ArcGIS SDKs and location services for developers responding to COVID-19.If you’re developing coronavirus disease 2019 (COVID-19) apps, whether to hack together a novel visualization or to deploy your expertise and aid in the response efforts, Esri offers a suite of no-cost location services and SDKs that you can use in your solutions._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...

Basic Viewer is a configurable app template that can be used as a general purpose app for displaying a web map and configuring a variety of tools. This app offers a clean, simple interface that accentuates the web map and includes a toolbar and floating panel.Use CasesDisplays a set of commonly used tools within a floating pane. This is a good choice for balancing the need for a collection of tools while still maximizing the amount of screen real estate dedicated to the map. The app includes the ability to toggle layer visibility, print a map, and show pop-ups in the floating pane.Provides editing capabilities in the context of a general-purpose mapping app. This is a good choice when your audience needs additional tools or information about the map to support their editing activities.Configurable OptionsUse Basic Viewer to present content from a web map and configure it using the following options:Choose a title, sub title, logo, description, and color scheme.Configure a custom splash screen that will display when the app loads.Use custom CSS to customize the look and feel of the app.Enable tools on a toolbar including a basemap gallery, bookmarks, layer list, opacity slider, legend, measure, overview map, etc.Enable an editor tool and an editor toolbar giving users editing capabilities on editable feature layers.Configure a printing tool that can utilize all available print layouts configured in the hosting organization.Configure the ability for feature and location search.Set up custom URL parameters that define how the app and web map appear on load.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The Address Point Editor Tool is a custom Esri Web AppBuilder for ArcGIS (Developer Edition) application for address point data. This tool is intended to be hosted by counties and other organizations that want to facilitate the creation and maintenance of authoritative data for addresses. The application is available to directly add to Web AppBuilder and as the source code to be extended. Please refer to the Address Point Editor Tool User Guide or the Configuration Guide for more details.

This web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis.

Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.

Quick access to the following band combinations and indices is provided:

• Agriculture : Highlights agriculture is bright green; Bands 6, 5, 2
• Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8
• Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3
• SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4
• Geology : Highlights geologic features; Bands 7, 6, 2
• Bathymetric : Highlights underwater features; Bands 4, 3, 1
• Panchromatic : Panchromatic images at 15m; Band 8
• Vegetation Index : Normalized Difference Snow Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)
• Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)
• SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))
• Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)
• Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)
• Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)

The application is written using Web AppBuilder for ArcGIS accessing imagery layers using the ArcGIS API for JavaScript.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 27 threshold values of temperature for the contiguous United States for 1950-2023. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2023. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 16 threshold values of precipitation for the contiguous United States for 1950-2023. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2023. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to downscaled climate projections for 27 threshold values of temperature for the contiguous United States for 2 SSP climate scenarios from 1950-2100. These services are intended to support analysis of climate exposure for custom geographies and time horizons. Sixteen downscaled global circulation models (GCMs) were chosen to be included in a weighted ensemble, optimized for the contiguous United States. More details on the models included in the ensemble and the weighting methodologies can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 2005 to 2100. Additionally, a modeled history runs from 1950 - 2005. The modeled history and future projections have been merged into a single time series. These annual increments support deriving a temporal average, such as a decadal or thirty-year period centered on a specific year. These time steps should not be used to make predictions about conditions for a specific year, especially at a pixel-level. Climate ScenariosClimate models use estimates of future greenhouse gas concentrations and human activities to predict overall change. These different scenarios are called the Shared Socioeconomic Pathways (SSPs). Two different SSPs are presented here: 2-4.5 and 5-8.5. The 2- or 5- represents the socioeconomic growth model. The 4.5 or 8.5 number indicates the amount of radiative forcing (watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but SSP2-4.5 aligns closest with the international targets of the COP-26 agreement for no greater than 2oC average global warming. SSP3-7.0 may be the most likely scenario based on current emission trends. SSP5-8.5 acts as a cautionary tale, depicting a worst-case scenario if reductions in greenhouse gasses are not undertaken. Variable DefinitionsSee the variable list and definitions here. Additional ServicesThree versions of the gridded climate projections are available from CRIS:LOCA2 Ensemble: a statistically downscaled 6-km resolution model. LOCA2 has SSP2-4.5, 3-7.0 and 5-8.5STAR-ESDM Ensemble: a statistically downscaled 4-km resolution model. STAR-ESDM has SSP2-4.5 and 5-8.5NCA5 Blended Ensemble: a merging of LOCA2 and STAR-ESDM ensembles at a 6-km resolution, as was done for the 5th National Climate Assessment (2023). NCA Blended Ensemble has SSP2-4.5 and 5-8.5Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable, SSP, and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

The Climate Resilience Information System (CRIS) provides data and tools for developers of climate services. This image service provides access to gridded historical observations for 16 threshold values of precipitation for the contiguous United States for 1950-2013. These services are intended to support analysis of climate exposure for custom geographies and time horizons. More details on the how the data were processed can be found in Understanding CRIS Data.Time RangesPixel values for each variable were calculated for each year from 1950 to 2013. Variable DefinitionsSee the variable list and definitions here. Additional ServicesTwo versions of the gridded hisorical observations are available from CRIS:nClimGrid: a 4-km resolution dataset generated by NOAA. This data was used to downscale the STAR-ESDM climate projections in CRIS.Livneh: a 6-km resolution dataset generated by Livneh et al. This data was used to downscale the LOCA2 climate projections in CRIS.Using the Imagery LayerThe ArcGIS Tiled Imagery Service has a multidimensional structure -- a data cube with variable and time dimensions. Methods for accessing the different dimensions will depend on the software/client being used. For more details, please see the CRIS Developer’s Hub along with this instructional StoryMap. To run analysis, first use the multidimensional tools Aggregate or Subset in ArcGIS Pro to copy the necessary data locally.Data ExportData export is enabled on the services if using an ArcGIS client. NetCDF or Zarr files are also available from the NOAA Open Data Distribution system on Amazon Web Services.

Metropolitan Atlanta Rapid Transit Authority (MARTA) data downloaded from the Google Transit Feed Specification (GTFS) public dataset here:http://www.itsmarta.com/app-developer-resources.aspxGeneral Transit Feed SpecificationThe MARTA GTFS feed is updated approximately four times per year, which is about how often MARTA service changes. It will be published to the same URL with each new update.google_transit.zip (10MB, ZIP) Effective Date: 2/4/2017 About GTFSThere are a number of resources for GTFS, the standardized data format for transit schedules originally developed for use on Google Transit. A good starting point is the official documentation maintained by Google. The site includes references, and examples. Translated to Esri Feature classes using the public-transit-tools scripts by mmorang.

In New Jersey, the Successor Solar Incentive Program (SuSI) was established by the New Jersey Board of Public Utilities in 2021 to provide incentives to eligible solar facilities to enable the continued efficient and orderly deployment of solar renewable energy generating sources throughout the State. This application contains a series of web-mapping tools that have been designed to assist solar developers with identifying and analyzing potential solar project sites based on the specific siting criteria for the following BPU-administered incentive programs:Competitive Solar Incentive Program (CSI)Community Solar Energy Program (CSEP)Dual-Use Pilot ProgramRemote Net Metering Program (RNM)Users of these incentive program-specific tools can gain insight into certain land use types that may be prohibited for siting solar under the programs by referencing the specially curated data layers available within each tool. The mapping tools do not support project siting or permitting approval or denial. Developers are responsible for determining if their project may be sited on State regulated or prohibited land use type(s) and consult with the applicable and/or appropriate agency.

(Below is the metadata from the Living Atlas)This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into well over 100 categories. See event descriptions for full details.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.Revisions:Feb 25, 2021: Revised service data upate workflow, improving stability and update interval.Process now checks for data updates every 5 minutes!Mar 3, 2021: Revised data processing to leverage VTEC parameter details to better align Event 'effective' dates with reported dates on Alert pages.Apr 17, 2023: Turned off popups for boundary Layers by default.Feb 1, 2024: Revised to leverage CAP v1.2 source endpoint. Update event link to use alert search.Feb 16, 2024: Revised event link to accomodate change in alert search endpoint.Jan 19, 2025: Added event 'Description' and 'Instructions', updated Pop-up.Jan 22, 2025: Exposed 'Hours Old' fields supporting last 'Updated', 'Effective', and 'Expiration' as +- age values for events.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This data set is part of the TPB Regional Transportation Data ClearinghouseThe service is a collection of the Regional Automatic Counts, DC Bicycle Counts, and Northern Virginia Bicycle Counts. Below is a description of each Count type included in the service, as well as the data included for each.The 2018 Pedestrian and Bicycle Automatic CountsData represents pedestrian and bicycle trips captured by a regional network of automatic counters (http://www.bikearlington.com/counter-data/) located in Arlington County, City of Alexandria, the District of Columbia, and Montgomery County for calendar year 2018. The raw data was downloaded via Commuter Page’s web services ( http://www.commuterpage.com/pages/tools-resources/tools-for-developers/) and processed, organized and summarized.Layers:2018 Pedestrian Automatic Counts (regional)2018 Bicycle Automatic Counts (regional)Tables:2016 Bicycle Automatic Counts, Daily (regional)2016 Pedestrian Automatic Counts, Daily (regional2016 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2017 Pedestrian Automatic Counts, Daily (regional)2017 Bicycle Automatic Counts, Daily (regional)2017 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2018 Bicycle Automatic Counts, Daily (regional)2018 Pedestrian Automatic Counts, Daily (regional)Disclaimer (taken from BikeArlington): Because the dashboard presents "raw" data direct from the system server and the devices in the field, it will sometimes include errors, or contain gaps or blank periods. As we continue to develop the dashboard, our goal is to provide the most accurate and useful information possible. This may involve presenting both data direct from the system as well as data that have been normalized statistically. We invite your help and active participation in this process. If you find problems with the data, have questions about particular time periods, or would like to share insights or interpretations, please feel free to communicate them to: bikepedcounts@arlingtonva.us.Washington, D.C. Bicycle CountsBicycle Counts collected for Washington, D.C. during AM Peak and PM Peak time periods. Counts were totaled and recorded in 15 minute intervals from 06:00 to 10:00 and 15:00 to 18:00. The Street, between which intersections, date, day of the week, parking, speed limit, bike lanes, one way street, were all recorded for each count location. Count data collected by TPB Staff for DDOT includes: Day Total, Peak Time Period Total, Helmet(yes/no) Total, Male Total, Female Total, Adult, Child by Station. The related table includes Total (Time Period Total), Helmet (yes), Helmet (no), Male, Female, Adult, Child Total, Male Total, Female Total, Adult, Child by Station.Layers:District of Columbia Bicycle Count Stations Totals, Monthly (FY 2014, 2015, 2016, 2017)Tables:District of Columbia Bicycle Counts, Hourly (FY 2014, 2015, 2016, 2017) VDOT Bicycle CountsAs part of TPB's Northern Virginia Technical Assistance program, Travel Monitoring staff conduct bicycle and pedestrian counts for the Virginia Department of Transportation (VDOT) at requested locations on a regular basis. The data was collected utilizing VDOT's Miovision cameras (video data collection). The cameras allow directional observations. The layer data provided was collected at the 24-hour summary level by mode as well as by direction where available. 15-minute increments for the 24-hour period per count location for bicycles and pedestrians by direction where available data is made available in the tables .Layers:VDOT Pedestrian Counts (FY 2013, 2014, 2015, 2016, 2017)VDOT Bicycle Counts (FY 2013, 2014, 2015, 2016, 2017)Tables:VDOT Bicycle & Pedestrian Counts - Hourly (FY 2013, 2014, 2015, 2016, 2017)

This data set is part of the TPB Regional Transportation Data ClearinghouseThe service is a collection of the Regional Automatic Counts, DC Bicycle Counts, and Northern Virginia Bicycle Counts. Below is a description of each Count type included in the service, as well as the data included for each.The 2018 Pedestrian and Bicycle Automatic CountsData represents pedestrian and bicycle trips captured by a regional network of automatic counters (http://www.bikearlington.com/counter-data/) located in Arlington County, City of Alexandria, the District of Columbia, and Montgomery County for calendar year 2018. The raw data was downloaded via Commuter Page’s web services ( http://www.commuterpage.com/pages/tools-resources/tools-for-developers/) and processed, organized and summarized.Layers:2018 Pedestrian Automatic Counts (regional)2018 Bicycle Automatic Counts (regional)Tables:2016 Bicycle Automatic Counts, Daily (regional)2016 Pedestrian Automatic Counts, Daily (regional2016 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2017 Pedestrian Automatic Counts, Daily (regional)2017 Bicycle Automatic Counts, Daily (regional)2017 Bicycle and Pedestrian Automatic Counts, Monthly (regional)2018 Bicycle Automatic Counts, Daily (regional)2018 Pedestrian Automatic Counts, Daily (regional)Disclaimer (taken from BikeArlington): Because the dashboard presents "raw" data direct from the system server and the devices in the field, it will sometimes include errors, or contain gaps or blank periods. As we continue to develop the dashboard, our goal is to provide the most accurate and useful information possible. This may involve presenting both data direct from the system as well as data that have been normalized statistically. We invite your help and active participation in this process. If you find problems with the data, have questions about particular time periods, or would like to share insights or interpretations, please feel free to communicate them to: bikepedcounts@arlingtonva.us.Washington, D.C. Bicycle CountsBicycle Counts collected for Washington, D.C. during AM Peak and PM Peak time periods. Counts were totaled and recorded in 15 minute intervals from 06:00 to 10:00 and 15:00 to 18:00. The Street, between which intersections, date, day of the week, parking, speed limit, bike lanes, one way street, were all recorded for each count location. Count data collected by TPB Staff for DDOT includes: Day Total, Peak Time Period Total, Helmet(yes/no) Total, Male Total, Female Total, Adult, Child by Station. The related table includes Total (Time Period Total), Helmet (yes), Helmet (no), Male, Female, Adult, Child Total, Male Total, Female Total, Adult, Child by Station.Layers:District of Columbia Bicycle Count Stations Totals, Monthly (FY 2014, 2015, 2016, 2017)Tables:District of Columbia Bicycle Counts, Hourly (FY 2014, 2015, 2016, 2017) VDOT Bicycle CountsAs part of TPB's Northern Virginia Technical Assistance program, Travel Monitoring staff conduct bicycle and pedestrian counts for the Virginia Department of Transportation (VDOT) at requested locations on a regular basis. The data was collected utilizing VDOT's Miovision cameras (video data collection). The cameras allow directional observations. The layer data provided was collected at the 24-hour summary level by mode as well as by direction where available. 15-minute increments for the 24-hour period per count location for bicycles and pedestrians by direction where available data is made available in the tables .Layers:VDOT Pedestrian Counts (FY 2013, 2014, 2015, 2016, 2017)VDOT Bicycle Counts (FY 2013, 2014, 2015, 2016, 2017)Tables:VDOT Bicycle & Pedestrian Counts - Hourly (FY 2013, 2014, 2015, 2016, 2017)