conducted in a variety of languages. Every year, the questionnaire is translated from English into Spanish, Russian, and Chinese. Some years, live translation services are provided by Language Line (including Hindi, Arabic, Farsi, and Haitian Creole). Typically, data collection begins in March of the study year and ends in December. The average length of the survey is 25 minutes.

LIMITATIONS The survey sampling methodology does not capture the following groups: households without any telephone service and (prior to 2009) households that only have a cell phone. The CHS also excludes adults living in institutional group housing, such as college dormitories.

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HIV medication adherence self-efficacy by degree of perceived neighborhood disorder.

Cumulative and monthly counts and rates of confirmed COVID-19 in Ottawa neighbourhoods, excluding cases linked to outbreaks in long-term care homes (LTCH) and retirement homes (RH). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day the data is pulled to provide the monthly update. Accuracy: Points of consideration for interpretation of the data: Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) the day prior to publication. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. A case (an individual with laboratory-confirmed COVID-19 infection) is assigned to an Ottawa Neighbourhood Study (ONS) geography based on the individual’s residential postal code and the ONS’s postal code conversion file. As the area served by a given postal code may cross multiple neighbourhoods, the ONS postal code conversion file identifies the proportion of each postal code that falls within a neighbourhood. Thus, for cases with postal codes falling within multiple neighbourhoods, a fraction of those cases will be assigned to each neighbourhood. Rates calculated from very low case numbers or for neighbourhoods with very small populations are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time. Update Frequency: Monthly Attributes: Data fields ONS Neighbourhood – text Cumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a neighbourhood, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that neighbourhood Cumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a neighbourhood, excluding cases linked to outbreaks in LTCH and RH Monthly rates (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a neighbourhood reported to OPH during the month of interest, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that neighbourhood. Monthly number of cases reported, excluding cases linked to outbreaks in LTCH and RH - number of residents with confirmed COVID-19 in a neighbourhood reported to OPH during the month of interest, excluding cases linked to outbreaks in LTCH and RH. Contact: OPH Epidemiology Team & Ottawa Neighbourhood Study Team | Epidemiology & Evidence, Ottawa Public Health

Several scholars have concluded that ethnic diversity has negative consequences for social trust. However, recent research has called into question whether ethnic diversity per se has detrimental effects, or whether lower levels of trust in diverse communities simply reflect a higher concentration of less trusting groups, such as poor people, minorities, or immigrants. Drawing upon a nationally representative sample of the German population (GSOEP), we make two contributions to this debate. First, we examine how ethnic diversity at the neighborhood level–specifically the proportion of immigrants in the neighborhood–is linked to social trust focusing on the compositional effect of poverty. Second, in contrast to the majority of current research on ethnic diversity, we use a behavioral measure of trust in combination with fine-grained (zip-code level) contextual measures of ethnic composition and poverty. Furthermore, we are also able to compare the behavioral measure to a standard attitudinal trust question. We find that household poverty partially accounts for lower levels of trust, and that after controlling for income, German and non-German respondents are equally trusting. However, being surrounded by neighbors with immigrant background is also associated with lower levels of social trust.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

Cumulative and monthly counts and rates of confirmed COVID-19 in Ottawa neighbourhoods, excluding cases linked to outbreaks in long-term care homes (LTCH) and retirement homes (RH). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day the data is pulled to provide the monthly update. Accuracy: Points of consideration for interpretation of the data: Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) the day prior to publication. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. A case (an individual with laboratory-confirmed COVID-19 infection) is assigned to an Ottawa Neighbourhood Study (ONS) geography based on the individual’s residential postal code and the ONS’s postal code conversion file. As the area served by a given postal code may cross multiple neighbourhoods, the ONS postal code conversion file identifies the proportion of each postal code that falls within a neighbourhood. Thus, for cases with postal codes falling within multiple neighbourhoods, a fraction of those cases will be assigned to each neighbourhood. Rates calculated from very low case numbers or for neighbourhoods with very small populations are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time. Update Frequency: Monthly Attributes: Data fields ONS Neighbourhood – text Cumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a neighbourhood, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that neighbourhood Cumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a neighbourhood, excluding cases linked to outbreaks in LTCH and RH Monthly rates (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a neighbourhood reported to OPH during the month of interest, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that neighbourhood. Monthly number of cases reported, excluding cases linked to outbreaks in LTCH and RH - number of residents with confirmed COVID-19 in a neighbourhood reported to OPH during the month of interest, excluding cases linked to outbreaks in LTCH and RH. Contact: OPH Epidemiology Team & Ottawa Neighbourhood Study Team | Epidemiology & Evidence, Ottawa Public Health

Grocery Store AccessHow do people get to a grocery store in your city? The option to travel quickly to a grocery store varies by location. Explore grocery store access in your neighborhood. Enter your ZIP code, city, or point of interest into the app’s search to see how many stores people there can reach in a 10-minute walk or drive. Interactive charts update as you move around the map. How does grocery access differ with neighboring areas, states, or across the US? This Esri map estimates that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. How does your city compare? Learn more about this map