Marco Minghini

The collection, curation and publication of geospatial information has been for centuries the sole prerogative of public sector organisations. Such data has been traditionally considered the reference source for datasets and cartographic outputs. However, new geospatial data sources (e.g. from the private sector and citizen-generated[1]) have emerged that are currently challenging the role of the public sector [2]. In response to this, governments are currently exploring new ways of managing the creation and update of their geospatial datasets [3]. Datasets of high relevance are increasingly produced by both private companies and crowdsourced initiatives. E.g., in 2022 Microsoft released Microsoft Building Footprints, a dataset of around 1 billion building footprints extracted from Bing Maps imagery from 2014 to 2022. More recently, in December 2022n Amazon Web Services (AWS), Meta, Microsoft, and TomTom founded the Overture Maps Foundation (https://www.linuxfoundation.org/press/linux-foundation-announces-overture-maps-foundation-to-build-interoperable-open-map-data), a joint initiative in partnership with the Linux Foundation with the aim to curate and release worldwide map data from the aggregation of multiple input sources including civic organisations and open data sources, especially OpenStreetMap data. These initiatives aim to improve the coverage of existing governmental geospatial information through the release of open data and a strong dependency on OpenStreetMap. In particular, the Overture initiative has the explicit goal to add quality checks, data integration, and alignment of schemas to OSM data. Recently, the Italian Military Geographic Institute (IGM, one of the governmental mapping agencies in Italy) has released a multi-layer dataset called “Database di Sintesi Nazionale” (DBSN, https://www.igmi.org/en/dbsn-database-di-sintesi-nazionale). The DBSN is intended to include geospatial information relevant to analysis and representation at the national level, with the additional purpose to derive maps at the scale 1:25,000 through automatic procedures. The creation of the DBSN builds on top of various information sources, with regional geotopographic data as primary source of information and products from other national public bodies (e.g. cadastral maps) as additional sources. The source is recorded in a specific attribute field for each feature in the database, with a list of codes referencing the various sources. Among the external sources used as input for the work of integration in the DBSN, OpenStreetMap was explicitly considered and used. One of the elements of novelty, at least in the Italian context, is the release of the DBSN under the ODbL licence (https://opendatacommons.org/licenses/odbl), caused by the fact that the inclusion of OSM data requires derivative products to be released with the same licence. Currently, the DBSN includes data covering only 12 out of the 20 Italian regions (Abruzzo, Basilicata, Calabria, Campania, Lazio, Marche, Molise, Puglia, Sardegna, Sicilia, Toscana, Umbria). The remaining ones will be released in the near future. The datasets have been downloaded from the official IGM website in January 2023. The DBSN schema is a subset of the specifications defined in the "Catalogue of Spatial Data - Content Specifications for Geotopographic Databases” (Decrete 10 November 2011) and is composed of 10 layers, 29 themes and 91 classes. We compared it with the OpenStreetMap specifications (based on the community-based tagging scheme at https://wiki.openstreetmap.org/wiki/Map_Features) and selected two main themes (buildings and streets). The analysis was performed through a set of Python scripts available under the open source WTFPL licence at https://github.com/napo/dbsnosmcompare. Firstly, we analysed—for buildings and streets in the IGM database—where OSM data was used as the primary source of information. The percentage of buildings derived from OSM is minimal, ranging from 0.01% in Umbria to 1.3% in Marche; regarding streets, the differences between regions increase, ranging from almost 0% in Abruzzo and Calabria to 94% in Umbria. Secondly, we calculated the area covered by buildings and the length of streets in both the IGM and OSM databases to understand how much OSM completeness is good, compared to the official IGM dataset. In the 12 regions, the area covered by buildings in OSM is on average about 55% of the corresponding area in IGM, while the percentage of the length of streets is about 78%. Anyway, these numbers are highly variable among regions, ranging between 32% in Calabria and 105% in Puglia for buildings, and between 46% in Calabria and 103% in Umbria for streets. These first results show that the main source information in the DBSN (namely the official regional data) is highly variable across the 12 regions, which required the IGM to find additional data sources to fill the gaps. OSM plays a minor role for integrating buildings in the database, while it demonstrates a high potential for contributing to street information. Results also show that, even with only a small contribution, some elements that are present in OSM are still not included in the DBSN. This can be due to at least two reasons: (i) the current workflow of selection of elements in OSM (through tags) does not include some potentially relevant elements; ii) the (ideally) daily update of OSM is able to bring in the database new features at a pace that is unbeatable by the IGM, and governmental organisations in general. While this study highlights the importance that OpenStreetMap has achieved as a reference source of geospatial information for governmental bodies as well, providing evidence of its contribution to the national database of the IGM, iit also paves the way for improving OpenStreetMap itself by importing data for various layers, benefiting from the release of the DBSN under the ODbL licence.

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Other people: Alessandro Sarretta

Published in 2020, the European strategy for data sets the vision for Europe to become a leader in a data-driven society by establishing so-called common European data spaces in all strategic societal sectors. Data spaces are envisioned as sovereign, trustworthy and interoperable data sharing environments where data can fairly flow within and across actors, in full respect of European Union (EU) values to the benefit of European economy and society. The development of data spaces is accompanied by a set of horizontal legislative measures, including, among others, an Implementing Act on high-value datasets under the Open Data Directive that lays down a list of datasets (many of which being geospatial) that EU Member States public sector organisations are required to make available for free, under open access licenses, in machine-readable formats and via Application Programming Interfaces (APIs). The talk will describe the activities around open source geospatial software and open geospatial data that the European Commission’s Joint Research Centre (JRC) has performed to support the development of the common European Green Deal data space, focused on environmental data sharing and instrumental to address climate changes and environmental challenges in line with the top priority of Von der Leyen’s Commission 2019-2024. A key enabler to bring public data into this data space is the infrastructure setup for the EU INSPIRE Directive, which is technically coordinated, maintained and operated by the JRC. The INSPIRE Directive itself, together with the Directive on public access to environmental information, are currently subject of an impact assessment that might lead to a revision of the legal framework (GreenData4All initiative). This is accompanied by an overall modernisation of the technical infrastructure, increasingly based on open source software both at the Commission side (GeoNetwork for the INSPIRE Geoportal, ETF for the INSPIRE Reference Validator and Re3gistry for the INSPIRE Registry) and at the Member States side, where FOSS4G tools are the primary choice for both serving and consuming data. Thanks to a number of INSPIRE Good Practices promoted by the community, new standards and approaches for data encoding and sharing (e.g. based on OGC APIs) are bringing additional value to the INSPIRE stack. The same set of approaches ensures the full alignment and complementarity between INSPIRE and the Implementing Act on high-value datasets, thus positioning open source geospatial software as a true enabler for the Green Deal data space.

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This study adopts a statistical approach based on Exploratory Spatial Data Analysis to identify underlying contribution patterns of OpenStreetMap (OSM). Univariate and multivariate analyses on a number of variables computed from OSM history on a regular hexagonal grid in Milan (Italy) allow to detect a number of both local clusters and local outliers, which shed light on the complexity of OSM temporal evolution driven by active local contributors and communities, data imports and mapping parties.

Compared to traditional geospatial data sources, a major advantage of OpenStreetMap (OSM) is the availability of its full history. In literature, OSM history has been exploited for a number of purposes. The most frequent is intrinsic quality assessment, which - in contrast to extrinsic assessment, where OSM quality is evaluated through comparison against a reference dataset - estimates OSM quality by only looking at its temporal evolution. OSM history has been also explored to gain insights into the project's contribution patterns, e.g. history and profiling of contributors; origin, amount, nature and frequency of edits; spatio-temporal evolution of the whole OSM database - or parts thereof, such as road networks and buildings - in specific areas, or after specific events like natural disasters; and spatial analysis of contributor and contribution patterns. This work fits into the context of OSM intrinsic assessment by proposing a statistical approach based on Exploratory Spatial Data Analysis, and in particular spatial association, aimed at uncovering underlying history-based patterns of OSM data. More in detail, spatial association is investigated in both the univariate and multivariate contexts, i.e. in the cases - respectively - when one variable and multiple variables (together) are examined. The univariate analysis is performed using the Local Moran’s I indicator, which provides a robust classification method to detect statistically significant patterns (compared to the hypothesis of randomness) and define the spatial association type at each location in the dataset. The association type reflects the local characteristics of the variable at each location and its surroundings. Hence it allows detecting clusters, i.e. local patterns of similar (either high or low) values, as well as outliers, i.e. local patterns of dissimilar values (either low values surrounded by high values or viceversa). Instead, the multivariate Geary’s c indicator is employed to detect local association patterns resulting from the joint spatial interaction of two or more variables. A multivariate pattern classification comparable to the one of the univariate case is achieved through a novel classification method developed by the authors. This consists of a comparison of local and global centrality measures (means and medians) for the computed distribution of the multivariate Geary’s c, to produce classification maps of clusters and outliers. The analysis is performed on Milan Province (Northern Italy), counting a population of more than 3 million inhabitants on a surface of about 1.500 km². This area is sampled using a regular hexagonal grid with side of the hexagon equal to 1000 m, producing a total of 684 cells. The analysis is focused on the history of OSM nodes only, with the following hypotheses: only nodes with at least one tag are considered; a new version of a node is counted only when there is a change in tags (not in geometry); only the nodes which currently exist in the OSM database are considered. With this in mind, for each grid cell a number of history-based variables are computed: total number of different contributors who have edited OSM nodes; average number of different contributors who have edited each OSM node; average date of creation of the OSM nodes; average date of last edit of the OSM nodes; average number of versions of the OSM nodes; average frequency of update of the OSM nodes. These values are derived from the processing of the OSM Full History Planet file (downloaded in May 2019) and its conversion into a SpatiaLite database after an intersection with the study area, followed by the computation of the variables for each grid cell. The univariate analysis, performed using the QGIS Hotspot Analysis plugin developed by the authors, highlights different spatial associations for the different variables. While some of them (such as total and average number of contributors and average version) clearly show clusters of high values in correspondence of the most urbanized areas and clusters of low values in the non-urban peripheral areas, spatial association patterns are more heterogeneous for other variables such as the average update frequency. Multivariate analyses are then performed to detect the spatial patterns derived from the joint interaction between two and more of the variables considered. Despite each variable has its own spatial pattern when taken alone, their combination (especially when adding more and more variables) highlights not only high and low-value clusters in urban and non-urban areas, but also other interesting clusters and outliers. These unveil peculiar contribution patterns resulting from active local contributors, data imports and mapping parties, and highlight areas where OSM development might need some improvement. Despite only preliminary, the methodology - which, to the authors’ knowledge, has been never adopted before in OSM-related research - looks extremely promising to process the complexity of OSM history and transform it into understandable, statistical-based indicators which can shed more light on the intricate phenomenon of OSM local development.

Lightning Talks

## OpenStreetMap intrinsic quality assessment using “Is OSM up-to-date?” web application Marco Minghini and Francesco Frassinelli

Approaches to analyze OpenStreetMap (OSM) intrinsically, i.e. based on the history of data, have become an established way to achieve a wide range of final goals, most importantly to study its quality. Started in 2017, “Is OSM up-to-date?” is an open source web application licensed under AGPL and mainly written in Python. It can be also run in the command line or inside a Docker container. The target beneficiaries of the software are OSM users and communities, who need to assess the quality of data in a given area to decide whether to use it or not as well as where mapping efforts should be best directed, and OSM researchers and scholars, who can use it as a tool to help in their OSM quality studies. The project has a dedicated wiki page (https://wiki.openstreetmap.org/wiki/Is_OSM_up-to-date), source code is hosted on GitHub (https://github.com/frafra/is-osm-uptodate) and a demo is available at https://is-osm-uptodate.frafra.eu. Using the OSM API, the web application generates history-based, quality-oriented visualizations of OSM nodes and ways having at least one tag for any rectangular user-selected region. Such visualizations (on top of a grayscale OSM basemap) are available for the following criteria: date of creation, date of last edit, number of versions, number of different contributors who edited that node or way, and frequency of update. When an OSM node or way is clicked, a popup shows its values for the previously mentioned criteria as well as the list of currently available tags and the links to the OSM iD editor (in edit mode on that node or way), history and details of that node or way (both linked to the OSM website). A number of additional features to further improve the analysis of quality is currently planned (see https://github.com/frafra/is-osm-uptodate/labels/enhancement).

## Deriviste - Click and Go Photo Mapping Christopher Beddow

There are many OSM editing tools, but only one that allows the user to click inside a Mapillary image to add new data to the map. Richard Fairhurst's Deriviste tool was a quick experiment that proved this was possible, but it's important to ask more questions. How useful is the tool? How accurate and precise is it? How can it be improved?

## Fifty states in 12 years and five minutes Minh Nguyễn

A whirlwind historical tour of the OpenStreetMap project in the United States.

## "MAPAbabae": Using OSM as a Tool for Mapping with Women and For Women Andi Tabinas, Arnalie Vicario

We would like to share how we, as part of the Social Welfare and Development agency of the Philippines (DSWD), have utilized OSM to have a collaborative and gender-inclusive approach of mapping points of interests catering to women.

## Reverse Geocoding Is Not Simple Ilya Zverev

Having seen a dozen of different geocoders, I did not expect to find myself writing another one. But here I am, tasked with making a reverse geocoder better than the industry-standard Nominatim. Turns out it is a fun and not so straightforward task. Let’s see what can go wrong.

Other people: Ilya Zverev Arnalie Vicario Andi Tabinas Minh Nguyễn Christopher Beddow