Chaos Computer Club - archive feed

TVLuke hat in den letzten Jahren viele Häuser im Flat-Design erstellt. Ein paar einfach Grundregeln, wie man sowas einfach umsetzen kann stellt er in diesem Video vor. Ein Vortrag im Rahmen der Fünf-Minuten-Termine. Die Idee ist einfach: An jedem vierten Mittwoch des Monats nutzen wir das volle Haus zum Open Space / Chaostreff und jede Person, die möchte, kann kurz vorstellen, was auch immer ihr vorschwebt. about this event: chaotikum.org

Beim Fotografieren des Nachthimmels muss man verschiedene Faktoren beachten, darunter die Belichtungsdauer und die ISO. Um dennoch leuchtstarke Fotos der Sterne zu erhalten kann man Stacking nutzen. Fabi erklärt, was das ist und wie das geht. Ein Vortrag im Rahmen der Fünf-Minuten-Termine at Home. Die Idee ist eigentlich einfach: An jedem vierten Mittwoch des Monats nutzen wir das volle Haus zum Open Space / Chaostreff und jede Person, die möchte, kann kurz vorstellen, was auch immer ihr vorschwebt. Diesmal wegen Corona-Krise von Zuhause. about this event: chaotikum.org

Maps! Maps! about this event: https://2020.stateofthemap.org/sessions/VTDQPZ/

Surprise, surprise! Surprise, surprise! about this event: https://2020.stateofthemap.org/sessions/7LWKCA/

Walkability is the foundation for urban life that is sustainable, inclusive, healthy, and dignified. Pedestrians First is a new open-source suite of tools for using OSM data to measure indicators of urban walkability. During this talk, we will examine the nature of walkable and unwalkable cities, we will discuss the opportunities and limitations of using OSM to measure walkability, and we will consider possible avenues for extending Pedestrians First in the future. Few modern cities are walkable. Around the globe, cities have been designed for cars rather than pedestrians, resulting in enormous costs to the environment, the economy, and even the social fabric of our communities. In the face of the climate crisis, there is an urgent need to build cities that are sustainable and inclusive. Responding to that need, the Institute for Transportation and Development Policy has released Pedestrians First, a tool that supports planners and decision-makers by using OSM to measure what matters for walkability in cities. This talk will describe what makes a city walkable, discuss the experience of using OSM to measure walkability, and explore possibilities for improving that measurement in the future. Because the experience of walking is so natural, it is easy to assume that planning walkable cities is equally simple. That is far from the case. To be walkable, a city must prioritize pedestrians not only in urban design but also in land use and transportation planning. Some factors, like sidewalk quality, matter at the small scale, the level of the street or the city block. Other factors matter at the level of the neighborhood, such as the proximity of destinations, and still others are at the level of the entire city, like the ability of a public transit system to move pedestrians from one neighborhood to another. We have found OpenStreetMap an invaluable tool in identifying and measuring the most important elements of walkability. Certain aspects of the OSM data model have been critical to the success of the project, especially its emphasis on representing connectivity, its typology of streets, and its relative uniformity around the world. However, other aspects have been challenging to work with, and prevent us from measuring other important indicators of pedestrian-friendliness. For example, it is still difficult to clearly represent the presence and quality of sidewalks in OSM and assessing the safety of street crossings remains a pressing concern. There are many opportunities to expand Pedestrians First. We are exploring possibilities that include computer vision to identify street assets, integration of traffic data, and measurement of multimodal access to opportunities. We hope to join conversations about how best to implement such approaches within the OpenStreetMap community. With offices in Brazil, China, India, Indonesia, Kenya, Mexico, and the United States, the Institute for Transportation and Development Policy has advocated for sustainable urban transport since 1985. A non-governmental organization striving for inclusive cities, ITDP is dedicated to openness of information and techniques. about this event: https://2020.stateofthemap.org/sessions/RANPLK/

A summary of trademark law basics and an explanation of the OSMF Trademark Policy and how it applies. The purpose of this talk is to give those interested a foundational explanation of what trademark law is and how the OSMF Trademark Policy works. There have been several questions raised to LWG regarding the OSMF Trademark Policy and how it applies in certain circumstances. Trademark law is often conflated with other types of intellectual property law and the scope of trademark rights is not well-understood. In addition, the LWG sometimes fields questions from OSM users or groups on how to use the Trademark Policy and forms and templates mentioned in the policy. This talk will go over some common questions and give the audience a chance to ask more. Outline for the talk: Trademarks 101: What is a trademark? (vs other forms of intellectual property, like copyright, data rights, patent, trade secrets, publicity rights) How do you get trademark rights? - Trademarks are jurisdictional - Acquiring rights via usage - Trademark registration Trademark use vs not trademark use - Using a term "as a mark" - Sponsorship/endorsement - What trademark doesn't protect - fair uses OSM trademarks: - The OSMF Trademark Policy - Common uses - Pre-policy use cases - Domains - Using the permission template Questions! about this event: https://2020.stateofthemap.org/sessions/JYWQX3/

Today, nearly 17% of the global road network was last edited by a corporate data-team member. We further investigate unique editing patterns among three corporations that have specific, localized impacts on the map. **Introduction** OpenStreetMap (OSM), the largest crowdsourced geographic database has garnered interest from corporations over the last four years. Today, major corporations including Apple, Facebook, and Microsoft have dedicated teams contributing to OSM. More than 2,300 OSM editors are associated with corporate data teams, up from approximately 1,000 in 2019 [1]. As of March 2020, nearly 17% of the global road network (measured per kilometer) was most recently edited by a corporate data-team member. Each corporation edits according to their own agenda; displaying unique patterns of edits with respect to types of features edited, mode (manual, import, or machine assisted), and locations and volume of edits. **Aim** We investigate the unique editing patterns associated with three corporations: Grab, Digital Egypt, and Tesla, the latter two’s editing activity has never previously been quantified. Differing from other corporations with high volumes of global editing [1], these corporations exhibit uniquely specific patterns. **Methodology** We use a combination of OSM data processing pipelines including tile-reduce, osm-qa-tiles, and osm-interaction tilesets [2] to extract and quantify the edits associated with the corporations. **Results and Discussion** **Grab** is a Singapore-based company active in South-East Asia offering ride-hailing transport food delivery, and payment services. Grab is actively editing OSM data since 2018 and has thus far edited 1.6M features. Grab’s focus on transport related services implies that a navigable road network is a priority. However, topology and navigation restrictions are difficult to encode. Grab dedicates efforts to improve road navigability. In Singapore, Grab has edited over 100,000 turn restrictions, comprising 95% of all turn restrictions in Singapore (and 7% of all turn restrictions globally). This represents a highly focused effort put in by a corporation in a specific place to build infrastructure needed to support their business. Overall, Grab’s efforts of improving data and building a community of editors in South-East Asia is beneficial for the OSM ecosystem. **Digital Egypt (DE)** aims to produce detailed and accurate GIS and Mapping data in Egypt, Middle East and Africa. Active in a part of the world with sparse geographic data coverage, Digital Egypt’s team of 24 mappers works to improve the accuracy of OSM, specifically as it relates to improving address information for improved geocoding in Egypt [3]. As of March 2020, DE has edited more than 2M features, more than 1.7M of these edits involve objects with address tags (e.g. addr:housenumber). These edits comprise 94% of the objects in Egypt that have an address tag. Similar to Grab, DE’s contributions improve the usability of OSM data for everyone. Unlike Grab, DE does not operate services built on top of this data infrastructure. Their hyperlocal focus and dedication to improving data for a country with a dearth of spatial data distinguishes them from other corporations using map data in a product. **Tesla**—an American manufacturer of electric cars—was revealed to be using OSM parking aisle information for their vehicle’s self-driving “summon” feature in a blog post in November 2019 [4]. To function properly, the summon feature requires detailed maps of parking aisles within larger parking lots. These data were relatively sparse, as their utility to the overall map is minimal compared to the actual road network. However, the number of parking aisles added to OSM in North America has increased by approximately 71% in 2018-2020 from an average of 322 ways per day in 2016-2018. While there is no official Tesla data-team that is mapping these features, in the days following the blog post, the number of new OSM editors adding parking aisles within their first day of editing jumped from an average of 1.5 to 10. A single blog post inspired dozens of Tesla fans to join OSM and add new parking aisles to the map, thereby mapping a specific feature type for a narrow purpose. These Tesla owners represent a new generation of hobby and “craft mappers” in OSM. Unlike traditional ‘craft-mappers’ considered to altruistically contribute map data about specific features, the Tesla mappers create data intended to be consumed by a corporation to enhance the experience for a select group of motorists. If looking to categorize these new mappers into prior community labels, their mapping practices have more in common with a “traditional craft-mapper” than a paid, corporate mapper. Grab, Digital Egypt, and Tesla represent three distinct cases of corporations consuming, contributing, and driving further interest in OSM. **References** https://docs.google.com/document/d/183aN5Ph2KGuR6iBE_i8j-mL8h2saH3EYU65WMrZWNsw/

Lightning Talk session ## [Divide and map. Now.](https://wiki.openstreetmap.org/wiki/Divide_and_map._Now.) *Jiri Vlasak* - [Website](https://www.damn-project.org/), [Slides](https://qeef.gitlab.io/talks/damn-lightning-talk/) The damn project helps mappers by dividing some big area into smaller squares that a human can map. ## Mapping Historically Black Colleges and Universities in OSM *Harrison Cole* Historically black colleges and universities (HBCUs) are schools that were established exclusively for Black Americans prior to 1964. As of June 19th, 2020, their campuses are disproportionately undermapped in OSM relative to institutions with predominately white student populations. After putting out the call to map these campuses, volunteers have come together to submit thousands of edits and ensure that the campuses are represented properly. In this talk, I give a brief overview of how far the effort has come, and what still needs to be done. ## DDD123-OSM: 2D and 3D render toolchain *Jose J. Montes* Introducing the toolset we are building to generate 3D models from OpenStreetMap data, used in our under-development "racing in your own city" game, and sharing some pictures and videos. ## OSGeo + OSM *Enock Seth Nyamador* - [Slides](https://enockseth.github.io/sotm-2020-osgeo-plus-osm-lightning-talk-enyamador/) Quick introduction to Open Source Geospatial Foundation (OSGeo) and highlighting projects for working with OSM Data. ## Tasking Manager 4 Tour *Wille Marcel* The new version of Tasking Manager, released on April 2020, has a new completely redesigned interface. This talk is a guided tour on the main parts of Tasking Manager 4. ## Uses of Mapping for Community Care During the Pandemic *Andi Tabinas* How were we able to use maps for community care during the COVID-19 situation in the Philippines? Through this lightning talk, I would like to share about how our volunteer organization, Mental Health AWHEREness, has been using maps and OSM data for community care in the Philippines. ## OpenStreetMap Data Pacific *John Bryant* Interest in open geospatial is growing in Pacific Island countries, where budgets and data availability are big problems, but the OpenStreetMap community in the region is still small. This project aims to introduce Pacific "geo" people to OSM by providing a weekly extract of 14 countries in a GIS-friendly format, bundled with a QGIS project and enhanced with some basic cartography. ## Hikar.js - bringing Hikar to the web *Nick Whitelegg* Hikar is an augmented reality app which overlays OSM ways on the camera feed of an Android device, and was presented last year at SOTM Heidelberg. The past year has seen a surge of interest in the three.js and A-Frame based augmented reality library AR.js, which allows AR apps to be developed for the web. This lightning talk summarises efforts so far to port Hikar to the web using AR.js. ## [Mapathon Keralam](https://wiki.openstreetmap.org/wiki/Kerala_State_IT_Mission) *Amitha K Biju* Mapathon Keralam is being coordinated by the Kerala State IT Mission, with the concept of Let Us Make Our Map. about this event: https://2020.stateofthemap.org/sessions/QYYC9T/

We outline methods for a) extracting the geometry of street blocks in urban centres using OSM and remote sensing data, b) generating approximate cadastral maps of a block given contained building footprints, and c) quantifying residents’ ability to navigate within blocks through topological analysis of cadastral maps. This topological metric, termed “spatial accessibility” and denoted k = 1, 2, 3, ..., determines whether areas of a city are informal settlements, as blocks where k > 2 contain cadastral parcels without direct access to formal road networks. We analysed 1 terabyte of OpenStreetMap data for 120 low and middle income (LMIC) countries. Determinations of whether a neighbourhood is underserviced depends greatly on local concerns, however topological analysis of building footprints in relation streetlevel access offers a pathway to near-universal criteria for determining whether a neighbourhood is potentially a slum or urban informal settlement. With a topological approach, it is possible to determine the number of building parcels a streetblock inhabitant must cross to access a potential services bearing street, i.e. one that enables for example access to emergency services, sanitation and piped water. Focusing on topological invariants allows us to analyse cities without respect to the specific morphology of their street network [1]. To create a global index of these under-serviced neighbourhoods, we extracted open-source data on building footprints and street networks and applied a topological analysis to each extracted street block to characterise the level of spatial accessibility. Using these techniques, we analysed 1 terabyte of OpenStreetMap (OSM) data to create an index of street block geometry, cadastral maps, and spatial accessibility calculations for 120 Low and Middle Income Countries (LIMCs) in the Global South. These results highlight the developing world’s most spatially inaccessible communities, enabling prioritisation of infrastructure investment and further study. The primary data universe for our work involves three entities: 1) administrative boundary polygons, 2) building footprint polygons, and 3) street network line-string collections. The administrative data boundaries were sourced from the Database of Global Administrative Areas, while building footprints and street networks were sourced from OSM. Since OSM does not provide direct downloads, the data under consideration was obtained from a mirror of OSM hosted by GeoFabrik. This data reflects a snapshot of OSM as of July 2019. Starting with the initial road network, the geometry of the street network can be extracted via the well-studied process of polygonization. In polygonization, the self-intersections of the street network are determined in order to define the block geometry. Most GIS software packages offer a polygonization feature (e.g. ST_MakePolygon from PostGIS). In implementation, however, we found a set-theoretic approach to be more stable and performant [2]. By buffering the one-dimensional line-strings comprising the street network with a small buffer radius, we obtain two-dimensional polygons capturing the outline of the street network. We then find the set-theoretic difference between the administrative boundary polygons and the buffered road-network polygon; this renders the negative area between the road network geometry as a collection of polygons. These negative areas are precisely the street blocks we are trying to extract. With each building’s access to the formal road network - or lack thereof - analysed, we can also provide suggestions to the local community about ways to connect each building to the existing road network while respecting existing building footprints. This process, known as re-blocking, is in general, NP-hard. In contrast to previous algorithmic re-blocking efforts involving stochastic graph search [3], we approach the problem using Steiner tree approximations to the optimal road network. To frame the universal access network as a Steiner tree problem, we segmented the building parcel boundaries to create “non-terminal” nodes at each segment boundary, and create a “terminal” node for each building parcel lacking direct street access by placing a node on the non-street parcel boundary closest to the building. We then construct a complete subgraph of all the terminal nodes, where the weight of each edge is the Euclidean distance between each pair of terminal nodes; each existing road segment has zero weight. Solving the minimum-spanning tree problem on this subgraph, and then recovering the original path segments, gives us the optimal new road network. In the short future, we hope to repeat our analysis on denser sources of data on footprints and street network data from other providers (such as private satellite imagery companies). We expect that while OpenStreetMap has good coverage in some areas, we can use data in which footprints are automatically extracted from satellite imager

OSM is more fun and useful with quicker access to fresh data. New web services, tools and file formats enable mappers to download and use edited data within minutes. Workflows for adding data to OpenStreetMap keep getting better - but once an edit is uploaded, it can take a long time to appear on hosted map tiles or regional OSM extracts. The experience of downloading and using OpenStreetMap can be improved by quick access to fresh data - a fast feedback loop makes contributing to OSM more enjoyable! The first part of this talk covers a new web service, Minutely Extracts (protomaps.com/extracts). Mappers can download rectangular or polygon regions of the world, with changes replicated every minute from planet.openstreetmap.org. I’ll show some example workflows for using Minutely Extracts with common GIS applications such as QGIS, and new tools for conversion to formats like GeoPackage and Shapefile. The second part of this talk describes OSM Express (osmx), a new spatially indexed file format powering Minutely Extracts, that supports in-place updates. Developers needing random access to OSM objects can consider embedding osmx as a library. Near real-time editing activity visualization is one possible use case. I’ll review the technical tradeoffs between using osmx, PBFs and other popular formats. Financial Background: The software is developed as open source, under a BSD license, and funded by the author's commercial OSM-based SaaS services as well as OSM contract software development. about this event: https://2020.stateofthemap.org/sessions/JDNTHK/

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

Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. We analyse the history of humanitarian mapping using OpenStreetMap History and OSM Tasking Manager (tasks.hotosm.org) data. We conduct a comprehensive quantitative analysis on a global scale and long term perspective to depict more than just snapshots of individual events. Results show that in regard to edits, users, projects, geographic diversity, almost all of these have experienced linear growth. But regarding user commitment and validation efforts we conclude that the humanitarian mapping community still faces huge challenges to achieve sustainability. ## Introduction Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. In the last few years, several researchers have analyzed humanitarian mapping practices (e.g. Zook et al. 2010, Soden et al. 2016, Dittus et al. 2017), however most of this work was either event-driven or focused on specific time periods and regions. The OSM ecosystem and the actors involved in it are constantly changing and emerging, for instance also due to the mapping activities of corporates (Anderson et al. 2019). ## Purpose of this study In our work we analyse the history of almost 10 years of humanitarian mapping in OpenStreetMap using the OSM Tasking Manager (tasks.hotosm.org). We conduct a comprehensive quantitative analysis on a global scale and long term perspective in order to depict more than just snapshots of individual events. We show how humanitarian mapping was impacted by major mapping disaster response events, but also widened its application to other domains such as disaster preparedness. Our approach follows two paths. One focuses on the mapping itself. The other focuses on the composition of the humanitarian mapping community. We provide insights on the following research questions: 1. Where, for what purpose and at what scale did humanitarian remote mapping actually happen in 2012-2020? 2. What is the impact of humanitarian contributions and how does it differ among user groups and over time? ## Methodology and Results ### Data Our analytical approach is characterized by the combination of the following data sets: (1) For general OSM mapping and user characteristics we use the whole history of OSM object versions and edits provided by the OSHDB framework (Raifer et al. 2019). (2) We were provided with a HOT Tasking Manager database dump covering the time between 11/2012 until 12/2019. ### Method Our method uses spatio-temporal time series analysis based on a hexagonal grid for all areas humanitarian mapping activity has been conducted. We investigate the evolution of humanitarian mapping with respect to several attributes including number of OSM contributions (created, modified, deleted OSM objects), percent of tasks mapped and validated in the Tasking Manager and number of OSM contributors involved. We set our findings and the geographical distribution of our results in context to the overall OSM mapping and user characteristics. ## Results Humanitarian mapping has grown almost linear with respect to the numbers of projects, unique users, tasks mapped and OpenStreetMap contributions. While at the same time it got more diverse in terms of spatial distribution of mapping activities and organizations involved. In 2018 more than 100 organizations were running projects on every inhabited continent except Australia. Peaks in mapping activities in the first quarter of 2015 and the third quarter of 2017 are reflecting activations of the community in responses to the Gorkha earthquake as well as to the hurricanes Harvey, Irma and Puebla earthquake. Although the number of mappers is growing, the humanitarian community lacks long term commitment. Almost 70% of humanitarian mappers contribute one day only. Another 10% drops out after ten days of being active. However the number of one day only contributors is steadily rising, whereas mappers involved on 5 or more days seem to be saturated since 2017. Nonetheless, the one day only contributors have a considerable impact on the data. They account for 10% of all edits, whereas contributors involved on 100 or more days account for 50%. This pattern is reflected by the composition of user actions as well. The number of mapping activities is growing almost linear, whereas the amount of validators stagnate. However, different organisations have varying degrees of success in recruiting new validators. The american red cross for instance accounts for 30 to 50% of all first time validators in the last 12 months. ## Discussion Humanitarian mapping with the tasking manager now exists for more than seven years. In terms of scale it is definitely a success story. We have shown edits, users, projects, geographic diversity, almost all of these have experienced linear growth. But with respect to the numbers of user commitment and val

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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