The FDOT GIS Roads with Local Names feature class provides spatial information on local name of the roadway. The name given to a section of roadway to identify it from other sections of roadway. Local names are important for emergency medical services and law enforcement. This information is required for all roadways, including Active Exclusives. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/localnam.zip

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.

These geocoded locations are based on the Allegheny County extract of Educational Names & Addresses (EdNA) via Pennsylvania Department of Education website as of April 19, 2018. Several addresses were not able to be geocoded (ex. If PO Box addresses were provided, they were not geocoded.)

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

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.

TransportationThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, displays primary roads, secondary roads, local roads and railroads in the United States. According to the USCB, "This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways."Interstates 20 and 635Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (TIGERweb/Transportation) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 155 (Series Information for All Roads County-based TIGER/Line Shapefiles, Current)OGC API Features Link: (Transportation - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Census Feature Class Codes (CFCC)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

The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. The SMA feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. The SMA Withdrawals feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA Withdrawal is defined by formal actions that set aside, withhold, or reserve Federal land by statute or administrative order for public purposes. A withdrawal creates a title encumbrance on the land. Withdrawals must accomplish one or more of the following: A. Transfer total or partial jurisdiction of Federal land between Federal agencies. B. Close (segregate) Federal land to operation of all or some of the public land laws and/or mineral laws. C. Dedicate Federal land to a specific public purpose. There are four major categories of formal withdrawals: (1) Administrative, (2) Presidential Proclamations, (3) Congressional, and (4) Federal Power Act (FPA) or Federal Energy Regulatory Commission (FERC) Withdrawals. These SMA Withdrawals will include the present total extent of withdrawn areas rather than all of the individual withdrawal actions that created them over time. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details.

This document provides an overview on the provisioning of GIS data to support NG9-1-1 services. This document is intended to provide guidance to local GIS and PSAP authorities on the following: The required GIS datasets to support the i3 Emergency Call Routing Function (ECRF) and Location Validation Function (LVF) The validation processes to synchronize the GIS datasets to the Master Street Address Guide (MSAG) and Automatic Location Information (ALI) datasets Geospatial call routing readiness The short term and long term NG9-1-1 GIS data maintenance workflow proceduresAdditional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.

There are many useful strategies for preparing GIS data for Next Generation 9-1-1. One step of preparation is making sure that all of the required fields exist (and sometimes populated) before loading into the system. While some localities add needed fields to their local data, others use an extract, transform, and load process to transform their local data into a Next Generation 9-1-1 GIS data model, and still others may do a combination of both.There are several strategies and considerations when loading data into a Next Generation 9-1-1 GIS data model. The best place to start is using a GIS data model schema template, or an empty file with the needed data layout to which you can append your data. Here are some resources to help you out. 1) The National Emergency Number Association (NENA) has a GIS template available on the Next Generation 9-1-1 GIS Data Model Page.2) The NENA GIS Data Model template uses a WGS84 coordinate system and pre-builds many domains. The slides from the Virginia NG9-1-1 User Group meeting in May 2021 explain these elements and offer some tips and suggestions for working with them. There are also some tips on using field calculator. Click the "open" button at the top right of this screen or here to view this information.3) VGIN adapted the NENA GIS Data Model into versions for Virginia State Plane North and Virginia State Plane South, as Virginia recommends uploading in your local coordinates and having the upload tools consistently transform your data to the WGS84 (4326) parameters required by the Next Generation 9-1-1 system. These customized versions only include the Site Structure Address Point and Street Centerlines feature classes. Address Point domains are set for address number, state, and country. Street Centerline domains are set for address ranges, parity, one way, state, and country. 4) A sample extract, transform, and load (ETL) for NG9-1-1 Upload script is available here.Additional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.

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.

This dataset demarcates the municipal boundaries in Allegheny County. Data was created to portray the boundaries of the 130 Municipalities in Allegheny County the attribute table includes additional descriptive information including Councils of Government (COG) affiliation (regional governing and coordinating bodies comprised of several bordering municipalities), School District, Congressional District, FIPS and County Municipal Code and County Council District.

This dataset is harvested on a weekly basis from Allegheny County’s GIS data portal. The full metadata record for this dataset can also be found on Allegheny County's GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the "Explore" button (and choosing the "Go to resource" option) to the right of the "ArcGIS Open Dataset" text below.

Category: Civic Vitality and Governance

Department: Geographic Information Systems Group; Department of Administrative Services

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).

Since its inception in 1991, the Data Access & Support Center (DASC) has served as the State of Kansas Geographic Information System (GIS) data clearinghouse. Created as a center for the archiving and distribution of geospatial data, DASC has worked to expand its service portfolio over the years. While data archiving and distribution are still at the core of DASC's mission, DASC also provides various geospatial services, including web-based application development and hosting, database development and integration, state and local coordination, technical support, and local GIS data backup. These services support the Kansas GIS Initiative and complement state and local GIS activities. DASC continues to develop, maintain, and host GIS applications for numerous state agencies, including the Kansas 911 Coordinating Council, Kansas Department of Agriculture, Kansas Division of Emergency Management, Kansas Department of Revenue, Kansas State Department of Education, Kansas State Historical Society, Kansas Department of Transportation, Kansas Water Office, and the Kansas Department of Wildlife & Parks. The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

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

505 Economics is comprised of doctoral and post-doctoral researchers based at the London School of Economics. We blend together experience in data science, GIS, artificial intelligence and economics.

Our department at LSE is ranked number 1 in Economic Geography in the world.

Get in touch to discuss how we can help you with your geospatial and economics projects.

We have previously: Created sub-national GDP measures using high resolution satellite imagery and deep learning for EU regions Created sub-national economic data for conflict zones using alternative data Extracted geographic features for African countries (e.g. POI, road network data) Created Computable general equilibrium (CGE) models

description: Building Footprints 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.; abstract: Building Footprints 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.

Court Buildings 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.

Publication Date: May 2025.

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.

description: Pharmacy Locations 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.; abstract: Pharmacy Locations 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.