Chaos Computer Club - archive feed

Lightning Talks Lightning Talks about this event: https://pretalx.com/sotm2019/talk/XQUHP3/

We map because we care to represent the world. Yet maps are never "true", they are shaped by their creators and their circumstances. Map-making is world-making: maps by different authors can give access to different worlds. So how can we make, share, and use maps that are created _by_ these worlds, and not just by a privileged few? How can vulnerable communities influence how they’re represented and affected by our maps? The popular view is that mapping the world is just an act of reflecting reality and reflecting nature. People rely on map-makers like us to show them the “true” spatial arrangement of the world. And yet, making a map also involves making assumptions, omissions, additions, and generalisations, and it feeds on our training, experiences, and personal perspectives as map-makers. A map is never really “true”, it is shaped by the hands of its creators, and it is always also a product of chance and its circumstances of making, sharing, and use. It can be said that map-making is world-making: maps shape our understanding of the world, and inform our actions in the world, and a map by a different author can give access to a different world. So how do we create a map of the world that is created _by_ the world, and not just by a privileged few? To fully appreciate the complexity of this challenge we need to extend our understanding of what counts as maps and mapping, and why. In order to understand our responsibility as map-makers, we need to become both _critical_ and _caring_ cartographers. This shift in perspectives and attitudes can help us understand better what can happen when our everyday mapping practice connects with real-world circumstances. It may also help us become more responsible as the volunteer workers of OSM. David and Martin will guide you through a careful and caring consideration of OSM based on personal experiences, and talk about the responsibilities we carry as we map and make the world during difficult times. For example, we may encounter circumstances where the OpenStreetMap approach to openness brings complications. Can the communities represented on the platform make decisions about how they’re being written about? Indigenous communities around the world regard their land as sacred and don’t want it to be mapped. Should we as a global community know when not to map? There are circumstances where the act of mapping itself can bring danger to the mapper. Do we have the right methods to assess such risks? What support can we offer those who take them? If we don’t, whose hand gets to shape the map instead? And what does all of this this mean for a do-ocracy, where decisions emerge slowly and can often contradict each other? Or to put it even more broadly, how can we diversify, democratise, denaturalise, and decolonise open mapping? Who cares, you ask? David and Martin have participated in the OSM ecosystem as mappers, researchers, and organisers, and have been involved in the Humanitarian OpenStreetMap Team and other global mapping initiatives. Based on their personal experiences they will introduce you to a careful and caring consideration of OpenStreetMap cartography, and talk about the responsibilities we carry as we map the world. They ask you to consider that maps are not _universal_, and that even in OpenStreetMap many different maps and mappings are possible. They propose that a better map may be a _pluriversal_ one, where many different perspectives can coexist. Can OpenStreetMap accommodate this? Which practices can help us produce maps that represent the world more equitably? More importantly, if we think that mapping can make a difference in a time of global precarity, inequalities, climate catastrophe, and mass extinction, then they ask _you_ to care. about this event: https://pretalx.com/sotm2019/talk/F9D8QG/

As one of the outreach activities in Japan, OpenDatathon which holds mapping party and Wikipedia editing event simultaneously, are regularly held. As a member of an Organizer group, I will introduce OpenDatathon case studies and consider their potential. In Japan, many OpenDatathon are held by various operators. It does the mapping party and editing of Wikipedia, other OpenData editing as one event. This contributes to the increase in opportunities to learn OpenData, the exchange of people involved in OpenData, and the increase of contributors. As a member of the group that organizes OpenDatathon, I will explain the purpose, effect, and spread of this initiative. The purpose of OpenDatathon's activities is different for each organizing group. Among the purposes, cases aimed at local information distribution are worth noting. It edits information on OSM and Wikipedia and uses it for the development of the local community. Because Civic Tech communities and local government organizations are often interested in it. For example, the library is collecting material about the history of the area. Writing on Wikipedia using them is important to them. The number of OpenDatathon hosted by the library is increasing. We also frequently support events organized by other organizations. That way, they can manage the event without having direct contact with OSM and Wikipedia experts. In addition, it is possible for people of various motivations to participate by incorporating the fun of walking around town. In order to edit OSM and Wikipedia, We are looking for historical or important topics in the local area. In addition, we may hold multiple events as a series of historical themes. We are planning a story in mapping, and creating a motivation for people to participate by incorporating a specific feature. As our future challenge, not only disseminate local information by themselves, they will organize hackathon by using the OSM geospatial database and the Wikipedia document data. We want to increase activities. The code and data are linked in an area-oriented. about this event: https://pretalx.com/sotm2019/talk/LBREW8/

Lightning Talks Lightning Talks about this event: https://pretalx.com/sotm2019/talk/MYFAAH/

Have you ever been lost inside a gigantic railway station? SNCF, the french railway company, is developing a pedestrian routing and navigation service to help travelers find their way inside and around railway stations. This talk exposes the challenges and how they have been addressed to provide a robust solution that can handle the great variety of data as well as routing through open spaces. SNCF, the french railway company, has been using OpenStreetMap to map railway stations for years. Since 2016, travelers can explore all 380 railway stations of the Greater Paris area through interactive maps deployed on their web site and on a mobile app. Browsing a map is good, but not good enough when you are lost inside a complex, multilevel, indoor (and/or outdoor) environment. It gets worse for someone in a wheelchair, elderly people, or anyone coming back from a family trip with more luggage than your hands could carry. What if we could bring pedestrian routing and navigation services to help travelers find their way inside and around railway stations? Could we do it using OSM data? This is the challenge SNCF has been trying to tackle for the past few months. Together with Jawg Maps and Carto’Cité, they held a detailed mapping campaign on 83 stations and developed a dedicated routing engine. This engine can seamlessly navigate indoors and outdoors, and achieves routing through open spaces such as pedestrian areas and station halls. This talk exposes the challenges we faced. It focuses on the data structure and the algorithmic strategies that have been defined to provide a robust navigation service, and its ability to handle the great diversity of railway stations. The talk also discusses how some parts of the data model originally designed for road navigation could be used for pedestrian routing – with some minor tweaks. If you thought mapping stairs was trivial, you would be surprised… about this event: https://pretalx.com/sotm2019/talk/AT9YPK/

<p>'Public Transport Navigation using OpenStreetMap by OsmAnd'</p> <p>The presentation describes stages of developing and starting of new feature of OsmAnd app.</p> <p>OsmAnd is the first among the large OSM apps that developed public transport support. Now we continue to improve this function further, add new modes of transport, and experiment with the settings.</p> <p>How we came to the idea of ​​public transport? In the presentation, we will explain why we use the PTv2 scheme, why this option is preferred, and how it works. In addition, in our report, we will consider the future support of GTFS.</p> <p>During the work we faced various difficulties, so we will explain what these difficulties were and how we tried to build at least something. We will also cover how OSM users can help us, and whether it is possible and how long it will take to improve the data.</p> <p>Lastly, we will present our results and plans, namely, what was our most significant success and how we want to build an editor for public transport..</p> <p>We launched the function at the beginning of the year, and throughout the year we tested it, supplemented it with new conditions for building a route and tried to take into account all possible conditions for building routes. Although the data in the OSM have not proved to be always correct, everything can be fixed by the community.</p> about this event: https://pretalx.com/sotm2019/talk/NWW7GF/

OpenMapTiles is an open-source set of tools for processing OpenStreetMap data into vector maps, which can be produced in various coordinate systems. OpenMapTiles is an open-source set of tools for processing OpenStreetMap data into zoomable and web-compatible vector tiles, which are ready to use in OpenLayers, Mapbox GL, Leaflet, QGIS as well as mobile applications. This talk gives an overview of the OpenMapTiles tools to produce vector tiles in coordinate systems other than Mercator while keeping all the features, such as interactivity to create powerful region-specific applications. Today’s de-facto global standard is a Spherical Mercator projection because it preserves shape and angles. The entire world looks like a square, which makes it easy to work with on a computer. However, such a transformation causes several issues like distortion of sizes where places around the equator are visually smaller than those near poles. That’s why new global coordinate systems were developed. There are also many local coordinate systems with a long tradition. They perfectly fit the need to display a particular country/area in the most acceptable way. They are heavily used, especially by governments. OpenMapTiles can serve as a base map for displaying data in a different coordinate system or for view/print purposes. There is also a hosting service based on OpenMapTiles called MapTiler Cloud, which offers the same ability to be displayed in a different coordinate system than Mercator. about this event: https://pretalx.com/sotm2019/talk/RYPLFZ/

Pic4Review now helps contributors to integrate open data properly in OpenStreetMap using street pictures validation. Discover the way it works and how this can help improve both OSM and open datasets. [Pic4Review](http://pic4review.pavie.info/) is a new editor to work on OpenStreetMap using open street-level pictures offered by Mapillary, OpenStreetCam and Wikimedia Commons. You can create thematic missions to work on a specific subject. The features are loaded in the tool, which automatically find available pictures around it, and offers a simple interface to answer questions and editing the features. At start, it was helping contributors adding details on existing objects, like accessibility of crossings, name of shops, levels of buildings... This offers a wide range of possibilities, and was already used by hundreds of contributors to edit around 100000 features worldwide. Now, it also offers to integrate open datasets into OpenStreetMap, the proper way. The complexity of integrating open data in OSM lies in the accuracy of these datasets : we should check if features are correctly located, and properly described. This can be done by surveying, or using freely available pictures of our streets. This is what Pic4Review offers to simplify : import your dataset, create a mission, and work in team to integrate the data which can be verified. This talk will present the problem of open data integration, the Pic4Review tool, and the process of using it for integrating open data. about this event: https://pretalx.com/sotm2019/talk/JGHWKY/

Closing Session Closing Session about this event: https://pretalx.com/sotm2019/talk/SWAGX7/

The OpenStreetMap provides probably the most comprehensive digital path network for pedestrians, which had been largely unused in its actual depth of detail so far. Based on findings from the TERRAIN project, this talk will go into detail how this network can be used for roadside-aware pedestrian navigation efficiently, and what challenges and mappings problems occurred. Furthermore, specific needs of various person groups will also have to be considered facing variable data quality. People primarily move on sidewalks, but this is hardly taken into account in generally available routing solutions. The OpenStreetMap provides probably the most comprehensive digital path network for pedestrians, which had been largely unused in its actual depth of detail so far. Through an efficient and transparent topological extension of the path network, more accurate routes for pedestrians and other means of transportation can now be determined, revealing hitherto unnoticed mapping problems and an undesirable previous focus on automotive mobility. As part of the TERRAIN project, an exemplary publicly available navigation system for blind persons was developed that demonstrates the special challenges of human locomotion based on the OpenStreetMap. Challenges and robust solutions concerning data quality, micro-mapping and transition areas, regional OSM semantics versus mapper interpretations, and path bundling are presented. A large part of the footpaths in the OpenStreetMap are listed in the form of annotations as sidewalks. This well-proven procedure enables a low-maintenance, robust and high coverage of the footpath network. However, a presentation with many paths inevitably leads to contradictions due to path bundling, which requires a more elaborate micro mapping, which is often not noticed by mappers up to now, and can now be shown clearly. But also regional micro mapping can lead to unexpected complications in addition to the increased effort, since a multitude of connection problems can occur which have to be considered. In addition, there are a large number of requirement profiles for children, adults, the disabled or the elderly who travel on foot, in wheelchairs or on scooters. A deeper examination of the map data enables a finer specialisation in different areas of application, which also uncovers and challenges previously unused data treasures and requires special handling in the software. about this event: https://pretalx.com/sotm2019/talk/ARMCT7/

In many regions of the world, a population’s access to essential services is unduly constrained by a lack of proper infrastructure. By performing accessibility analysis using OSM data, we can understand how route infrastructure impacts access to essential services and use that information to inform an intervention. This talk explores accessibility analysis performed to understand the impact of footbridge construction in eSwatini and introduces a python framework enabling users to perform similar analysis. Bridges over rivers are fundamental pieces of human infrastructure which enable safe crossings for the populations who use them. However, many rural regions of the world lack these critical bridges, and as a result, access to essential services is restricted for millions of people. In pursuit of tackling poverty caused by rural isolation, we have begun experimenting using remote analysis techniques to quantify bridge need and impact. To these ends, accessibility analyses using OSM and other data have proven to be extremely valuable. By comparing a population’s baseline accessibility to its access in scenarios such as a flood or post bridge construction, one can better understand the transportation dynamics of a region and even estimate the number of people that would be impacted with a bridge construction, for example. During the course of running various accessibility models, a python framework tebetebe was built around OSRM [Open Source Routing Machine] which simplifies the process of running different scenarios. This framework is built with footbridges in mind but is generalized so it may be applied to other applications. This talk explores the accessibility analysis process, its caveats and results achieved in the Kingdom of eSwatini. Finally, a walkthrough of the python framework tebetebe is given so that other users may perform similar analyses. about this event: https://pretalx.com/sotm2019/talk/7FBMMM/

OSM2World is an open-source renderer capable of creating rich 3D worlds from OpenStreetMap data. It is now available as a web application powered by WebGL. OpenStreetMap contains a wealth of data that can be used to build a 3D representation of our world. Some of it, such as buildings mapped using the [Simple 3D Buildings](https://wiki.openstreetmap.org/wiki/Simple_3D_Buildings) tagging convention, is intended primarily for 3D use cases. Other feature classes, such as roads and railways, urban infrastructure, or landuse information, aren't usually mapped with 3D in mind, but are nevertheless essential ingredients of a 3D world. In either case, unlocking the full potential of OSM data and converting it into a representation that's suitable for typical use cases in the domain of 3D rendering requires specialized software tools. [OSM2World](http://osm2world.org/) is one of these tools. Fully open source, it can be used to export 3D data for modelling software, animations, and games, as a library in other programs, and as an interactive map on the web. Until recently, however, the lack of a powerful 3d rendering solution for the web put constraints on the features available without a local installation of the software. Today, the WebGL standard allows real-time 3D rendering in all major browsers. A new web frontend for OSM2World builds on these capabilities to allow users to explore the world in 3D, based entirely on open data. This talk demonstrates the abilities of OSM2World and its new web frontend, showcases some impressive 3D mapping around the world, and briefly explores how the software works behind the scenes. about this event: https://pretalx.com/sotm2019/talk/V7QPGG/

Osmose-QA is a Quality Assurance tool pointing issues on map, but also able to compare external data set to OpenStreetMap. First part is about the new support of MapCSS rules in Osmose-QA and the opportunity to share rules with JOSM and other tools. Second part is on integration of external data from Mapillary photography objects extracted by AI and from OpenData. It is also on the challenge to deals with lots of OpenData. At the beginning, Osmose-QA is a Quality Assurance tool pointing issues on map, but also able to compare external data set to OpenStreetMap. First talk part is about the new support of MapCSS rules in Osmose-QA. MapCSS language was primarily designed as way to style map objects, it look like the web styling language CSS. The base of MapCSS is to use selectors to filter objects. JOSM already use it for map styling and validator. This new implementation of MapCSS in Osmose-QA allows to write rules more easily. It also allow to reuse JOSM validator rules set. Next step would be to run Osmose rules in JOSM. It’s a great opportunity to share rules across tools and projects. But come the question on how to share and manage this rules while considering tools particularities. Second talk part is on external data comparison (aka conflation) with OpenStreetMap. It permits to point issues and evaluate the coverage, lastly it allows fixing and integration into OpenStreetMap. The more we wait, the more there is OpenData sets available. How Osmose-QA can try to addresses this issue of already too many OpenData set available. Beside OpenData from authorities we also have street objects extracted by Mapillary AI from street photography. Finally, what about the limit of the current model and how we may go further. about this event: https://pretalx.com/sotm2019/talk/JJKH8H/

As of today we have OpenStreetMap but it doesn't fit all data and some data is not recommended for submission. We've got user reviews request in OsmAnd and Maps.Me and we would like to collaborate with OpenStreetMap community to create independent open platform for reviews. I would like to talk about how data could be stored and moderated and would like to explain how that data will be further integrated with OpenStreetMap and will be contributed back to OSM! One of the main contribution to OSM database itself will be justified places and details of places itself. I will explain how tools will validate License sanity and avoid Import problem to OSM i.e. how every edit will be administered by OSM-user. In the end we would like to have seamless OSM user experience integration and create open OSM-satellite project. *OpenPlaceReviews - A new way for local reviews* Open. Collaborative. Trustworthy. Openness - make all the reviews data open, so every project can access and contribute back. Community driven - build a community to make decision about moderation, data structure together. Decentralization - develop a decentralized system from the Day 1 to avoid scalability issues in the future. Trust - create tools to detect fraud, spam & keep all the data open to prevent any data manipulation. Monetization - make business subscriptions once the project is matured and spread the income between applications, authors, moderators and network operators. Further Description will be provided. Further Description will be provided. Further Description will be provided. about this event: https://pretalx.com/sotm2019/talk/LBGPCD/

Norway has deployed OSM on a national level for journey planning and is taking initiatives to expand the user, and usage of OSM in Norway and abroad. Entur’s mission is to enable and increase the use of public transport in Norway. Entur operates the national registry for all public transport in Norway, collecting data from 60 public transportation operators. The registry contains data about 21,000 daily departures on 3,000 routes. This data is open and free of use for app and service developers. We would very much like to present our success story of using OSM data as the foundation for all interchanges and routing in our national journey planner (used in the app: Entur, and http://en-tur.no), and the surprisingly easy challenges we faced due to the quality of OSM data, and the way we solved the large scale challenges, such as missing roads around the country. Why we are going to continue to build on the usage of OSM, OpenTripPlanner and other open source platforms on a European wide base to create an ever-improving public transport information distribution. We are actively engaging with the Norwegian community to build relations, improve quality and mapping methods, even arranging beer meetups. We are of course providing all our data openly, making it easy for OSM to make good use of for example our national stop place database, or for our NeTEx based time table data to be used in innovative solutions. about this event: https://pretalx.com/sotm2019/talk/CXVMJ8/

Replacing the classics "Continue for about 200 m then turn left" by guidance instructions in more natural language automated from landmarks are still a research topic but aims to allow users to move with more confidence. The objective is to test it on an indoor/outdoor pedestrian route calculator. Anything that can be used as a landmark is extracted from OpenStreetMap, then categorized and classified to annotate the route: relevance, visibility, relative position… Replacing the classics "Continue for about 200 m then turn left" by instructions in more natural language with landmarks incorporation. It aims to allow users to move with more confidence in and around railways stations. Guidance instructions in more natural language automated from landmarks are still a research topic. Innovation & Research branch of SNCF, French National Railway Company, has delegate to Makina Corpus the task to implementation the state of the art in this field. The objective is to test it and then improve an indoor and outdoor pedestrian route calculator under development. Anything that can be used as a landmark is extracted from OpenStreetMap, then categorized and classified. The landmarks are evaluated to annotate the route: relevance, visibility, relative position.… The process have to deals with indoor and outdoor but also as points or large landmarks. We talk on subjects like evaluate how OpenStreetMap objects are suitable to be landmarks. We also expose how check for objects visibility based only on OpenStreetMap data, or how to allow landmark to be visible from outside a building or room depending on the outdoor or indoor status of the observer. The railway stations context has the particularity to require support of transition between indoor and outdoor. about this event: https://pretalx.com/sotm2019/talk/ZJYT7F/

In this research, the OSM-Notes feature is mainly viewed as data that can be examined speedily from OSM data globally in terms of the content of the notes posted and the location of users. Data from volunteered geographic information (VGI) and public surveying are essentially different; while a great quality and quantity of data is available for VGIs, discussions about data quality are scarce (Senaratne et al., 2017). As the use of VGI in business expands, attempts have been made to develop quality verification tools (cf. KeepRight and Validation OSM) with the understanding that, in addition to position accuracy, data quality equally depends upon the diversity and interaction of the number of users involved in data generation (Haklay et al, 2010). OSM-Notes is a capability for describing errors and discussing OSM data and fixme, although there are few research cases (Seto et al., 2017). This feature is new and was only added to OSM.org in April 2013. It allows users to specify and comment on any point on the map, and the history of comments can be accumulated and closed when the problem is solved. Unlike the fixme tag (allows contributors to mark objects and places that need further attention in the form of a "note to self" or request for additional mapping resources), this function does not directly associate with OSM data, but there is no need to have an OSM account. In this research, the OSM-Notes feature is mainly viewed as data that can be examined speedily from OSM data globally in terms of the content of the notes posted and the location of users. The purpose of the research is to analyze the context of OSM-Notes use through a “GIS” approach (Cope & Elwood, 2009). This analysis was conducted on data dumped from Planet OSM (https://planet.openstreetmap.org/) as April 2019. Since planet-notes.osn (about 782MB) is a special binary format, we used an enhanced parser able to separately output open (unresolved discussions) and closed (resolved discussions) based on osn2osm. After converting to .osm format data, the set was combined with Natural Earth's border data and population data and treated as spatial data. As a result, there were OSM-Notes postings in 237 countries: 415,433 open and 129,887 closed records. By counting the number of OSM-Notes postings by country based on whether they are open or closed, we determined that the majority were in the United States, Germany, and Russia. Moreover, it became clear that Japan, Canada, Korea, and Taiwan are the regions where there are a large number of submissions in urban areas (based on Natural Earth’s definition). In addition to these, many OSM users have posted from many countries, including Iraq, Ukraine, and Ecuador, and it is clear that active discussions are being held by many contributors. In these countries, mapping for humanitarian assistance is commonplace, as other reference resources are unavailable, so it is necessary to improve data quality through the use of OSM-Notes. OSM-Notes even has a function that allows non-OSM users to post, as is the case with 50% or more of the posts in Spain, Korea, etc. This data can be used to analyze urban trends and spatial features within a single country. For example, according to the analysis for Japan, OSM-Notes has many posts about the location of shops and POI (Point of Interest), and suggestions based on Maps.ME, Facebook, and Pokémon go. This is considered to be the main reason that anonymous posting is permitted. It is also worth noting that very few users post only a single note. Thus, by analyzing OSM-Notes, it is possible to grasp hot issues between users of OSM data. Overall, OSM mapping was found to be more common in developed countries with active mapping, but similarly, bug reports from non-OSM applications as developing countries that require discussion among OSM users, and as a new aspect. Moreover, how to accept it is an important issue in considering the process of constructing OSM data. However, because the functional relationship between OSM-Notes and the data on OSM cannot be specified directly, the nature of the feature is also difficult to grasp directly compared to the fixme tag. about this event: https://pretalx.com/sotm2019-at/talk/GRR3N3/

This talk discusses different ways how to improve the search experience for domain-specific maps. No map comes without a facility to search for places. As a result quite a few open-source geocoders using OpenStreetMap data have been developed and have matured in the last years. Nominatim, Pelias, Photon, Carmen, there are many to choose from. They all have in common that they offer a general purpose search for addresses, places and, to a more limited extent, places of interest. OpenStreetMap has inspired the creation of many domain-specific maps. Be it maps for specific features like brew pubs, camping or power infrastructure or for activities like cycling, hiking or child entertainment. A general purpose search on these maps is often not satisfying. The special features might simply not be available in the generic geocoders. And when they exist, they do not get the prominence appropriate for the map. This talk explores ways to improve the search experience for such special-interest maps. We start with simple approaches to boost results using existing web APIs and then look into different ways for creating a small custom domain-specific search engine. We discuss advantages and disadvantages of the different approaches and show common pitfalls for implementations. Although some knowledge in system administration and programming is expected, the focus is on solutions for small and medium projects that are realized with limited resources. about this event: https://pretalx.com/sotm2019/talk/PJE8GK/

Services that provide money advice, access to food provision, digital access and community support are key to supporting those facing poverty. “Our Falkirk” is a simple mapping platform for service discovery that allows enriched OSM data to be easily described, mapped and shared through the concept of data ‘themes’. Falkirk Council is one of 32 Local Authorities in Scotland. Located in the centre of Scotland with a population of around 155,000 people, 1 in 5 people and 1 in 4 children in the Falkirk Council area are estimated to be living in poverty. Fairer Falkirk is Falkirk Council’s strategic response to the rising poverty in the Falkirk Council area. It brings community planning partners together and sets out in detail a series of practical, deliverable, and achievable programmes aimed towards mitigating the impact of poverty on individuals and families. Services that provide money advice, access to food provision, digital access and community support are key to supporting those facing poverty, but ensuring that local people and front-line staff have access to up-to-date information relating to these services is challenging. With funding from the Open Data Institute (ODI)’s Local Government Geospatial Data Stimulus Fund, Fairer Falkirk and their technology partner thinkWhere have harnessed the richness and flexibility of OpenStreetMap to create “Our Falkirk”: an online, map-based tool to allow local people to easily access information on services in the area. The presentation will look at how we’ve created a simple mapping platform for service discovery that allows enriched OSM data to be easily described, mapped and shared through the concept of data ‘themes’. We’ll look at some of the key challenges and opportunities identified through this process, and show how taking an Open Data approach has allowed Falkirk Council to democratise access to vital information through the streamlining of data creation of publication. We’ll look at potential next steps and sign-post the resources now available as a result of this fully open–sourced project. about this event: https://pretalx.com/sotm2019/talk/YENWFX/

This study examines OpenStreetMap data quality at different stages of a participatory mapping process developed for understanding inequalities in healthcare access of informal urban residents in Africa and Asia. Recent studies have examined quality intrinsically and extrinsically. However, in both cases, the data production processes are often not completely transparent to researchers, therefore limiting possibilities for systematic data quality analysis of the processes leading to OpenStreetMap update. Globally, the lack of detailed quality spatial data of informal urban settings, such as slums, is increasingly becoming a concern to both researchers and development agencies (Hachmann, Jokar Arsanjani, & Vaz, 2017; Kuffer, Pfeffer, & Sliuzas, 2016). One potential for making spatial data available is through Volunteered Geographic Information (VGI) which is opening up new possibilities of data production in recent years and facilitating the emergence of several initiatives aimed at “putting the most vulnerable people on the map” (MissingMaps.org, 2018; Shekhar, 2014). The increasing availability of volunteered and crowdsourced geographic information, in particular OpenStreetMap (OSM), has led to plethora of scientific studies with emphasis on evaluating the quality of the OSM data. The quality assessment results are usually presented in the form of tables, diagrams, map and statistics per given area (Barron, Neis, & Zipf, 2014; Sehra, Singh, & Rai, 2017). Some recent studies have examined OSM data quality without using any external data; the so called intrinsic approach (Barron et al., 2014). In contrast to intrinsic approach, other studies commonly used what is referred to as the extrinsic approach where the OSM data is compared with external datasets such as the UK Ordnance Survey data or National Park Service lists (Haklay, 2010; Zipf, 2017). In both approaches, the data production processes are often not completely transparent to researchers therefore limiting possibilities for systematic data quality analysis of the processes leading to OpenStreetMap update. This presentation examines OSM data quality at different stages of a participatory mapping process developed as part of an ongoing research project focused on understanding inequalities in healthcare access of slum residents in the Global South. The following research questions are addressed: (1) What is the level of spatial data quality one can expect at different stages of the mapping process leading to final update of the OpenStreetMap database? (2) What are the factors influencing quality? Our exploratory method applies recently developed data analytics framework for spatio-temporal analysis of OpenStreetMap History Database (OSHDB) to our study areas vis-à-vis the participatory mapping process workflow. OSHDB framework will serve as a mediating framework to allow flexible analyses of OSM full history data completeness in our study areas. A multi-country case study associated with an ongoing research project of the National Institute for Health Research Global Health Unit on Improving Health in Slums at University of Warwick is used. This Unit focuses on health services in slums through the study of seven slum sites across two continents (Asia and Africa), with the ultimate aim of finding optimal ways to deliver health services to slum dwellers (Lilford, 2017). The historical data sets are derived from the following stages in the participatory mapping process: before online mapping (i.e. digitisation from satellite imagery), after online mapping and validation but before ground-truthing, and, after ground-truthing. The before-and-after estimates at each mapping stage are discussed together with lessons learnt, and feedback, from the project including comments from fieldworkers and supervisors. We thus present initial results from a spatial data quality assessment of the mapping process workflow used to map our study areas and update the OpenStreetMap database. about this event: https://pretalx.com/sotm2019-at/talk/MWS9ZC/

The OSM data model has facilitated rapid growth of community-created geodata which third parties can build on. But as more accuracy is needed in routing, cartography, and other uses, is this data model good enough? We are trying to represent spaces as flows, which result in fundamental compromises and inaccuracies. This talk will discuss real-world cases where this compromise is increasingly problematic. OpenStreetMap was designed to enable ordinary people to create open geodata that anyone can use and maintain easily. Traditional GIS concepts such as layers are dispensed with in order to make editing simple and accessible. In the same way that the web would never have taken off if HTML were not so accessible and tolerant of mistakes, this simplicity in OSM has meant a low barrier to involvement. However, as OSM is becoming more widely used in the mainstream, the need for accuracy and quality is becoming more and more important. Cyclists need detailed turn data to enable high-quality routing that takes full account of safety. Satnav companies, need lane data, which is difficult to represent accurately. Pedestrian routing is barely in its infancy and high quality routing for people walking or using wheelchairs is hard to achieve. At its root, OSM tries to represent spaces as flows (lines). This results in fundamental compromises and inaccuracies. What is good for routing is not always good for cartography, and vice-versa. For instance, a street containing cycleways with pavements either side is usually represented as a single line with attributes. However, it is extremely challenging to represent properly all the parts of the street, and in general people simply don't bother: a single line with large numbers of attributes is unwieldy to edit (even when hidden by editor GUIs), and just as challenging for a router to interpret. Continual changes in the width of a street cannot be easily represented without segmenting the street heavily and creating a mess. Temporary disappearance of lanes makes editing complex. Routing ultimately ends up as a lowest common denominator result. The alternative method of representing this same street is as a series of individual lines. But this is equally problematic. In this model, the street loses its coherence as a single entity - humans think of it as a street with multiple uses (walking, cycling, driving, trees). Where people have done this, attributes such as street names need to be kept in sync, and in practice separate pavements often fail to have names attached. Concepts such as the ability to cross from one side of the road to the other (or even switch lanes) are not modelled, with the result that a router may take the user to the end of the street then back down. And cartography ends up showing a series of parallel lines which looks messy and does not match the human perception of a street. The bicycle tagging page on OSM provides a perfect demonstration of the current problem: https://wiki.openstreetmap.org/wiki/Bicycle It shows the complexity of representing many common scenarios, with increasingly incomprehensible tagging combinations. No router implements anything like all of this, and even expert OSM contributors would shy away at bothering to add this data. Cycleways indeed are a good general example of inconsistent tagging. Cycleways separate to a road are sometimes tagged as an attribute of the street and sometimes as a separate geometry. What about a hybrid/stepped cycle lane of the kind seen in Copenhagen - is that a cycle lane or a cycleway? Do lane counts include cycle lanes or not? How is obstructive car parking represented? Is the one-way indication applicable to the cycle lane on the road? And so on. Another example is junctions. Should traffic signals be treated spatially (i.e. represent the location of the traffic heads), or should they be treated linearly so that routing works properly? How should the linear model work accurately when there is only a single geometry for multiple directions? Have a look at the roads around the Arc De Triomphe in Paris - it is completely impossible for a routing engine to work out exactly how many signal delays should actually be attributed based on the presence of the marking of traffic signals in the data: https://www.openstreetmap.org/#map=17/48.87391/2.29536 This talk will discuss these cases, and provide a starting point for discussion on what should be done to improve the situation. As people are ever keener to add more detail to the map, and as more and more mainstream users look to OSM, we have to ask whether the current model is arguably creaking too heavily. Is there a way that we can represent spaces as a set of interconnected flows in some way? The speaker, Martin Lucas-Smith, is one of the developers of CycleStreets, one of the earliest and most established dedicated cycle routing engines. As such, he has spent many years considering the kinds of tradeoffs represented by the current OSM data model. abou

More than 17 million edits globally have been made by paid contributors in the last five years. We look at the long history of corporate involvement in OSM and then dig into the data to quantify the impact this latest evolution of corporate involvement is having on the map and explore the interactions between paid and volunteer mappers. OpenStreetMap (OSM) is both a map and the active community of over a million mappers that create and maintain it. Participation in OSM has largely been studied in terms of motivation and the resulting data quality. Today, the community is comprised by many different interest groups including craft/hobby mappers, humanitarian mappers, professional mappers, and more. The last few years have seen a dramatic growth in a specific group of mappers: corporate editors. These are mappers hired by corporations and edit the map as part of their employment. In November 2018, the OSM Foundation published the _organized editing guidelines_ that outline a number of steps all groups engaged in organized editing activities (including corporate data teams), should take to promote transparency, openness, and engagement with other mappers—especially local community. This work identifies ten corporations that are complying with these guidelines and explores their mapping activities. We found these corporations have cumulatively edited over 17M objects globally in the last five years, of which 9M were edited in 2018 [1]. First, we traced the history of corporate involvement in OSM to show that while this growing phenomena of corporate editing is new, it represents just the latest stage in a long history of corporations both contributing to and benefitting from OpenStreetMap. Next, we used historical quarterly-snapshot OSM-QA-Tiles to quantify where the ten corporations are active on the map and what types of edits they are performing. We find these edits are global in geographical scope, yet vary per corporation in location and edit type: Corporations heavily impact road networks, yet non-corporate mappers maintain the majority of all edits by mapping more buildings and points-of-interest [1]. To date, this research has quantified and contextualized the growing phenomena of corporate editing in OSM and identified the need for more in-depth analysis to more descriptively explain the impact to the map and volunteer mappers in these regions where corporate-editors are active. To further explore these impacts, we need to dig deeper into the editing record to describe the evolution of the map. For this, we are building upon open-source OSM data-processing tools to construct new vector tiles with with full OSM editing histories [4]. These new historical analysis tiles allow us to efficiently explore the evolution of the map in these regions. This allows us to better contextualize and visualize the interactions between corporate editors and volunteer mappers at scale. Previous research has shown that the road network typically gets mapped first and the map builds up from there [2]. To this extent, we will explore the notion of _map seeding_ whereby paid editors create the first version of the road network, seeding the map for others to maintain and grow. Supporting such an idea is the concept of _densification_ of the map, where some mappers prefer to edit where there is existing—though incomplete or sparse—map data, instead of a beginning with a blank section of map [3]. The concept of such editing patterns highlights the nuances in effectively measuring the impact of paid editing on the map. In other words, this question is more complicated than “are corporate editors taking over?” The first part of the research presented here was recently published [1]. The deeper exploration of the data to identify and explain the impact to the map and local communities is current, ongoing research. At State of the Map, I will briefly summarize the completed work to better set the context, and then present the results of our ongoing investigation on the impact to the map and community. 1. Anderson, J., Sarkar, D., & Palen, L. (2019). Corporate Editors in the Evolving Landscape of OpenStreetMap. ISPRS Int. J. Geo-Inf. 8, 232. 2. Ciepłuch, B., Mooney, P., & Winstanley, A. C. (2011). Building Generic Quality Indicators for OpenStreetMap. 19th Annual GIS Research UK (GISRUK), 5. Retrieved from http://eprints.maynoothuniversity.ie/2483/ 3. Corcoran, P., Mooney, P., & Bertolotto, M. (2013). Analysing the growth of OpenStreetMap networks. Spatial Statistics, 3, 21–32. https://doi.org/10.1016/j.spasta.2013.01.002 4. OSM-Wayback Utility available at https://github.com/osmlab/osm-wayback about this event: https://pretalx.com/sotm2019-at/talk/W8BA9K/

Satellite imagery has materially enhanced OpenStreetMap and improved editing and validation. In this talk we will talk about recent enhancements we've made to get even more information from satellite imagery. DigitalGlobe, now known as Maxar Technologies, has been providing two sources of imagery directly for OpenStreetMap (OSM) users for tracing and validating. Over the past couple years, we’ve received a lot of excellent feedback from OSM users and we are making a few changes to better serve OSM volunteers. Part of this presentation will address some of the common questions we are asked, address some of the common feedback, and dive into the upcoming changes to the services that we provide. We want to do this to clear up any confusion or future questions the community might have. We will further demonstrate how we have integrated Maxar imagery into the OpenStreetMap ecosystem. In both iD and JOSM, we have developed a custom plugin that connects to the commercial satellite imagery service and provides a carousel view of available imagery in the visual extent of the editor. Images are arranged temporally providing opportunities to compare areas against different time periods. The plugin also applies the metadata from the image as tags on the feature, including source:imagery, source:imagery:layerName, source:imagery:sensor, and source:imagery:date. Leveraging these services and applying the metadata of the imagery enhances the mapping experience, enriches OSM data, and improves the end-user benefits. about this event: https://pretalx.com/sotm2019/talk/TVSA9F/

This talk will present the rise of active, self-standing grassroots communities in Haiti, Western and Central Africa since 2010 resulting from a unique set of continued support actions replicable in other territories, by an ensemble of speakers from (at least) France, Burkina Faso, Senegal and Togo This breakout session will produce a narrative about how since 2010, OpenStreetMap communities emerged in Haiti and expanded to Western and Central Africa after 2012 partly as the result of a combined set of support actions spanning technical and organizational OSM trainings, voluntary and professional OSM mapping projects, OSM local and international volunteering programs, documentation as well as preparedness and crisis mapping work... Tied to the overall support of the global OSM community and the commitment of Haitian and African mappers, these actions allowed the OSM project to come forth in the form of a network of organized and self-standing communities and economic stakeholders in Haiti, Western and Central Africa cognizant of and active with OpenStreetMap, Open data and free geomatics. Like in Milano's Bird of Feathers sessions last year, this talk will be collectively built with African mappers involved in these multi-years activities and ideally co-facilitated by those from Western Africa successful in getting visas and traveling to Heidelberg. This talk is meant to introduce a Bird of Feathers session opened to anyone active or interested in growing OSM the grassroots way in the hard environment of the countries of the Global South and especially southern countries of Africa. ### Speakers * Nicolas Chavent (France) * Séverin Ménard (France) * Amadou Ndong (Senegal) * Aimée Sama (Togo) * Innocent Dibloni (Burkina Faso) * Saliou Abdou (Benin) * Racky Ly (Ivory Coast) Additional scholars from Africa may also join. about this event: https://pretalx.com/sotm2019/talk/NCSSPK/

This talk will share lessons that OSM and OSMF members can learn about the multi-years collective dynamics around OpenStreetMap which unfold in French-speaking southern countries of Africa and the Caribbean with the view of identifying paths for local OSM grassroots groups evolution towards a formal OSMF local chapters. It has been some years that OSM and OMSF members have been discussing the topic of OSMF local chapters over emails, wiki, physically through BoF sessions at SotM events. In 2019 the Ugandan NGO MapUganda applied to be the OSMF local chapter in this country. This talk will share lessons that OSM and OSMF members can learn about the multi-years collective dynamics around OpenStreetMap which unfold in French-speaking southern countries of Africa and the Caribbean. In these countries, since the start local OSM actions (outreach, communication, mapping and training) have been organized collectively. These actions lead to the de facto building of first informal collective of mappers which organized internally over the years and for some incorporated as local associations mandated to the promotion and support of the OSM project playing the role of a de facto OSM Chapter. The talk will look at the collective practices of these de facto OSMF local chapters in the light of the OSMF requirements but also look at the OSMF local chapters requirements in the light of this multi-years OSM associative experience with the view to identify itineraries to the building of OSMF local chapters. Like in Milano's BoF sessions, this talk will be collectively built with African mappers involved for many years in these activities and ideally co-facilitated by those from Western Africa successful in getting visas and traveling to Heidelberg. This talk is meant to introduce a BoF session opened to anyone active or interested in supporting the growth of OSMF local chapters connected to the pioneering grassroot collective in the hard environment of the countries of the Global South and especially southern countries of Africa. about this event: https://pretalx.com/sotm2019/talk/VWBV88/

We propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock using OpenStreetMap data, and (ii) estimating the energy use of buildings individually with a back-end model. For mitigating climate change, it is crucial to minimize energy use in buildings while maintaining decent wellbeing (1). Buildings contribute more than a quarter of the global energy-related emissions (2). On the one hand, buildings are among the lowest-hanging fruit for mitigation—with technological solutions available for energy-neutral or even energy-positive buildings. On the other hand, deep decarbonization is challenging because of the heterogeneity in the building stock (2). Moreover, achieving decent well-being conditions for everyone could increase the energy consumption of buildings. For instance, in countries like India where global warming is likely is intensify the deadly effects of heatwaves, air-conditioning is considered an important adaptation strategy. Currently, building energy demand, e.g. for heating and cooling, is mostly estimated for individual buildings with data-intensive models, or with fudge factors in highly aggregated models globally. For rapid, wide-spread mitigation, such methods are not sufficient either to develop solutions that can be transferred across urban areas, nor to develop tailored solutions based on local data everywhere (3). This situation confines impactful climate action to a limited group of cities. There are entire regions with pressing mitigation and development issues that are left behind, in particular rapidly-urbanizing urban areas in the global South (4). The rise of big data and artificial intelligence offer new opportunities to model building energy demand for urban areas, and can even take into account geographical diversity (3). Such data could inform municipal policymakers on spatially stratified but city-wide strategies for climate change mitigation in buildings. However, large-scale spatially-explicit models that compute the minimum energy demand satisfying essential thermal comfort in buildings are still missing. Here, we propose a model that uses only open data for estimating the minimal energy use of individual buildings for heating and cooling at scale. The workflow is divided in two main blocks: (i) predicting at scale a 3D building stock, and (ii) estimating the energy use of buildings individually with a back-end model. In a first step, we use a machine learning model to learn and predict buildings heights from OpenStreetMap and ground truth LiDAR data. The latent energy use of a building can be approximated by its shape and its height (5). However, the height attribute is sparsely populated in OSM. Previous research (6) has demonstrated that building heights can be predicted from features describing buildings and their surroundings. We compare several machine learning architectures and input features spaces. In particular, we are developing a hybrid convolutional neural network based on OSM raster data and scalar features (7). Such architecture enables to take full advantage of both the spatial and the higher-level information available in OSM. We will test how well the model transfers to new cities, and how to improve generalization. In a second step, a back-end model computes the latent, minimal energy demand of each building for heating and cooling. The model accounts for simplified building characteristics, local climatological conditions and ancillary factors. Climatological conditions strongly influence building energy use. The latent energy demand is a simple metric that does not account for occupancy patterns or appliance use. However, this metric provides a lower bound for the energy necessary for thermal well-being, and it is simple enough so that it can be computed for any building. Preliminary results indicate that on average the energy demand in the studied areas can be reduced by a large amount while maintaining decent thermal comfort. However, strategies for energy demand reduction depend on building stock vintage, cultural standards, and the local climate. Our framework provides new insights from OpenStreetMap for more detailed and globally consistent analyses of mitigation strategies in cities. 1. N. D. Rao, P. Baer, “Decent Living” Emissions: A Conceptual Framework. Sustainability (2012) 2. O. Lucon et al., in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)] (2014) 3. F. Creutzig et al., Upscaling urban data science for global climate solutions. Global Sustainabil

Mapathons and Imports represent a Quality Challenge for the OSM community. This presentation focuses on Buildings. It presents Metrics and show progress of tools development to monitor and correct Quality problems in the OSM database or before importing. Mapathons and Building import projects represent major OSM Quality problems to monitor. Potentiel 3.0 and OSM-RDC started a project in 2018 to monitor Data quality problems. We present the work done with metrics to monitor quality problems among various Projects (see [OpenDataLabRDC Blog Articles about Building Quality](https://opendatalabrdc.github.io/Blog/#!index_en.md) ). Individual building geometries are evaluated and classified (ie. orthogonal, quasi-orthogonal or irregular). Topological errors are detected (ie. incomplete polygon, self-crossing, overlaps). Geojson visualisations and Statistics let evaluate a project. Output of List of osm_id's for various problems (ie. topological error, irregular building) let's import simply into JOSM with Overpass queries. Current development is to go a step further and correct buildings quasi-orthogonal angles in an OSM file taking into account neighbor buildings to avoid distorsions with blocks of buildings aligned on a street (See [OQ_Analysis](https://github.com/pierzen/OQ_Analysis)). Programming is based on the Osmosis schema, Python and PostgreSQL-PostGIS. OSM files to analyse can be imported from various sources (ie. Geofabrik, Overpass, osm export). Each OSM file (specific date-Zone) is converted to PosGIS and stored in a specific PostgreSQL schema via Osmosis. The topological analysis and geometry measures are made with PostGIS functions. about this event: https://pretalx.com/sotm2019/talk/3BZCXA/

Lightning Talks Lightning Talks about this event: https://pretalx.com/sotm2019/talk/RLZJG9/

Indexing OSM data in your PostGIS database for fast spatial queires is not as straight foreward as one migth hope. And with each release of PostgreSQL / PostGIS there are more options to try out. This talk will explain different spatial indexing concepts and best practices in PostGIS and present some benchmarking results. When working with OpenStreetMap data in a database you learn pretty soon that you need to index the columns (and rows) which you use for filtering in order to have fast running queries. PostgreSQL offers a variety of these [table access methods](https://youtu.be/W6B8-srOsrs) but when it comes to its spatial extension PostGIS, developers could only use one for the geometry fields for years: The GiST index - an implementation of the [R-tree](https://en.wikipedia.org/wiki/R-tree) search tree concept. But during the last releases new methods were made available, namely BRIN and sp-GiST. Not many resources are yet available to figure out which index strategy to apply for which data processing or analytical workflows. Therefore, I created a simple [benchmark](https://github.com/FxKu/postgis_indexing) to find it out. So far, only for artifical data, but for this years StoM it is planned to extend the experiments to OSM datasets. This talk will explain the different characteristics of each spatial index and present some performance comparisons in terms of query speed, overhead on writes, index building time and index size. It will also cover general indexing best practices such as subdividing geometries, [partial indexing](https://wiki.openstreetmap.org/wiki/User:Species/PostGIS_Tuning#Indices) and introduce new concepts such as [covering indexes](https://info.crunchydata.com/blog/why-covering-indexes-are-incredibly-helpful). about this event: https://pretalx.com/sotm2019/talk/CAD93S/

OpenStreetMap's processes are carefully designed to minimize the privacy footprint of the mappers. Nonetheless, the principle that any edit shall be attributable means that some data is still recorded. An overview is given which data is recorded at all and which of it gets available to whom. OpenStreetMap surveys data about places, not about persons. Thus privacy sensitive data in the geodata is very rare and always unwanted. Likewise, the principle of distributing the data allows data consumers of the geodata to stay as anonymous as they want. Mappers can contribute with pseudonyms, although not completely anonymous. However, for both the social features of OpenStreetMap and the purpose of attribution for the edited data it is necessary to collect some privacy related data. A useful starting point to see your own data footprint is the tool HDYC. All the edits you make are grouped into changesets, and the editing software will urge you to add a comment per changeset. As both the edits and the changesets carry dates it is possible to track your activity times. The nature of some edits may suggest local knowledge thus that you actually have been at the mapped places. The kind of edited objects and the mapping standards can be checked by every other contributor as well. Most often, this is simply a reason to be proud of the achievements, but you should know and check before it might get a matter of concern. Using separated accounts for different mapping tasks is often an appropriate solution and well-accepted in the community. OpenStreetMap offers additional ways to interact: you can leave comments on changesets and notes on the map, you can discuss on mailing lists or the forum, or you can answer questions on help. You can write documentation on the wiki. All of these channels have their own rules with regard to how long the data is stored, how it is searchable, and whether you can hide or delete your contributions. The talk presents the data traces of each channel and discusses strategies to get the desired degree of privacy. about this event: https://pretalx.com/sotm2019/talk/XRL7VK/

Abschlussveranstaltung der Datenspuren 2019 about this event: https://datenspuren.de/2019/fahrplan/events/10469.html

This study adopts a statistical approach based on Exploratory Spatial Data Analysis to identify underlying contribution patterns of OpenStreetMap (OSM). Univariate and multivariate analyses on a number of variables computed from OSM history on a regular hexagonal grid in Milan (Italy) allow to detect a number of both local clusters and local outliers, which shed light on the complexity of OSM temporal evolution driven by active local contributors and communities, data imports and mapping parties. Compared to traditional geospatial data sources, a major advantage of OpenStreetMap (OSM) is the availability of its full history. In literature, OSM history has been exploited for a number of purposes. The most frequent is intrinsic quality assessment, which - in contrast to extrinsic assessment, where OSM quality is evaluated through comparison against a reference dataset - estimates OSM quality by only looking at its temporal evolution. OSM history has been also explored to gain insights into the project's contribution patterns, e.g. history and profiling of contributors; origin, amount, nature and frequency of edits; spatio-temporal evolution of the whole OSM database - or parts thereof, such as road networks and buildings - in specific areas, or after specific events like natural disasters; and spatial analysis of contributor and contribution patterns. This work fits into the context of OSM intrinsic assessment by proposing a statistical approach based on Exploratory Spatial Data Analysis, and in particular spatial association, aimed at uncovering underlying history-based patterns of OSM data. More in detail, spatial association is investigated in both the univariate and multivariate contexts, i.e. in the cases - respectively - when one variable and multiple variables (together) are examined. The univariate analysis is performed using the Local Moran’s I indicator, which provides a robust classification method to detect statistically significant patterns (compared to the hypothesis of randomness) and define the spatial association type at each location in the dataset. The association type reflects the local characteristics of the variable at each location and its surroundings. Hence it allows detecting clusters, i.e. local patterns of similar (either high or low) values, as well as outliers, i.e. local patterns of dissimilar values (either low values surrounded by high values or viceversa). Instead, the multivariate Geary’s c indicator is employed to detect local association patterns resulting from the joint spatial interaction of two or more variables. A multivariate pattern classification comparable to the one of the univariate case is achieved through a novel classification method developed by the authors. This consists of a comparison of local and global centrality measures (means and medians) for the computed distribution of the multivariate Geary’s c, to produce classification maps of clusters and outliers. The analysis is performed on Milan Province (Northern Italy), counting a population of more than 3 million inhabitants on a surface of about 1.500 km². This area is sampled using a regular hexagonal grid with side of the hexagon equal to 1000 m, producing a total of 684 cells. The analysis is focused on the history of OSM nodes only, with the following hypotheses: only nodes with at least one tag are considered; a new version of a node is counted only when there is a change in tags (not in geometry); only the nodes which currently exist in the OSM database are considered. With this in mind, for each grid cell a number of history-based variables are computed: total number of different contributors who have edited OSM nodes; average number of different contributors who have edited each OSM node; average date of creation of the OSM nodes; average date of last edit of the OSM nodes; average number of versions of the OSM nodes; average frequency of update of the OSM nodes. These values are derived from the processing of the OSM Full History Planet file (downloaded in May 2019) and its conversion into a SpatiaLite database after an intersection with the study area, followed by the computation of the variables for each grid cell. The univariate analysis, performed using the QGIS Hotspot Analysis plugin developed by the authors, highlights different spatial associations for the different variables. While some of them (such as total and average number of contributors and average version) clearly show clusters of high values in correspondence of the most urbanized areas and clusters of low values in the non-urban peripheral areas, spatial association patterns are more heterogeneous for other variables such as the average update frequency. Multivariate analyses are then performed to detect the spatial patterns derived from the joint interaction between two and more of the variables considered. Despite each variable has its own spatial pattern when taken alone, their combination (especially when adding more a

Ein paar Denkanstösse über den Umgang mit persönlichen Daten Eine kleine Einführung in die Problematik des Datenschutzes. Wie Daten gesammelt und analysiert werden, wielche Schlüsse sich daraus ziehen lassen am Beispiel der Rasterfahndung., Terrorismusbekämpfungsgesetz, Datenfallen im Netz, Verteidigungsstrategien about this event: https://datenspuren.de/2019/fahrplan/events/10190.html

This presentation describes the nature and life-cycle of carto-vandalism through a data-driven analysis of harmful edits originated from Pokémon Go players. It also assesses how the OSM community reacts to vandalism. The utilization of OpenStreetMap (OSM) data by mainstream tech companies has been on the rise in recent years. Two prominent examples are Snapchat and Pokémon Go that became OSM data consumers in 2017. Snapchat [reports](https://bit.ly/2YAzv70) 190 million daily active users in 2019. Pokémon GO was used by 28.5 million users daily during its peak popularity in 2016 and it still managed to engage more than [10 million](https://bit.ly/2VJS3EO) users monthly in 2018. The large user base of these application puts OSM in an unprecedented spotlight which can be considered a huge success for the project. On the other hand, increased attention comes with side effects. Acts of [vandalism](https://bit.ly/2HBLE4R) manifested in the data no longer stay within the OSM community but will be visible to a worldwide audience. This increased visibility of errors caused by malicious actions (e.g. fake place names, fictive data) can potentially undermine the reputation of the OSM project. In August 2018, a case of [anti-semitic vandalism](https://bit.ly/2HHPRno) surfaced on Snapchat's online maps and also made it to various mainstream media outlets, such as the [BBC](https://bbc.in/2JLO4QY), [Time](https://bit.ly/2VJwpkd) or [The New York Times](https://nyti.ms/2onZJJS). Another type of vandalism can be observed in connection with Pokémon Go, where users modify the underlying OSM data by adding [fictional map features](https://bit.ly/2EjFSnD) (e.g. parks, footpaths and lakes) to gain benefits in the game. OSM’s vulnerability to vandalism is often considered one of its drawbacks directly related to data quality. Despite this and other negative effects on the OSM project, carto-vandalism [1] has only been addressed sporadically in the literature. One study identified motivations behind such actions [2], while some other studies characterized different types vandalism based on investigations of community forums and mailing lists [1] and documented cases of vandalism [3]. According to Linus’s law, the collaborative nature of OSM ensures that vandalism will be discovered and corrected [4]. However, it is unreasonable to expect that all harmful contributions will be found by community [5], therefore, automatic detection of vandalism with rule-based methods is of interest [3, 6]. The OSM community also developed a set of tools to battle vandalism. Using Pokémon Go as an example, this study focuses on the nature and life-cycle of harmful edits with an emphasis on the OSM community's response. Based on OSM changeset comments and discussions, the study first identifies Pokémon related vandalism together with changesets that fixed them or reverted them. This duality allows to study not only the act of vandalism itself, but also the community's response. By analyzing changesets committed by Pokémon Go players, the presentation describes in detail what kind of fictive information these players tend add. A better understanding of this will allow to develop more targeted rules for vandalism detection systems. However, it is important to note that not all Pokémon Go players vandalize OSM and some of them are probably valuable members of the OSM community. It is less obvious, though, if those who started their OSM careers as vandals can be converted to real contributors. As OSM relies heavily on its community, this study seeks empirical evidence of vandals converted to OSM contributors. This would help utilizing OSM's increased visibility to engage and retain more contributors. Vandalism directly affects data quality, therefore we provide a first description of the life-cycle of carto-vandalism analyzing a large pool of events and considering both spatial and temporal constraints. Our initial data analysis shows more than 1,000 revert changesets in response to Pokémon Go vandalism. Since OSMappers often revert more than one changeset at once (e.g. https://bit.ly/2YC7U5f), this number corresponds to more vandalism cases in reality. This provides sufficient amounts of data for our investigation. [1] Ballatore, A. (2014). Defacing the map: Cartographic vandalism in the digital commons. The Cartographic Journal, 51(3), 214-224. [2] Coleman, D., Georgiadou, Y., & Labonte, J. (2009). Volunteered geographic information: The nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research, 4(1), 332-358. [3] Neis, P., Goetz, M., & Zipf, A. (2012). Towards automatic vandalism detection in OpenStreetMap. ISPRS International Journal of Geo-Information, 1(3), 315-332. [4] Haklay, M. (2010). How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and planning B: Planning and design, 37(4), 682-703. [5] Goodchild, M

The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been developed further significantly. This talk will give an update on the newest developments and the emerging community around the application. The Tasking Manager is OpenStreetMap’s most used software tool to organize mapathons, community mapping initiatives and professional mapping teams. Over the last year it has been further developed significantly. This talk will give an update on the newest developments and the emerging community around the application. Based on tales of data storytelling representatives of the development team of the Tasking Manager are going to discover remarkable curiosities and insights out of a wider application and user analysis, of course backed by hard stats. From this starting point we explain the solutions we implemented in Tasking Manager in the last year to improve the user experience and increase the data quality by mappers using the tool. Three stories will be told, about group of users that are coming to the Tasking Manager frequently and how the changes affected their flow. New mappers, validators and project creators are the main actors of the exciting adventures of combating a low beginners retention rate, lifting data relicts, guiding a mapper through the labyrinth of OSM contribution and learning about the magic of people’s using technology to achieve what they want. We will talk about the new ingredients of the Tasking Manager, from the redesign of the users interface and the interaction among them, to how it now fits into a wider spectrum with much more applications of the OpenStreetMap ecosystem in order to improve direct access to more efficient mapping and data validation. about this event: https://pretalx.com/sotm2019/talk/7Q97AH/

OpenStreetMap enables people to use third-party apps that seem to be more suitable for privacy-conscientious users, but are we as users really private when using OSM-based apps? In mobile applications users generally don’t have control over what gets transmitted over the network. This means that people even in the age of the GDPR are not aware what they expose about themselves when using Android or iOS apps. This talks goes through a few popular open and closed source OSM apps and shows what gets transmitted about you and to whom. Additionally, it shows whether removing ads through in-app purchase gives users of those apps more privacy. Furthermore it will be demonstrated which tracking services are most often used and which risks of de-anonymization could arise from this. Also a brief discussion whether and how software developers can make their software more privacy-friendly will take place. Are users aware which data gets sent to third parties and are they consenting? Do they have a choice? And is this tracking generally bad or can it improve user experience in return? Are there apps which are suitable for people who want to stay private? Is it possible to prevent tracking and how? This talk is going to compare several OSM apps in regards to their privacy levels, but also show how closed data, commercial competitors are performing. about this event: https://pretalx.com/sotm2019/talk/7LMH8R/

In this talk we try give an overview on how to use GraphHopper to provide a more flexible routing (based on weather information, road class, road width, ...) and how this could be also used for visualization purposes or data analysis. In its first version 0.1 the open source GraphHopper routing engine was able to store just the distance and the car speed and access for every road. Since then many things have changed and improved in version 0.12 and beyond more data can be stored even without knowing Java or GraphHopper internals, but still the storage of those properties is done efficiently. A world wide graph with some basic useful road properties like highway, toll, tunnel, bridge, ferry, width, height, surface, maxspeed and access fits into a routable graph of under 25GB, i.e. just 60% of the planet PBF. GraphHopper allows you to keep this either in memory if you need high speed or serve the graph from the hard drive (incl. cache) to keep the costs low or use your development laptop. Developers and data analysts are enabled to store more features while preserving fast data access. The advantage of the graph-based storage of GraphHopper over a usual database is that the road connectivity can be directly exploited. This is a work in progress and we'll show what is already possible to provide a more flexible routing, data analysis and in-browser visualization with Leaflet. E.g. for routing purposes so called "what if" scenarios can be done to show the impact of a bridge construction via isochrones or reachable areas and also potential problems for HGV vehicles can be outlined. about this event: https://pretalx.com/sotm2019/talk/P8AG7K/

Wie ist der Stand der IT-Sicherheit bei Verbraucherprodukten? Wie könnten europaweit verbindliche Vorgaben zur IT-Sicherheit gemacht werden? Wie lässt sich die IT-Sicherheit eines Produkts transparenter machen? Informatikerin Anja Hirschel und der Europaabgeordnete Patrick Breyer schlagen ein Bewertungssystem zur IT-Sicherheit von Produkten vor und haben einen entsprechenden Antrag eingereicht. Wie nützlich wäre eine „IT-Sicherheitsampel“ (ähnlich Ernährungsampel) oder bestimmte Icons, die klar zeigen, ob ein Produkt aktualisierbar ist, verschlüsseln kann usw.? Einreichungstext des Forschungsprojektes: When buying goods with embedded digital technology, like smart products (e.g. connected cars, mobile phones, 'Smart TVs' or any other ‘smart’ products that make up the Internet of Things), which IT security features are to be subject to the contract? The answer should be clear for the consumer. With the Internet of things, 'smart' devices start affecting the world in a direct and physical manner (e.g. car technology). IT devices that are insecure and vulnerable to integrity and availability threats increasingly risk our lives and property. Consumers will get more and more familiar with the digital world, and in particular with 'smart' goods. Such growing digital literacy will favour the demand for easy access to more detailed information about smart goods and about how to facilitate their use. The Pilot Project will aim to make the new 'Digital Contract' rules easily readable for consumers thanks to the development of an IT security rating system for smart goods. This IT rating system could for instance consist in 'traffic lights' or icons that would show whether a device will be automatically updated, whether encryption will be applied to stored data, or other security features. This information will trigger the consumer's rights and the manufacturer's liability. According to the Digital Content Directive, suppliers of digital goods and services will have to provide updates to smart goods, which is not just important to make them function longer, but also to increase cybersecurity. The Directive provides for objective requirements for the conformity of the goods and services, including performance features such as those related to security, which the consumer may reasonably expect. Thanks to the rating system in 'smart' goods, consumers will for instance know whether such updates happen automatically. In order to foster EU innovation in the highly competitive field of the Internet of Things (IoT), the European industry needs to attract EU consumers with consumer friendly features in the development of their products. The legal protection of consumers, and the legal certainty about such protection, are key in developing future markets and make the EU compete worldwide, while keeping high level EU standards of consumer protection. Defining a common set of standard rules to rate smart goods and their contractual mechanisms could be an asset for European SMEs wishing to make their products consumer friendly. This can also support the EU-level development of 'legal design' tools on contract rules to be further developed by industry players in the field of IoT products, in partnership with lawyers and data protection experts. JUSTIFICATION: The European legislator has endeavoured to bring clear legal solutions for consumers, especially when buying 'smart goods', with a Directive on Contracts for the Supply of Digital Content and Digital Services, and with a Directive on the Sale of Goods, both adopted in 2019. However, practical solutions are needed to make sure that consumers can identify and compare the IT security features of 'smart goods' and exercise their contractual rights in this respect. about this event: https://datenspuren.de/2019/fahrplan/events/10437.html

Lightning Talks Lightning Talks about this event: https://pretalx.com/sotm2019/talk/KMP9X7/

We are working to map all the solar panels (photovoltaic, "PV") in the world. Why? The data can be used directly to reduce carbon emissions from power generation. We will share our experiences of surveying, aerial mapping and machine vision to find all the hundreds of thousands of solar panels in our countries. Together with a small group called OpenClimateFix, we are working to map all the solar panels (photovoltaic, "PV") in the world. Why? Because if we combine this with short-term forecasting of cloud and sunshine, we can directly predict the solar power electricity generation ahead of time. This means we don't need to burn as much coal or gas as backup. So, it can be valuable to know the exact location and characteristics of each solar PV installation - large solar farms, and small domestic installations. In this talk we'll discuss how this all fits together. We'll talk about solar power tagging/mapping in OSM, for both large and small, to make it easy for people to map but also useful for the power network analysis. We'll also share our explorations of crowdsourcing and automation machine vision, and the use of other data sources such as government open data to guide the mapping process. We have already mapped a significant portion of the UK's solar capacity, and trialled some crowdsourcing and machine vision tools. We will show visualisations and analyses of the work that's been done so far, and consider how to scale this worldwide. about this event: https://pretalx.com/sotm2019/talk/EBHGTW/

In this talk, large scale data production events in OSM are identified, characterized, and their spatio-temporal patterns and impacts are analyzed. The results show that remote mapping events produce more data today than bulk imports, yet that the former type has a more lasting impact on representation, hence pointing towards possible steps for maximizing the positive influences of events of different types. Volunteered geographical information often visions data as a product of individual actions. In OpenStreetMap (OSM) however, contributions are frequently made as part of large-scale data production events. These events, which can take multiple forms (e.g. organized activities of local chapters, mobilization of global communities, and imports of externally collected datasets), do contribute much to the OSM project. Nevertheless, they also hold the potential to significantly affect local representations by changing the development course of data and community, thus biasing representation. Hence, it is important to identify and understand such events, as well as their impacts upon the data. This talk sets out to contribute to the study of these issues by identifying large-scale data production events in OSM, classifying them, and analyzing their spatio-temporal patterns and impacts. For this, we use the OSM History Database (OSHDB) tool to extract the cumulative number of contribution operations (i.e. the operations made as part of each contribution) by month for different areas. Assuming that in the absence of events the cumulative distribution of monthly operations over time would follow an S-shaped form (since data grows constantly, and even more so when the community grows, until it reaches some form of saturation), we fit a logistic curve to each of these time series. Events are identified as months where observed values are significantly higher than the ones predicted by the fitted curve. Thus, events are defined not only in terms of their absolute size but also according to their relative weight in the development of the data. In the subsequent step, events are clustered into types according to different measures, e.g. the maximal number of contributions made by one user and the share of creations, deletions, tag changes, and geometry changes in all contributions, representing their nature in terms of centralization and contribution themes. The results show that a significant share of all OSM contributions is made as part of an event, with some data regions almost entirely dominated by these. Furthermore, it does not seem that the number of event contributions is decreasing over time. Looking deeper into the nature of events, we identify two different event types based on the contribution of individuals – local events and remote mapping events – and several bulk import event types, diverging mostly in the share of creations in the events’ contributions. Computing the number of events over time shows that while data creation imports were the most frequent type of events early on, over the last years remote mapping events are contributing the most data. Locally based events also show a significant increase in data production. However, these types of events are not distributed evenly across the globe, with import events frequent mostly in countries with developed economies and remote mapping events being more common in the least developed regions of the world. Interestingly, the negative (and expected) correlation between the time of the event and its impact on the data exists only for import events and not for remote mapping events. Hence, mapping and analyzing large-scale events allows relating the nature representation to socio-economic effects. This talk further breaks down the spatio-temporal patterns of events, investigating whether the temporal patterns for different regions follow the global ones or are there clusters of temporal change as well. Furthermore, we study the nature of events’ impacts, presenting how the values of measures such as the stability of events’ contributions and change in the number of active mappers vary by event type and area. These results, beyond promoting a deeper understanding of events and representation in OSM, allow assessing the implications for the project of current and expected trends in OSM data production, hence facilitating the formulation of global and local steps aimed at maximizing events’ positive impacts and controlling their adverse influences. about this event: https://pretalx.com/sotm2019-at/talk/9NVD77/

OpenStreetMap provides a lot of valuable information about urban green spaces, but the numerous and conceptually overlapping OSM tags that describe such features lead to spatially heterogenous representations in OSM. We developed an exploratory data analysis methodology to identify locally relevant OSM tags for mapping green spaces in a specific area and compared the extracted OSM features to administrative data to evaluate the level of completeness in regard to urban green spaces. Urban green spaces provide a variety of important ecosystem services such as micro-climate regulation, increase of biodiversity and the provision of recreational and cultural services for citizens. Thus, they are an important factor for the quality of life in cities (Bolund and Hunhammar, 1999). However, in order to take advantage of these services citizens need to have sufficient information about the location and qualities of nearby green spaces. Within the project “meinGrün” we are addressing this issue by developing a web-based recommendation service which helps citizens find suitable green spaces that satisfy their personal needs. OpenStreetMap (OSM) plays an important role in this project, since it provides a lot of valuable information about urban green spaces such as their location and the amenities they provide (playgrounds, benches, toilets etc.) However, its spatially heterogeneous data quality, especially in regard to the level of completeness, provides challenges for its usage in a recommendation system. Therefore, the integration of OSM data for our purposes requires a prior assessment of the completeness of urban green spaces. The completeness of certain geographic objects is one of the main fields of investigation in regard to OSM data quality. In recent years several studies investigated the completeness of OSM data with respect to the road network (Barrington-Leigh and Millard-Ball, 2017), buildings (Hecht et al., 2013) or land use features (Jokar Arsanjani et al., 2015). Urban green spaces, on the other hand, were rarely the focus of completeness studies. Ali et al. (2016) developed a method to quantify the plausibility of vegetation-related tags being assigned to specific OSM features and Lopes et al. (2017) evaluated the potential of OSM for extracting information about natural local climate zones. Since both of these studies do not explicitly address the completeness of urban green spaces, we developed a new methodology for this purpose. In contrast to buildings and highways, this poses unique challenges due to the variety of vegetation-related OSM tags and the many different forms of urban vegetation ranging from large parks over private gardens to roadside greenery. OSM tags that describe natural objects are numerous and sometimes conceptually overlapping e.g. some features could be tagged as leisure=park or leisure=garden. This leads to different representations of urban green spaces in OSM across different geographical regions. Defining one set of relevant OSM tags to measure the completeness of urban green spaces that can be applied everywhere is therefore not possible. To solve this issue, we developed an explorative data analysis methodology based on OSM and satellite imagery to identify locally relevant OSM tags that indicate urban green spaces. The analysis is based on statistical and graphical methods to evaluate the association between a certain OSM tag and the presence of vegetation. After the relevant tags have been identified, features representing green spaces are extracted from OSM and compared to an administrative data set to assess the level of completeness. As a basis for this comparison, the study area is divided into patches of homogenous land use based on natural and human-made barriers such as the road network, rivers or objects that mark changes in land use (fences, walls, etc.). On this basis, features from both data sets are joined and the level of completeness is assessed using different extrinsic data quality measures. In our talk we will present our methodology along with the results of the completeness assessment for the City of Dresden, which is a pilot city of “meinGrün”, a project funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI). References: Ali, A., Sirilertworakul, N., Zipf, A., Mobasheri, A., 2016. Guided classification system for conceptual overlapping classes in OpenStreetMap. ISPRS Int. J. Geo-Inf. 5, 87. Barrington-Leigh, C., Millard-Ball, A., 2017. The world’s user-generated road map is more than 80% complete. PLOS ONE 12, e0180698. https://doi.org/10.1371/journal.pone.0180698 Bolund, P., Hunhammar, S., 1999. Ecosystem services in urban areas. Ecol. Econ. 29, 293–301. https://doi.org/10.1016/S0921-8009(99)00013-0 Hecht, R., Kunze, C., Hahmann, S., 2013. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int. J. Geo-Inf. 2, 1066–1091. Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., 2015. Qualit

Von Firewalls und VPNs hat wohl jeder schon gehört - jedoch sind die technischen Details häufig hinter GUIs versteckt, so dass man von den eigentlichen Abläufen dahinter nur wenig sieht. In diesem Vortrag wollen wir auf zwei neue Technologien in Linux näher eingehen: dem iptables-Nachfolger nftables und dem modernen WireGuard VPN-Tunnel. Wir greifen verschiedene Implementierungsdetails auf und zeigen, was die Unterschiede und Vorteile gegenüber Alternativen sind. Gerichtet ist der Vortrag an alle Interessierte, wobei Linux-Admins besonders für die Anwendung in der Praxis profitieren können. Der Vortrag wird in Kombination mit einem darauf folgenden Workshop angeboten. about this event: https://datenspuren.de/2019/fahrplan/events/10399.html

There are 250 million refugees and IDPs in informal settlements that are routinely excluded from population and settlement datasets as well as Sustainable Development Goals (SDGs) assessments. Here, we share results from ongoing research to map and assess SDG indicators at global informal settlements using OSM data and satellite imagery. We present a new OSM-driven schema for monitoring SDG progress that counters the exclusion of informal settlements from other assessments. In 2015, the United Nations introduced 17 Sustainable Development Goals (SDGs) and 169 associated targets to be met as part of its 2030 Agenda. One hundred and ninety three countries declared their commitment to “leave no one behind” in the shared pursuit of SDGs, yet 250 million people around the globe are estimated to be missing from SDGs progress assessments. This presentation focuses on one category of the “Missing Millions”: refugees and internally displaced people (IDPs) living in informal settlements in over 60 countries. In part due to the lack of reliable information on the locations of informal settlements, refugee and IDP populations are systematically excluded from national censuses, representative surveys, global settlement and population data sets, and settlement-level SDG progress assessments. If implementation and monitoring of SDG goals are to be truly inclusive of these highly vulnerable populations, a globally systematic, accurate, and open database of informal settlement locations is required. In order to locate and characterize informal settlements across the globe, we have used multi-sensor satellite (i.e. Landsat, Sentinel-2, and Planet Dove) imagery, machine learning pattern recognition, and crowd-sourced data collection through visual interpretation of high resolution satellite imagery. With informal settlements identified and using Uganda as a relatively data-rich case study, we developed a spatially discrete and temporally informed settlement-level schema for monitoring SDG initiatives and evaluating progress since 2015. To do so, we examined the distribution of OSM tags relevant for SDG goal indicators, built a database of unique OSM flags for specific SDG indicators, identified relevant OSM features within and nearby informal settlements in Uganda, measured when each feature was first added to the OSM database, and assessed the variability in SDGs progress across a global sample of informal settlements. The presence of SDG-relevant OSM features at some informal settlements may reflect past humanitarian or development mapping, while other informal settlements with less attention in OSM may lack any SDG-relevant OSM features. Since we also sought to assess SDG indicators purely through satellite image analysis, we measured the agreement and complementarity of various remotely sensed measurements of SDG indicators and OSM features. Amenities or public services represented in OSM data such as the designation of a building as a school or health center, and sites of portable drinking water have been essential to formalizing the relationship between satellite-derived estimates of SDG proxy indicators and SDG-relevant features in OSM since these features are not readily identified with satellite imagery and thus are complementary to satellite image-derived indicators. Finally, with an eye on scaling up our assessment to the global-level, we evaluated the relevance of the Uganda-based SDG schema for other geographic regions and specific informal settlements. The results of this research highlight the value of fusing place-specific OSM data with spatially expansive satellite imagery for assessing progress toward specific SDGs. Further, the analytical framework developed for this project may inform future OSM volunteer campaigns to map and evaluate settlement attributes of vulnerable populations or help guide development practitioners and programming. Many challenges associated with the considerable variability in OSM tags within and between informal settlements remain for developing a settlement-level schema for assessing SDG progress using OSM data. Further, the absence of SDG-relevant OSM features across so many informal settlements highlights the need for a rigorous assessment of potential bias in settlement-level OSM data availability. Future work thus includes a cross-validation between remote sensing and OSM-derived SDG indicators at informal settlements as well as field-based assessment of the settlement schema. Results from this effort will inform how on-the-ground contributors and global end users of OSM data can adopt the proposed settlement SDG schema to better align data collection for SDGs assessment. about this event: https://pretalx.com/sotm2019-at/talk/LKLEWQ/

OSM Teams is a software framework for building team-based applications on top of OpenStreetMap. We will present how the software is built, why we think it's a good tool for communities, and how you can integrate your application with the framework. OpenStreetMap is first and foremost a community platform. A lot of OSM mapping emerges from grassroots collaborations, from local neighborhood communities to large-scale mapping initiatives. These collectives need tools to communicate, collaborate and sustain their combined motivation around mapping, and to that end we present OSM Teams. OSM Teams is a software framework for building team-based applications on top of OpenStreetMap. Development Seed built Teams internally to coordinate mapping projects, and to share tasks and statistics across mapping applications built on top of OSM. By building an authentication and authorization framework on top of the OSM login, we enable a second identity layer for teams that can be shared throughout apps. If adopted by the wider community, OSM Teams would bring structure to organizational editing. Teams would also support individual mappers by empowering them to discover new communities and causes, and to better understand who else is editing the map. With this presentation, I would like to start a technical and community focused conversation about running organizations, teams, and communities using this framework. Specifically the questions I would like to bring to the community are: how does this fit with existing community patterns? How do we reduce complexity for new mappers? What new application possibilities does this open up? about this event: https://pretalx.com/sotm2019/talk/XHGBU7/

<p>... ein gesellschaftliches und solidarisches Patch <br> <strong>TL;DR</strong><br> Was können wir in einer Umgebung der Anpassung tun, um kritisches Bewusstsein zu fördern und die Abhängigkeit von Expert*innen zu mindern?</p> <p><strong>Ausgangslage:</strong><br> Staatliche und Konzerninteressen prägen längst die Art, wie wir Technik nutzen und Medien konsumieren. Es wird uns leicht gemacht, uns mit den vorherrschenden Verhältnissen zu arrangieren. Spaß am Gerät und Medien, reichen heute nicht mehr aus, um auch kritisches Bewusstsein zu fördern und aus der Konsumentenrolle auszubrechen!</p> <p><strong>Inhalt des Vortrags:</strong></p> <ul> <li>Warum finden sich so viele Menschen mit der <s>unterdrückten</s> angepassten USER-Rolle ab?</li> <li>Was können wir gegen die vorherrschende Passivität und Resignation machen?</li> <li>Wie können wir die Abhängigkeit von Expert*innen mindern?</li> <li>Wie können wir gezielt ein <strong>kritisches Bewusstsein</strong> fördern, das auch zu einer <strong>kritischen und solidarischen Praxis</strong> führt?</li> <li>Warum sind wir in der Gefahr, <strong>selbst zu Unterdrücker*innen</strong> zu werden?</li> </ul> </br></br> <p><strong>Anschließend Meetup für Fragen/Diskussion:</strong><br>14:15 im Seminarraum</p> about this event: https://datenspuren.de/2019/fahrplan/events/10450.html

JOSM is almost as old as OSM but few people really know what it takes to maintain your preferred editor. We'll present the development model of JOSM and who's part of its active community: developers, translators, testers, plugin authors, end users, sponsors, etc. We'll talk about the project difficulties, the major achievements made in the past years, what work is currently in progress and what will happen in the near future! JOSM is almost as old as OSM but few people really know what it takes to maintain your preferred editor. We'll present the development model of JOSM and who's part of its active community: developers, translators, testers, plugin authors, end users, sponsors, etc. We'll talk about the project difficulties, the major achievements made in the past years, what work is currently in progress and what will happen in the near future! about this event: https://pretalx.com/sotm2019/talk/88ZHKQ/

Hacker tüfteln mit verspielter Hingabe. Hacker machen Angst, indem sie ihr Wissen für Machtspielchen oder zur Machtdemonstration nutzen. Hacker mit entsprechenden ethisch moralischen Grundmoralvorstellungen klären auf, damit kein Raum für Machtspielchen bleibt. Mit diesem Vortrag möchten wir speziell bei funkbasierten Zutrittssystem aufklären. Wir zeigen anhand eines Beispiels im 900MHz-Band Signalanalysen und was man bei der Funkkommunikation beachten sollte. Dazu verwenden wir Software Defined Radio (SDR). Darin werden komplexe Verarbeitungsschritte in der Signalverarbeitung mittels Software gelöst. Das ist zwar langsam, bietet hingegen unglaubliche Flexibilität. ? about this event: https://datenspuren.de/2019/fahrplan/events/10441.html

The project created from the cooperation between GeoLab (Universidad de Antioquia), and SiCLas (group of cyclists), both present in Aburrá Valley (Colombia), proposes a collaborative mapping by bicycle users as an urban transport mode. The data generation from existing cycling infrastructure will allow an improvement of OSM database, and an optimization in route calculation. In addition, the incorporation of surveys will allow the generation of thematic maps, such as the association of gender with mobility. The cities of the Aburrá Valley, with Medellín as a core city, present a network of bike paths, and the theme of sustainable and non-motorized mobility is gaining increasing importance in public policies in the region. Meanwhile, everyday cyclists, who use the bicycle on a daily basis as a mode of transport, know that this cycling infrastructure has lacks in several aspects, such as a fragmented presence in the territory, non-direct routes, among others. In addition, the cycling infrastructure in OpenStreetMap is not completely mapped or has erroneous information, this represents a problem for cyclists who wish to transport themselves through the city and identify the most appropriate route to their destination. It does not have accurate and relevant information for users such as the location of bicycle parking, bike-share stations and multimodal stations, among others. These reasons represent a disincentive for more people to choose the bicycle as a mode of transport. For these reasons, this project aims to map the existing formal cycling infrastructure, urban transport routes used by cyclists, to optimize route calculation, and aims to create a platform that allows the visualization of safety data, infrastructure, environmental problems (air quality) and gender mobility, among others, in which the information of shared eventualities can be collected and uploaded by the users. From the cooperation created between the GeoLab research group of the University of Antioquia – YouthMappers chapter, and the SiClas group of cyclists, both entities present in the Aburrá Valley, a considerable number of cycling activists will be mapping the cities of the valley with the OSM platform and tools open. The discussion of the existing tags in OSM for the mapping of cycling infrastructures is a topic of interest in the project, and can contribute to a discussion of the creation of new tags in OSM. The status of current information will be verified and corrected and the missing routes mapped. The tools to generate the thematic maps will be KoBo Collect, Mappillary, among others. about this event: https://pretalx.com/sotm2019/talk/KATR7E/