Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

Displacement risk indicator showing how many households within the specified groups are facing either housing cost burden (contributing more than 30% of monthly income toward housing costs) or severe housing cost burden (contributing more than 50% of monthly income toward housing costs).

The Cost of Risk metric shows how much the city spends on handling risks (like insurance, legal expenses, or accident payouts) compared to how much money it collects overall.The performance measure dashboard is available at 5.17 Total Cost of Risk.Additional InformationSource: Peoplesoft and ACFRContact: Laura CalderContact E-Mail: laura.calder@tempe.govData Source Type: ExcelPreparation Method: The total expenses in Fund 2661 (The Risk Management cost center) is divided by the total revenue from Annual Comprehensive Financial Report to calculate the total cost of Risk.Publish Frequency: AnnualPublish Method: ManualData Dictionary (pending update)

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.

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.

LOW TRANSPORTATION COST INDEXSummaryThe Low Transportation Cost Index is based on estimates of transportation expenses for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e. CBSA). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 (hh_type6_) as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters (t_rent). Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.InterpretationValues are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the transportation cost index, the lower the cost of transportation in that neighborhood. Transportation costs may be low for a range of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community.

Data Source: Location Affordability Index (LAI) data, 2012-2016.Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 11.

References: www.locationaffordability.infohttps://lai.locationaffordability.info//lai_data_dictionary.pdf

To learn more about the Low Transportation Cost Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Alaska Energy Authority Power Cost Equalization (PCE) program by community. The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity."AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE. RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca."(AEA, 2017)Source: Alaska Energy AuthorityThis 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 - it has been primarily compiled from AEA PCE Fiscal Year Utility Report PDFs. For more information and for questions about this data, see: AEA Power Cost Equalization

This publication contains tabular data used to evaluate the effects of fuel treatments and previously burned areas on daily wildland fire management costs. The data represent daily Forest Service fire management costs for a sample of 56 fires that burned between 2008 and 2012 throughout the conterminous United States. Included in the data is a suite of spatially derived variables used to control for variation in daily fire management costs, including topography, fire weather, fuel loading, remoteness, and human populations-at-risk. These data were extracted using daily fire progression maps produced using the methods outlined in Parks (2014).

The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.

Communities served by entities that are eligible for the Alaska Energy Authority's (AEA) Power Cost Equalization (PCE) program. The power cost equalization program supports rural Alaskans who live in areas where energy costs are significantly higher than urban areas in meeting the cost of electricity. Eligibility is determined by the Regulatory Commission of Alaska under Alaska Statutes 42.45.100-170."AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE. RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca."(AEA, 2017)Source: Alaska Energy AuthorityThis 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: AEA Power Cost Equalization

Crawford County, Pennsylvania address points. Location and information of address points in Crawford County,Pennsylvania. This feature service has restricted fields available for this open data version. Certain fields have been redacted. The full dataset (as seen via the GIS mapping applications) is available via cost by contacting Crawford County. The full dataset available at cost provides all records that are not redacted by law. Data is public use and unrestricted.

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Free and reduced lunch data for each participating public school in Alaska. This data set includes the number of students receiving free lunches and reduced price lunches, and the percentage of the students enrolled in either of these programs. Students qualify for free and reduced meals under the National School Lunch Program.Where possible the data is mapped at the location of School that is associated with the program - however some data rows represent non-school entities. See source DEED data center https://education.alaska.gov/cnp/nslp for source dataSource: Alaska Department of Education & Early Development, School Nutrition Programs

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.

This packaged data collection contains two sets of two additional model runs that used the same inputs and parameters as our primary model, with the exception being we implemented a "maximum corridor length" constraint that allowed us to identify and visualize the corridors as being well-connected (≤15km) or moderately connected (≤45km). This is based on an assumption that corridors longer than 45km are too long to sufficiently accommodate dispersal. One of these sets is based on a maximum corridor length that uses Euclidean (straight-line) distance, while the other set is based on a maximum corridor length that uses cost-weighted distance. These two sets of corridors can be compared against the full set of corridors from our primary model to identify the remaining corridors, which could be considered poorly connected. This package includes the following data layers: Corridors classified as well connected (≤15km) based on Cost-weighted Distance Corridors classified as moderately connected (≤45km) based on Cost-weighted Distance Corridors classified as well connected (≤15km) based on Euclidean Distance Corridors classified as moderately connected (≤45km) based on Euclidean Distance Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in raster GeoTIFF (.tif) format.

Areas that have been determined to be eligible for support for broadband and voice service from the FCC’s final Alternative-Connect America Cost Model (A-CAM version 2.3). A-CAM calculates costs per location in all rate-of-return carrier census blocks for the entire country.For more information, see https://www.fcc.gov/maps/a-cam-offer-map/

Beginning in 2005, the Division of Community and Regional Affairs began collecting prices of heating fuel and unleaded gasoline in 100 select communities. The communities have remained constant since the project’s inception. The prices for heating fuel in these 100 communities are collected via a telephone survey of each fuel retailer. Survey methodology has evolved over time; however, the reported prices should be considered representative of what a community resident would have paid for a gallon of heating fuel (including tax) on the day of contact.

More Metadata"Bedrock" is one of four layers which collectively describe the subsurface geologic formation, structural geology and surficial deposits. "Bedrock" is the subsurface expression of the geologic formations near the ground surface as mapped by outcrops and well borings. The geology of Loudoun County, Virginia, was mapped in 1988 through 1991 under a cooperative agreement between the U.S. Geological Survey (USGS) and the Loudoun County Department of Environmental Resources. This geologic map was compiled in 1993 from a series of detailed published and unpublished field investigations at scales of 1:12,000 and 1:24,000. Some of these same data were compiled as a digital geologic map at 1:100,000 scale (Burton and others, 1992a) and were the basis for a cost-benefit analysis of the societal value of geologic maps (Bernknopf and others, 1993). The data was later revised and published by USGS in the Open File Report, MAP OF-99-150, GEOLOGIC MAP OF LOUDOUN COUNTY, VIRGINIA By Scott Southworth, W.C. Burton, J.S. Schindler, and A.J. Froelich1 with contributions on the geology of the Piedmont province by A.A. Drake, Jr., and R.E. Weems and an aeromagnetic survey by D.L. Daniels, W.F. Hanna, and R.E. Bracken.

This is a representation of all primary roads as well as parks and institution roads that the Iowa Department of Transportation has the responsibility to maintain. Each maintenance garage is displayed in a different color to show where one area of responsibility ends and another begins. Updated August 2016.

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