ESD provides capital grant funding from the Regional Council Capital Fund available for the State’s Regional Economic Development Council Initiative, which helps drive regional and local economic development across New York State in cooperation with ten Regional Economic Development Councils (“Regional Councils”). Capital grant funding is available for capital-based economic development projects intended to create or retain jobs; prevent, reduce or eliminate unemployment and underemployment; and/or increase business or economic activity in a community or Region. One of the program categories within the program will provide enhanced incentives for projects located in economically distressed areas (census tracts) where investments are needed to spur economic growth. The definition of economically distressed areas (census tracts) can be found below.

For more information and full program guidelines, please see the full program guidelines within the 2025 Available Resources at: https://regionalcouncils.ny.gov/

Economically distressed area shall mean the following based on the census tract for where the project is located:

Severely distressed census tracts shall have at least 25 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least five of the criteria listed below:Moderately distressed census tracts shall have at least 25 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least three of the criteria listed below:Slightly distressed census tracts shall have at least 100 households receiving public assistance income in the 2023 ACS 5-year estimate and meet at least two of the criteria listed below:o
Population loss between the 2023 ACS 5-year estimate and the 2019 ACS 5-year estimate – an absolute loss in population.o
Unemployment rate (2023 ACS 5-year estimate) higher than the State’s rate.o
Private sector employment growth rate (2023 ACS 5-year estimate) over the preceding 5 years was lower than the State’s OR private sector employment (2023 ACS 5-year estimate) as a percentage of total employment was less than the State’s.o
Percentage of households receiving public assistance (2023 ACS 5-year estimate) was greater than the statewide percentage.o
Poverty rate (2023 ACS 5-year estimate) was greater than the State’s poverty rate.o
Per Capita Income change (2023 ACS 5-year estimate) over the preceding five years was less than the growth in the consumer price index (CPI) for all urban consumers nationally OR per capita income was less than the State’s per capita income.

Attributes:

Field Name

Data Type

Description

Census Tract

Number

The 11 digit geoid associated with each census tract in New York State. Census tracts are small, relatively permanent statistical subdivisions of a county that average about 4,000 inhabitants.

Stress Level

Number

The stress level number (1-4) associated with the census tract.

Stress Level Description

Text

The stress level description (Not Distressed, Slight Distress, Moderate Distress, Severe Distress) associated with the census tract.

Stress Level Color

Text

The stress level color (Gray, Light Orange, Dark Orange, Red) associated with the census tract.

Analysis of ‘Census Tract Economically Distressed Areas 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d1eb9a2a-7402-4947-a44a-0ac743d33008 on 12 February 2022.

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

This is a copy of the statewide Census Tract GIS Tiger file. It is used to determine if a census tract (CT) is DAC or not by adding ACS (American Community Survey) Median Household Income (MHI) data at the CT level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated MHI estimates. Created by joining 2016 DAC table to 2010 Census Tracts feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

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

This is a copy of the statewide Census County GIS Tiger file. It is used to determine if a county is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the county level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2016 EDA table to 2010 block groups feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

This is a copy of the statewide Census Block Group GIS Tiger file. It is used to determine if a block group (BG) is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the BG level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2016 EDA table to 2010 block groups feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.

The Appalachian Regional Commission uses an economic classification system to identify and monitor the economic status of counties and census tracts in the Appalachian Region.

This map displays the FY 2017 classification of the Region's counties into one of five economic levels (distressed, at-risk, transitional, competitive, and attainment) and the designation of distressed areas (census tracts).

Analysis of ‘Census Block Group Economically Distressed Areas 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ac57065c-1179-421b-968f-e8010700189c on 12 February 2022.

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

This is a copy of the statewide Census Block Group GIS Tiger file. It is used to determine if a block group (BG) is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the BG level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2016 EDA table to 2010 block groups feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are defined before tabulation block delineation and numbering, but are clusters of blocks within the same census tract that have the same first digit of their 4-digit census block number from the same decennial census. For example, Census 2000 tabulation blocks 3001, 3002, 3003,.., 3999 within Census 2000 tract 1210.02 are also within BG 3 within that census tract. Census 2000 BGs generally contained between 600 and 3,000 people, with an optimum size of 1,500 people. Most BGs were delineated by local participants in the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated BGs only where the PSAP participant declined to delineate BGs or where the Census Bureau could not identify any local PSAP participant. A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within census tract. Within the standard census geographic hierarchy, BGs never cross county or census tract boundaries, but may cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. BGs have a valid code range of 0 through 9. BGs coded 0 were intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. For Census 2000, rather than extending a census tract boundary into the Great Lakes or out to the U.S. nautical three-mile limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore. The Census Bureau assigned a default census tract number of 0 and BG of 0 to these offshore, water-only areas not included in regularly numbered census tract areas.

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

These data represent the predicted (modeled) prevalence of Frequent Mental Distress among adults (Age 18+) for each census tract in Colorado. Frequent Mental Distress is defined as experiencing more than 14 mentally unhealthy days within the past 30 days in which mental health was "not good." Health conditions for measuring mental health include stress, depression, and problems with emotions.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Because of their restricted access to financial resources, couples undergoing economic distress are more likely to live in disadvantaged neighborhoods than are financially well-off couples. The link between individual economic distress and community-level economic disadvantage raises the possibility that these two conditions may combine or interact in important ways to influence the risk of intimate violence against women. This study examined whether the effect of economic distress on intimate violence was stronger in disadvantaged or advantaged neighborhoods or was unaffected by neighborhood conditions. This project was a secondary analysis of data drawn from Waves 1 and 2 of the National Survey of Families and Households (NSFH) and from the 1990 United States Census. From the NSFH, the researchers abstracted data on conflict and violence among couples, as well as data on their economic resources and well-being, the composition of the household in which the couple lived, and a large number of socio-demographic characteristics of the sample respondents. From the 1990 Census, the researchers abstracted tract-level data on the characteristics of the census tracts in which the NSFH respondents lived. Demographic information contains each respondent's race, sex, age, education, income, relationship status at Wave 1, marital status at Wave 1, cohabitation status, and number of children under 18. Using variables abstracted from both Wave 1 and Wave 2 of the NSFH and the 1990 Census, the researchers constructed new variables, including degree of financial worry and satisfaction for males and females, number of job strains, number of debts, changes in debts between Wave 1 and Wave 2, changes in income between Wave 1 and Wave 2, if there were drinking and drug problems in the household, if the female was injured, number of times the female was victimized, the seriousness of the violence, if the respondent at Wave 2 was still at the Wave 1 address, and levels of community disadvantage.

The Colorado Department of Public Health and Environment has developed community-level estimates for adults in a set of 14 important health condition and risk behavior indicators. The dataset includes indicators on adult asthma prevalence, cigarette smoking prevalence, coronary heart disease prevalence, percent of adults who delayed medical care due to cost, diabetes prevalence, binge drinking and heavy alcohol consumption, percent of adults with fair or poor health status, mental distress, percent of adults with no routine medical checkup in the past 12 month, obesity and overweight prevalence, percent of adults that did not report doing physical activity or exercise, and percent of adults with frequent physical distress. These four-year estimates (2013-2016) have been produced for each census tract in the State of Colorado based on modeled survey data collected in the Colorado Behavioral Risk Factor Surveillance System (BRFSS) and incorporating population, race, gender, and age estimates for each census tract from the American Community Survey. CDPHE's Community Level Estimates are output from statistical models used to generate health condition and risk behavior estimates for smaller geographies than traditional surveillance systems report. The estimates are produced using a multilevel model that incorporates individual Colorado Behavioral Risk Factor Surveillance System (BRFSS) survey responses in addition to socio-demographic and contextual information about each census tract from the U.S. Census (American Community Survey). The individual survey responses related to a health condition or risk behavior from the Colorado BRFSS are nested within geographic boundaries (counties) where both individual characteristics (demographic) as well as sociodemographic characteristics can be used to model the probability of having a health condition or risk behavior at the census tract geography.

The Colorado Department of Public Health and Environment has developed community-level estimates for adults in a set of 14 important health condition and risk behavior indicators. The dataset includes indicators on adult asthma prevalence, cigarette smoking prevalence, coronary heart disease prevalence, percent of adults who delayed medical care due to cost, diabetes prevalence, binge drinking and heavy alcohol consumption, percent of adults with fair or poor health status, mental distress, percent of adults with no routine medical checkup in the past 12 month, obesity and overweight prevalence, percent of adults that did not report doing physical activity or exercise, and percent of adults with frequent physical distress. These four-year estimates (2013-2016) have been produced for each census tract in the State of Colorado based on modeled survey data collected in the Colorado Behavioral Risk Factor Surveillance System (BRFSS) and incorporating population, race, gender, and age estimates for each census tract from the American Community Survey. CDPHE's Community Level Estimates are output from statistical models used to generate health condition and risk behavior estimates for smaller geographies than traditional surveillance systems report. The estimates are produced using a multilevel model that incorporates individual Colorado Behavioral Risk Factor Surveillance System (BRFSS) survey responses in addition to socio-demographic and contextual information about each census tract from the U.S. Census (American Community Survey). The individual survey responses related to a health condition or risk behavior from the Colorado BRFSS are nested within geographic boundaries (counties) where both individual characteristics (demographic) as well as sociodemographic characteristics can be used to model the probability of having a health condition or risk behavior at the census tract geography.

Analysis of ‘State of New York Mortgage Agency (SONYMA) Target Areas by Census Tract’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/54c83793-f5bc-4411-93f6-15a5761c6cdb on 27 January 2022.

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

Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.

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

The Community Development Financial Institutions (CDFI) Fund, a division of the US Department of the Treasury, administers the New Markets Tax Credit (NMTC). The NMTC Program incentivizes community development and economic growth through the use of tax credits that attract private investment to distressed communities. This layer depicts area that are NMTC Qualified.New Market Tax Credit Program Note that the latest eligibility criteria use Census American Community Survey (ACS) 2016-2020 estimates.

Investors are able to defer paying taxes on capital gains that are invested in Qualified Opportunity Funds that in turn are invested in distressed communities designated as Opportunity Zones by the governor of each state. Census tracts 86, 87, 90, 91, 92, 93, 94, 110.01, 111.01 and 118 have been designated by the Washington State Department of Commerce as Opportunity Zones. This layer dissolves the aforementioned Census Tracts.

Rhode Island Enterprise Zones are authorized by the Distressed Areas Economic Revitalization Act of the Rhode Island General Laws (RIGL) §42-64.3 and are limited to not more than five (5) contiguous U.S. census tracts or portions thereof. Exceptions to the five (5) census tract rule are prescribed in RIGL §42-64.3-5. The zones are delineated by U.S. census 2010 boundaries and defined under RIGL §42-64.3-5.

Enterprise zone designation and re-designation are based on a number of distressed criteria including poverty, unemployment, and median household and per capita incomes. As well as non-demographic factors like economic development opportunities and potential, and defined course of action plans that including local incentives, resources and services. The Distressed Areas Economic Revitalization Act which spawned the Enterprise Zone Program was created to combat substantial and persistent levels of unemployment, blight, the spread of obsolete, dilapidated, and abandoned industrial and commercial structures and shrinking tax bases by stimulating economic revitalization, promote employment opportunities, and encourage business development and expansion in distressed areas.

Enterprise Zone Designation Process: The Central Falls/Cumberland Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1992; the zone was subsequently re-designated in 1996, 2001, 2006 and 2011. The zone is set to expire on December 31, 2016. The Cranston Enterprise Zone’s original designation was the result of a state wide RFP process, establishing the Port of Providence/Cranston Enterprise Zone; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1992; the zone was subsequently re-designated in 1996, 2001, 2006 and 2011. The zone is set to expire on December 31, 2016. The East Providence Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1995; the zone was subsequently re-designated in 1999, 2004, 2009, and 2014. The zone is set to expire on December 31, 2019. The Bristol/Warren (Mt. Hope) Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1993; the zone was subsequently re-designated in 1998, 2003, and 2008. The zone is set to expire on December 31, 2013. The Pawtucket/Lincoln Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1992; the zone was subsequently re-designated in 1996, 2001, 2006 and 2011. The zone is set to expire on December 31, 2016. The Pawtucket II Enterprise Zone’s original designation was the result of legislation submitted by the city of Pawtucket, passed by the Rhode Island General Assembly, signed in to law by the Governor and authorized by the Rhode Island Enterprise Zone Council in 2013. The zone’s designation was retroactive to January 1, 2013, as a result, the original 5-year designation expires on December 31, 2017. The Portsmouth/Tiverton Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1995; the zone was subsequently re-designated in 1999, 2004, 2009, and 2014. The zone is set to expire on December 31, 2019. The Port of Providence Enterprise Zone’s original designation was the result of a state wide RFP process, establishing the Port of Providence/Cranston Enterprise Zone; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1992; the zone was subsequently re-designated in 1996, 2001, 2006 and 2011. The zone is set to expire on December 31, 2016. The Providence II Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1993; the zone was subsequently re-designated in 1997, 2002, 2007 and 2012. The zone is set to expire on December 31, 2017.The West Warwick Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1993; the zone was subsequently re-designated in 1998, 2003 and 2009. The zone is set to expire on December 31, 2013. The Woonsocket/Cumberland Enterprise Zone’s original designation was the result of a state wide RFP process; all subsequent re-designations were granted by the Rhode Island Enterprise Zone Council. The original designation proposal was approved in 1992; the zone was subsequently re-designated in 1996, 2001, 2006 and 2011. The zone is set to expire on December 31, 2016. A detailed description of each Enterprise Zone boundary can be reviewed within the 'Fields' metadata section under Field 'EZone_Name' - List of Values.

These data represent the predicted (modeled) prevalence of Frequent Physical Distress among adults (Age 18+) for each census tract in Colorado. Frequent Physical Distress is defined as experiencing more than 14 physically unhealthy days within the past 30 days in which physical health was "not good." Health conditions for measuring physical health include physical illness and injury.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Digital Distress Metric:Four variables from the U.S. Census American Community Survey were used: The percent of homes with no internet access, Using only cellular data, as well as The percent of homes relying on mobile devices only, or Having no computing devices. Data was obtained for all U.S. census tracts and categorized into low, moderate, and high digital distress.The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: Percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; Percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); Percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); Median maximum advertised download speeds; and Median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: Percent population ages 65 and over; Percent population 25 and over with less than high school; Individual poverty rate; Percent of noninstitutionalized civilian population with a disability: and A brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.

This estimate of the percent of distressed housing units in each Census Tract was prepared using data from the American Community Survey and the Allegheny County Property Assessment database. The estimate was produced by the Reinvestment Fund in their work with the Allegheny County Department of Economic Development.

Analysis of ‘Opportunity Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/543389fc-3901-43d9-ad74-5a866d3707f7 on 27 January 2022.

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

Investors are able to defer paying taxes on capital gains that are invested in Qualified Opportunity Funds that in turn are invested in distressed communities designated as Opportunity Zones by the governor of each state. Census tracts 86, 87, 90, 91, 92, 93, 94, 110.01, 111.01 and 118 have been designated by the Washington State Department of Commerce as Opportunity Zones. This layer dissolves the aforementioned Census Tracts.

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DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.