The Resiliency Exposure Inventory is an index that evaluates the levels of climate adaptation capacity across neighborhoods in New York City. There are many factors that contribute to a location's resiliency to various threats - this analysis takes a holistic view, combining and analyzing datasets to compare and contrast strategies to become more resilient to the threats of outdoor heat, indoor heat, and coastal flooding. Higher scores indicate greater adaptation levels, and each neighborhood's score is relative to other neighborhoods; a neighborhood with the highest score is assessed to have the greatest relative level of adaptation measures, but overall adaptation can always be improved. This tool was developed to provide a unified way to view levels of adaptive capacity across locations, specifically neighborhoods (NTAs). OMB developed this baseline inventory of the city's existing resiliency measures based on a suite of relevant metrics, or Key Performance Indicators (KPIs). More than 30 unique KPIs are grouped into Categories, which represent adaptation strategies. The Inventory does not replace or conflict with existing efforts or data. It seeks to measure factors and efforts that reduce exposure (increase adaptive capacity) to climate threats, which would leave fewer people, areas, or assets at risk despite varying levels of threat and vulnerability.

Content

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year.

NOTE: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Dean Rose on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

A dataset listing New York counties by population for 2024.

Exploring Potential Tax and Water Liens in NYC

Investigating Geographic Distribution and Effects of Local Policies

By data.world's Admin [source]

About this dataset

This dataset captures properties in New York City that have tax and/or water liens potentially eligible to be included in the next lien sale. Explore the city's fiscal landscape with information about borough, lot, tax class code, building classes, community board, council district, house number street name and zip code. This data is updated monthly with new liens being added from the most current month back to 12 months prior. By analyzing this data you can gain greater insight into New York City’s financial conditions over time as well as how this affects individual properties throughout the city. This data is provided by the New York City open data portal is subject to terms of use outlined on its website so please refer to it for any additional information regarding usage rights

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How to use the dataset

This data is sourced from New York City's Open Data portal. By exploring this dataset you can search for properties with possible tax and/or water liens located in a particular area or neighborhood or by a specific attribute such as the house number or street name. You can also take a look at particular subsections of potential eligible lien sale records over time – for example you could look at all potential water debt liens only during a certain month – to get some more specific insights into what tax and water liens may be available at certain points in time. To use this dataset please note the following important tips: • Start by familiarizing yourself with each column’s field meaning (using our table above); • When searching for records use quotation marks if you are looking up something which is two words (e.g “construction”) ;
• Use an underscore _for replacing spaces if necessary e.g “west_village”;
• Be aware that Boroughs are referenced by their full names (e.g Manhattan, Queens, etc);
• If using wild cards (*) make sure not to put them on either side of your query - e.g instead of Lastname * use Lastname*.
We hope this guide was helpful and good luck exploring!

Research Ideas

• Real Estate Investment Analysis: Create a platform or tool using this dataset to assist individuals looking to invest in properties with potential tax and water liens. The platform/tool should provide insights into the best locations for purchasing real estate based on location, tax class code, building class and council district data points from this dataset.
• Tax Foreclosure Notifications: Use this dataset to create an automated notification system which informs registered users when a property they are interested in is coming up for sale with a lien in the next sale date.
• Local Planning Solutions: Leverage data from this dataset to identify areas where there might be concentration of properties with tax liens that could potentially benefit from local planning solutions such as community grants, affordable housing initiatives etc.. This could help municipalities deploy resources more effectively towards restoring distressed properties instead of letting them slip out of their control via foreclosure at lien sales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: tax-lien-sale-lists-1.csv | Column name | Description | |:---------------------|:-----------------------------------------------------------------| | Month | The month in which the lien sale is eligible. (String) | | Borough | The borough in which the property is located. (String) | | Lot | The lot number of the property. (Integer) | | Tax Class Code | The tax class code of the property. (Integer) | | Building Class | The building class of the property. (String) | | Community Board | The community board in which the property is located. (Integer) | | Council District | The council district in which the property is located. (Integer) | | House Number | The house number of the property. (Integer) | | Street Name | The street name of the property. (String) | | Zip Code | The zip code of the p...

LISTOS_Ground_QueensCollege_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Queens College ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The dataset includes each individual’s initial admittance date into DOC custody, the date they were designated unfit to proceed in court, their DOC discharge date and facility, and their total length of staying DOC and length of stay from when they were designated to when they were discharged. Individuals are found unfit to proceed in court by a psychiatric examiner. If found unfit to proceed, individuals are transferred out of DOC custody to the New York State Department of Health, New York State Office of Mental Health and e New York State Office for People with Developmental Disabilities.

The Agency Report Table aggregates pay and employment characteristics in accordance with the requirements of Local Law 18 of 2019. The Table is a point-in-time snapshot of employees who were either active or on temporary leave (parental leave, military leave, illness, etc.) as of December 31st of each year the data is available (see Column "Data Year"). In addition, the Table contains snapshot data of active employees in seasonal titles as of June 30th. To protect the privacy of employees, the sign “<5” is used instead of the actual number for groups of less than five (5) employees, in accordance with the Citywide Privacy Protection Policies and Protocols. The Pay and Demographics Report, and the list of agencies included is available on the MODA Open Source Analytics Library: https://modaprojects.cityofnewyork.us/local-law-18/

Each row represents a group of employees with a common agency, EEO-4 Job Category, pay band, employee status and demographic attributes, which include race, ethnicity and gender.

Dataset was introduced by Toby Seagaran in his book Programming Collective Intelligence.

The columns are: Departure airport code, Arrival airport code , Time of Arrival(24h), Time of Departure(24h), Cost (USD)

Problem Definition

Planning a trip for a group of people from different locations all arriving at the same place is always a challenge, and it makes for an interesting optimization problem. In our situation group members are from all over the country and wish to meet up at a prticular location say New York. They will all arrive on the same day and leave on the same day, and they would like to share transportation to and from the airport. There are dozens of flights per day to New York from any of the family members’ locations, all leaving at different times.

For more information and examples check out github.com/Agrover112/fliscopt/examples

LISTOS_Ground_NewHaven_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the New Haven ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

Dataset contains monthly counts, from 2001 to present, of individuals receiving regular unemployment insurance benefits, as well as the total amount of benefits received from New York State.

Data are provided for the state, 10 labor market regions, and counties. State counts can include everyone who receives benefits through New York State (including out-of-state residents) or only state residents who do so (excluding out-of-state residents).

Regular unemployment insurance includes: Unemployment Insurance (UI) Compensation, Compensation for Federal Employees (UCFE), Unemployment Compensation for Ex-Service Members (UCX), Shared Work (SW) and Self Employment Assistance Program (SEAP). It excludes federal extensions and 599.2 training.

This dataset lists executed evictions within the five boroughs for the years 2017-Present (data prior to January 1, 2017, is not available). The data fields may be sorted by 20 categories of information including Court Index Number, Docket Number, Eviction Address, Marshal First or Last Name, Borough, etc..

Eviction data is compiled from New York City Marshals. City Marshals are independent public officials appointed by the Mayor. Marshals can be contacted directly regarding evictions, and their contact information can be found at https://www1.nyc.gov/site/doi/offices/marshals-list.page.

LISTOS_Ground_YaleCoastal_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) ground site data collected at the Yale Coastal ground site during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

LISTOS_Ground_Rutgers_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) Rutgers ground site data collected during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

The Agency Report Table aggregates pay and employment characteristics in accordance with the requirements of Local Law 18 of 2019. The Table is a point-in-time snapshot of employees who were either active or on temporary leave (parental leave, military leave, illness, etc.) as of December 31st of each year the data is available (see Column "Data Year"). In addition, the Table contains snapshot data of active employees in seasonal titles as of June 30th. To protect the privacy of employees, the sign “<5” is used instead of the actual number for groups of less than five (5) employees, in accordance with the Citywide Privacy Protection Policies and Protocols. The Pay and Demographics Report, and the list of agencies included is available on the MODA Open Source Analytics Library: https://modaprojects.cityofnewyork.us/local-law-18/

Each row represents a group of employees with a common agency, EEO-4 Job Category, pay band, employee status and demographic attributes, which include race, ethnicity and gender.

The Electoral Integrity Project at Harvard University and the University of Sydney (www.electoralintegrityproject.com) developed the AVE data, release 1.0. The dataset contains information from a three-wave panel survey designed to gather the views of a representative sample of ordinary Australians just before and after the 2nd July 2016 Australian federal elections. The survey monitored Australian voters’ experience at the polls, perceptions of the integrity and convenience of the registration and voting process, patterns of civic engagement, public confidence in electoral administration, and attitudes towards reforms, such as civic education campaigns and convenience voting facilities. Respondents were initially contacted in the week before the election between 28 June and 1 July and completed an online questionnaire lasting approximately 15 minutes. This forms the pre-election base line survey (wave 1). The same individuals were contacted again after the election to complete a longer survey, an average of 25 minutes in length. Respondents in wave 2 were contacted between 4 July and 19 July, with two thirds completing the survey after the first week. About six weeks later, the same respondents were interviewed again (wave 3) beginning on 23 August and ending on 13 September. The initial sample contains 2,139 valid responses for the first wave of questionnaires, 1,838 for the second wave (an 86 percent retention rate), and 1,543 for the third wave (84 percent retention rate). Overall, 72 percent of the respondents were carried over from the pre-election wave to the final wave. The following files can be accessed: a) dataset in Stata and SPSS formats; b) codebook; c) questionnaire. The EIP acknowledges support from the Kathleen Fitzpatrick Australian Laureate from the Australian Research Council (ARC ref: FL110100093). **** EIP further publications: BOOKS • LeDuc, Lawrence, Richard Niemi and Pippa Norris. Eds. 2014. Comparing Democracies 4: Elections and Voting in a Changing World. London: Sage Publications. • Nai, Alessandro and Walter, Annemarie. Eds. 2015 New Perspectives on Negative Campaigning: Why Attack Politics Matters. Colchester: ECPR Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2014. Advancing Electoral Integrity. New York: Oxford University Press. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. Eds. 2015. Contentious Elections: From Ballots to the Barricades. New York: Routledge. • Norris, Pippa. 2014. Why Electoral Integrity Matters. New York: Cambridge University Press. • Norris, Pippa. 2015. Why Elections Fail. New York: Cambridge University Press. • Norris, Pippa and Andrea Abel van Es. Eds. 2016. Checkbook Elections? Political Finance in Comparative Perspective. Oxford University Press. ARTICLES AND CHAPTERS • W. Frank. 2013. ‘Assessing the quality of elections.’ Journal of Democracy. 24(4): 124-135.• Lago, Ignacio and Martínez i Coma, Ferran. 2016. ‘Challenge or Consent? Understanding Losers’ Reactions in Mass Elections’. Government and Opposition doi:10.1071/gov.3015.31 • Martínez i Coma, Ferran and Lago, Ignacio. 2016. 'Gerrymandering in Comparative Perspective’ Party Politics DOI: 10.1177/1354068816642806 • Norris, Pippa. 2013. ‘Does the world agree about standards of electoral integrity? Evidence for the diffusion of global norms.’ Special issue of Electoral Studies. 32(4):576-588. • Norris, Pippa. 2013. ‘The new research agenda studying electoral integrity’. Special issue of Electoral Studies. 32(4): 563-575.57 • Norris, Pippa. 2014. ‘Electoral integrity and political legitimacy.’ In Comparing Democracies 4. Lawrence LeDuc, Richard Niemi and Pippa Norris. Eds. London: Sage. • Norris, Pippa, Richard W. Frank and Ferran Martínez i Coma. 2014. ‘Measuring electoral integrity: A new dataset.’ PS: Political Science & Politics. 47(4): 789-798. • Norris, Pippa. 2016 (forthcoming). ‘Electoral integrity in East Asia.’ Routledge Handbook on Democratization in East Asia. Tun-jen Cheng and Yun-han Chu. Eds. Routledge: New York. • Norris, Pippa. 2016 (forthcoming). ‘Electoral transitions: Stumbling out of the gate.’ In Rebooting Transitology – Democratization in the 21st Century. Mohammad-Mahmoud Ould Mohamedou and Timothy D. Sisk. Eds. • Pietsch, Juliet; Michael Miller and Jeffrey Karp. 2015. ‘Public support for democracy in transitional regimes.’ Journal of Elections, Public Opinion and Parties. 25(1): 1–9. DOI: 10.1080/17457289.2014. • Smith, Rodney. 2016 (forthcoming). ‘Confidence in paper-based and electronic voting channels: Evidence from Australia.’ Australian Journal of Political Science. ID: 1093091 DOI: 10.1080/10361146.2015.1093091 dx.doi.org/10.1080/07907184.2015.1099097 • Van Ham, Carolien and Staffan Lindberg. 2015. ‘From sticks to carrots: Electoral manipulation in Africa, 1986-2012’, Gover... Visit https://dataone.org/datasets/sha256%3A9efcfe40123531a7f785369bae96a30beb0f41c1ce7334bc7c398a54be5e69f5 for complete metadata about this dataset.

Critical habitat includes all marine waters within the delineated boundaries. For the inland waters of Washington state (2006 designation), the contiguous shoreline is defined by the line at a depth of 20 feet (6.1 meters) relative to extreme high water. For the coastal marine waters along the U.S. west coast (2021 revision), the contiguous shoreline is defined by the line at a depth of 20 feet (6.1 meters) relative to mean high water. See the final rules (71 FR 69054 and 86 FR 41668) for descriptions of areas excluded from this critical habitat designation. For the inland waters of Washington state (2006 designation), military areas excluded due to national security impacts were not clipped out of the data.For the coastal marine waters along the U.S. west coast (2021 revision), military areas excluded due to national security impacts (i.e., the Quinault Range and its 10 kilometer buffer) were clipped out of the data.Link to NOAA Fisheries final rule pageLink to eCFRLink to InPortLink to NOAA Fisheries Critical Habitat Webpage

New York State Health Indicators

Examination of County and Region-level Data

By Health Data New York [source]

About this dataset

The New York State Community Health Indicator Reports (CHIRS) provides an incredible resource of data to analyze the health of all communities in this state. This dataset contains more than 300 indicators across 15 health topics, which are organized by region and county. These indicators include important information such as event counts, percent/rates, confidence intervals, measure units,quartiles and many more. Whether you're a researcher or a policymaker interested in public health issues in this state - this dataset can be used to inform your decisions by creating powerful visuals with it's wealth of data points. Use this dataset to explore different factors that could be impacting public health outcomes and discover key insights around public health trends in the Empire State!

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How to use the dataset

This dataset contains data on more than 300 health indicators for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. It can be used to analyze different trends in population health from a local and state-level perspective. Here is a guide on how to use this dataset:

• Familiarize yourself with the data columns: Have an understanding of what each column represents in order to have a better grasp of what type of analyses you will be able to do with this dataset. Additionally, look into other potential features that may not be included within this dataset but could help you with your research or analysis.
• Clean and prepare the data: Make sure that the data is up-to-date and free of errors by cleaning it up prior to conducting any analysis or research project. Some cleaning steps may include inspecting for accuracy, addressing missing values/outliers, formatting irregularities etc.
• Generate questions related to public health issues: Brainstorm ideas around public health topics or possible implications based on your curiosities then use those questions as stepping stones when conducting further research or analysis into this particular healthcare dataset..
• Visualize key information through visual plots/charts: Create charts and graphs which could significantly give out important insights by providing visualization capabilities that would allow users valuable information in an understandable manner such as indicating correlations between certain factors or determining frequency distributions among others.. 5 Develop conclusions from your exploratory findings : Through careful calculation using thoughtfully designed formulas as well as chart interpretation draw meaningful conclusions from continuous observation assessments performed within the contents of this healthcare related base answer pertinent queries raised at hand efficiently thereby leaving no room for ambiguity in user’s overall comprehension about subject matter discussed herein ensured efficient completion processes executed timely objectives justly desired

Research Ideas

• Comparing health indicators across different New York state counties and regions: This dataset can be used to compare the health indicators of different New York county and region levels, helping identify areas of strength or weakness in an area's public health conditions.
• Examining changes over time: By analyzing data from multiple years, this dataset can be used to understand patterns in changes of public health outcomes throughout NY state regions since 2012.
• Generating targeted public health initiatives and interventions: Understanding the geographical distribution of positive or negative public health outcomes could help generate targeted policy interventions more effectively tailored to local needs than a one-size-fits-all approach

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: community-health-indicator-reports-chirs-latest-data-1.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------------------------------| | County Name | Name of the county in New York State. (String) | | Health Topic Number | Number assigned to each hea...

In August 2005, New York State Legislature passed "Billy's Law" which is requiring biannual reports of State and local monitoring of out-of-state residential facilities that house New York State children who are placed in such facilities for specialized services including specialized educational services. Reports are to be produced on first day of September and February, containing name and location of each facility, number of children placed, general population served by each facility, the number of individuals served, the age, race, gender and nature of any disabilities and the types of services provided by each out of state facility.