The purpose of the design review process is to: Allow for special projects to be approved by the Zoning Commission after a public hearing and a finding of no adverse impact; Recognize that some areas of the District of Columbia warrant special attention due to particular or unique characteristics of an area or project; Permit some projects to voluntarily submit themselves for design review in exchange for flexibility because the project is superior in design but does not need extra density; Promote high-quality, contextual design; and Provide for flexibility in building bulk control, design, and site placement without an increase in density or a map amendment.

The Chinatown Design Review Boundary as established by DCMR Title 10 Chapter 24. The purpose of Chinatown Design Review is to ensure the contribution of proposed buildings and public space projects to the Chinese identity of Chinatown.

This ownership dataset utilizes a methodology that results in a federal ownership extent that matches the Federal Responsibility Areas (FRA) footprint from CAL FIRE's State Responsibility Areas for Fire Protection (SRA) data. FRA lands are snapped to county parcel data, thus federal ownership areas will also be snapped. Since SRA Fees were first implemented in 2011, CAL FIRE has devoted significant resources to improve the quality of SRA data. This includes comparing SRA data to data from other federal, state, and local agencies, an annual comparison to county assessor roll files, and a formal SRA review process that includes input from CAL FIRE Units. As a result, FRA lands provide a solid basis as the footprint for federal lands in California (except in the southeastern desert area). The methodology for federal lands involves:

The BEPS Program was created by Title III of the Clean Energy DC Omnibus Act of 2018. The BEPS is a minimum threshold of energy performance that will be no lower than the local median ENERGY STAR score by property type (or equivalent metric). The standards were created to drive energy performance in existing buildings to help meet the energy and climate goals of the Sustainable DC plan — to reduce greenhouse gas emissions and energy consumption by 50% by 2032. DOEE established the first set of Standards on January 1, 2021. Standards will then be set every 6 years, creating BEPS Periods (BEPS Period 1, BEPS Period 2, etc.). The 2021 Building Energy Performance Standards and a Guide to the 2021 BEPS are available for viewing on DOEE’s website.To improve transparency and help building owners understand how their building performs relative to the BEPS, DOEE is publishing this BEPS Disclosure that compares a building’s benchmarking data with the BEPS and provides an estimate of the building’s distance from the standard and estimated performance requirement.Please note that this dataset is based on information currently available to DOEE using calendar year 2019 benchmarking data provided by the building owner. Some buildings are still being evaluated and therefore have been designated as “Under Review” in this dataset. Building owners that believe their 2019 calendar year data is incorrect should contact the Benchmarking Help Center (info.benchmark@dc.gov). Additionally, buildings that meet certain criteria may request a variance from the standards by submitting a variance request form on the DOEE website.

June 2016 VersionData Content:This data set contains all CalWater 2.2.1 Planning Watersheds (PWS) where CDFW has documented coho salmon to be present during or after 1990. It was developed for the express purpose of assisting with Coho salmon recovery planning efforts. NOTE: Acreages are calculated for area inside California only. It is important to note that this data set does not attempt to model the entire possible distribution of the species. Rather, it only represents planning watersheds intersecting the known distribution, which is based on where the species has been observed and reported. While the distribution data may indeed represent the extent of the species, generally the upstream extent of the distribution only represents the location of positive sampling or other observations.. Therefore, this data set likely represents an underestimation of the absolute geographic distribution of the species. Data Source:This watershed level data set was derived by intersecting Calwater PWS with point and line features depicting Coho salmon distribution. These features are derived from a subset of data contained in thedevelopers file geodatabase (©Environmental Science Research Institute (ESRI) 2016). This is an ongoing project developed by CDFW Northern Region Data Management and GIS with assistance from CDFW Biogeographic Data Division and Pacific States Marine Fisheries Commission. These data are based upon confirmed observations of Coho salmon. Effort has been made to identify and correct watersheds that were erroneously selected due to inaccuracies from using data of different scales. The observation data that are the basis for the distribution were compiled from a variety of disparate sources including but not limited to CDFW, U.S. Forest Service, National Marine Fisheries Service, timber companies, and the public. Forms of documentation include CDFG administrative reports, personal communications with biologists, observation reports, and literature reviews.This derived dataset is meant to be continually updated as additional information is acquired. As such, any copy of this dataset is considered to be a snapshot of the known Coho Distribution at the time of release. It is incumbent upon the user to ensure that they have the most recent version prior to making management or planning decisions.Data Usage: Examples of appropriate uses include: Coho salmon recovery planning Evaluation of future survey sites for Coho Validating Coho distribution models Examples of inappropriate uses include: Using this data to make parcel or ground level land use management decisions. Using this data set to prove or support non-existence of coho at any spatial scale. Assuming that Coho are prevalent throughout the entire watershed. All users of this data should seek the assistance of qualified professionals such as surveyors, hydrologists, or fishery biologists as needed to ensure that such users possess complete, precise, and up to date information on Coho salmon distribution and water body location. Please refer to "Use Constraints" section below.

Reviews of small and large scale development projects within Cumberland County, NC.

This map compares any two years' per capita income for counties in the U.S.Data downloaded from GeoFred application. GeoFred provides geographical economic data from the St. Louis Fed (the Federal Reserve Bank of St. Louis)Source: U.S. Bureau of Economic Analysis, Personal Income per capita, retrieved from FRED, Federal Reserve Bank of St. Louis

Pre-planning application locations. This data set depicts the location of pre-applications. The regulatory review process can usually be expedited if the applicant elects to participate in a pre-application conference with District engineers and environmental scientists early in the project planning process. A meeting with District staff can help the applicant and the project designers to better understand District rules and regulations, and help District staff understand the project. The District staff can outline procedures to facilitate submittal of a complete application or explain permitting requirements, as needed. Any potential permitting problems could be identified at the meeting.

This data set is a graphical representation of the Coastal Areas Facilities Review Act (CAFRA) boundary, which legislates land use within the coastal area. This boundary was dissolved from the Coastal Planning Areas in New Jersey data set.

This layer contains Gross Domestic Product (GDP) Per Capita - the total value of goods produced and services provided, divided by the total population in each country, from 1960 to 2016, expressed in 2016 US Dollars. Expressing the GDP in "per capita" terms allows for better comparisons across countries. Total GDP is available in an accompanying layer. GDP as a measure has been largely criticized as an incomplete measure of productivity and wealth, as it does not take into account production in the informal economy, quality of life, degradation to the environment, or income distribution. However, GDP is an internationally comparable measure, used in everything from banks setting interest rates to political campaign speeches.Source: World Bank, World Development Indicators.

This is a feature point layer of the 124 licensed personal care homes (PCHs) in Manitoba. All licensed PCHs in Manitoba are required to comply with minimum standards of care as set out in the Personal Care Home Standards Regulation under the Health Services Insurance Act. The Licensing and Compliance Branch of Manitoba Health Seniors and Active Living monitors compliance through regular review processes. PCH operators are required to take the necessary steps to address concerns identified in the course of reviews within specified time lines and must provide status updates until concerns have been addressed. PCH licences are reviewed and renewed annually and review findings are used to inform decision-making. The dataset includes the following fields (Alias (Name): Description) Regional Health Authority (Regional_Health_Authority): The name of the Regional Health Authority in which the facility is located. Community (Community): The name of the community in which the facility is located. Facility (Facility): The name of the licensed personal care home. Facility Key (Facility_Key): Primary key used to query records in the Summary Reviews table. Facility Label (Facility_Label): An abbreviated facility name suitable for use as a label in a map. Address (Address): The street address of the facility. Postal Code (Postal_Code): The postal code for the facility. Phone Number (Phone_Number): The phone number for the facility. Proprietary Status (Proprietary_Status): Refers to the ownership of the facility, either Proprietary or Non-proprietary. Language (Language): The designated language of the facility, either English or Bilingual. Bed (Beds): The number of beds in the facility. Status of Licence (Status_of_Licence): The status of the facility’s license. Possible values are Unencumbered, Under Review, or With Conditions. Owner/Operator (Owner_Operator): The individual or company that owns the facility. Website (Website): The URL for the website of the facility. Latitude (Latitude): The latitudinal coordinate in decimal degrees. Longitude (Longitude): The longitudinal coordinate in decimal degrees.This feature point layer forms part of the data for the Manitoba Personal Care Home Reporting app.

This table contains URL links to reports on personal care home reviews completed by the Licensing and Compliance Branch of Manitoba Health, Seniors and Active Living. All licensed personal care home facilities in Manitoba are required to comply with minimum standards of care as set out in the Personal Care Home Standards Regulation under the Health Services Insurance Act. The branch monitors compliance through regular review processes. Operators of facilities are required to take the necessary steps to address concerns identified in the course of reviews within specified time lines and must provide status updates until concerns have been addressed. Licences are reviewed and renewed annually and review findings are used to inform decision-making.The table includes the following fields (Alias (Name): Description)Regional Health Authority (Regional_Health_Authority): The name of the Regional Health Authority in which the facility is located.Community (Community): The name of the community in which the facility is located.Facility (Facility): The name of the licensed personal care home.Facility Key (Facility_Key): Primary key used to link this table with the feature point layer Licensed Personal Care Homes.Review Date (Review_Date): The date the review was conducted.Review Type (Review_Type): The type of review conducted. Possible values are Regular Standards, Unannounced, Modified, Pre-Opening, or Other.Summary Link (Summary_Link): The URL link to the report.This table forms part of the data for the Manitoba Personal Care Home Reporting app.

All Business Licenses that are Active, Renewed, In Review, and/or Closed within the City of West Sacramento. Dataset is updated nightly.

The Draft Budget 2025 Explorer provides insight into how the budget is created, what elements make up the budget, user-friendly interactive charts, graphs and tables to enhance financial literacy and transparency, an update on service reviews as well as highlights from over 100 lines of services that are advancing Council’s strategic priorities. The draft budget is broken down by Committee, department and service areas as described in the Table of City Services and Standing Committee reporting structure. With direction from Council, the budget is drafted and tabled for review by each Standing Committee and adopted by Council. Aside from the draft budget considered by Committees, there are four external boards who debate their budget separately. These budgets are represented in the overviews but are not broken down by committee or included in the rates, fees and charges reports. For more information on these budgets please visit the agenda for the budget tabling meeting. • Committee of Adjustment • Ottawa Police Services • Ottawa Public Health • Ottawa Public Library For complete details on the budget visit the Budget, finance and corporate planning page.Date Created: November 19th, 2024Update Frequency: As required.Accuracy, Completeness, and Known Issues: If at any point in time the figures found in this tool differ from the draft budget books or presentations at Committee, the draft budget books will be considered the accurate data.Attributes: 1_Operating_overview_expenditure1_Operating_overview_revenueAll City programs and services are funded through the City’s operating budget, which supports the dependable delivery of services that residents rely on every day.2_Capital_program_by_committee2_Capital_program_by_funding_src2_Capital_program_by_service_categoryCity infrastructure and assets are funded through the capital budget. Most of that funding goes to maintaining and fixing existing infrastructure as described in the Comprehensive Asset Management analysis. As funding allows, the City continues to fund growth, build new infrastructure and invest in the future. 3_Reserve_fund_DiscretionaryreservesReserve funds are monies set aside to fund capital expenditures, similar to having personal savings accounts for future needs. They are also used to manage unexpected expenses and to support the City’s finances for the long-term.4_Rates_fees_and_chargesRates are utility charges dependant on usage for water consumption and sewer surcharges that are found on residents' water bills. Fees are charged to users of many City services to cover part or all of the costs of providing the service. Examples of where fees are applied include transit fares, recreation program fees, planning applications and childcare fees. Development charges are one-time fees levied by municipalities on new residential and non-residential properties to help pay for a portion of the growth-related capital infrastructure requirements.5_Exp_brkd_by_committee_OPERATING5_Exp_brkd_by_committee_CAPITALThe draft budget is broken down by Committee, department and service areas as described in the Table of City Services and Standing Committee reporting structure. Each Committee is responsible for a specific portion of the operating and capital budget. Each Committee hears from Community delegations and debates the items assigned to them. Councillors can ask for amendments to each section of the budget and then all sections of the budget are brought back to Council for final a vote on adoption.6_ How_the_city_of_ottawa_compares?See how the City’s taxation compares to other major Canadian cities from 2012-2024.Data Steward: Suzanne Schnob – Financial Services ManagerData Steward Email: fcsdposting@ottawa.caDepartment or Agency: Finance and Corporate Services DepartmentBranch/Unit: Financial Strategies, Planning and Client Services

This online map displays California’s active Underground Gas Storage (UGS) projects and wells associated to UGS projects. Project data and well data are provided by CalGEM’s Well Statewide Tracking and Reporting System (WellSTAR). Wells are displayed by well type and the association to a UGS project.CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).WellSTAR homepageUpdate Frequency: As Needed

This raster file represents land within the Mountain Home study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. To build a Random Forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

Record lots are defined by the Department of Buildings (DOB) – Office of the Surveyor (OS) - DC Surveyor. They are official, platted, recorded subdivision lots created by the D.C Surveyor’s Office in compliance with the Subdivision Ordinance of the District of Columbia (must have public street frontage etc). Typically, these lots are numbered 1 through 799 with no number being used more than once in a Square. Exceptions to this rule:When the 1-799 range has been exhausted within a square, the Surveyor’s Office assigns numbers from 1200 or may even use 8000 and aboveFor reasons unknown, 42 Squares have record lot numbers greater than 799 but less than 1200Additionally, in most case scenario’s, a piece of property must be a Record Lot before a building permit will be issued for that site in the District of Columbia, and all proposed Record Lots are carefully reviewed by Zoning Administration officials for compliance with the city’s Zoning Ordinances. Other agencies that review new record lots besides OS are Office of Zoning, Office of Planning, the Dept. of Public Works, Historic Preservation and DDOT.Record lots are defined only when requested by property owners, normally when they are seeking a building permit. Record lots are recorded in Plat Books and Subdivision Books in the Office of the Surveyor. These documents are bound volumes of historical representations of the locations of property lines, and they include record dimensions, though typically no bearings of lines. These lots are located within squares, which usually correspond to one or two city blocks. Certain record lots can also be classified as “of-lots”. An "Of-Lot" is the D.C. Surveyor’s Office term for describing “Remaining/Part of Original Lot X”In the record lot feature class, if a domain value of 1 resides in the “OF_LOT” field, you can assume that at one time the original lot was modified. Typically, any of these of-lots will also have a tax lot overlaying them since it is a piece or remainder of a Record Lot.

Design Review Equity Areas are areas of Seattle where applicants for development projects going through the City’s Design Review program are required to work with staff from the Department of Neighborhoods (DON) to customize their community outreach plan to the needs of historically underrepresented communities.

Equity Areas are identified based on local demographic and socioeconomic characteristics from the US Census Bureau. Equity Areas are census tracts having a census-tract average greater than the city-as-a-whole average for at least two of the following characteristics:

1. Limited English proficiency, identified as percentage of households that

are linguistically isolated households.

2. People of Color, identified as percentage of the population that is not non-Hispanic white; and

3. Income, identified as percentage of population with income below 200% of the federal poverty level.

For more information please see Director’s Rule for Early Community Outreach for Design Review. Additional resources and FAQs are available on DON’s Early Community Outreach webpage.

Data Source: US Census Bureau’s American Community Survey 2016 Five-Year Estimates.

This map will be evaluated and updated every three years.This layer is used in the SDCI Web Map.

This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThis dataset consists of hexagons 50-acres in area, or several city blocks. The dataset covers the following tree canopy categories:Existing tree canopy percentPossible tree canopy - vegetation percentRelative percent changeAbsolute percent changeAverage maximum afternoon temperature (F)Tree canopy percentage & average afternoon temperature (F)For more information, please see the 2021 Tree Canopy Assessment.