Displacement risk indicator classifying census tracts according to single-family home sale prices in census tracts where at least 100 single-family homes exist. We classify arms-length transactions only along two dimensions:The median price of sales within the census tract for the specified year, balancing between nominal sale price and sale price per square foot.The change in median sale price (again balanced between nominal sale price and price per square foot) from the previous year.

All rateable land within the local area is categorised as either residential or business. The City determines the category of your property based on its dominant use. There are 3 categories: residential, business, business CBD.This layer shows the different business rate zones.View the interactive map More information on business rates

A land area that has distinct management/use authorities or agreements for Forest Service action. Includes: Cost Share Agreement Areas, Exchange Authority Areas, Land Adjustment Plan Areas, Forest Reserves, and Secretary's Order Areas. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

The dataset contains constructed unique geospatial identifier for buildings. A buildings UBID is the north axis aligned "bounding box" of its footprint represented as the centroid (in the GDAL grid reference system format), which is represented by the first set of characters before the first dash, and four cardinal extents, which are represented by the four sets of numbers after the first dash (North, East, South, West),The data has been constructed by spatially joining the latest (2019) building footprints published in DC Open Data with the Common Ownership Lot shapefile. The UBIDs were coded using US DOE’s Implementation code. Please note that the current data set may include some unnecessary structures identified as buildings. These included sheds, overhangs, bus stops, and other structures that do not need to be assigned a UBID. An updated version of the UBID dataset will be released when this issue is resolved. This project is the result of the US DOE Better Buildings Building Energy Data Analysis (BEDA) Accelerator. US DOE is working with stakeholders including state and local governments, commercial and residential building data aggregators, property owners, and product and service providers to develop the UBID system and to pilot it in real-world settings. US DOE and its partners are demonstrating the benefits of UBID in managing and cross-referencing large building datasets and in reducing the costs and enhancing the value proposition of leveraging building energy data. UBIDs For more information regarding UBIDs please visit: https://www.energy.gov/eere/buildings/unique-building-identifier-ubid

Mortality Rates for Lake County, Illinois. Explanation of field attributes: Average Age of Death – The average age at which a people in the given zip code die. Cancer Deaths – Cancer deaths refers to individuals who have died of cancer as the underlying cause. This is a rate per 100,000. Heart Disease Related Deaths – Heart Disease Related Deaths refers to individuals who have died of heart disease as the underlying cause. This is a rate per 100,000. COPD Related Deaths – COPD Related Deaths refers to individuals who have died of chronic obstructive pulmonary disease (COPD) as the underlying cause. This is a rate per 100,000.

This data set provides the calculated annual average price of residential homes sold, by home type, within Peel and the area municipalities since 2005. Data is compiled from monthly data released by the Toronto Real Estate Board’s Market Watch reports.NoteAverage annual home price by type for Peel and each of the area municipalities has been calculated using monthly sales and dollar volume. For years 2005 to 2011, data was first aggregated based on TREB districts.

Displacement risk indicator showing the number of property transactions of single-family homes recorded by the King County Assessor that can be classified as "flips" (meaning that the home had previously been sold within the past year and that the sale price had increased between sales at least twice as fast as the increase in regional housing Consumer Price Index during the same time period). Summarized at the census tract level; available for every year from 2004 through the most recent year of available data.

St. Louis County Missouri Tax Rates 2020

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.

The northern Sierra Nevada foothills wildlife connectivity project modeled wildlife corridors for 9 focal species between 238 landscape blocks within the northern Sierra Nevada foothills and neighboring ecoregions. We followed the least-cost corridor techniques described by Beier et al. (2007). This analysis identified the least-cost corridor, or the best potential route for each species, between neighboring landscape blocks. The data needed for a least-cost corridor analysis are a resistance raster and landscape blocks. The resistance raster is the inverse of the species distribution model (SDM) output (i.e., Maxent or BioView habitat models, which rank habitat suitability across the landscape from 0-100 for each species). We identified habitat patches for each focal species within each landscape block, and connected those habitat patches using the least-cost corridor models. The least-cost corridor model does not identify barriers, risk and dispersal. We removed urban areas and areas of unsuitable/non-restorable habitat from the corridors and then inspected the corridor to make sure they were continuous. We examined the amount of predicted suitable habitat in each corridor, and measured the distance between habitat patches within each corridor to make sure it was within the maximum dispersal distance for that focal species. If the corridors did not meet these rules then habitat patches on the border of the corridor were added to meet the selection requirements. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

The northern Sierra Nevada foothills wildlife connectivity project modeled wildlife corridors for 9 focal species between 238 landscape blocks within the northern Sierra Nevada foothills and neighboring ecoregions. We followed the least-cost corridor techniques described by Beier et al. (2007). This analysis identified the least-cost corridor, or the best potential route for each species, between neighboring landscape blocks. The data needed for a least-cost corridor analysis are a resistance raster and landscape blocks. The resistance raster is the inverse of the species distribution model (SDM) output (i.e., Maxent or BioView habitat models, which rank habitat suitability across the landscape from 0-100 for each species). We identified habitat patches for each focal species within each landscape block, and connected those habitat patches using the least-cost corridor models. The least-cost corridor model does not identify barriers, risk and dispersal. We removed urban areas and areas of unsuitable/non-restorable habitat from the corridors and then inspected the corridor to make sure they were continuous. We examined the amount of predicted suitable habitat in each corridor, and measured the distance between habitat patches within each corridor to make sure it was within the maximum dispersal distance for that focal species. If the corridors did not meet these rules then habitat patches on the border of the corridor were added to meet the selection requirements. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center

Polygon boundaries representing finance area rates for private road charges. The data was created for taxation purposes. See HRM Tax Rates dataset for more information. Metadata

The Cost Surface layer was constructed using a combination of several geospatial layers resulting in a series of polygons covering the entire estimated occupied range plus ten miles (EOR+10). The following geospatial layers were used to create the cost surface layer: LPC Crucial Habitat, Modified Cropland Classification, Ecoregions, ESD (soils), and Percent Potential Habitat within 1 Mile. Habitat Evaluation Guide (HEG) cost values for each polygon within the newly created layer were calculated in accordance with the HEG Estimator Tool. HEG costs are represented visually scaled 0 –10 with 0 representing no cost areas and 10 representing the highest estimated cost areas. Projection:USA_Contiguous_Albers_Equal_Area_Conic_USGS_version. Datum:NAD 83. Spatial ref. 102039

This polygon feature class represents property tax rate boundaries in Peoria County, Illinois. Boundaries reflect the 2023 tax year.

Please note, this spreadsheet refers to 'Outstanding debt', meaning the amount the council owes the customer. These accounts are therefore in credit.

Limitations on data:

Where ratepayers are individuals, for example sole traders, we keep their personal details private. The information is exempt under Section 40 (2) of the Freedom of Information Act.

(Business Rates data: list of Business Rates properties which have had credit balances written on)

Labour market indicators for local and unitary authorities, counties and regions in Great Britain for a 12 month period. Please note that the spatial layer the source data have been joined to is Local Authority Districts (December 2016) Ultra Generalised Clipped Boundaries in England. This layer has been used to improve online load time and may be used for country level visualisations. Please DO NOT use this layer for area or length calculations.Source: ONSUpdate Frequency: QuarterlyLast Update: 16 Oct 2018Next Update: 22 Jan 2019

Hosted feature service containing rental pricing information for Chatham County from 2006, 2011, & 2015 broken down by Census Block Groups.

The source of the rental cost information was the MLS. The data was geocoded, spatially joined with the Census Block Groups, and summarized statistically to present meaningful content for the Chatham County Board of Commissioners. The data within this service was created to help assess the state of affordable housing in Chatham County in 2015 during various affordable housing summits. The home price information was the only data set chosen to be displayed in a web mapping application in an attempt to not inundate the decision makers with too much data.Although this service is not consumed in a web mapping application it still provides meaningful information that can be updated on an as needed basis with more current information to further assess the affordable housing situation in Chatham County, NC.Chatham GIS SOP: "HFS-39"

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.