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.

In a geographic information system (GIS) study, a spatial join was conducted using QGIS to integrate three datasets: Afstand tot Koelte (ATK), CBS Buurten 2021, and BAG Verblijsobjecten (VO) across three provinces. The resulting output comprised a point dataset which included the ATK and buurten for each VO.It was observed that approximately 5% of the VOs lacked ATK data. Consequently, a field calculation was initiated to ascertain the direct distance, as the crow flies, to the nearest cool place ("koele plek"). A key distinction is that ATK data is based on walking distances along road networks, whereas direct distance measurements do not incorporate such networks. Comparative analysis revealed marginal differences between the two methods, with ATK data generally showing distances 50-100 meters greater.Subsequently, a selection process isolated the VOs categorized as residential ("woning,") which were then exported to an Excel format for further analysis. This was followed by the creation of a distinct list of neighborhoods ("buurten.") Utilizing the 'COUNTIFS' formula, a summation of distances per neighborhood was calculated, leading to the computation of their respective percentages. This methodology integrates spatial data analysis and quantitative techniques to understand geographic proximity and distribution.

OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for 21 Pacific Island Countries, in a GIS-friendly format. The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly and contains data for Cook Islands, Federated States of Micronesia, Fiji, Kiribati, Republic of the Marshall Islands, Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Guam, Northern Mariana Islands, French Polynesia, Wallis and Futuna, Tokelau, American Samoa as well as data on the Pacific region. The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

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