FOSS4G 2024 Academic Track

Public participation is of utmost importance for community mobilization and engagement, so that through their networks and relationships, both within and outside the community, they create space through social action. According to Goodchild (2007), there is a demand to generate information that helps vulnerable communities to strengthen relationships with the government responsible for promoting important interventions to bring about change. It is possible to use the sensitivity and ability to inform the needs of each resident, from their perception, and understand the needs of their community. Therefore, it is important to use open and free mapping tools that represent the community's demands, producing information that allows collective autonomy to carry out strategies that involve the public authorities through co-optation and coalitions aimed at community-based interventions (Silva, 2014).

In this sense, OpenStreetMap (OSM) stands out as a tool for collaborative mapping and community interventions in highly vulnerable urban areas, such as favelas. Given OSM's participatory and open nature, it allows the creation of maps with records of features of various kinds, but it is also of great value as a platform for community training and the development of geospatial skills (Bortolini and Camboin 2019).

The objective is to analyze from the literature how OpenStreetMap has been used for training and empowering local actors in projects aimed at community-based interventions in favelas.

A systematic literature review was carried out with the support of artificial intelligence tools (Chat GPT-4, Elicit, Semantic Scholar, Chat pdf), bibliography management software (Zotero), and software for visualizing bibliometric networks (VosViewer) combined with other research methodologies (P.I.C.O., Bardin) to assist in the overall evaluation of the literature. Although AI tools have great power to aid the review, they do not replace the need for critical judgment and human expertise, demanding confidence in the knowledge of the contents and scientific methodology.

The following keywords were used in the platforms of WoS and Scopus collection lists in the first search: [Collaborative mapping, Community intervention, OpenStreetMap, Community empowerment, Community mobilization, Citizen participation]. In this first search, some filters were established, such as the publication date within the last 10 years and articles that were open access. Based on these filters, 43 articles were found that fit these specifications in the Web of Science, with the vast majority in English. From this first literature search, there also arose the need to increase the number of articles that most closely aligned with the theme. The keywords were adjusted based on these articles. In this second search, other databases were also included for the research, such as Scopus and Google Scholar. The use of artificial intelligence tools Elicit and Semantic was essential to find articles using the keywords that were most repeated in the main articles. Still in this second search, these adjusted keywords were entered into Chat-GPT 4 to generate search strings under the acronym P.I.C.O (Population; Intervention; Comparison; Outcome) for use in the Web of Science.

Keywords for the second search: [Collaborative mapping, informal settlements, urban slums, OpenStreetMap, public participation, community engagement, community-based intervention, community intervention].
Final search string: [("Collaborative mapping" OR "participatory mapping") AND ("community intervention" OR "community-based intervention") AND ("OpenStreetMap" OR OSM) AND ("community empowerment" OR "empowerment") AND ("community mobilization" OR "community engagement") AND ("citizen participation" OR "public participation") AND (favelas OR "informal settlements" OR "urban slums")]. Finally, twenty articles were identified in the Web of Science, Google Scholar, and Scopus databases.

Based on these 43 articles, a synthesis framework is being built that aims to systematize information about the works found, informing: 1) Source/Base/Collection, 2) Reference according to ABNT, 3) Name of the Journal, 4) Contact of the main author - email, 5) Country of affiliation of the authors, 6) Country of the mapped community, 7) Problem/Objective/Hypothesis, 8) Methodology, 9) Materials used, 10) Techniques used, 11) Main results, 12) Does it work with Favela? 13) What is the nature of the community-based intervention? 14) Did favela residents operate the OpenStreetMap? 15) Where is the favela and/or intervention? 16) If mapping in a favela, what features and attributes were mapped? 17) Were integrated digital and analog cartographic technologies used? Which ones? 18) Were methods used for community appropriation of cartographic tools and data? 19) Was educational material provided? Indicate link, 19) Was a method for evaluating the tools and processes used implemented? 20) Were community impact indicators used? 21) Are effective impacts felt by the community reported? Which ones?

By constructing this framework, we are evaluating aspects such as the geographic diversity in the use of OSM, indicating the platform's flexibility and adaptability, or the still limited participation of local actors in mapping their communities. By compiling data on the nature of community-based interventions, techniques and methodologies used, and community impact indicators, we aim to identify common patterns in the types of interventions that have been most effective. Furthermore, the analysis of reported impacts can indicate tangible benefits of these projects.

The analysis of how projects addressed training and education, including the provision of educational materials and methods for community appropriation of cartographic tools and data, can indicate strategies used to empower local communities. We are also analyzing the features and attributes mapped specifically in favelas, to identify the main challenges and specific needs of these areas, supporting the indication of demands for improvements in methodologies and mapping tools in these urban contexts.

Thus, we are building a comprehensive framework on the current state of the use of OpenStreetMap for training and intervention in favelas, identifying gaps, challenges, and opportunities for future research and projects.