This data layer is an element of the Oregon GIS Framework. This theme delineates urban growth boundaries (UGBs) in the state of Oregon. The line work was created by various sources including the Oregon Department of Land Conservation and Development (DLCD), the Oregon Department of Transportation (ODOT), Metro Regional Council of Governments (Metro), county and city GIS departments, and the Oregon Department of Administrative Services - Geospatial Enterprise Office (DAS-GEO). UGB areas consist of unincorporated lands surrounding a city that show where the city plans to grow over the next 20 years. When a city needs to develop more residential, commercial, industrial, or public land, it annexes the needed area from its UGB. If a city runs out of needed land within the UGB, it can expand its UGB. Original UGBs were established under the Oregon Statewide Planning Goals in 1973 by the Oregon State Legislature (Senate Bill 100). Goal 14 of the statewide planning program is, "To provide for an orderly and efficient transition from rural to urban land use, to accommodate urban population and urban employment inside urban growth boundaries, to ensure efficient use of land, and to provide for livable communities." The process and requirements for designating and amending UGBs are in Oregon Administrative Rules, Chapter 660, Division 24 (OAR 660-024). Designating or amending a UGB requires a public process, as required by Planning Goal 1, followed by approval by both the city and county elected officials and acknowledgement by the DLCD. This process includes the city submitting a Post Acknowledgement Plan Amendment (PAPA) to DLCD to review for consistency with Goal 14. The PAPA submittal includes GIS files that delineate the changes to the UGB. DLCD aggregates the local GIS layers into the statewide UGB layer. UGB line work and attributes are verified with the city PAPA submittals entered in DLCD’s tabular database to ensure that all UGB updates reported to DLCD have been included in this dataset. UGBs that are currently in the appeal process at the time of publication of this layer are not included. The effDate attribute indicates the year in which the UGB amendment was acknowledged by DLCD. In 2022, DLCD acknowledged amendments to the following UGBs: Central Point, Dayton, Phoenix, and Turner. Corrections were also made to the Astoria and Condon UGBs to reflect the current acknowledged boundary.

This data set contains features representing growth boundaries in the San Francisco Bay Region. The data set was developed by Greenbelt Alliance as part of their open space preservation mission. The Metropolitan Transportation Commission (MTC) updated the feature set in late 2019 as part of the jurisdiction review process for the BASIS data gathering project. Changes were made to the growth boundaries of the following jurisdictions based on their BASIS feedback: Antioch, Fremont, Livermore, Marin County, Pittsburg, Pleasanton, and San Ramon.The original features were created by referencing the local measures adopted by voters, city councils/boards of supervisors, or both and map images developed by local government. Web links to the measures and images are included in the attribute table. Greenbelt Alliance is responsible for updating the links to that information if their links change or the documents are taken offline. MTC's updates were made to a limited set of features using shapefiles downloaded from jurisdiction websites, hand editing features by referencing map images provided by jurisdictions, and erasing feature areas using base data features where one jurisdiction's growth boundary overlapped another jurisdiction's city limit. The MTC edits were limited, or related, to features receiving comments from jurisdiction reviewers.While commonly known as either Urban Growth Boundaries (UGBs) or Urban Limit Lines (ULLs), there are no standard designations so they are referred to by a number of designations in the attribute table (name). A couple of the alternative designations include Rural Urban Boundary and City Centered Corridor. Regardless of the designation used, growth boundaries are intended to control where development should be encouraged and discouraged. In this case, the polygons represent the areas where development should occur, and development outside the boundary is discouraged. Development proposed for areas outside the boundary is usually allowed based on special review and approval processes designated by the adopting jurisdiction.Jurisdictions are not required to adopt growth boundaries so the development restrictions imposed by them only apply to the cities and counties that create them. In the case of growth boundaries adopted by counties, they are usually developed with the consent and agreement of the cities adjacent to the county growth boundary.

This theme delineates urban growth boundaries (UGBs) in the state of Oregon. The line work was created by various sources including the Oregon Department of Land Conservation and Development (DLCD), the Oregon Department of Transportation (ODOT), Metro Regional Council of Governments (Metro), county and city GIS departments, and the Oregon Department of Administrative Services - Geospatial Enterprise Office (DAS-GEO). UGB areas consist of unincorporated lands surrounding a city that show where the city plans to grow over the next 20 years. When a city needs to develop more residential, commercial, industrial, or public land, it annexes the needed area from its UGB. If a city runs out of needed land within the UGB, it can expand its UGB. Original UGBs were established under the Oregon Statewide Planning Goals in 1973 by the Oregon State Legislature (Senate Bill 100). Goal 14 of the statewide planning program is, "To provide for an orderly and efficient transition from rural to urban land use, to accommodate urban population and urban employment inside urban growth boundaries, to ensure efficient use of land, and to provide for livable communities." The process and requirements for designating and amending UGBs are in Oregon Administrative Rules, Chapter 660, Division 24 (OAR 660-024). Designating or amending a UGB requires a public process, as required by Planning Goal 1, followed by approval by both the city and county elected officials and acknowledgement by the DLCD. This process includes the city submitting a Post Acknowledgement Plan Amendment (PAPA) to DLCD to review for consistency with Goal 14. The PAPA submittal includes GIS files that delineate the changes to the UGB. DLCD aggregates the local GIS layers into the statewide UGB layer. UGB line work and attributes are verified with the city PAPA submittals entered in DLCD’s tabular database to ensure that all UGB updates reported to DLCD have been included in this dataset. UGBs that are currently in the appeal process at the time of publication of this layer are not included. The effDate attribute indicates the year in which the UGB amendment was acknowledged by DLCD. In 2022, DLCD acknowledged amendments to the following UGBs: Central Point, Dayton, Phoenix, and Turner. Corrections were also made to the Astoria and Condon UGBs to reflect the current acknowledged boundary.

Urban Growth AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, displays Urban Growth Areas (UGA) in the United States. Per the USCB, “UGAs are legally defined entities in Oregon and Washington and are used to regulate urban growth. UGA boundaries, which need not follow visible features, are delineated cooperatively by state and local officials in Oregon and Washington and then confirmed in state law.”Metro Urban Growth AreaData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Urban Growth Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 62 (Series Information for 2020 Census Urban Growth Area (UGA) State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Urban Growth Areas - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit:Urban Growth AreasUrban Growth Area Maps (Washington)Urban Growth Boundaries (Oregon)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

This data provides the major urban area boundaries used when setting noise limits for commercial, industrial and trade premises. The Environment Protection Regulations 2021 and the incorporated …Show full descriptionThis data provides the major urban area boundaries used when setting noise limits for commercial, industrial and trade premises. The Environment Protection Regulations 2021 and the incorporated Noise Protocol document, Noise limit and assessment protocol for the control of noise from commercial, industrial and trade premises and entertainment venues (EPA publication 1826) set the limits for commercial, industrial and trade noise. There are two methods for setting the noise limits depending on the location of the residence or other noise sensitive area. • major urban areas – large regional towns, cities and Melbourne, or • rural areas. The major urban area boundaries are aligned with: • The Melbourne metropolitan Urban Growth Boundary (UGB) (Vicmap Planning - Department of Environment, Land, Water and Planning, 2020) • If outside of the Melbourne UGB, the UGB of any other municipality with a population greater than 7,000 persons. Obtained from the relevant authority upon request or digitised from current published documentation. • If outside of a UGB, the Urban Centre and Localities (UCL) boundary (Australian Bureau of Statistics, 2016) of an urban centre with a population greater than 7,000 persons, including land within the whole of any Residential, Industrial, Commercial or Urban Growth zone from the Planning Scheme Zones (Vicmap Planning - Department of Environment, Land, Water and Planning, 2020) that are crossed by the UCL boundary. The dataset is generated each fortnight based on current planning scheme zones combined with the Melbourne UGB, the Greater Bendigo UGB, and the Urban Centre and Locality (UCL) ASGS Ed 2016 Digital Boundaries in ESRI Shapfile format’ [sic] from https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.004July 2016?OpenDocument.

The Urban Growth Boundary (UGB) dataset represents the nine cities, urban (incorporated) municipality urban growth boundaries within the County of Sonoma. The UGB is a planning tool used to limit growth around a city and promote efficient provision and expansion of city services outside city limits.As defined by the Sonoma Local Agency Formation Commission (LAFCO; May 2003): An Urban Growth Boundary is a voter-enacted boundary for those City's which reside within the County of Sonoma. The boundary depicts the maximum growth over a specific period of time (typically 20 year time frame) as desired by the voters (residents) within the City's and acts as a control measure by the people. The jurisdiction of the boundary resides with the City's and thus, any modifications and/or amendments must be voted on by the registered voters within the City limits. Therefore, LAFCO has no jurisdiction as to the establishment of the urban growth boundaries beyond that of the City's Sphere of Influence (a.k.a. SOI). See Data Quality - Attribute Accuracy tab for boundary modification history.Related Resource:Permit Sonoma GIS HomepageSonoma Local Agency Formation Commission (LAFCO)Annexation Report (The annexation report represents unincorporated lands annexed into city jurisdictions.)Refer to Map series per jurisdiction -City of CloverdaleCity of CotatiCity of HealdsburgCity of PetalumaCity of Rohnert ParkCity of Santa RosaCity of SebastopolCity of SonomaTown of Windsor

OverviewORS 477.490 requires Oregon Sate University (OSU) and the Oregon Department of Forestry (ODF) to develop a statewide wildland-urban interface (WUI) map that will be used in conjunction with the statewide wildfire hazard map (ORS 477.490) by the Oregon State Fire Marshal to determine on which properties defensible space standards apply (ORS 476.392) and by the Building Codes Division to determine to which structures home hardening building codes apply (ORS 455.612).Rules directing development of the WUI are listed in OAR-629-044-1011 and 629-044-1016. A comprehensive description of datasets and geospatial processing is available at https://hazardmap.forestry.oregonstate.edu/understand-map. The official statewide WUI map is available on the Oregon Wildfire Risk Explorer at https://tools.oregonexplorer.info/viewer/wildfire.Following is an overview of the data and methods used develop the statewide WUI map.Wildland-Urban InterfaceCreating a statewide map of the WUI involved two general steps. First, we determined which parts of Oregon met the minimum building density requirements to be classified as WUI. Second, for those areas that met the minimum building density threshold, we evaluated the amount and proximity of wildland or vegetative fuels. Following is a summary of geospatial tasks used to create the WUI.Develop a potential WUI map of all areas that meet the minimum density of structures and other human development - According to OAR 629-044-1011, the boundary of Oregon’s WUI is defined in part as areas with a minimum building density of one building per 40 acres, the same threshold defined in the federal register (Executive Order 13728, 2016), and any area within an Urban Growth Boundary (UGB) regardless of the building density. Step One characterizes all the locations in Oregon that could be considered for inclusion in the WUI on building density and UGB extent alone. The result of Step One was a map of potential WUI which was then further refined into final WUI map based on fuels density and proximity in Step Two.Compile statewide tax lots.Map all eligible structures and other human development.Simplify structure dataset to no more than one structure per tax lotCalculate structure density and identify all areas with greater than one structure per 40 acresAdd urban growth boundaries to all the areas that meet the density requirements from the previous step.Classify WUI based on amount and proximity of fuel. The WUI is also defined by the density and proximity of wildland and vegetative fuels (“fuels”). By including density and proximity of fuels in the definition of the WUI, the urban core is excluded, and the focus is placed on those areas with sufficient building density and sufficient fuels to facilitate a WUI conflagration. Consistent with national standards, we further classified the WUI into three general classes to inform effective risk management strategies. The following describes how we refined the potential WUI output from step one into the fina

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural.

To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.

If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights.

We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may:

prefer to use an uncontrolled classification, or

prefer to create more than three categories.

To accommodate these uses, our final tract-level output dataset includes the "raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories.

The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural).

  For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 

Data Dictionary: DD_Urbanization Perceptions Small Area Index.

This service provides a tract-level dataset illustrating the outcome of machine learning techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural. Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural.

This hosted feature layer has been published in RI State Plane Feet NAD 83.THIS IS A FUTURE LAND USE MAP CREATED IN 2006. THIS DOES NOT SHOW CURRENT 2025 LAND USE LAND COVER.The Land Use 2025 dataset was developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. It evolved from a GIS overlay analysis of land suitability and availability and scenario planning for future growth. The analysis focused on the 37% of the State identified as undeveloped and unprotected in a land cover analysis from RIGIS 1995 land use land cover data. The project studied areas for suitability for conservation and development, based on the location of key natural resources and public infrastructure. The results identified areas with future use potential, under three categories of development intensity and two categories of conservation.These data are presented in the Plan as Figure 121-02-(01), Future Land Use Map. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The intent of the Plan is to bring together the elements of the State Guide Plan such as natural resources, economic development, housing and transportation to guide conservation and land development in the State. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas.These data have several purposes and applications: They are intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, these data are a guide to assist the state and communities in making land use policies. It is important to note these data are a generalized portrayal of state land use policy. These are not a statewide zoning data. Zoning matters and individual land use decisions are the prerogative of local governments. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. It depicts where different intensities of development (e.g. parks, urban development, non-urban development) should occur by color. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the USB, most land is served by public water service; many areas also have public sewer service, as well as, public transit. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts.NOTE: These data will be updated when the associated plan is updated or upon an amendment approved by the State Planning Council. NOTE: Wetlands were not categorized within the Land Use 2025 dataset.When using this dataset, the RIGIS wetlands dataset should be overlaid as a mask. Full descriptions of the categories and intended uses can be found within Section 2-4, Future Land Use Patterns, Categories, and Intended Uses, of the Plan. https://www.planning.ri.gov/documents/guide_plan/landuse2025.pdf

This system provides the user with a facility to select a state and county combination to determine if the selected county is part of an Office of Management and Budget (OMB) defined Core Based Statistical Area (CBSA). The system has been updated with OMB area definitions published for FY 2009.

This index represents the integrated probability of development occurring sometime between 2020 and 2080 at the 30 m cell level. It was based on models of historical patterns of urban growth in the Northeast, including the type (low intensity, medium intensity and high intensity), amount and spatial pattern of development, and incorporates the influence of factors such as geophysical conditions (e.g., slope, proximity to open water), existing secured lands, and proximity to roads and urban centers. The projected amount of new development is downscaled from county level forecasts based on a U.S. Forest Service 2010 Resources Planning Act (RPA) assessment. A complementary product, Probability of Development, 2040, Northeast U.S., estimates the probability of development over a shorter time-scale. The derivation of the integrated probability of development layer was complex. Please consult the technical documentation for a full description of the background data used, the computation of integrated probabilities from a stochastic model, and information about the related urban growth model. The following is a summary of the five major steps of the derivation: 1) Determining historical patterns of growthTo understand how past patterns of development have occurred, historical data from NOAA (for Maine and Massachusetts) and the Chesapeake Bay Watershed Landcover Data Series were obtained for the years 1984 (Chesapeake Bay only), 1996, and 2006. The data were used to model the occurrence of six different development transition types: New growthundeveloped to low-intensity (20-49% impervious surface; e.g., single-family homes)undeveloped to medium-intensity (50-79% impervious surface; e.g., small-lot single-family homes)undeveloped to high-intensity (80-100% impervious surface; e.g., apartment complexes and commercial/industrial development) Intensificationlow- to medium-intensitylow- to high-intensitymedium- to high-intensity Separate models were developed to represent development patterns at model points representing landscapes differing along two dimensions: intensity of development and amount of open water. Predictor variables in the models account for the intensity of existing development and landscape context (e.g. intensity and distance of nearest roads, amount of open water). Analysis of the historical data was based on dividing the landscape into “training windows,” 15km on a side, to determine the historical distribution of transition types and the total amount of historical development. 2) Application to current landscapesFuture patterns of development were projected based on the observed historical patterns. As the first step in this process, the entire Northeast was subdivided into 5km “application panes,” each of which was the center pane of a (3 x 3) “application window”, 15 km on a side. Each of these overlapping application windows was then matched to the three most similar training windows on the basis of intensity of development from the UMass integrated landcover layer, (derived in turn from the 2011 National Landcover Database and other sources), as well as geographic proximity, amount of open water, and density of roads. . For each application window, according to how it mapped on to the dimensions of development and open water modelled above, the relative probability of each of the six development transition types was determined on a scale of 30m cells. 3) Predictions for changing land-useFuture urban acreage by county was predicted as part of an assessment for the U.S. Forest Service 2010 Resources Planning Act. The derivation of this product, the new growth forecasted for the 70 years between 2010 and 2080 was transformed into demand in units of 30m cells. Demand for each county (or census Core Based statistical Area, where relevant) was allocated to the corresponding application windows based on the average of the total amount of historical development in the three matched training windows. 4) Combining models of past and predictions for the futureThe relative probability of a transition type occurring in each cell in a window was used to distribute the allocated demand of new growth throughout the window. The result was an actual probability of development for the transition occurring sometime between 2020- 2080 at the 30 m cell level. Already existing urban land-use was intensified (i.e., transitions 4-6) in proportion to historic patterns determined from the matched training windows, and distributed according to the probability of those transition types across the cells in the window. The combining of probabilities and demand to distribute development to cells was done for each transition type in turn; thus, each cell received a separate probability of being developed through each of the six transition types. Through the application of this process in every application window, an actual probability of development was determined for each cell with reference to nine slightly different contexts corresponding to each of the overlapping windows in which the pane was situated. 5) Smoothing and integrationAn additional step was used to create a smooth and continuous probability of development surface, not subject to abrupt differences along arbitrary boundaries. Cell by cell, actual probabilities of development from each of the overlapping windows were combined such that the closer to a window’s center a cell was located, the more weight the probability derived from it was given. Consequently, each cell had one weighted average probability that was part of a continuous probability of development surface for each transition type. Finally, the probability of development by each of six transition types was integrated for each cell. More weight was given to new growth, such that the probability of undeveloped land becoming urban had more impact than the probability of an intensification of development. The final product is a single layer of the integrated probability of development by 2080, extending across the entire Northeast on the scale of 30 m cells.Known Issues and Uncertainties As with any project carried out across such a large area, the Probability of Development dataset is subject to limitations. The results by themselves are not a prescription for on-the-ground action; users are encouraged to verify, with field visits and site-specific knowledge, the value of any areas identified in the project. Known issues and uncertainties include the following:Although this index is a true probability, it is best used in a relative manner to compare values from one location to anotherThe GIS data upon which this product was based, especially the National Land Cover Dataset (NLCD), are imperfect. Errors of both omission and commission affect the mapping of current development and in turn, models of the probability of future development. Likewise, the forecasts in the 2010 Resources Planning Act assessment, the basis of the projected demand for new growth, contains uncertainties. While the model is anticipated to generally correctly indicate where development is likely to occur, predictions at the cell level are not expected to be highly reliable.Users are cautioned against using the data on too small an area (for example, a small parcel of land), as the data may not be sufficiently accurate at that level of resolution.This model is built on the assumption that future patterns of development will match patterns in the past.It is important to recognize that the integrated probability of development is highest near existing roads, largely because the urban growth model does not attempt to predict the building of new roads and the development associated with them, nor does it incorporate county or town level planning for infrastructure. Because proximity to roads is an important and dominant predictor of development at the 30- m cell level in the model, the integrated probability of development surface is heavily weighted towards existing roads. It is not specifically designed to predict where a subdivision might be developed in the future.

This feature dataset contains a snapshot of all King County parcels from September 2020, with all of the "additional relevant criteria" data used in Method 2 of the LCI opportunity area determination described below.There are two methods by which a property may qualify as being in an opportunity area:Method 1. Property meets all three of the following "specified criteria" in King County code 26.12.003.(a) Areas "located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County; (b) "located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP codes in King County; and (c) "are within the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within one-quarter mile of a residence, or are outside the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within two miles of a residence." (King County Code 26.12.003)Data results related to Method 1 are shown in the LCI Opportunity Areas dataset on the King County GIS Open Data site. In this dataset, the parcels where the "CriteriaAllYN" column is equal to "Y" also represents those parcels.Method 2. If a property does not qualify under Method #1, a project may qualify if: "the project proponent or proponents can demonstrate, and the advisory committee determines, that residents living in the area, or populations the project is intended to serve, disproportionately experience limited access to public open spaces and experience demonstrated hardships including, but not limited to, low income, poor health and social and environmental factors that reflect a lack of one or more conditions for a fair and just society as defined as "determinants of equity" in KCC 2.10.210." (King County Code 26.12.003)Conservation Futures (CFT) values the use of multiple sources of data and information to demonstrate that a property is in an opportunity area. Applicants are welcome to provide additional criteria and data sources not identified in this report to demonstrate that a property is in an opportunity area. These sources are provided in the document here: Understanding the Data Report.

A Home for Everyone is the City of Boise’s (city) initiative to address needs in the community by supporting the development and preservation of housing affordable to residents on Boise budgets. A Home for Everyone has three core goals: produce new homes affordable at 60% of area median income, create permanent supportive housing for households experiencing homelessness, and preserve home affordable at 80% of area median income. This dataset includes information about all homes that count toward the city’s Home for Everyone goals.

While the “produce affordable housing” and “create permanent supportive housing” goals are focused on supporting the development of new housing, the preservation goal is focused on maintaining existing housing affordable. As a result, many of the data fields related to new development are not relevant to preservation projects. For example, zoning incentives are only applicable to new construction projects.

Data may be unavailable for some projects and details are subject to change until construction is complete. Addresses are excluded for projects with fewer than five homes for privacy reasons.

The dataset includes details on the number of “homes”. We use the word "home" to refer to any single unit of housing regardless of size, type, or whether it is rented or owned. For example, a building with 40 apartments counts as 40 homes, and a single detached house counts as one home.

The dataset includes details about the phase of each project when a project involves constructing new housing. The process for building a new development is as follows: First, one must receive approval from the city’s Planning Division, which is also known as being “entitled.” Next, one must apply for and receive a permit from the city’s Building Division before beginning construction. Finally, once construction is complete and all city inspections have been passed, the building can be occupied.

To contribute to a city goal, homes must meet affordability requirements based on a standard called area median income. The city considers housing affordable if is targeted to households earning at or below 80% of the area median income. For a three-person household in Boise, that equates to an annual income of $60,650 and monthly housing cost of $1,516. Deeply affordable housing sets the income limit at 60% of area median income, or even 30% of area median income. See Boise Income Guidelines for more details.Project Name – The name of each project. If a row is related to the Home Improvement Loan program, that row aggregates data for all homes that received a loan in that quarter or year. Primary Address – The primary address for the development. Some developments encompass multiple addresses.Project Address(es) – Includes all addresses that are included as part of the development project.Parcel Number(s) – The identification code for all parcels of land included in the development.Acreage – The number of acres for the parcel(s) included in the project.Planning Permit Number – The identification code for all permits the development has received from the Planning Division for the City of Boise. The number and types of permits required vary based on the location and type of development.Date Entitled – The date a development was approved by the City’s Planning Division.Building Permit Number – The identification code for all permits the development has received from the city’s Building Division.Date Building Permit Issued – Building permits are required to begin construction on a development.Date Final Certificate of Occupancy Issued – A certificate of occupancy is the final approval by the city for a development, once construction is complete. Not all developments require a certificate of occupancy.Studio – The number of homes in the development that are classified as a studio. A studio is typically defined as a home in which there is no separate bedroom. A single room serves as both a bedroom and a living room.1-Bedroom – The number of homes in a development that have exactly one bedroom.2-Bedroom – The number of homes in a development that have exactly two bedrooms.3-Bedroom – The number of homes in a development that have exactly three bedrooms.4+ Bedroom – The number of homes in a development that have four or more bedrooms.# of Total Project Units – The total number of homes in the development.# of units toward goals – The number of homes in a development that contribute to either the city’s goal to produce housing affordable at or under 60% of area median income, or the city’s goal to create permanent supportive housing for households experiencing homelessness. Rent at or under 60% AMI - The number of homes in a development that are required to be rented at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 61-80% AMI – The number of homes in a development that are required to be rented at between 61% and 80% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 81-120% AMI - The number of homes in a development that are required to be rented at between 81% and 120% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details.Own at or under 60% AMI - The number of homes in a development that are required to be sold at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.