FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps. FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and/or cartographic design with others. Second, users can generate customized flow symbols to support different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbols, choropleth, and base maps to contextualize flow patterns with location references and characteristics. Finally, the web-based architecture of FlowMapper supports server-side computational capabilities to process and normalize large flow data and reveal natural patterns of flows.

This table provides wholesale trade sales data based on origin, destination, and industry cross-tabulations from the Annual Wholesale Trade Survey. Origins include all provinces and territories, while destinations encompass all provinces and territories, the United States, China, and the rest of the world. Industry details are available at the 2, 3 and 4 digit North American Industry Classification System (NAICS) levels.

This table provides manufacturing sales data based on origin, destination and industry cross-tabulations from the Annual Survey of Manufacturing and Logging. Origins include all provinces and territories. Destinations include all provinces and territories, the United States of America, and the rest of the world. Industry detail is available at the 2, 3 and 4 digit NAICS level.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhood How do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app. Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state). On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access. As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car? How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access. Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery

The table Origin-Destination is part of the dataset LEHD Origin-Destination Employment Statistics (LODES) -- New York, available at https://columbia.redivis.com/datasets/rw0s-5nc9rxp6k. It contains 460120831 rows across 13 variables.

Map Of Origin - Destination In Puebla Capital City
This dataset falls under the category Public Transport Other.
It contains the following data: Origin - Destination Map in Puebla Capital City
This dataset was scouted on 2022-09-30 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://datos.pueblacapital.gob.mx/como-nos-movemos-2018-2021See URL for data access and license information.

The data and codes for the paper titled “Enhanced prediction of origin-destination flows by considering heterogeneous mobility patterns with community detection and graph attention networks”.

This map shows volumes of commercial truck traffic that originate and arrive (destination) by block group (2020 Census)Limitations of Replica vehicle volume data: The map shows quarterly daily average vehicle volume at road link level provided by Replica, Inc. It includes estimates of volumes of passenger vehicles, buses, on-demand vehicles, and commercial trucks. SACOG staff only validated passenger vehicle volume and bus at road links with observed data. Due to lack of observed truck volume, the estimates of truck volume used in this map were not validated. Users should be aware of the limitations of truck volume in applications.

(StatCan Product) Customization Details: Table A. By province of origin/destination (five-year period) presents information on migration to and from Canadian provinces and territories by Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table B. By age group (five-year period) presents information on in-migrants, out-migrants and net-migrants by the following age group categories: 0-17 years, 18-24 years, 25-44 years, 45-64 years, 65+ years and Total for Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table C. By type of migration and sex (five-year period) presents information on in-migrants, out-migrants and net-migrants by the type of migration (intraprovincial, interprovincial and international) by sex (Male, Female or Both) for Alberta (entire province), all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta from 2004 to 2009. Table D. Flows by CD of origin/destination, or by CMA/non-CMA of origin/destination (five-year period) presents information on where Alberta's migrants/immigrants are moving to and where they've moved from by all 19 Alberta Census Divisions, the CMA of Edmonton, the CMA of Calgary and Non CMA Alberta and internationally from 2004 to 2009. Table E. Median income of migrant taxfilers (single year) is NOT INCLUDED. Annual Migration Estimates - The data consist of estimates of migration flows between census divisions (CDs) or census metropolitan areas (CMAs), by sex and broad age groups. The statistics are derived from the annual tax file provided by the Canada Revenue Agency. Intraprovincial migration: movement of people between two CDs or CMAs located within the same province. The CD/CMA of departure is the CD/CMA of origin and the CD/CMA of arrival is the CD/CMA of destination. Interprovincial migration: movement of people between CDs and CMAs located in two different provinces. The province of departure is the province of origin and the province of arrival is the province of destination. International migration: movement of people between an area in Canada and another country. Migration flows: migration flows for any given CD or CMA. The flows are listed in descending order of net migration for the most recent year of migration. Migration flows: migration flows for any given CD or CMA. The flows are listed in descending order of net migration for the most recent year of migration. There are five standard data tables that are normally available for this product: Table A. By province of origin/destination (five-year period); Table B. By age group (five-year period); Table C. By type of migration and sex (five-year period); Table D. Flows by CD of origin/destination, or by CMA/non-CMA of origin/destination (five-year period); Table E. Median income of migrant taxfilers (single year); Annual Migration Estimates by Census Division/Census Metropolitan Area.

The dataset provided here is an output of the Track & Know project, shared with the scientific community. The dataset consists of aggregate origin-destination (OD) flows of private cars in London augmented with feature data describing city locations and dyadic relations between them. The geographical location of each cell in the OD graph is not provided, for privacy protection, since the extension of each area is relatively small.

The dataset was first used in the following publication:  Gevorg Yeghikyan, Felix L. Opolka, Bruno Lepri, Mirco Nanni, Pietro Lio`. Learning

Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks. 2020 IEEE International Conference on Smart Computing (SMARTCOMP), to appear.

Network of 45 papers and 68 citation links related to "Exploration on origin–destination-based travel time variability: Insights from Seoul metropolitan area".

This table contains information about the origin and destination of emissions to water of nutrients and heavy metals. These data are part of the environmental accounts. The origin of the emissions to water can be divided into the Dutch economy (disaggregated by private households and industries) and the rest of the world. The destination can be divided into absorption by producers, drain off emissions to the rest of the world and contribution to the environmental problem. The figures in this table can be compared in a consistent way to macro-economic indicators. Furthermore, water emission accounts indicators can be derived for the emissions of heavy metals into water and the eutrophication of surface waters. These indicators can be used for supporting water policy. Data available from: 1995 Status of the figures: To obtain coherent and consistent time series, the complete time series are (re)calculated. The latest insights, particularly with regard to the emission factors are included in the calculations. Changes as of January 30, 2015: None, this is a new table. This table replaces a similar table containing data till 2010, where except for the years 1995, 2000, 2005 and 2010 from the emission inventory also the interjacent years were provided (see paragraph 3. Links to relevant tables and articles). When will new figures be published? New figures are published biannually in October, simultaneously with the appearance of the publication of the environmental accounts. The first update will take place in October 2016.

This repository contains the input data associated with the research paper titled "The Role of Spatial Features and Adjacency in Data-driven Short-term Prediction of Trip Production: An Exploratory Study in the Netherlands". The paper is currently under review for publication in the IEEE Transactions on Intelligent Transportation Systems. The data shows the origin destination information for Mezuro zones in the Netherlands for March 2017. This information are provided in a format of a relational table in .CSV format. The details on data preparation steps are provided in the pdf file inside MiRRORS data.rar and it is titled MiRRORS description 20191220.pdf. The Mezuro zones are defined in the shapefiles directory within MiRRORS data.rar. This project's main source of data (origin destination table) is named od_day_hour_20170301_0.CSV located inside MiRRORS data.rar. This dataset is the source to investigate the travel patterns in the Netherlands.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

Rail industry origin and destination of transported commodities, including provinces and territories, regions, US and Mexico, annual.

This table is about the socio-economic category of emigrants. Of all emigrants who left the Netherlands in a calendar year, their socio-economic category was 90 days before departure. The figures can be broken down by gender, age, migration background and country of destination. The leveling point for the socio-economic category is 90 days before emigration. The leveling moment for the background characteristics such as age is the moment of emigration.

These emigration figures may, due to a different methodology, differ from those in other tables on StatLine. More information on this different method can be found in paragraph 4.

Data available from: 2000-2011

Status of the figures: The figures in this table are final.

Changes as of 17 April 2020: None, this table has been discontinued.

When are new figures coming? No longer applicable.

The Low Exposure Routing (LER) modeling methodology has already been evaluated at the vehicle level in our previous work. In this study, we propose a novel framework to quantify the impact of LER at the transportation system level applying different technology penetration rates. Under the framework, we employ the truck origin-destination from regional transportation demand model, to generate truck trips in the City of Riverside. Then, the calibrated BEAM model, an agent-based simulation, simulates trips through reinforcement learning and dynamic daily planning technique to reach maximum utility at the transportation system level. Finally, it shows that with a 100% penetration rate of LER and a strict 10% time increase threshold, the air pollutant exposure reduced up to 16% at city level with a slight trade-off of travel time.

Methods In this project, we aim to tackle this challenge by developing an activity-based traffic simulation model for the City of Riverside, CA and evaluate the transportation system-level impacts of the truck low-exposure routing (LER) technology for mitigating the impacts of truck emissions on communities. Based on the existing passenger transportation model developed, truck demand and trips are generated using Southern California Association of Governments (SCAG) model. Based on the long-range regional transportation plans in SCAG, we extract zone-based truck trips origin-destination table; thus, we got daily passenger car and truck trips for Riverside City. Since zone-based origin-destination (OD) table offers no exact coordinates and time of a day, this study utilizes employment data provided by the Employment Development Department (EDD) to give deterministic truck trips coordinates and utilizes PeMs data to calibrate the exact time of travel. Those trips are used as input for BEAM, a framework for a series of research studies in sustainable transportation. We then apply activity-based traffic simulation and Low Exposure Routing to Heavy Duty-Diesel Truck (HDDT) trips in the city, showing that the communities will benefit from reduced exposure to air pollutants by adjusting HDDT routes. Finally, we evaluate LER at the transportation system level under different technology penetration rates.

(hhdt_od_10.csv) Zone-based Origin-Destination table (OD TABLE) from SCAG is necessary for generating three different truck trips: Heavy-Duty Diesel Truck Trips, Medium-Duty Diesel Truck Trips, and Light-Duty Diesel Truck Trips.
(pems_output_i10e.xlsx) PeMs collects real-time traffic data from over 39,000 individual detectors along with the freeway system crossing major cities, monitoring traffic volume by categories, such as number of trucks on the road. Thus, we can estimate deterministic time of travel by reading PeMs data. Since PeMs data is one-hour interval format, we randomly distribute these trips into corresponding hour. 
(truck_related_employment.csv) The Employment Development Department (EDP) record business companies in Riverside City, which we can use them for potential truck trip position generation. Thus, for the truck trips inside Riverside City, we assign coordinates of companies in the corresponding zone to the truck trips as their origin and destination positions. For those truck trips just crossing city, we assign the closest boundaries points coordinates to them.
(riverside_new.xml) The network files for Riverside City is derived from OpenStreetMap.com, which is osm (OpenStreetMap) format file. The raw network file is opened in Java OpenStreetMap Editor and transformed into pbf file. After we place this pbf file in the R5 folder under BEAM repository, the initial simulation automatically generates network.dat and physsim.xml where all map-related data is stored.
(households.csv, person.csv, plans.csv) The minimum requirements needed for conducting a BEAM run are households, persons, vehicle fleet, vehicle types, map data, and configuration. We assign virtual household and virtual driver for these truck trips.
(linkAttributes_RIV.csv) To determine vehicle emission factors (in the unit of gram/mile), link-based traffic activities (e.g., average traffic speed) is needed as an input. We get the linkspeed from BEAM Model Results and use them here.
(facilityAttributes.csv, blockAttributes.csv) facility and residential block are prepared as the sensitive receptors.

Source: U.S. Census Bureau, OnTheMap Application and LEHD Origin-Destination Employment Statistics (Beginning of Quarter Employment, 2nd Quarter of 2002-2014). Selection area is TID 36 - Downtown boundary. Note: Educational Attainment is only produced for workers aged 30 and over.

This repository contains a GeoPackage of edge-bundled line geometries between the centroids of all https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics" target="_blank" rel="noopener">NUTS 2 regions in continental Europe. The centroids of the NUTS 2 regions are derived from the 2021 version of the regions. The spatial layer contains just the edge-bundled lines, and no values for the flows. The coordinate reference system used is the https://epsg.io/3035" target="_blank" rel="noopener">ETRS89-extended / LAEA Europe (EPSG:3035) commonly used by The European Union.

This data is made to support the visualization of complex origin-destination matrix mobility data on the NUTS 2 level in Europe. Straight line geometries between origin and destination points can lose their legibility when the number of flows gets high.

Usage

To use the spatial layer, combine the provided GeoPackage with your origin-destination matrix data, such as migration, student exchange, or some other flow data. The edge-bundled flows has a directionality-preserving column for joining the flows (OD_ID). This can be done in QGIS/ArcGIS with a table join or in R/Python with a data frame merge.

Data structure

Production code

The spatial layer was produced by the https://doi.org/10.5281/zenodo.14532547">Edge-bundling tool for regional mobility flow data, which is a fork of a similar tool by Ondrej Peterka (2024), which is based on the work of Wallinger et al., (2022).

References