The city of Maricá, in the state of Rio de Janeiro (Brazil), is historically affected by natural disasters, which mainly hit the less affluent areas of the city, where people who have fewer resources to deal with risks and damage. On the other hand, city halls in small and medium-sized cities don't always have a spatial data infrastructure or a Web mapping platform that allows for the integration of data layers related to disaster risk reduction (DRR). Given this panorama, a collaborative mapping platform is proposed, with layers related to DRR infrastructure. Seven areas vulnerable to disasters (neighbourhoods: Jardim Atlântico, Itaocara, Cajueiros, Mumbuca, Centro, Bananal, Jaconé), have been adopted as areas of interest (AOI) for this pilot project, which is an initiative of the Virtual Institute for Sustainable Development - IVIDES. org, in cooperation with the YouthMappers UFRJ chapter (Rio de Janeiro, Brazil), chaired by Dr. Raquel Dezidério Souto, chairwoman of IVIDES.org and associated researcher of the Laboratory of Cartography of the Federal University of Rio de Janeiro (GeoCart-UFRJ). Throughout 2024 and 2025, various training sessions have been carried out in order to map the seven AOI collaboratively. In order to execute the project and provide interactive data visualization, two Web maps were created, one for the acquisition an initial mapping of data in OpenStreetMap (OSM) and the other for the display of validated data in a private environment, where changes by others can be controlled. The first Web map (for raw data mapping and visualization) shows layers with data retrieved dynamically from the OpenStreetMap database using Overpass API queries, which were included in the dynamic layers of a structure developed with uMap. In this first Web map [1] it is possible to check the coverage of existing data related to the research topic in OSM and the two more used editors for OSM - iD and JOSM, can be accessed to collaborate with data missing in these AOI. The second Web map [2], developed with the Leaflet JavaScript library and the WordPress content management system (CMS), with the “Leaflet map” plug-in installed and active, containing the layers with OSM data that have already been validated, plus the static layers that have been added with data from the public authorities, such as data on the sirens at the hydrological or geological warning and alarm stations, which issue alarms to evacuate areas before a disastrous event occurs. A total of 16 layers are being considered initially: Areas subject to flooding; Areas subject to mass movements; Classification of the coast as its vulnerability to the coastal erosion; Schools (public and private); Geological alert and alarm stations; Hydrological alert and alarm stations; Hydrological monitoring stations; Meteorological monitoring stations; Hydrants; Hospitals (public and private); Clinic centers (public and private); Hotels; Churches; Pluviometers; Villages and towns; and Localities. For the Web map project developed with the WordPress CMS, custom icons were designed, which can be found on the GitHub [3]. The icons were designed with a black border to make them more visible on the map. The files were formatted in Scalable Vector Graphics (.SVG) and Portable Network Format (.PNG). The second format was adopted for the Web map in WordPress, as it was better for visualization. The Web map project developed with uMap adopted simple symbols, which are present in its standard collection of symbols. Some aggregations are made for layers with a lot of data (with the clustering resource provided by uMap), in order to improve visualization and navigation on the map. The strengths of this methodology, developed for the city of Maricá, but which could be adopted by other municipalities, are that it relies on the collaborative effort of anyone minimally trained to map on the OSM and the socialization of information on disaster-prone areas with the population. However, some difficulties have been encountered, such as: i) some people have difficulty using programs and equipment needed to interact with the map; ii) weak Internet signals in locations far from large urban centers; iii) a lack of data for certain categories, such as data on the vulnerability of areas to disasters. All these difficulties have been encountered by many other researchers carrying out collaborative research and mapping. This initiative is related to the United Nations 2030 Agenda [4] and the Sustainable Development Goals (SDGs) [5], especially: SDG 3 - Ensure healthy lives and promote well-being for all at all ages; SDG 11- Make cities and human settlements inclusive, safe, resilient and sustainable; SDG 13 - Take urgent action to combat climate change and its impacts; and SDG 17 - Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development. With the progress of this research, we hope to collaborate in providing information support, not only for prevention and mitigation operations, but also for consultation with the resident population, in order to contribute to DRR in the areas covered.
[1] http://u.osmfr.org/m/1013950/
[2] https://ivides.org/infomarica
[3] https://github.com/raqueldeziderio/leaflet_wp
[4] https://sdgs.un.org/2030agenda
[5] https://sdgs.un.org/goals

The OpenStreetMap (OSM) ecosystem is very vast, making it complicated to investigate user preferences for OSM-related software or topics. This ecosystem has an important community component and a large set of software resources that serve many different purposes, such as retrieving, editing, validating or converting data, collecting data in the field, routing, geoservices... These programs are increasingly present in the daily lives of OSM users, especially in countries outside the US-Europe axis (such as Latin America and Africa), where new users are becoming interested in OSM and the applications, which
is reflected in the significant increase in the number of contributions on the map. This research aims to survey the publication profile of the weeklyOSM [1], analyzing the content of the issues published on the last ten years (#272 to 768), since it began to be organized with the OSM Blog Collector [2], a system with free and open source code, designed, programmed and improved by TheFive [3] and which has been used to manage the content of the weekly publishings since its number 272, in September 2015. The weeklyOSM reports news from the reporting week, is produced by mappers and OSM enthusiasts and is independent from organizations and companies [4]. It covers currently 15 languages and has been informing the OSM user community without fail since its release as the German weekly OSM-Wochennotiz [5], whose initial issue was published on July 23, 2010. Its workflow naturally favors sampling OSM-related topics from a wide variety of sources, such as social networks, blogs, OpenStreetMap Foundation channels or institutional news sites. Thus, by analyzing their content, it is hoped that the results will serve as a proxy for understanding the interest in certain categories and the usage of certain software by OSM users. The methodological flow started with the initial export of the articles stored in the OSM Blog Collector (OSMBC), in a file in .CSV format. The records were then imported into a PostgreSQL database and analyzed using SQL. Of the initial 32,261 records (articles) and 35 categories, the records prior to number 272 were excluded and the categories were standardized to conform to the actual weeklyOSM's categories (About us, Breaking news, Community, Did you know that..., Education, Events, Humanitarian OSM, Imports, Licenses, Local chapter news, Mapping, Mapping campaigns, Maps, Open Data, OpenStreetMap Foundation, OSM in action, OSM in the media, OSM research, Other Geo Things, Picture, Programming, Releases, Software, Upcoming Events). Thus, after the initial processing, 18,672 records (articles) were obtained, classified into the current 24 categories and considered as the initial set for the analysis. In a second stage, queries were made regarding the occurrence of 102 software in the articles (e.g. “Panoramax”), saving the results files in .csv format and making them available on GitHub [6], with its
subsequent classification into 14 software groups (3D model, aerial imagery, API, converter, data extraction, data quality, desktop editor, library, mobile editor, notes editor, OSM based service, routing, street level imagery, tagging) and re-importing them into the database in a new table, forming a second set of records. Making the data and results available on GitHub guarantees the transparency and reproducibility of the analysis. The search for software and the design of the categories were inspired by the sources [7-11]. From the starting set for the analysis, the initial and final numbers of the category (e.g. “Mapping”) were collected, and the total number of articles in the category was calculated, resulting in a ranking showing the most popular categories for publishing articles. From the second set (only the records of the 102 selected software), the software' indices were calculated, which correspond to the software's participation in the group to which it belongs. The results found in the first stage of the analysis show that the ten most popular weeklyOSM categories to associate with articles were: “Mapping” (2,778 articles), “Other geo things” (2,331), "Community" (2,212), “Did you know that...” (1,177), “Events” (1,153), “Maps” (1,064), “Software” (1,060), "Programming" (963), “Humanitarian OSM” (821) and “OpenStreetMap Foundation” (782); and the full ranking can be accessed on the GitHub [12]. In the second stage of the analysis, with the set of data which contains only the records of articles that included the 102 selected software, two main statistics were obtained: i) the number of articles for the 14 groups of software and ii) the participation index of a given software in the group to which it belongs. For the first processing (i), the five groups with the highest number of articles were: “Desktop editor” (1,193), “OSM based service” (968), "Routing" (524), “Mobile editor” (522) and “Street Level Imagery” (424); and the full results are found on the GitHub [13]. For the second processing (ii), there was a greater variety of software in the “OSM based service”, “Desktop editor” and “Mobile editor” groups; the complete set of graphs are found on the GitHub [14]. As a result of the research, we could infer that weeklyOSM, over the last ten years, has expressed the variety of themes and programs related to OSM, with a large representation of content pertinent to “mapping” and “community”, which are at the heart of the OSM ecosystem. Thus, could be inferred that the analysis had a representative result from the point of view
of reality; and c) in addition to this “panoramic” view of the content in general, it was possible to observe the participation of the software in their respective group, inferring their relevance in the context of the collaborative mapping with OSM. Limitations of the research include the difficulty in performing queries on very heterogeneous strings, requiring greater attention from the researcher and the static nature of the analysis, making it more time-consuming to carry out the workflow. With the publication of this research, we hope to highlight the wide variety of information resources available to OSM contributors and, in particular, to highlight the role of weeklyOSM as a representative vehicle for information on the OSM ecosystem, freely accessible to the community and which has fulfilled its objective without fail since its first edition on 2010.

[1] weeklyOSM Editorial Team. weeklyOSM. https://weeklyosm.eu
[2] weeklyOSM Editorial Team. OSM Blog. https://blog.openstreetmap.de/blog/2010/07/osm-wochennotiz-nr-1/
[3] TheFive et al. OSM Blog Collector. https://osmbc.openstreetmap.de/
[4] weeklyOSM Editorial Team. Blog OSM Histoire. https://blog.openstreetmap.de/mitmachen/
[5] TheFive. TheFive's OpenStreetMap user profile. https://www.osm.org/user/TheFive
[6] Souto, R. D. weeklyOSM-stats. https://github.com/raqueldeziderio/weeklyOSM-stats
[7] Comparison of editors. OpenStreetMap Wiki. https://wiki.openstreetmap.org/wiki/Comparison_of_editors
[8] ToastHawaii. OSM APPs Catalog. https://osm-apps.org/?category=edit
[9] OpenStreetMap contributors. List of OSM based services. OpenStreetMap Wiki.
https://wiki.openstreetmap.org/wiki/List_of_OSM-based_services
[10] https://software.wambachers-osm.website/ ("Improve the map" category)
[11] Editor usage stats. OpenStreetMap Wiki. https://wiki.openstreetmap.org/wiki/Editor_usage_stats
[12] Souto, R. D. https://github.com/raqueldeziderio/weeklyOSM-stats/tree/main/statistics
[13] Souto, R. D. https://github.com/raqueldeziderio/weeklyOSM-stats/tree/main/selection
[14] Souto, R. D. https://github.com/raqueldeziderio/weeklyOSM-stats/tree/main/graphics_software_in_group