Chaos Computer Club - archive feed

Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed. Most field program managers have their go-to field preparation checklist - this often includes a data model, their preferred data collection tools, field survey timeline, to name a few. We are often cautioned about the importance of community entry, and it is right, you will not be able to just enter the community and start mapping as people will get curious, ask questions and possibly become suspicious or hesitant to accept your data collection activities. At HOT, we employe participatory mapping methods and encourage local people to map their communities. However, sometimes with even all the correct steps followed, your activities can be hindered due to factors outside of your control. In this session, we will explore one of HOT’s field mapping projects implemented in Kampala in collaboration with the Kampala Capital City Authority that aimed to map community-level flood risk in a local suburb along the Nakamiro Channel catchment area. Despite all the correct steps taken, community entry in a specific jurisdiction felt impossible and field mapping could not be carried out. In this session, our aim is to first discuss what went wrong and how our field team approached this situation and later invite participants/attendees to share similar challenges experienced in the field and how these situations were overcome or addressed. about this event: https://2020.stateofthemap.org/sessions/SAEWDP/

This study explores the following research questions using OpenStreetMap-based mapping approach and healthcare facility survey from one of seven slums being studied in Africa and Asia. What are the differentials of spatial proximity to health care providers in informal settlements like slum? What are some of the lessons learnt from using OpenStreetMap-based mapping approach for slum health research? Preliminary findings suggest that residents can access four categories of healthcare facilities (Clinics/Maternity Centres; Patent Medicine Stores; Traditional/Faith Healers; Eye Health Centre) within a walking distance (under 1km) where Clinics and Maternity Centres are farthest from most residents. Background. The United Nation Sustainable Development Goal (SDG) 3 seeks to ensure universal health coverage for everyone irrespective of geographical location by 2030. Anecdotal evidence exists on the possibility of attainment of the goal at household level in slum areas most especially in Africa. Recent studies suggest further work on the advancement of empirical evidence on slum health [1], [2]; especially in Africa where slum population growth is reported to be at the same level with urban population growth [3]. There is the need to understand the dimensions of spatial proximity to healthcare facilities in Nigeria towards achieving SDG 3 [4]; especially in slum areas where little evidence exists. Spatial access to appropriate healthcare is even more relevant given the rapid rise in Covid-19 cases globally. The lack of detailed quality spatial data is a concern to both researchers and development agencies [5]. In an attempt to contribute to the knowledge gap in slum health studies, this study draws on two data sets (field validated OpenStreetMap data and healthcare facility survey data) from an ongoing research project to examine spatial proximity to healthcare facilities (HCFs) in Sasa, an informal urban “slum” area in Nigeria. The decision to focus on spatial proximity is based on findings from a household survey, in an ongoing project, which suggest that one of the main reasons given by respondents for choosing HCFs is proximity. Conceptually, there are two main schools of thought about spatial proximity [6]; this study considers proximity as a distance measure defined quantitatively. The ongoing research project is a National Institute for Health Research (NIHR) Global Health Unit on Improving Health in Slums at University of Warwick [7]. This Unit focuses on health services in slums through the study of seven slum sites in Africa and Asia and aims at finding optimal ways to enhance health services. We thus present initial results from one of the study sites in Africa. The following research questions are explored. What are the differentials of spatial proximity to health care providers in informal settlements like slum? What are some of the lessons learnt from using OpenStreetMap-based mapping approach for slum health research? Method. An OpenStreetMap-based data collection methodological approach was developed and implemented [8]. A spatially-referenced sampling frame was generated through a combination of: remote participatory mapping from satellite imagery; local participatory mapping and ground-truthing; and the identification of dwellings of each validated structure. Additionally, a healthcare facility survey was conducted to capture types of facilities etc. The following categories of HCFs are drawn from survey data and used for analyses: four Clinics and Maternity Centres (CMC); twenty-two Patent Medicine Stores (PMS); five Traditional and Faith Healers (TFH); and, one Eye Health Centre (EHC). Two-fold analyses are conducted. First, two measures of spatial proximity (spatial network and Euclidean distances) to different types of HCFs within the site are computed using field validated OpenStreetMap (OSM) network data. Bivariate analysis is performed to test sinuosity (ratio of network and Euclidean distance). Additionally, comparative analyses of combined means and medians (using k-independent samples median tests) for categories of HCFs are performed. Second, a reflective exercise is undertaken to outline some of the lessons learnt during the research process related to the OSM-based approach. Result. The presentation will discuss the outcome of the two-fold inquiry outlined. Preliminary results show strong positive correlation (r=.97; 99% CI) between the two spatial proximity measures suggesting that Euclidean and network spaces are quite similar in terms of accessibility to health care services within Sasa slum. Overall sinuosity index is 1.16 suggesting that the non-linear nature of network routes to HCFs contributes to 16% more than the Euclidean metric. The combined network distance grand mean (with standard deviations) and grand medians for each of the categories are as follows: 727m (±299) and 766m for CMC; 579m (±256) and 563m for PMS; 589m (±240) and 589m for TFH; and, 503m (±204) and 490m fo

To assess the possible human and financial losses of earthquakes and to estimate the long-term earthquake risk that many people on Earth are exposed to, detailed knowledge of buildings is paramount. This encompasses not only the position, size, and type of buildings, but also the reconstruction value and the number of people inside the building at any time. Using OpenStreetMap data and further open data, we are implementing an open, global, dynamic, purely algorithmic, and reproducible exposure model for the probabilistic description of the aforementioned parameters for every building on Earth, growing and changing with every edit in OpenStreetMap. Earthquakes are threatening many regions in the world with constantly increasing risk due to rapid urbanization and industrialization. To improve resilience and preparedness, we need to estimate the risk of earthquakes with the greatest possible detail. For this, exposure models are used that translate the physical earthquake hazard to building damage, human and financial losses. The level of detail in the risk model directly depends on the level of detail in the exposure model. So far, exposure models are usually described as aggregated building-type descriptions for larger geographic areas, from city districts to even larger administrative units. We present our new open, dynamic, and global exposure model based on OpenStreetMap that does not stop at administrative boundaries but rather attempts to classify and describe every building on Earth with the greatest level of detail. Our open-source model extracts all possible exposure indicators, i.e. footprint shape, number of stories, occupancy type, shape of the roof, etc. and combines the OpenStreetMap data with other open datasets if available. Using these indicators, the model assesses in a probabilistic way the possible building classes, the number of people inside the building depending on the time and day, and the reconstruction value. In areas with incomplete building coverage, the classical aggregate-based exposure models are combined with our model to deliver a probabilistic description of the entire building stock. To achieve a better spatial distribution of buildings in areas of incomplete coverage, we estimate the likely locations of buildings through remote sensing using again open data only, mainly Sentinel-I radar data. Due to the near-realtime computations of our model, it directly profits from the growth of OpenStreetMap and with about 5 million buildings added each month, the areas of incomplete coverage are constantly shrinking, making way for our building-specific exposure model. about this event: https://2020.stateofthemap.org/sessions/KNU7L3/

Trufi Association NGO offers an open-source journey planner app for formal and informal transport, based on public transport mapped in OpenStreetMap. In this extended talk, I would like to explain, how the participants can be customize the app to their own city, region, and country. With Trufi, mappers and developers can customize an open-source journey planner app for their own city, region or country. Especially in emergent countries in Africa, Middle and South America and Southeast Asia, where informal traffic (no stops, undocumented routes) is mainly in use. For the public transport data part, OSM is used to map the routes, OSM2GTFS or our own tools are used to create GTFS, which is hosted in OTP. Besides the obvious tasks (map routes, customize app code), many more important steps need to be done: Get a team together, find develop perfect UX for your city, release, finance the server and map costs, do marketing, convince governments to collaborate, approach users, run analysis on data, improve OpenStreetMap routes, offer solutions for drivers, etc. I would like to talk about these necessary steps, with stories from our implementations in Bolivia, Ghana, Ethiopia, and Colombia. Finally, I would like to discuss in an extended Q&A part whether OpenStreetMap is made for 100s of (bus) routes - we had fruitful discussions with pros and cons on that in the talk-de mailing list in July 2019 that we could continue. about this event: https://2020.stateofthemap.org/sessions/373NDC/

The study contributes towards some best practices of carrying out community mapping exercise and, distribution of results freely on OSM and spatial data portal like MASDAP for further studies or decision making. Thus the study focused on preparing for mapping - what to map, how to map and how to record the data; the mapping exercise itself; downloading and digitizing of data in map production; and how to use the maps to aid in decision making. The greatest threat to the people, property and economy of Malawi are natural hazards. According to Misomali (2014), since 1946, of all the 298 times the country has been impacted by hazards, 89% of those have been natural hazards while the remaining 11% have been human-generated events. According to MacOpiyo, records indicate that in the last 100 years, the country has experienced about 20 droughts in the last 100 years while in the last 36 years alone, the country has experienced eight major droughts, which have affected over 24 million people (MacOpiyo, 2017). The problem that exists almost every year in Malawi is that it is hit by floods in quite a number of districts. Households, infrastructures: roads, schools, praying houses are affected much alike like cultivation fields. These disasters impact negatively on the social-economic growth of the communities involved and the country at large to some extent. For example MacOpiyo pointed out that in 2015 Malawi experienced a once-in-500-years flood which impacted more than 1.1 million people (MacOpiyo, 2017). Therefore the study aimed at collecting exposure data which was used for production of flood risk maps. These maps were in turn used in Atlas production. Primary and secondary data was used in this study. Secondary data came from disaster profile from the Department of Disaster and Management Affairs (DoDMA) in Malawi which helped in identification of flood prone areas. Secondary data which was exposure data such as buildings, toilets, roads, bridges and schools among others was collected using handheld GPS with an accuracy of 3m. Choice of this data was based on how it impacts on the social-economic activities of the concerned communities. Java OpenStreet Map (JOSM) software was used for digitizing the collected exposure data by overlaying it with satellite imagery and creating attributes of that data. Thereafter, this was uploaded into a GIS environment for conducting symbolization and map visualization. Various maps at different scales were produced which showed location of different areas that were affected by floods. These maps are used to inform the affected communities on areas that are prone to floods. The maps are shared on OpenStreeMap, Malawi Spatial Data Portal (MASDAP) and at regional and district level through workshops. Thus communities should know where to settle or not as well as where they can carry out their socio-economic activities and not be affected by flood. This also helps in building resilience among communities. The study has shown that there is little interventions on the ground to help in reducing the root cause of floods in most parts of the country. There are a lot of mixed reasons as to the causes of floods that affect people in these areas. Living in low lying areas is the main attributed that was found for communities to be heavily affected by floods. Another reason is the siltation within rivers which is causing the rising-up of river beds is another cause of flooding in the country. These have heavily affected the poorly constructed buildings and infrastructures found in the flood prone areas. Another problem that was observed during the course of this study is lack of knowledge and information on disaster risk management. This has resulted in communities being affected when flood disaster strikes. As a way forward, the study proposes that the government and other stakeholder must equip communities with long term interventions in building their capacities and resilience to reduce vulnerabilities. This might be in form of building of dykes along river banks that floods; enforcement of proper construction standards when putting up infrastructures and that communities must also strive to build permanent dwelling houses with strong foundations. about this event: https://pretalx.com/state-of-the-map-2020-academic-track/talk/TEAKVH/

Recently, OpenStreetMap (OSM) shows great potentials in providing massive and freely accessible training samples to further empower geospatial machine learning activities. We developed a flexible framework to automatically generate customized training samples from historical OSM data, which in the meantime provide the OSM intrinsic quality measurements as an additional feature. Moreover, different satellite imagery APIs and machine learning tasks are supported within the framework. After more than a decade rapid development of volunteered geographic information (VGI), VGI has already become one of the most important research topics in GIScience community [1]. Almost in the meantime, we have witnessed the ever-fast growth of geospatial machine learning technologies in intelligent GiServices [2] or addressing remote sensing tasks [3], for instance land use land cover classification, object detection, and change detection. Nevertheless, the lack of abundant training samples as well as accurate semantic information has been long identified as a modelling bottleneck of such data-hungry machine learning application. Correspondingly, OpenStreetMap (OSM) shows great potentials in tackling this bottleneck challenge by providing massive and freely accessible geospatial training samples [4]. More importantly, OSM has exclusive access to its full historical data [5], which could be further analyzed and employed to provide intrinsic data quality measurements of the training samples. Therefore, a flexible framework for labeling customized geospatial objects using historical OSM data allows more effective and efficient machine learning. This work approaches the topic of labeling geospatial machine learning samples by providing a flexible framework for automatically customized training samples generation and intrinsic data quality measurement. In more detail, we explored the historical OSM data for twofold purposes of feature extraction and intrinsic assessment. For examples, when training a building detection convolutional neural networks (CNNs), the OSM features with tags as building=residential or building=house are certainly of interests while the data quality of such features might play an important role later in the CNNs training phase. Therefore, besides the acquisition of the user-defined OSM features, we provide additional intrinsic quality measurements. Currently, we consider some basic statistics, such as the areas of buildings tagged with different OSM tags, the amount of distinct contributors in the last six months, or the equisdistance of polygons with landuse=cropland etc , since the existing research suggested that the lower equisdistance of the current polygon, the better relative quality of the polygon, which due to the further refining and editing from users [6]. In the future, one could also easily extend the current framework and develop other sophisticated quality indicators for specific “fitness-for-use” purposes. Heterogeneous remote sensing APIs are supported within the framework, user’s option ranges from commercial satellite image providers (e.g., Bing or Mapbox) to government satellite missions (e.g., Sentinel-hub), even user-defined tile map service (TMS) API. Correspond to OSM features, the satellite image would be automatically downloaded via TMS and tiled into proper size. Moreover, this framework also supports different machine learning tasks, like classification, object detection, and semantic segmentation, which requires distinct sample formats. The preliminary test is performed to extract the geographical information of water dams with OSM tag waterway=dam, which enables the training of water dams detection CNNs, where users could easily change the geospatial water dams to customize objects as long as the corresponding OSM tags are identified. This work aim to promote the application of geospatial machine learning by generating and assessing OSM training samples of user-specified objects, which not only allows user to train geospatial detection models, but also introduce the intrinsic quality assessment into the “black box” of the training of machine learning models. Based on a deeper understanding of training samples quality, future efforts are needed towards more understandable and geographical aware machine learning models. References [1] Yan, Y., Feng, C., Huang, W., Fan, H., Wang, Y. & Zipf, A., (2020) Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience, International Journal of Geographical Information Science. [2] Yue, P., Baumann, P., Bugbee, K. & Jiang, L., (2015). Towards intelligent GIServices. Earth Sci Inform 8, 463–481. [3] Zhu, X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F. & Fraundorfer, F. (2017) Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8-36. [4] Li, H., Herfort , B., Zipf , A. (2019

The collaboration between the Italian Alpine Club (CAI) and OpenStreetMap (OSM) officially began with the signing of an agreement between CAI and Wikimedia Italia, the Italian chapter of the OSM Foundation, in 2016. The first activity was to define a standard to be used for CAI objects to be mapped using the wiki, and this started the mapping. Three years after that signature much has been done with a surge in the last year also thanks to the funding of CAI through the project "CONTRACT FOR THE DATA IMPLEMENTATION SERVICE IN THE INFOMONT SYSTEM". This financed one person to carry out different activities: - the data entry in OSM with the procedure used and the situation region by region - the development of software released under a FOSS license able to obtain CAI data from OSM and carry out some conversion and reporting operations - training activities in the different sections of the CAI During the presentation will be made a history of the activities of the CAI with OSM, the results obtained so far and the various features of the software developed. The collaboration between the Italian Alpine Club (CAI) and OpenStreetMap (OSM) officially began with the signing of an agreement between CAI and Wikimedia Italia, the Italian chapter of the OSM Foundation, in 2016. The first activity was to define a standard to be used for CAI objects to be mapped using the wiki, and this started the mapping. Three years after that signature much has been done with a surge in the last year also thanks to the funding of CAI through the project "CONTRACT FOR THE DATA IMPLEMENTATION SERVICE IN THE INFOMONT SYSTEM". This financed one person to carry out different activities: - the data entry in OSM with the procedure used and the situation region by region - the development of software released under a FOSS license able to obtain CAI data from OSM and carry out some conversion and reporting operations - training activities in the different sections of the CAI During the presentation will be made a history of the activities of the CAI with OSM, the results obtained so far and the various features of the software developed. about this event: https://2020.stateofthemap.org/sessions/CDES3T/

In an ideal map, every point is reachable to another. However, in OSM data for instance, only 98.59% of Singapore’s nodes are reachable to each other by a path. In this talk, we identify OSM map problems by checking the connectivity of the road network using strongly connected component algorithms and introduce a creative visualisation to help map ops pinpoint the fix effortlessly. Using this approach, we have fixed thousands of map problems in SEA. In an ideal map, every point is reachable to another. Like any crowd-sourced product, it is a challenging goal for OSM to be ideal because the edits are from contributors with various backgrounds. For instance, only 98.59% of Singapore’s nodes are reachable to each other. This could cause significant problems when routing from one point to another for any business use case. In June 2019, some contributor mistakenly tagged one of the five major expressways in Singapore with Access=No, which subsequently caused all the routing through the expressway to fail. In this talk, we address the issue by using strongly connected component algorithms to identify such map problems and building a creative visualisation to help map analysts pinpoint the fix effortlessly. Using this technique, we identify map errors such as two one-way roads meeting each other with opposite directions; duplicate nodes causing roads disconnected; parking lots not connected to main road network and more. The detected map errors spread everywhere on the map that motivates us to build a creative visualisation to help map analysts pinpoint the erroneous nodes/ways. Using this approach, we have fixed thousands of map problems in SEA. about this event: https://2020.stateofthemap.org/sessions/URVEBF/

The “localness” of data is often described as a major factor for the authenticity of (geo-) information in OpenStreetMap. However, the exact meaning and relevance of “localness” remain controversial. We compare proposals made for the “measurement”, i.e. for the empirical operationalization, of “localness”. Based on this, two convincing operationalizations were selected and implemented in order to contrast regional differences in “localness”. Our analysis allows the identification of regions in which exceptionally high proportions of data are mapped remotely – mostly regions in the Global South. Bearing this in mind, we discuss how “localness” is negotiated in the OSM community. A frequently reproduced mantra of the debate on Volunteered Geographic Information (VGI) can be summarized by the term ”localness”: It emphasizes the actual or at least desired local production of geographical information. Through new tools in Web 2.0 it is assumed that now a large number of “ordinary” people “on the ground” can generate knowledge about their everyday environment. This postulated “local expertise” operates as a claim to truth of VGI. In contrast to “conventional” geodata, whose truth claim is more likely to be based on professional and technical expertise, VGI is often legitimized by its authenticity due to its “localness” (Goodchild 2007, 220 or Elwood et al. 2012, 584). In fact, relatively little is known about how “local” VGI data actually are. Although “localness” is accepted as a central quality feature of VGI (Barron et al. 2014), it is hardly taken into account in approaches to measuring the quality of VGI (Senaratne et al. 2016, 161). However, we compare existing methods for measuring “localness”, with particular emphasis on the different geographical scales on which they work. Additonally, considering that the importance of “localness” is controversially discussed in the OSM community, further research is conducted in OSM wikis, blogs, group chats, at conferences and with interviews. The key interest is to explore the significance and relevance of “localness”, and simultaneously investigate the implications of an interference of local mappers with remote mappers. The presented research follows a mixed methods approach. Firstly, a synopsis of all methods that have been already used to measure “localness” is implemented. The effort to determine where OSM users have their origin is necessary because this information is not saved within the OSM user data or cannot be read simply from IP addresses (Quinn 2016, 6). In addition, the methods aim to find out where the density of local information is high. Many of the used methods work best at a regional scale, only some function for global data. Since we are most interested in the latter, we have implemented two methods that are possible on a global scale. Using the first OSM changeset of a user, we determine the density of local mappers across the world (Neis 2013). Furthermore, the sum of all used OSM keys or the sum of the different OSM keys in an area per number of OSM elements can be used to identify areas with a higher density of local content. Zielstra et al. (2014, 1227f) have already used this method on a regional level for selected mappers; with Zipf et al. (2019) we are able to calculate this on a global scale. The second part of the study consists of qualitative interviews and analysis of documents such as OSM wikis, blog pages and group chats. After a preliminary research, the aim is to dive into exemplary local communities in which conflicts between local and non-local mappers become evident. For instance, the contested meaning of localness for humanitarian mapping between local map guards and international aid organisations will be further investigated. In addition, the emerging issue of the relationship between maps produced with the aid of “artificial intelligence” and local mapping communities will be explored. Frequently, “localness” is considered an important indicator for the authenticity of data in OSM. The research carried out should therefore answer the question whether locally produced data in OSM are “better” than data not produced locally. Moreover, we also approach conflicts between local and external mappers by examining debates and discussions within the OSM community. This research thus helps to gain a better understanding of the conflicts that exist within OSM and between different user groups within the OSM community. References Barron, C., Neis, P., & Zipf, A. (2014). A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis. Transactions in GIS 18(6), 877–895. Goodchild, M.F. (2007). Citizens as sensors. The world of volunteered geography. GeoJournal 69(4), 211–221. Elwood, S.A., Goodchild, M.F., & Sui, D.Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the American Association of Geographers 102(3), 571–590. Neis, P. (2013). The Ope

Plenty of research about behavioural differences between men and women for years ago. According to a scientific article in 2013 by Lewis, on average women may have better verbal memory and social cognition, whereas men may have better motor and spatial skills. Moreover, spatial skill is really needed for mapping, especially as a mapper volunteer in OSM that everyone can make their own map. It also has been known that male mappers more dominate OSM mapping than female mappers. Nevertheless, in some mapper communities, the number of female mappers more than male mappers, for example in Humanitarian OpenStreetMap Team Indonesia. 19 from 30 mappers in HOT Indonesia are female, yet does it affect the performance and quality of mapping in OpenStreetMap? It is true that men mappers more dominate OpenStreetMap (OSM) mapping than women mappers. Some mapper communities have the number of female mappers than males mappers. For instance in Humanitarian OpenStreetMap Team Indonesia (HOT Indonesia), 19 from 30 mappers in HOT Indonesia are female. Moreover, it is a fact that men and women have some behavioural differences and it might affect their working performance, especially in OSM mapping. According to the number of changesets, addition, deletion and modification in OSM mapping that tracked through OSMCha, it can be seen the difference between female and male mappers when they are mapping some objects in OSM. OSMCha offers many features for reviewing OSM, one of the features is to review a changeset and mapper details that include the mapper username, the number of changesets that mapper has contributed in OSM. Besides, it has changeset-map for visualising of changeset on OSM and the addition, deletion, and modification can be seen by other OSM users. Thus, through those data, it can be seen how is the difference between females and males when they are contributing data in OSM, also how the quality of OSM map that they create. I believe, those differences can be helpful for creating better data for OSM and invite another female to contribute in OSM. about this event: https://2020.stateofthemap.org/sessions/AWT7K8/

Contributors of OpenStreetMap data for Mozambique, a country in Southern Africa, were classified into four distinct groups. The most active group included 25% of all contributors, most of them long-term contributors, and most features were last edited by members of this group. One can therefore conclude that the quality of the data is likely to be good, however, it lacks in completeness and the number of edits per feature is low. Even though no absolute statements about data quality can be made, the analysis provides valuable insight into the quality and can inform efforts to further improve the quality. OpenStreetMap (OSM) has made it possible for any volunteer to contribute geographic information, regardless of their level of experience or skills. Since the task of creating geographic information is no longer exclusively performed by trained professionals, data quality can be a concern. Uncertainty about the quality of data contributed by volunteers has been cited as a hindrance to its use (Mooney and Morgan, 2015). As the number of OSM contributors continues to grow, gaining knowledge about their characteristics and the kind of data they contribute is important. Data quality can be assessed extrinsically, i.e. against reference datasets, or intrinsically, i.e. by analysing the data itself. OSM data quality has often been assessed extrinsically by comparing it to other reference datasets (Girres and Touya, 2010; Haklay, 2010; Neis et al, 2012; Helbich et al, 2012; Mooney and Corcoran, 2012; Fan et al, 2014; Dorn et al, 2015). However, such reference data is not always available and therefore intrinsic assessment methods have been employed (Anderson et al, 2018; Barron et al, 2014). For example, analysing contributors and their contributions can answer questions, such as: What kind of contributors (e.g. experienced vs newcomers) have worked on the data in the area? In which areas should the data be validated or updated (e.g. where data has been contributed by newer contributors or older non-recurring contributors). In this study, contributors and their contributions to OSM in Mozambique, a country in Southern Africa, were analysed in order to gain insight into the quality of the data. We chose Mozambique because in 2019, it received a significant amount of attention in the OSM community following the floods and damages as a result of cyclones Idai and Kenneth. The OSM contributors were characterised in three steps: 1) OSM history data, containing information about the contributors and their contributions, was downloaded; 2) using cluster analysis, OSM contributors were classified according to their contribution characteristics; 3) based on the classification, the OSM data contributors were characterised in Mozambique in order to get insight into the quality of the data. OSM history data provides a record of all the edits (or changes) performed on OSM features. Each edit results in an increment in a feature’s version number. Each version of a feature is associated with a contributor (called ‘user’). The results of the cluster analysis revealed four distinct classes of contributors. The most active class of contributors had 2,552 volunteers (25% of all contributors in the area), with on average the highest numbers of changesets, total contributions, node contributions, way contributions and ways and nodes for which they were the last user to modify them. These volunteers are ‘older’ contributors who have sustained their contributions in the Mozambique area over a long period of time, with the first contributions dating back around 15 years. Compared to the other contributors, they have mapped on more days than the others and the average number of edits per feature is higher. Nevertheless, 99.6% of buildings and 84% of ways in the data had been edited twice at most. Buildings are almost entirely concentrated within the centre of Mozambique, in the areas for which the mapathons were conducted. In some European countries, the number of edits per feature is much higher. Similar to the results of other OSM contribution analyses (Neis and Zipf, 2012), most of the data generated in Mozambique has been contributed by a small group of active contributors who have dedicated a significant amount of time to this. Studies have suggested that such contributors are more likely to be experienced and knowledgeable about the project (Bégin et al, 2013; Budhathoki and Haythornthwaite, 2013; Barron et al, 2014; Yang et al, 2016) and are therefore more likely to produce data that is of good quality (Barron et al, 2014; Yang et al, 2016; Anderson et al, 2018). Even though no absolute statements can be made about the quality of the OSM data for Mozambique, analysing contributors and their contributions provides valuable insight into the quality of the data and can inform efforts to further improve the quality. The results of this study show how one can gain a better understanding of the community that contributes data in a specifi

Non-government organizations and local government units use geographic data from OpenStreetMap (OSM) to target humanitarian aid and public services. As more people start to depend on OSM, it is important to study data completeness in order to identify unmapped regions so that OSM volunteers can focus their attention on these areas. In this study, we propose a method to measure the data completeness of OSM building footprints using human settlements data. Non-government organizations and local government units use geographic data from OpenStreetMap (OSM) to target humanitarian aid and public services. As more people start to depend on OSM, it is important to study data completeness in order to identify unmapped regions so that OSM volunteers can focus their attention on these areas. In this study, we propose a method to measure the data completeness of OSM building footprints using human settlements data. Specifically, we use Facebook’s High Resolution Settlement Layer (HRSL), a dataset of built-up areas derived from satellite images, as a proxy for ground truth building footprints. We then measure data completeness by getting the “percentage completeness” of pixels which is computed using the total percentage of pixels within the intersection of the human settlement layer and the OSM building footprints. The method can be broken down into three steps: (1) convert the human settlement layer into a vector; (2) perform a spatial join to find the intersection between the vectorized human settlement layer and the building footprints; and (3) calculate the data completeness based on pixels from the vectorized human settlement layer that intersect with the building footprints. Chepeish and Polchlopek [1] conducted a similar study measuring data completeness in OSM building footprints. We differentiate our work from Chepeish and Polchlopek [1] in three ways. First, for the human settlement layers, Chepeish and Polchlopek [1] used WorldPop which has a spatial resolution of 100 meters [2] while we used the High Resolution Settlement Layer (HRSL) from Facebook which has a spatial resolution of 30 meters [3]. Second, for the data processing, their group rasterized the building footprints while our group vectorized the human settlement layer. Third, for calculating the data completeness, their group used a combination of geographic information system (GIS) and machine learning (ML) while our group solely used GIS. Using building footprints from January 2020 and human settlement layers dated June 2019 and October 2018, the percentage completeness is 32.75% and 10.89% for the Philippines and Madagascar, respectively. We found that in the Philippines, most of the unmapped pixels are in rural areas. When the pixels are aggregated to the municipality-level and plotted as a scatter plot of the urban percentage completeness vs. the rural percentage completeness, the municipalities appear to group together into two categories: sparsely mapped and thoroughly mapped. A possible explanation is that there are not enough OSM volunteers to map all municipalities and the OSM community focuses on thoroughly mapping high population municipalities rather than moderately mapping all municipalities. Interestingly, poverty incidence data from the Philippine Statistics Authority is not correlated with data completeness. Complete or incomplete OSM data in an area is not an indicator of wealth or poverty. As this work has garnered interest from humanitarian organizations such as the Humanitarian OpenStreetMap Team (HOTOSM), to whom regularly updated information on OSM data completeness is extremely valuable, we looked into ways to automate workflows in QGIS by using the built-in workflow builder tool (i.e. Processing Modeller) and by using the QGIS API. However, as we consider scalability and reproducibility important for this line of work, we ultimately deemed QGIS to be unfit for our use case. QGIS is not scalable because the data processing is not easily parallelizable and it is also not easily reproducible by developers who do not have training with GIS software. Thus, we decided to migrate our workflow to GeoPandas and rasterio and open-source our code [4]. Our workflow improved because (1) we were able to speed up the process by migrating to the cloud and increasing the computing resources; and (2) we were able to improve the reproducibility by allowing us to communicate our work more effectively to people who aren’t familiar with GIS. For future research, we recommend exploring other human settlement datasets The Global Human Settlement Layer (GHSL), for example, has a spatial resolution of 30 meters [5] which is comparable to WorldPop and HRSL. We also encourage further data analysis on the percentage completeness in order to get insights on how to improve the process of contributing to OSM. [1] Chepeish, E., Polchlopek, J. (2018), Estimate OSM building coverage completeness by comparing vs WorldPop raster, GitHub repository, https://g

The number of OSM contributors every year tends to increase. But not all are sustainable contributors. For example in Indonesia, there is a lot of OSM training and Mapathon but it is suspected that there are not many local contributors of all time. For this reason, extracting information from OSM accounts that have been registered since a few years ago that classified as a rare mapper. The method is by recording the top 500 accounts in Indonesia, identifying local accounts based on profiles and heatmaps, sending questionnaires, and summarizing them. The results can be used by the Indonesian OSM community to increase the sustainability of the contribution of local people in OSM. In many developing countries, the number of OSM contributors tends to increase every year. But the amount of increase has not been directly proportional to the sustainability of contributions to OSM. Indonesia is an interesting example of developing countries with many OSM agendas from various parties (Grab, HOTOSM, RedCross, IFRC, OSM Indonesia, National Disaster Agency, etc.) including training and Mapathon. In general, OSM was introduced in Indonesia by HOT OSM since 2012. Until 8 years later there were not many local mappers who contributed sustainably. It is also interesting to explore as a basis for increasing sustainable contributions to OSM by local mappers or mappers who have a certain area of ​​interest. Tools from Pascal Neis help to identify the top 500 newest mappers in Indonesia. As well as the user profile page and heatmap to identify the background mapper and their interest areas. After being selected the "local" accounts are then grouped based on their activity level. Then send structured messages through all their OSM accounts. The results of the interviews are then grouped based on the activity of contributing and the age of the account. Then the statistics are determined based on the diversity of answers. The results of this interview are expected to be useful for all stakeholders who directly and indirectly deal with OSM. Concrete steps for an awareness-raising strategy contribute to OSM on an ongoing basis in every OSM event held. about this event: https://2020.stateofthemap.org/sessions/C9NGG3/

This presentation describes the ongoing work at the Urban Analytics Lab at the National University of Singapore, developing novel methods to assess building completeness at a multi-country scale, as part of a broader project of generating 3D city models on a large-scale using OpenStreetMap. Quality assessment of OpenStreetMap (OSM) data has been an important topic since the inception of the project. Much research has been done on this topic by many research groups around the world, and it can mostly be seen as permutations of three aspects: (1) spatial data quality element(s) in focus (e.g. positional accuracy, completeness), (2) theme (e.g. amenities, buildings, roads), (3) geographical area (e.g. particular city or country); e.g. positional accuracy of cultural features in Italy. Completeness is one of the principal quality aspects of geospatial data, and our research focuses on developing a method to assess the completeness of buildings in OSM on a large scale (spanning several countries). While there are many robust OpenStreetMap completeness techniques and studies developed, they mainly focus on limited areas, mostly developed countries with ground truth data at hand for comparisons. Doing the same for less developed regions is rare as the lack of authoritative data inherently hampers it, and the methods hardly ever scale: such an analysis done simultaneously for more than one administrative region is seldom carried out as there are other research challenges such as disparate urban morphology, different data sources and standards to bridge in order to facilitate ground truth, and varying understanding of what a building is. Furthermore, the development of a method that would scale across dozens of countries is limited by computational resources. We are currently developing a method that uses several indicators derived from remote sensing, which are available on the global level, that may hint at the building completeness and would scale across the world. A regression model to predict the approximate volume of buildings in a given area is trained in areas in which there is an indication of high completeness of buildings in OSM. OSM building completeness is estimated by comparing the number of mapped buildings against their expected (predicted) amount in reality. The method has the potential to scale at a worldwide level, and completeness is estimated for a grid of resolution of approximately 1x1 square kilometres, and simultaneously for administrative regions to enable cross-country comparisons. The work is being implemented in Google Earth Engine, mostly relying on imagery and indicators such as normalised difference built-up index (NDBI) and normalised difference vegetation index (NDVI). Preliminary results suggest a substantial disparity in OSM building completeness around the world, with areas that are entirely complete to those with inadequate completeness. This presentation aims to report the progress of the ongoing work and encountered challenges in the project such as selecting indicators that are consistently available on the global level, scaling the method to the global level, accounting for different mapping practices around the world, different notions of a building, and varying morphologies of cities and urban areas. We also investigate the relation between OSM building completeness and socio-economic parameters such as GDP to understand their relationships to mapping intensity and quality. These may offer the potential to be used as additional predictors. This work is part of a broader project conducted at the Urban Analytics Lab at the National University of Singapore on investigating the potential of generating 3D city models by extruding building footprints in OpenStreetMap to a building height that is predicted using artificial intelligence. This ongoing portion of the project is the crucial first step in the project, as it will enable us to understand what is the completeness of building footprints in OpenStreetMap around the world and manage expectations about the potential coverage of 3D city models. about this event: https://pretalx.com/state-of-the-map-2020-academic-track/talk/YHEMFS/

The Philippines is to be considered one of the most-LGBT friendly countries in the World. In 2019, it was able to host the largest pride celebration in Asia. Amidst all this, crimes against LGBTQ+, discrimination, and bullying is still rampant in the country. The Sexual Orientation and Gender Identity Expression (SOGIE) Bill is still continuously being delayed. It is intended to prevent various economic and public accommodation-related acts of discrimination against people based on their sexual orientation, gender identity or expression. Despite of being tolerated, the LGBT community is still far from being accepted by society. Evidence of our community have been written on books, told in stories, presented in movies and yet the community has not left its mark in data. Spreadsheets, research, books have identified spaces where community activities happen but this are not shown on any map online. Our spaces are mere descriptions or addresses on tables and paragraphs. This talk would be about how we would be more represented on OpenStreetMap so as to provide emphasis on being on the map. MapBeks is an online community of mapping volunteers that advocates for diversity inclusion and representation focused specifically for Lesbians, Gays, Bisexuals, Transgendered, Queer, Inter-sexed, etc. (LGBTQI+) on OpenStreetMap. As part of its advocacy is to map-out and locate all HIV facilities (testing, counselling, and treatment hubs) in the Philippines. It has researched, collated, and validated various sources to build an updated and comprehensive online database with location data. Currently, it has identified 650 HIV testing and counselling centers all over the Philippines and already mapped out 140 (20%) of the facilities on OSM using MapContrib.xyz. We would like to share our experiences in building our small community of LGTQ+ advocates, and digital volunteers. We hope we can inspire the world with our endeavour to make change from what little we have. The talk will discuss the following: 1. The STATUS QUO- LGBT places are not that much represented on OSM/ tagging/ lack of data 2. How was Map Beks able to start up as a local community and how it was able to reach out to the growing LGBT and PLHIV community 3. Its current projects and advocacies 4. Its plans for the future about this event: https://2020.stateofthemap.org/sessions/L3RTUK/

OpenStreetMappers are a diverse group of people. This short segment will introduce you to another person that makes the project what it is. Enjoy this little break from longer talks and get to know a conference delegate that you can talk to at the conference. As OpenStreetMap is made by us all, it's important to get to know each other and this can form a nice ice-breaker or give you suggestions on conversation starters. Gregory has two unique OpenStreetMappers for us to meet and chat to. One has only been a member of the project for a couple of years, getting involved due to a call from the Humanitarian OpenStreetMap Team(HOT) but also some local projects to map solar panels. The other OSMer has been involved for more years, and has started running out of new things to map in her local area so helps the project in other ways. about this event: https://2020.stateofthemap.org/sessions/FZCM39/

Do you get together with other mappers in your town? Would your group benefit from a bit more support? In this talk you will learn about the newly reformed Local Chapters & Communities Working group and our effort to support mapping groups all over the world. Attend this talk to learn more about the Local Chapters and Communities Working Group (LCCWG). This talk will share our current initiatives as well as invite ideas from participants, and will precede the annual Local Chapters Congress, so hopefully we'll see you at both! Who are the Local Chapters & Communities Working Group? Reformed in November 2019, the LCCWG are a small group of OpenStreetMap enthusiasts and community leaders interested in finding and implementing ways for the Foundation to support the growth of local communities. The LCCWG hopes to facilitate a global exchange of ideas and support among local leaders, and work together to create strong local communities. Right now we have 3 focus areas: building local community cohesion, sharing ideas and best practices globally. We hope to encourage established communities to further organise themselves and eventually formally affiliate with the Foundation as one of its Local Chapters. We will review the role of Local Chapters within the Foundation and the interactions between them. Based on our findings we will make recommendations to the Board as to how the affiliation scheme can be improved to provide a stronger case for local communities to eventually become Local Chapters, or possibly suggest creating new affiliation models such as less-formal user groups. Interested in representing your community on the Working Group? Start by joining the conversation today! Find out more about the LCCWG on the Wiki https://wiki.osmfoundation.org/wiki/Local_Chapters_and_Communities_Working_Group. about this event: https://2020.stateofthemap.org/sessions/DVR7ME/

In Cusco, Peru, during the last 2 years, GAL Center worked with students using OSM and associated tools, such as Kobo as educational tools, mainly for research into social problems that the students themselves identify in their locality. Projects such as “Sexist advertising mapping”, “Sexual health” and “Garbage mapping in Larapa” were the result of this work. This year, MapImpact is one of the HOT Microgrants and we will work with high school students and YouthMappers Chapters that we help to create in universities. In this talk, I will tell you more about how we work MapImpact in GAL: our objectives, our methodology, our results and why we would like it to be replicated in other places. MapImpact is a GAL Center project in Cusco, Peru, which aims to make more students aware of the problems that afflict their locality, to achieve this they will have to investigate these problems using OSM and associated tools such as Kobo Collect, as the main research tool. To develop this project GAL had a previous experience of 2 years, in which we work with high school students in different provinces of Cusco and help in the creation of the first YouthMappers Chapters in Peru, projects such as “Sexist advertising mapping”, “Sexual health” and “Garbage mapping in Larapa” were the result of this work. This year we will work MapImpact also with university students and our intention is for students to understand digital maps not only as a tool to generate geospatial data, but also as a tool of social impact, that is, the process not end when they save the changes in OSM, they have to use that information to make visible a problem that afflicts their location. In this talk we would like to share with you the whole process we follow to reach this point. about this event: https://2020.stateofthemap.org/sessions/3AJAEF/

We have seen an explosion of OSM mapping in the last few years around maps for development and humanitarian uses, particularly in Africa. During this time it has also become clear that sustaining this essential mapping work, and keeping maps up to date, was going to be a primary concern. Building a healthy mapping ecosystem around mapping for development will not necessarily be able to follow the same model as it has in more developed countries. In this talk, I will share the culmination of my research on sustainability with the World Bank’s Global Facility for Disaster Reduction and Recovery and their Open Cities Africa project, and some ways that we can best support mappers and grow a healthier global OSM ecosystem. Two years ago, at SOTM in Milan, I and several colleagues held a panel discussion around sustainability challenges that were common to working with OSM in developing countries. Mappers spoke up about their struggles continuing to map, keeping maps up to date, and growing their mapper communities in places with very few resources. This research has now concluded with a white paper on the topic, and many new learnings along the way. In this talk, I’ll review the outcomes of "Sustainability in OpenStreetMap", https://opendri.org/resource/sustainability-in-openstreetmap/, a publication under OpenDRI at the World Bank. While many groups and projects are arising to do mapping throughout the world, and many for a social purpose, we found a number of challenges to keeping mapping moving forward and overcoming hurdles. These include financial, technological, social, and political challenges, each with its own kind of possible solutions. I also looked into the varieties of OSM actors that would be most likely found in contexts of OSM in development, and what challenges they each may face. These actors include governmental mappers, independent consultants, businesses and startups, nonprofit organizations of a wide variety, international NGOs, formal and informal chapters, and more. Finally, I identified some solutions and supports that are most needed to create a sustainable OSM environment in low-resource geographies. In this talk I will share about all of these findings, and we will discuss the best ways to support a strong international mapper ecosystem. about this event: https://2020.stateofthemap.org/sessions/XAKBJT/

Want a postcard? Looking for somebody to send a postcard to? Me too! Let's discuss how people in OpenStreetMap come together, which pleasant and otherwise experiences we had meeting other mappers, and how to express gratitude and make people feel a bit closer to each other — with postcards. Each time I visit a SotM conference far from home, I'm collecting postal addresses from everyone who follows my news channel. And I send postcards: "Hello from Aizuwakamatsu!" We rely on digital too much: nothing we can touch, nothing we can put on a shelf. Virtual maps, virtual gratitude. With this project, OSM postcrossing, I plan to give every mapper a chance to have something tangible as an outcome of participating in our project. But to get there, we must think of what brings us here, and what experiences we have as members of the community. The premise is simple: ask somebody for their postal address. Except people are reluctant to give it: the address is a private information, and privacy is important. Have you tried to upload a gpx trace, to see how we value it? A lot. So what do you do? Do you send a postcard to a random mapper? Do you publish your address for everyone to see? Should you stay back from the official real-world services and back away to the comfort of virtuality? about this event: https://2020.stateofthemap.org/sessions/CKYTVS/

How I used OSM data to visualize gender disparity in street names for all of Brazil. The result shows how women are underrepresented in street names in the country, and raises questions on who is chosen to be commemorated in street names. Using OSM road data and a database of gender popularity for names, I created a map visualization to show gender disparity in street names in Brazil. Streets named after women represent only a small proportion of the streets in Brazil. This proportion is even smaller when we consider the length instead of the number of streets. In Brazil, streets are typically named after prominent historical figures, including politicians, business people, military, religious figures, artists, and academics, among others. The small representation of women among these reveals who is chosen to be regarded as prominent, and thus commemorated in a street name, and who isn't. Another interesting aspect is that the map allows up to see certain areas and neighborhoods where female street names predominate. Upon quick visual examination, these appear to be usually in neighborhoods at the periphery of large cities. More in-depth research could indicate if there's a spatial pattern here and if it is related to other social phenomena. The interactive map was based on the Road Orientations Map by Vladimir Agafonkin. I used Mapbox and mbtiles for the map visualization. To process the source data, I used Geofabrik extracts of OSM data and Postgres / PostGIS scripts. The interactive map is here https://medidasp.com/projetos/genero-ruas/#12/-23.5617/-46.6469 and the github repo is here https://github.com/bplmp/genero-ruas-mapa about this event: https://2020.stateofthemap.org/sessions/HLFEER/

Map Kibera has been working for the past two years with some of Kenya’s county governments to create maps of their primary features and funded projects. After implementing a Participatory Budgeting process, these counties realized that without good maps it was difficult for people to not only allocate resources, but to work with citizens to identify needs and prioritize funds. Map Kibera has been assisting counties to map key features and projects in OSM by working with youth from the local communities. The maps not only serve to connect citizens to the budgeting process and hold county government accountable for the funded projects, but, they have also become central to county functions in all areas. This talk will share all about the process used and outcomes. Map Kibera has been helping communities map out their local projects by collecting data and creating digital maps that they can use for planning and decision making. The Participatory Budget Mapping project was conducted in 3 counties in Kenya: West Pokot, Baringo and Makueni. These counties had already been part of an annual process of participatory budgeting, with locals weighing in on how budgets should be spent in their counties. In early 2018, Map Kibera along with partner GroundTruth Initiative began working with the World Bank in Kenya to initiate Community Participatory Mapping, by training the local citizens on how to map their county-funded projects using OpenStreetMap, Open Data Kit, and Kobo Toolbox. The project also enabled citizens to track the progress and quality of those projects, allowing them to hold the government accountable for delivering what had been promised during the budgeting sessions. The results of the mapping are displayed on a dedicated website and printed maps for budgeting sessions, which often take place in rural villages. The project has been able to: 1. Visualize existing and/or new government-funded (county and national level) development projects which will enable the counties to know which projects have been completed, which are in progress and the projects that are pending. 2. Perform a needs assessment analysis through the Participatory Budget meetings where the counties engage the citizens and together determine the distributions of the projects. 3. Assist citizens to monitor progress of projects and hold the government accountable for delivering on its promises. 4. Transfer knowledge of mapping in OSM to county government representatives directly, in offices of M&E, GIS, ICT, and Budgeting. Using OpenStreetMap and sharing the data with the counties is a huge milestone for Map Kibera as this will encourage more institutions and people using OpenStreetMap within both communities and county government. This session will share the process and tools being used in the project and early outcomes. about this event: https://2020.stateofthemap.org/sessions/LDGZ37/

This talk with survey the bright and dark history of companies and professional involvement in OpenStreetMap, lay out the challenges that we face now, and chart steps forward to figuring this out together. I want to reset the vision of the position of companies in OSM, starting by connecting back in time to when it was all more fluid in our community. Only later did some draw a sharp distinction between volunteer and professional activities in our project. The reality of the relationship of companies and professionals in OpenStreetMap from the very earliest days until today is ... complicated. There's incredible mutual benefit and purpose. There are super hard issues to address when large amounts of resources are mustered, among the constellation of many kinds of actors and motivations in OpenStreetMap. The reality is that OpenStreetMap is transformative, and that companies in OSM first come for the data, may fumble along the way, and stay for the shared mission to change how maps are made in the open. I want to reset the vision of companies place in OSM, starting by connecting back in time to when it was all more fluid in our community. For example, a month after I met Steve Coast in 2005, I was setting up meetings with Google. Helped secure Yahoo! maps imagery in 2007. Companies hosted many of the early days mapping parties. Professional cartographers were among the projects most original enthusiasts. The point was to change how mapping was done -- including and especially at companies. Only later did some draw a sharp distinction between volunteer and professional activities in our project. The reality of the relationship of companies and professionals in OpenStreetMap from the very earliest days until today is ... complicated. There's incredible mutual benefit and purpose. There are super hard issues to address when large amounts of resources are mustered, among the constellation of many kinds of actors and motivations in OpenStreetMap, whether they be hobbyists, developers, students, researchers, non-profits and on and on. The reality is that OpenStreetMap is transformative, and that companies in OSM first come for the data, then stay for the mission. There are of course fumbles along the way. But as a community we've lacked a way to take a clear eyed view of the challenges and vital role companies and professionals have played in what OSM has become today. In part that fault lies with companies themselves, who are risk averse to delving into our wild and wooly communications. Those that do, can take a lot of heat. This talk with survey the bright and dark history of companies and professional involvement in OpenStreetMap, lay out the challenges that we face now, and chart steps forward to figuring this out together. about this event: https://2020.stateofthemap.org/sessions/RHDUV9/

The talk will focus on the Turkish law egulating the acquisition, collection, dissemination and trading of spatial data falling within the responsibility matrix of Turkish National Geographic Information System, effective since February 20, 2020. With the law, acquisition, collection, dissemination and trading of spatial data which is defined within the National Spatial Data Responsibility Matrix by third party individuals or legal entities are subject to prior application fees and approval of the Ministry of Environmental and Urban Affairs. The talk will reflect and report the developments in Turkey after the law, effects and implications drawn focusing on the national spatial sector, OSM, and the Turkish OSM community. On January 30, members of the Turkish parliament voted in favour of the proposed amendment to the law regulating the acquisition, collection, dissemination and trading of spatial data falling within the responsibility matrix of Turkish National Geographic Information System. The law has officially been put into practice on February 20, with nationwide uncertainties on how it will be enforced, and on what level. With the law, the sole responsibility and authority on the national spatial data index is given to the Ministry of Environmental and Urban Affairs. Acquisition, collection, dissemination and trading of spatial data which is defined within the National Spatial Data Responsibility Matrix by third party individuals or legal entities are subject to prior approval of the ministry. Moreover, the approval will be subject to a fee of 25₺ for native, 50₺ for foreign parties per each 1/1000 plan corresponding to the study region(s) from the national topographical grid. The data layers which are included in the national spatial data responsibility list is as follows: 1. Coordinate Reference Systems and Geographical Grid Systems 2. Administrative Units 3. Geographical Names 4. Cadastre 5. Buildings 6. Addresses 7. Elevation 8. Orthophoto 9. Transportation Networks 10. Hydrography 11. Geology 12. Land Cover 13. Land Use 14. Soil Types 15. Protection Areas 16. Natural Risk Regions 17. Infrastructure 18. Energy Resources 19. Mines 20. Public Health and Safety 21. Populaiton Demographics 22. Environmental Monitoring Facilities 23. Industrial Facilities 24. Agricultural Facilities 25. Public Administration Regions 26. Flora and Fauna 27. Habitat Zones 28. Biogeographical Zones 29. Sea and Saltwater Regions 30. Atmospherical Data 31. Meteorological Data 32. NUTS Data The talk will reflect and report the developments in Turkey after the law, effects and implications drawn focusing on the national spatial sector, OSM, and the Turkish OSM community. about this event: https://2020.stateofthemap.org/sessions/JAG7JD/

Current presentation aspires to contribute to an overall assessment of the OSM map in Athens, Greece. The OSM content is assessed in terms of completeness and precision. Various official mapping sources and ground truth data are employed in order to measure the current state of the map. Current research aspires to contribute to the assessment of the OSM map in Athens, Greece in terms of completeness and precision. The researcher chose Athens as, according to international published research regarding the phenomenon of Volunteered Geographic Information, it is initially assumed that it will have the maximum quality level, as Athens is the most populated city of the country. The analysis includes mainly quantitative evaluation methods. To the level that access to editable - data is feasible, various GIS techniques are employed while in other cases the assessment is performed through samples in various regions of the municipality. While evaluating, all the known properties and characteristics of Volunteered Geographic Information are considered. Eventually, a short discussion related to similarities and differencies from other published OSM assessments in other countries, completes this short initial presentation. Apart from official mapping sources, ground truth data, collected through the use of in-car GPS devices, in certain areas of the city, are providing valuable insights regarding the level of quality. The research focuses on certain geographic entities, including the geometry of the road network, the street naming and addressing, various administrative units, some building footprints, parks and POIs. about this event: https://2020.stateofthemap.org/sessions/V9J9JS/

When people come to OpenStreetMap for the first time, their expectations are sometimes at odds with what the OpenStreetMap community is doing. If you have been puzzled by an OSMer telling you that OpenStreetMap is not a map, that openstreetmap.org is not aiming to compete with Google Maps, or by their stubborn refusal to remove a private trail from the map, then this talk is for you. It will explain the basic tenets of the OpenStreetMap community and how they apply in practice. This talk will explain some of the often-heard but little-documented basic concepts in OSM, like * we are not a map (but a database) * the map does not matter (the community does) * openstreetmap.org is not aimed at the public (but at mappers) * we map what's on the ground (not what the government or the landowner wants) Building on that, the talk will also outline why the OSM community is often skeptical about filling an empty map with data imports, about AI contributions, or about automatic edits, and why OpenStreetMap is not a business directory. The plan is to explain the basic ideas and give examples of their effects for data users or for everyday mapping practice. Where the concepts are controversial or subject to discussion, these controversies will be mentioned but not followed in depth. This talk will focus on "traditional" values in OpenStreetMap because they are omnipresent. After hearing these explanations, newcomers will have a better chance of understanding where people come from when they say things like "we are not a map", and will be better prepared to form their own opinion. about this event: https://2020.stateofthemap.org/sessions/DYXWDC/

We built open-source infrastructure that allows the community to integrate open data reviews of POI into the OpenStreetMap ecosystem. This enables any application or website to make use of a reviews layer, and to benefit from the shared data pool that is created by a combined user base of participating applications. We built it to ensure that people all over the world can freely share their insights about things that matter to them without being confined to proprietary data silos. Mangrove is a non-profit initiative to create a public space on the Internet where people can freely share insights with each other and make better decisions based on open data. Our goal is to create an Open Data Ecosystem for Reviews of places, companies, websites, books, and more. We built open-source infrastructure that enables any application or website to integrate a reviews layer, and to benefit from the shared data pool that is created by a combined user base of participating applications. In this talk, we are going to introduce to the OpenStreetMap community the technology that is available, and we are going to show how OSM-based projects could benefit from an integration with Mangrove. Furthermore, we are introducing the non-profit Open Reviews Association, ORA, who became the custodian of the Mangrove technology and open dataset. We invite anyone to join as a member in order to shape the direction of the project and help achieve its vision. about this event: https://2020.stateofthemap.org/sessions/AA8RXP/