These data were used in research to evaluate the accuracy of selectivity estimation in multiway spatial joins. Five queries were executed, each consisting of ten real datasets.

There is no description for this dataset.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

The slope_iris_sword in FIM HF IRIS can be used with the Recalculate_Discharge_in_Hydrotable_useFIMHFIRIS.py script to regenerate the hydrotable for OWP HAND-FIM, where the discharge will be recalculated using slope_iris_sword. Consequently, the synthetic rating curves (SRCs) will be updated based on the new discharges (see more details in https://github.com/NOAA-OWP/inundation-mapping/wiki/3.-HAND-Methodology). The script can also be used to regenerate hydrotables using river slopes from other sources, such as NextGen HF, provided they are linked to the FIM HF flowlines.

The feature classes for FIMHF_IRIS and NextGenHF_IRIS are provided in formats of geopackage (.gpkg) and geodatabases (.gdb), which can be accessed using ArcGIS, QGIS, or relevant Python packages for inspection, visualization, or spatial analysis of slope_iris_sword.

More information can be found at: Chen, Y., Baruah, A., Devi, D., & Cohen, S. (2025). Improved River Slope Datasets for the United States Hydrofabrics [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15099149

Number of incidents counted within census tracts based on different spatial join approaches.

This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

A. SUMMARY This dataset contains the list of intersecting Analysis Neighborhoods and ZIP Codes for the City and County of San Francisco. It can be used to identify which ZIP codes overlap with Analysis Neighborhoods and vice verse. B. HOW THE DATASET IS CREATED The dataset was created with a spatial join between the Analysis Neighborhoods and ZIP codes. C. UPDATE PROCESS This is a static dataset D. HOW TO USE THIS DATASET This dataset is a many-to-many relationship between analysis neighborhoods and ZIP codes. A single neighborhood can contain or intersect with multiple ZIP codes and similarly, a single ZIP code can be in multiple neighborhoods. This dataset does not contain geographic boundary data (i.e. shapefiles/ GEOMs). The datasets below containing geographic boundary data should be used for analysis of data with geographic coordinates. E. RELATED DATASETS Analysis Neighborhoods San Francisco ZIP Codes Supervisor District (2022) to ZIP Code Crosswalk Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods

The data for this analysis was obtained through a UC-Davis Coursera course as ElectionData2012.gdb, with polygon layers Counties and PrecinctVotingData. Both of those were loaded into a blank map document, followed by the World Light Grey Canvas basemap.

Then, the author conducted a Spatial Join of the PrecinctVotingData layers TO the Counties layer (target layer). A right click on the fields total_votes and proposition_37_yes_votes enabled the execution of a Sum merge operation for those fields.

After the spatial join, the author went into the Properties of the Join layer, selected Symbology, used the quantity gradient, selected sum_proposition_37_yes-votes as the field for symbology and normalized by the sum_total_votes field. Further, the author formatted the symbology such that the data was represented as a percentage (of the sum_total_votes) and used only 1 decimal place.

The author then went into the Label s tab of the Properties window, chose the County label style for the NAMES field, and edited the label to have a 1-pt. halo around the county names, centered on their feature.

From the attribute table of the Join, the author right-clicked the "sum_total_votes" and the "sum_proposition_37_yes_votes" fields and used the statistics function to gather the sum of the YES votes and the sum of the total votes for the state as a whole, for use in the final, shared map. Revisions were also made to layer names for the benefit of the final map.

Several previously published reports and geographic information system (GIS) data layers were used to code information on site attributes for each assessment plot using the spatial join tool in ArcMap. This information was used for an analysis of dieback and non-dieback habitat characteristics. The results of this analysis are presented in this table which depicts the probability of heavy to severe canopy dieback occurring at some time at a particular 30 x 30 m pixel location within the study area.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons?

-All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery, satellite data, and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct highly detailed polygons. This meticulous process ensures maximum accuracy and consistency. -We verify our polygons through multiple quality assurance checks, focusing on accuracy, relevance, and completeness.

What's More?

-Custom Polygon Creation: Our team can build polygons for any location or category based on your requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard GIS formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data

With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market and location analyses to identify growth opportunities. -Pinpoint the ideal locations for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute location-based marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

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LocationsXYZ is trusted by leading brands to unlock actionable business insights with our accurate and comprehensive spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI datasets. Request your free sample today and explore how we can help accelerate your business growth.

This repository provides code and data used in "Social Equity of Bridge Management" (DOI: 10.1061/JMENEA/MEENG-5265). Both the dataset used in the analysis ("Panel.csv") and the R script to create the dataset ("Panel_Prep.R") are provided. The main results of the paper as well as alternate specifications for the ordered probit with random effects models can be replicated with "Models_OrderedProbit.R". Note that these models take an extensive amount of memory and computational resources. Additionally, we have provided alternate model specifications in the "Robustness" R scripts: binomial probit with random effects, ordered probit without random effects, and Ordinary Least Squares with random effects. An extended version of the supplemental materials is also provided.

Added County and PrecinctVotingData to a spatial join. The fields total_vote and proposition_37_yes_votes were set to sum the merge. After joining, the symbology was set to jenks with five classes. The labels were turned on then turned the duplicate labeling off. The final steps were to create the maps and add all the map properties.

🇺🇸 United States English A complete, historic universe of Cook County parcels with attached geographic, governmental, and spatial data. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. Additional notes:Non-taxing district data is attached via spatial join (st_contains) to each parcel's centroid. Tax district data (school district, park district, municipality, etc.) are attached by a parcel's assigned tax code. Centroids are based on Cook County parcel shapefiles. Older properties may be missing coordinates and thus also missing attached spatial data (usually they are missing a parcel boundary in the shapefile). Newer properties may be missing a mailing or property address, as they need to be assigned one by the postal service.

• Spatial join of precintvotingdata in Counties *Change the symbology for quantities of normalization yes_votes.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed by combining crown TSR spatial data, information gathered from Rural Lands Protection Board (RLPB) rangers, and surveyed Conservation and Biodiversity data to compile a layer within 30 RLPB districts in NSW. The layer attempts to spatially reflect current TSRs as accurately as possible with conservation attributes for each one. Dataset History The initial process in production involved using the most up to date extract of TSR from the crown spatial layer as a base map, as this layer should reasonably accurately spatially reflect the location, size, and attributes of TSR in NSW. This crown spatial layer from which the TSR were extracted is maintained by the NSW Department of Lands. The TSR extract is comprised of approximately 25,000 polygons in the study area. These polygons were then attributed with names, IDs and other attributes from the Long Paddock (LP) points layer produced by the RLPB State Council, which contains approximately 4000 named reserves throughout the study area. This layer reflects the names and ID number by which the reserves were or are currently managed by the RLPB's. This layer was spatially joined with the TSR polygon layer by proximity to produce a polygon layer attributed with RLPB reserve names and ID numbers. This process was repeated for other small datasets in order to link data with the polygon layer and LP reserve names. The next and by far the most time consuming and laborious process in the project was transferring the data gathered from surveys undertaken with RLPB rangers about each reserve (location, spatial extent, name, currency conservation value and biodiversity). This spatial information was annotated on hard copy maps and referenced against the spatial join making manual edits where necessary. Edits were conducted manually as the reference information was only on hard copy paper maps. Any corrections were made to the merged layer to produce an accurate spatial reflection of the RLPB reserves by name and ID. This manual editing process composed the bulk of the time for layer production as all reserves in each RLPB district in the study area had to be checked manually. Any necessary changes had to then be made to correct the spatial location of the reserve and ensure the correct ID was assigned for attributing the conservation data. In approximately 80% of cases the spatial join was correct, although this figure would be less where long chains of TSR polygons exist. The majority of time was devoted to making the numerous additions that needed to be incorporated. A spreadsheet based on the LP point layer was attributed with the LP point [OBJECTID] in order to produce a unique reference for each reserve so that conservation and biodiversity value data could be attributed against each reserve in the spatial layer being produced. Any new reserves were allocated [OBJECTID] number both in the GIS and the spreadsheet in order to create this link. All relevant data was entered into the spreadsheet and then edited to a suitable level to be attached as an attribute table. Field names were chosen and appropriate an interpretable data formats each field. The completed spreadsheet was then linked to the shapefile to produce a polygon TSR spatial layer containing all available conservation and biodiversity information. Any additional attribute were either entered manually or obtained by merging with other layers. Attributes for the final layer were selected for usability by those wishing to query valuable Conservation Value (CV) data for each reserve, along with a number of administrative attributes for locating and querying certain aspects of each parcel. Constant error checking was conducted throughout the process to ensure minimal error being transferred to the production. This was done manually, and also by running numerous spatial and attribute based queries to identify potential errors in the spatial layer being produced. Follow up phone calls were made to the rangers to identify exact localities of reserves where polygons could not be allocated due to missing or ambiguous information. If precise location data was provided, polygons could be added in, either from other crown spatial layers or from cadastre. These polygons were also attributed with the lowest confindex rating, as their status as crown land is unknown or doubtful. In some cases existing GIS layers had been created for certain areas. Murray RLPB has data where 400+ polygons do not exist in the current crown TSR extract. According to the rangers interviewed it was determined the majority of these TSR exist. This data was incorporated in the TSR polygon by merging the two layers and then assigning attributes in the normal way, ie by being given a LP Name and ID and then updated from the marked up hard copy maps. In the confidence index these are given a rating of 1 (see confindex matrix) due to the unknown source of the data and no match with any other crown spatial data. A confidence index matrix (confindex) was produced in order to give the end user of the GIS product an idea as to how the data for each reserve was obtained, its purpose, and an indication to whether it is likely to be a current TSR. The higher the confindex, the more secure the user can be in the data. (See Confidence Index Matrix) This was necessary due to conflicting information from a number of datasets, usually the RLPB ranger (mark up on hard copy map) conflicting with the crown spatial data. If these conflicting reserves were to be deleted, this would lead to a large amount of information loss during the project. If additions were made without sufficient data to determine its crown status, currency, location, etc (which was not available in all cases) the end user may rely on data that has a low level of accuracy. The confindex was produced by determining the value of information and scoring it accordingly, compounding its value if data sources showed a correlation. Where an RLPB LP Name and ID point was not assigned to a polygon due to other points being in closer proximity these names and ID are effectively deleted from the polygon layer. In a number of cases this was correct due to land being revoked, relinquished and/or now freehold. In a number of cases where the TSR is thought to exist and a polygon could not be assigned due to no info available (Lot/DP, close proximity to a crown reserve, further ranger interview provided no info, etc etc). For these cases to ensure no information loss a points layer was compiled from the LP points layer with further info from the marked up hard copy maps to place the point in the most accurate approximate location to where the reserve is though to exist and then all CV data attached to the point. In many of these cases some further investigation could provide an exact location and inclusion in the TSR poly layer. The accuracy of the point is mentioned in the metadata, so that the location is not taken as an absolute location and is only to be used as a guide for the approximate location of the reserve. Topology checks were conducted to eliminate slivers in the layer and to remove duplicate polygons. Where two crown reserves existed on the same land parcel, the duplicate polygon was deleted and unique attributes (Crown Reserve Number, Type, and Purpose) were transferred. Once the polygon layer was satisfactorily completed, a list of the LP points not allocated to polygons was compiled. Any points (reserves) that were said to have been revoked or relinquished were then removed from this list to provide a list of those that are said to be current. An extract of the LP points layer was then produced with only the aforementioned points. These points were then attributed with the same conservation and biodiversity data as the polygon layer, in an attempt to minimise the amount of information loss. Dataset Citation "NSW Department of Environment, Climate Change and Water" (2010) Travelling Stock Route Conservation Values. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/198900d5-0d06-4bd0-832b-e30a7c4e8873.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho

Shows the mean July max temperatures for each neighborhood in the city of Toledo.Workflow:- Spatial Join of July Temperatures (census tracts) and Neighborhoods- On the new layer, Summary Statistics of Max July Temp by name- With the new layer, Join Mean Max Temp to Neighborhoods by name- Data > Export features to shapefile

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

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.