The North Carolina state and local government metadata profile as adopted by the NC Geographic Information Coordinating Council. The document and other information can be found at: https://it.nc.gov/documents/files/gicc-smac-state-local-gov-metadata-profile.

This inventory includes all data sets scheduled for release between July 2016 and December 31, 2018.

This dataset contains model-based place (incorporated and census designated places) level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the 2019 Census TIGER/Line place boundary file in a GIS system to produce maps for 29 measures at the place level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

The Local Geohistory Project aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government. This repository contains the data used to populate the project website. The tab-separated values (TSV) files containing the data are available in the data folder, and metadata is available in the metadata folder.

Currently, the open dataset only contains information related to New Jersey and Pennsylvania, with several scattered events concerning neighboring jurisdictions, mostly that currently border either state.

This repository does not contain the application code, which can be found in the Application repository, nor does it contain the table data for the bundled calendar extension.

Infectious disease experts have predicted a pandemic, saying it was not a question of if but when. Drawing on experiences with severe acute respiratory syndrome (SARS), avian influenza (H5N1), and novel influenza A (H1N1), the World Health Organization (WHO) and other health authorities, such as the Centers for Disease Control and Prevention (CDC), urged nations and local governments to prepare pandemic response plans. Many ministries of health and subnational departments of health around the world have activated those plans in response to coronavirus and are sharing data as required by the updated International Health Regulations.Esri's work with health organizations and government leaders has proven location intelligence from geographic information system (GIS) technology and data to be critical for the following:Assessing risk and evaluating threatsMonitoring and tracking outbreaksMaintaining situational awarenessEnsuring resource allocationNotifying agencies and communitiesThe current coronavirus disease pandemic presents an opportunity to build on the experience and readiness of Esri's existing global user community in health and human services. Through real-time maps, apps, and dashboards, GIS will also facilitate a seamless flow of relevant data as a component of the response from local to global levels. A compelling case exists for building on top of the public health GIS foundation that is already in place both in the United States and around the world.After reading this paper, leadership and senior staff should understand the following:The necessity to apply location intelligence to public health processes in coronavirus responseHow GIS can support immediate and long-term actionWhat resources Esri provides its customers

Foster City GIS Services: ArcGIS Server Connection https://services7.arcgis.com/CYn8XGt0yVlPlS5X/ArcGIS/rest/services

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

California Local Reliability Areas.

Local options on alcohol and marijuana in communities across Alaska.Source: Alcohol and Marijuana Control Office. This data 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: https://www.commerce.alaska.gov/web/amco/

This map application features information about local public transportation including MBTA Green Line and bus services.

A Subwatershed represents the area where precipitation naturally drains to a common water feature. Subwatersheds are part of a larger system of drainage areas within our larger watersheds like the Cuyahoga, Rocky, and Chargin Rivers. In turn, those watersheds are a part of a larger "basin". For Cuyahoga County and much of its surrounding area, our subwatersheds and watersheds drain into Lake Erie and the Great Lakes Basin.

Each of the small subwatersheds has information about its "parent" watershed group and associated websites, which provide detailed profiles of conditions and issues in the subwatershed.

One key characteristic of watershed health is the portion of its land area that is "impervious", such as roadway or roofs. For each subwatershed, we've indicated its impervious cover percentage. The Center for Watershed Protection provides guidelines on appropriate practices for watersheds based on their impervious cover. For example, highly urbanized areas (highly impervious) may only benefit from limited practices, such as retrofitting stormwater systems or replacing traditional parking surfaces with "pervious" surfaces. Less urbanized areas (less impervious) might benefit more by preserving headwater drainage and wetlands.

See the layer "Local Subwatersheds, By Percent Imperviousness" and the accompanying report from the Center for Watershed Protection: \dpsterfps01.ad.cuyahoga.cc\GIS\GIS DATA\Planning Commission\Greenprint\Documents\CenterForWatershedProtection\ELC_USRM1v2trs.pdf

Dataset represents centerlines of major water project canals that are managed by local area government agencies or entities. This dataset does not contain major State or Federal canals. The original data were from many sources including NHD,USBR,DWR,and contained errors in the attributes and locations. These errors were rectified by Jeff Galef of DWR Delta Levees Special Investigations Branch, using 2005 and 2006 NAIP imagery and Central Valley Aerials Express. These updates were as of 2009. Conflicts between this original data source and any new linework added was resolved using NAIP imagery from 2012. Digitizing was done at approximately 1:9000 scale. Many unnamed canals were identified using USGS topo maps and ESRI Street Map. Additional canal features were added in November 2017 which were inadvertently not included in the initial dataset.

Geospatial data about US Cities and Towns (Local). Export to CAD, GIS, PDF, CSV and access via API.

Libraries dataset current as of 2011. LAGIC is consulting with local parish GIS departments to create spatially accurate point and polygons data sets including the locations and building footprints of schools, churches, government buildings, law enforcement and emergency response offices, pha.

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

Building a resource locator in ArcGIS Online (video).View this short demonstration on how to build a simple resource locator in ArcGIS Online. In this demonstration the presenter publishes an existing Web Map to the Local Perspective configurable application template. The resulting application includes the ability to locate and navigate to different health resources that would be critical in managing a surge of displaced people related to a significant event impacting public health._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 29 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7