{"$schema": "https://c3voc.de/schedule/schema.json", "generator": {"name": "pretalx", "version": "2026.3.0.dev0", "url": "https://pretalx.com"}, "schedule": {"url": "https://pretalx.com/state-of-the-map-2022-academic-track/schedule/", "version": "0.9", "base_url": "https://pretalx.com", "conference": {"acronym": "state-of-the-map-2022-academic-track", "title": "State of the Map 2022 - Academic Track", "start": "2022-08-19", "end": "2022-08-21", "daysCount": 3, "timeslot_duration": "00:05", "time_zone_name": "Europe/Rome", "colors": {"primary": "#E23E36"}, "rooms": [{"name": "Auditorium B", "slug": "1599-auditorium-b", "guid": "1a03f98b-1bf7-5d17-bb6a-feb4c6d07bc6", "description": null, "capacity": 252}], "tracks": [], "days": [{"index": 1, "date": "2022-08-19", "day_start": "2022-08-19T04:00:00+02:00", "day_end": "2022-08-20T03:59:00+02:00", "rooms": {}}, {"index": 2, "date": "2022-08-20", "day_start": "2022-08-20T04:00:00+02:00", "day_end": "2022-08-21T03:59:00+02:00", "rooms": {}}, {"index": 3, "date": "2022-08-21", "day_start": "2022-08-21T04:00:00+02:00", "day_end": "2022-08-22T03:59:00+02:00", "rooms": {"Auditorium B": [{"guid": "f418843d-4f15-562b-bf53-7131f9210a76", "code": "RBZHX7", "id": 23290, "logo": null, "date": "2022-08-21T08:55:00+02:00", "start": "08:55", "end": "2022-08-21T09:00:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-23290-opening-session-academic-track", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/RBZHX7/", "title": "Opening Session - Academic Track", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "The opening session of the Academic Track at the State of the Map 2022 conference.", "description": "", "recording_license": "", "do_not_record": false, "persons": [{"code": "3UBLLA", "name": "Yair Grinberger", "avatar": null, "biography": "I am the co-chair of the Academic Track. I specialize in making test submissions.", "public_name": "Yair Grinberger", "guid": "9a9c1f4c-554f-5a44-90dd-91222bc44914", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/3UBLLA/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/RBZHX7/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/RBZHX7/", "attachments": []}, {"guid": "deb26ce9-1a5f-533b-974a-017b4225c69d", "code": "JRN9DN", "id": 19380, "logo": null, "date": "2022-08-21T09:00:00+02:00", "start": "09:00", "end": "2022-08-21T09:20:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19380-increasing-openstreetmap-data-accessibility-with-the-analysis-ready-daylight-distribution-of-openstreetmap-a-demonstration-of-cloud-based-assessments-of-global-building-completeness", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JRN9DN/", "title": "Increasing OpenStreetMap Data Accessibility with the Analysis-Ready Daylight Distribution of OpenStreetMap: A Demonstration of Cloud-Based Assessments of Global Building Completeness", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "A recent release of new scientific datasets generated from OpenStreetMap exemplifies the need for analysis-ready repositories of OSM data that require minimal pre-processing. We created the  Analysis-Ready Daylight OpenStreetMap Distribution to provide researchers with the opportunity for simple cloud-based SQL queries of nearly 1B OSM features. We demonstrate the capabilities with intrinsic and extrinsic data coverage assessments of OSM buildings globally.", "description": "Despite being one of the most open and freely available spatial datasets, OpenStreetMap (OSM) data accessibility remains a challenge. Data accessibility measures how easily end-users can access and use a given dataset for their needs [1]. Because OSM data is intended to be rendered as a map or ingested into routing engines, it is often not easily consumable by data analysts. Pre-analysis workflows require OSM data to be downloaded, parsed, and converted into more common formats, which means that novice end-users of OSM may lack the experience to readily access and use OSM data in decision-making.\n\nIncorporating communities into spatial decision-making processes, such as mapping, is important because a). community members are experts on their communities and b). have a larger stake in final solutions which directly impacts their lived-experiences[2]. OSM empowers a variety of communities, including local governments[3], digital humanitarian groups[4], and even student groups [5], to help navigate and understand places of respective importance.\n\nResearch by Nirandjan et al. recently lowered barriers to using OSM data as a reference dataset of critical infrastructure [6]. After categorizing and quantifying particular types of OSM features, the authors released the data in formats more common in geospatial analysis, such as GeoTiffs [6]. This article\u2019s popularity (ranked 90th percentile on the publisher\u2019s website) demonstrates the importance of making OSM data\u2014and datasets derived from OSM\u2014more accessible by means of familiar data structures compatible with common tools. If OSM data were more accessible for analysis, could we see it used in more geospatial research and innovation at large [7]?\n\nWhile many community-maintained tools exist to convert, extract, and download OSM data, each requires domain knowledge of the unique OSM data structure (nodes, ways, and relations). Furthermore, working at the country or planet-scale requires extensive computational resources. To further lower the barriers to entry for OSM data analysis and extraction, we created the Analysis-Ready Daylight OpenStreetMap Distribution (ARD-OSM). ARD-OSM is published on the registry of open data (RODA) on Amazon Web Services (AWS), where it is freely available to anyone [8]. This dataset containing 1B OSM features is optimized for use with Amazon Athena, a serverless interactive query engine on AWS. Additionally, ARD-OSM has  resolved the OSM data format into common geometries such as points, lines, and polygons. Data also includes pre-computed valuable attributes such as length, surface area, quadkeys, and geographic bounding boxes which are stored as additional metadata. To demonstrate the analytical capabilities of this dataset, next, we will perform a global OSM building density assessment.\n\nBuilding density is a common metric in OSM quality research, often used to assess map coverage and completeness, such as studied by Yeboah et al. [9]. Measuring building density requires counting all of the buildings within a defined unit of spatial analysis. We use zoom-level 11 map tiles to create an analysis grid that encompasses the global built environment in fewer than 1M tiles. Then, we divide the building count by the area of each map tile to obtain the number of buildings mapped per square kilometer.\nSince every feature in ARD-OSM includes the zoom-level 15 quadkey of the map tile in which it exists, we can use a SQL GROUP BY expression instead of a geospatial operator for aggregation. Here is the short query used to count the number of buildings in each zoom-level 11 map tile: \n\n```sql\nSELECT \tsubstr(quadkey, 1, 11) as z11_tile,\n\t \tcount(id) as number_of_buildings\nFROM \tanalysis_ready_daylight \nWHERE tags[\u2018building\u2019] IS NOT NULL AND release = \u2018v1.12\u2019\nGROUP BY substr(quadkey, 1, 11)\n```\nIn May 2022, running in AWS region us-east-1, this query took 15 seconds and cost just USD $0.10. The results of this query show the density of mapped buildings in OSM to be highest in Europe with additional areas of high density where Humanitarian mapping campaigns have been active such as Nepal, South Eastern Asia, and isolated parts of Africa. This is consistent with the findings of Herfort et al. [10].\n\nHow should these densities be interpreted? Do denser regions have higher levels of building completeness in which most or all buildings are mapped? Building density is an intrinsic data quality measure, to further contextualize these findings, we need to perform an extrinsic assessment by comparing our results against an external dataset. A recent study confirmed the viability of referencing population data for building density assessment [11]; and Orden et al. demonstrate a three-step methodology using Facebook\u2019s High Resolution Settlement Layer (HRSL) first requiring both vectorization and spatial aggregation to assess building completeness with respect to population in both the Philippines and Madagascar [12].\n\nBecause the HRSL is also published via RODA [13], it can be easily joined to our results. Once HRSL data is incorporated to obtain a measure of buildings mapped per square kilometer per person, we find that parts of Europe remain in the top tiers of density with the most buildings mapped per person. Nepal and many parts of South Eastern Asia, however, are no longer in the same top tier of map coverage. While there are many mapped buildings, the higher populations of these regions reveals that there are still many areas where the buildings have yet to be mapped. This yields a generally lower level of completeness overall than initially identified, which remains consistent with the findings in [10]. Additionally, parts of the United States and New Zealand actually appear more complete with areas of lower density coinciding with regions of lower population, yielding a higher measure of map completeness than before. \n\nThis case study cheaply and easily reproduced popular methods for both intrinsic and extrinsic data quality assessments of OSM building coverage without needing to download nor pre-process any OSM data. The analysis was done completely in the cloud on AWS using free and open data in RODA. Additional metadata in ARD-OSM enabled the query to run efficiently and cost-effectively. The same methodology can be applied to investigations of any other object type in OSM from hospitals to ice cream shops. We also recognize that ARD-OSM does not solve the needs of researchers looking to work with OSM history data. Other tools such as the OpenStreetMap History Database are better suited for those types of historical analyses [14].\n\nThe release of OSM-based datasets, such as Nirandjan et al. [6], shows a desire for more researchers to  use OSM data. While OSM data is freely and openly available, researchers must take many steps to download, transform, and ingest the data into an analysis workflow. To solve this, we have made ARD-OSM available in RODA. This analysis-ready dataset contains 1B features\u2014nearly every OpenStreetMap object\u2014in common geospatial feature types such as points, lines, and polygons. To additionally aid researchers, features are enriched with additional metadata describing their location and physical attributes such as length or surface area. From a data accessibility perspective, we anticipate ARD-OSM in future research and innovation, curated by a wide range of end-users, to readily integrate OSM data for decision-making processes which bring communities closer together.", "recording_license": "", "do_not_record": false, "persons": [{"code": "LSXXGM", "name": "Jennings Anderson", "avatar": "https://pretalx.com/media/avatars/LSXXGM_6jNdYxL.webp", "biography": "GeoInformation Scientist researching OpenStreetMap as both a map and an online community. I collaborate closely with researchers across both industry and academia to develop OSM research agendas and develop analysis-ready datasets to support researchers in the community.", "public_name": "Jennings Anderson", "guid": "a9b578f6-8aa7-5258-b614-2adaf6c03c3f", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/LSXXGM/"}, {"code": "ZTSLB3", "name": "Timmera Whaley Omidire", "avatar": null, "biography": "Timmera Whaley Omidire is a recent graduate from University of Arkansas, Department of Geosciences and Center for Advanced Spatial Technologies. Her expertise is in geographic information science, spatial decision support systems, environmental science, and environmental justice.", "public_name": "Timmera Whaley Omidire", "guid": "9ba4af6f-14ad-52b8-953f-166126289f43", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/ZTSLB3/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JRN9DN/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JRN9DN/", "attachments": [{"title": "Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/JRN9DN/resources/anderson_omidire_sotm2_UIJNXSN.pdf", "type": "related"}]}, {"guid": "93f68732-6e4e-51fa-95f4-379a85115f35", "code": "JNJKYR", "id": 19556, "logo": null, "date": "2022-08-21T09:30:00+02:00", "start": "09:30", "end": "2022-08-21T10:00:00+02:00", "duration": "00:30", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19556-osm-sidewalkreator-a-qgis-plugin-for-automated-sidewalk-drawing-for-osm", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNJKYR/", "title": "OSM Sidewalkreator - A QGIS plugin for automated sidewalk drawing for OSM", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Sidewalks are a relevant part of the living space in urban environments, but there are still few mapped sidewalks. In recent years, the mapping of sidewalks has grown in importance among the OSM and academic communities. To cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled \"OSM SidewalKreator\" to automatically draw for OSM the geometries of sidewalks crossings and kerb crossing interfaces. Furthermore, the tool gives the user the capacity to control the process. Then, deepen, improve, and increase the amount of sidewalk mapping in OpenStreetMap to improve accessibility and mobility worldwide.", "description": "Sidewalks are a relevant part of the living space in urban environments. The existence of sidewalks and their condition is fundamental to locomotion in general and is critically important in mobility groups such as cyclists, wheelchair users, blind people, the elderly, and children. Also, the displacement along sidewalks can ensure safety from traffic, contributing to the well-being of citizens [1].\nThere is still an open debate about the best way to represent sidewalks in the OpenStreetMap community. Some users claim that they should be represented only as tags of the streets, using compound tags such as \"sidewalk:left/right:surface=*\", arguing that over-representation can pollute the map and create unnecessary complexity [2]. Biagi [3] and many OSM users nowadays [4] have been showing the representation of sidewalks as separated geometries as allegedly their best representation in OSM. There are many listed advantages [4]: crossings may be represented as lines, with the kerb interfaces as points (8 in a regular 4-way intersection); the actual traversing length will be represented (as it will include block corners and crossings); independence from the digitizing direction, as \"left\" and \"right\" may swap if someone inverts the way direction; ease of representation for pedestrian islands. Moreover, some cases cannot be represented correctly using the tag scheme or will need some cumbersome solutions, as it shall represent the portion of the sidewalk that is orthogonally projected from the street. Therefore, if a property is different on both sides, one may need to split the highway into four segments to represent it correctly. There are also other issues, e.g. geometric properties such as the distance from the sidewalk to the street will stay unclear.\nRegardless of the form chosen for representation, there are still few mapped sidewalks. For example, according to Taginfo [5], as of April of 2022, there are approximately 201 million ways with the \"highway\" key, but only 16.8 million (7.61%) are tagged as \"highway=footway\", considering the key \"footway\", there are only 4.8 million (2.45% of 201 million) ways tagged as such (58% sidewalk, 41% crossing), considering the tag \"sidewalk=*\", there are only 2.6 million (1.3% of 201 million) ways tagged. So, considering that most features are located in urban environments [6;7], where the major part of streets may have a sidewalk, the sidewalks are underrepresented in both schemes. Historically, it has been an issue, as[8] showed that in Berlin, only 5.6% of the Highways have a \"sidewalk\" tag, growing to only 8.2% in 2017 [9].\nRecently, the mapping of sidewalks has grown in importance among the OSM and academic [10;11;12;13] communities. For example, the Open Sidewalks Initiative [14]. They are both a community and a project, providing dedicated mapping with an elaborate scheme on how to map in a pedestrian-centered way, but only manually. Drawing sidewalks and crossings is time-consuming and can be error-prone. This effort can be inferred by examining the OpenSidewalk's own Tasking Manager [15], where in the most near-completion project [16], each task has taken 9.4 minutes to be mapped, but 22.7 minutes be validated. Thus, considering just crossing mapping, for the 1046 existing tasks, it will take approximately 163.9 hours of mapping and 395.7 hours of validation. This total encompasses an area of just approx. 6.17 km2, only 0.65% of the urban area of the city of Sao Paulo, for example.\nTo cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled \"OSM SidewalKreator\" [17], which aims to automatically draw for OSM the geometries of sidewalks, crossings and kerb crossing interfaces, along with the basic descriptive tags. This tool gives the user the capacity to control and supervise the entire process. It contains a user-friendly GUI (Guided User Interface) that enables and disables the buttons according to the step in the process. The plugin methodology, encompassing the steps that the plugin runs through are basically: 1) Fetch Interest OSM data (highways and optionally buildings and addresses, when available) from a bounding polygon given by the user; 2) Generate a table for standard widths for the values for the \"highway=*\" tag, to provide widths to highways that have no \"width=*\" key; 3) remove ways that aren't streets, according to a value of width below 0.5m in the table; and split into segments at road intersections; 4) generate the sidewalk geometries based on a per-segment buffer (optionally checking if it doesn't overlaps buildings), dissolve, and a negative then positive buffer to create rounded corners (if wanted by the user) and then finally extract line geometries (inner holes outlines); 6) generate the crossings geometry, using vector that that grows iteratively in a direction perpendicular to the segment or parallel to adjacent segments (according to user's choice), until an intersection with the sidewalks (dissolved as a single multipart geometry) is found, there are also filtering options, to avoid badly generated crossings, such as too long geometries or too close to other crossings; 7) split the sidewalk geometries, since sidewalks properties (smoothness, surface material, etc.) may differ many times in the same block, it can split based on voronoi polygons of building centroids and/or addresses, or with a minimal length, minimal number of segments, only block fa\u00e7ades or don't split; 8) output all features, to a single .geojson ready to be imported at JOSM editor, where more inspection on generated data can be carried out. The plugin also outputs other auxiliary files as a .txt with a pre-filled changeset comment and intermediary files for debugging. The tool's first use test was performed in April 2022 to map the Campus Centro Polit\u00e9cnico of the Federal University of Paran\u00e1, with transportation engineering students, for the Horus Nav Project [18] an open-source tool for route optimization for blind people.\n    The key idea that guides the present work is to provide a tool to help intermediary to advanced users to speed up the tedious task of manually drawing sidewalks, crossings and kerbs. The tool does not intend to be a fully automated (a challenging task [12;13]) one-click solution but always calls the user to check out the results, step-by-step, using the resourcefulness from QGIS and JOSM. After the data importing, the user is also encouraged to carry out a validation project on a Tasking Manager. The present work also advocates for the representation of sidewalks as separate geometries as an ideal way to represent the sidewalk network. However, the tag scheme still can be helpful in some specific situations, such as representing explicitly that some highway does not have a sidewalk on one or both sides. Taking advantage of previously available data, including tag schema, is one of the features that might be included in future releases of OSM SidewalKreator and integrating properly with previously drawn sidewalks and crossing restrictions. Finally, by joining awareness in mapping communities, detailed instructions, and tools for automation, we can deepen, improve, and increase the amount of sidewalk mapping in OpenStreetMap and present a solid foundation for improving accessibility and mobility in cities around the world.", "recording_license": "", "do_not_record": false, "persons": [{"code": "7RKZCJ", "name": "Kau\u00ea de Moraes Vestena", "avatar": "https://pretalx.com/media/avatars/7RKZCJ_ybfGHu2.webp", "biography": "Kau\u00ea de Moraes Vestena, Master in Geodetic Sciences (UFPR - Universidade Federal do Paran\u00e1, 2021), Cartographic and Surveying Engineer (UFPR, 2017) and Technician in Surveying (UTFPR - Universidade Tecnol\u00f3gica Federal do Paran\u00e1, 2012). Currently a Doctoral student in Geodetic Sciences at UFPR, with research focus on Accessibility Mapping with Open Tools and Data. With experience in Terrestrial Mobile Mapping Systems research and extension activities in the area of collaborative mapping.", "public_name": "Kau\u00ea de Moraes Vestena", "guid": "dde20889-e850-5408-8893-d48ab1927a60", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/7RKZCJ/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNJKYR/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNJKYR/", "attachments": [{"title": "Slides_for_presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/JNJKYR/resources/vestena_et_al_sotm2022_4FR9eti.pdf", "type": "related"}]}, {"guid": "decf8c0d-09b1-5527-80d1-f413272b8e94", "code": "YU9JHN", "id": 19388, "logo": null, "date": "2022-08-21T10:00:00+02:00", "start": "10:00", "end": "2022-08-21T10:30:00+02:00", "duration": "00:30", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19388-comparative-integration-potential-analyses-of-osm-and-wikidata-the-case-study-of-railway-stations", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/YU9JHN/", "title": "Comparative Integration Potential Analyses of OSM and Wikidata \u2013 the Case Study of Railway Stations", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "In this work, we present analyses using a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM.", "description": "OpenStreetMap(OSM) is one of the richest and most diverse sources of geographic information. However, it lacks a fundamental property vital for spatio-semantic analyses: hierarchical structure and semantic linkage. OSM provides links to existing knowledge graphs (structured data that conforms to a specific ontology) e.g., via the wikidata=* tags. The usage of these link-tags is currently limited to a small percentage of both OSM and Wikidata objects. Efforts were undertaken to enhance the geographic linking, linking nearby objects of the same type and semantic linking [1-3]. On the side of the hierarchical and semantic structuring of OSM, the WorldKG knowledge graph[4] provides a semantic mapping of a large subset of OSM. While the free and open OSM tagging scheme is a fundamental part of the OSM project that enabled its success, WorldKG overcomes the inherent lack of structure this tagging scheme represents, paving the way for a knowledge-graph integration of the OSM dataset. Still, open knowledge graphs and OSM are not fully integrated. \n\nThe following analyses provide a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM. In this work, OSM is compared to Wikidata, one of the largest open knowledge graph projects from the Wikimedia Foundation that provides structured storage to other Wikimedia projects such as Wikipedia. Wikidata can, in many aspects, be compared to OSM by its community structure, its free and open nature, and simple contribution framework. In this work, the two datasets are first compared in size, data structure, and distribution. Later, we extend our analyses with a community comparison. The presented analyses also examine how two separate online communities with similar interests have evolved.    \n\nGrasping the size of the two projects is a straightforward task and visible on their websites: OSM features around 1 billion elements [5], while Wikidata is much smaller with over 97 million objects, of which approximately 9 million have geographic coordinates. The topic of railway stations was chosen because these objects have a comparable definition and are well represented in both datasets with ca. 130k and 100k elements in OSM and Wikidata, respectively, indicating integration potential. In OSM, railway stations are mapped by the tags 'railway=station' or 'railway=halt'. In Wikidata, the 'instance of (P31)' property containing 'Q55488' value represents Railway Station (object type).    \n\nBy defining generalizable comparison indicators, the presented work provides a framework and source code (available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-wikidata-comparison under the GNU Affero General Public License v3) for VGI project description, comparison, and monitoring. Similar approaches have been established for OSM contributors [6], for single OSM elements [7], and for small geographic regions [8]. For data collection in Wikidata, Wikidata API (https://www.wikidata.org/w/api.php) and Wikidata SPARQL endpoint were used. For Wikidata objects mapped with 'Railway Station', their revision history containing user information, timestamps, and a number of properties was collected. Overall contributions were collected from all users who have contributed to at least one object typed 'Railway Station'. OSM data collection was done using the ohsome API (https://ohsome.org) to extract all railway stations mapped in OSM, including their history and all edits made by the users who edited these railway stations. In addition to a general comparison between the datasets, we derived five sets for a more detailed comparison: OSM with links to Wikidata (59,441 elements), OSM without links to Wikidata (74,659), Wikidata that have links from OSM and are typed as railway stations (45,050), Wikidata without links to OSM but with geocoordinates (54,594) and Wikidata without links to OSM and without geocoordinates (6,714).   \n\nOur first analysis regarding the growth rate of the two sources showed that OSM has reached a saturated state regarding the number of railway stations, where only a few stations were added since mid-2020. Wikidata, on the other hand, still experiences a stable number of new stations that are added to the project. The two datasets depict no clear temporal correlation hinting towards two independent communities, meaning that edits in OSM are not followed by edits in Wikidata and vice versa. Despite the similar size of the two datasets at a global scale, the two datasets show significant discrepancies on a country level. For example, in China, Wikidata features only 39% of the stations present in OSM while having more than double the amount of stations in the United Kingdom. While the lack of stations seems reasonable considering the overall lack of stations in Wikidata, the overabundance of stations in the UK hints towards a data issue that needs more detailed analyses before integration.  \nIn terms of properties/tags of each object, we observed that Wikidata has, on average more properties per object than OSM. Since Wikidata is a knowledge graph, it also contains links to other objects that can help enrich existing objects increasing this discrepancy even further. OSM objects with links to Wikidatda have almost double the tags compared to those without links. This could either be a quality indicator or an indicator that only famous stations, which are very well mapped in OSM, are also linked to Wikidata. Wikidata objects without links from OSM and geocoordinates have the least number of properties, hinting at their lower quality.    \n\nNext, we present the community analysis. There were around 8.4 million contributors in OSM in total, and 48k unique users have contributed to either creation, deletion, or updating of the railway station objects. In Wikidata, the number of overall contributors is much smaller, i.e., 24k out of which 14k have contributed to Railway objects. The revisions for Wikidata objects are around 11 times higher than that of OSM revisions. This is evident as Wikidata railway stations have more properties than OSM railway stations. This could also be because OSM contributors have a wide variety of objects to map, whether a bench or a tree. In contrast, Wikidata contributors may focus on details and enrichment of prominent objects of public interest. In OSM, adding a new object to the map may take priority over extensive tagging of existing objects. A similar trend is observed for average stations created by each contributor wherein, on average, Wikidata contributors have created five times and, with median statistics, two times more objects than OSM contributors. This may be due to the higher number of bots and imports in Wikidata. While OSM users generally map a specific area that can only feature a limited number of railway stations, Wikidata users may import railway stations from other sources without limiting themselves to a certain geographical unit.   \n\nTo conclude, we notice that both communities have great potential to integrate these sources on the topic of railway stations. This potential increases daily with other topics reaching a mature data state in Wikidata and other knowledge graphs. OSM can benefit from the wide range of semantic information linked to objects, while Wikidata can benefit from the precise geoinformation and completeness OSM offers. Yet, care needs to be taken to take both communities on board as each user base exhibits unique data collection styles that need to be respected.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3JDZE9", "name": "Moritz Schott", "avatar": "https://pretalx.com/media/avatars/3JDZE9_0p6k6pE.webp", "biography": "- 2020 - present: Research Assistant at the GIScience Research Group of the Institute of Geography in Heidelberg\n Project: IDEAL-VGI - Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information\n - 2015 - 2019: Master of Science (MSc) in Geography at Heidelberg University\n Thesis: Under the Spell of Community Happenings \u2013 Analysing the Effects of Mapping Events on OpenStreetMap Contributors\n - 2012-2015: Bachelor of Science (BSc) in Geography at Freie Universit\u00e4t Berlin", "public_name": "Moritz Schott", "guid": "647bb5de-2db7-5bab-84c4-01445d832de9", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/3JDZE9/"}, {"code": "9GZ9SL", "name": "Alishiba Dsouza", "avatar": "https://pretalx.com/media/avatars/9GZ9SL_RgybEzY.webp", "biography": "Alishiba Dsouza is a Ph.D. student in the DSIS research group at the University of Bonn. Alishiba is pursuing her\nPh.D. in knowledge graph creation for volunteered geographic information (VGI). Her research interests include\nVGI, semantic Web, knowledge graphs, and representation learning.", "public_name": "Alishiba Dsouza", "guid": "5b79b889-c096-56cd-9901-3aff6c3a8ed0", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/9GZ9SL/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/YU9JHN/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/YU9JHN/", "attachments": [{"title": "Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/YU9JHN/resources/Comparative_Wikidata_D_QErzpl1.pdf", "type": "related"}]}, {"guid": "a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a", "code": "GPMSLW", "id": 18598, "logo": null, "date": "2022-08-21T10:30:00+02:00", "start": "10:30", "end": "2022-08-21T10:35:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-18598-inequalities-in-the-completeness-of-openstreetmap-buildings-in-urban-centers", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/GPMSLW/", "title": "Inequalities in the completeness of OpenStreetMap buildings in urban centers", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness within all 13,135 urban centers covering about 50% of the global population.", "description": "The collaborative maps of OpenStreetMap (OSM) have become a major source of geospatial baseline data for humanitarian organisations, companies and public authorities. Describing the elements of spatial data quality (e.g. positional accuracy, completeness, temporal quality) for the OSM dataset is a key prerequisite to provide the potential stakeholders with the necessary information to decide on the fitness for use of a data set for their particular application\u00a0[1]. Without information on spatial data quality there are serious barriers to the adoption and usage of new sources such as OSM.\n\nA large community of researchers has analyzed the quality of OSM data in comparison to authoritative reference data sets, by means of remote sensing and using intrinsic measures [2\u20134]. It has been acknowledged that the OSM data in general is strongly biased, in part due to a much larger contributor basis in countries in the global North as a consequence of socio-economic inequalities and the digital divide [5, 6]. Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness in regions home to a population of 3.5 billion people (about 50% of the global population). First, we propose a machine learning regression method based on generalized additive models (GAMs) to assess OSM building completeness within all 13,135 urban centers (as defined by the European Commission [7]). The analysis utilizes an extensive collection of open building data from commercial and authoritative sources as training data and builds upon very recent technological advances to utilize OSM full-history data for spatio-temporal data analysis on the global scale [8]. This allow us \u2013 for the first time \u2013 to present a comprehensive assessment of the evolution of urban OSM building completeness which encompasses all data contributed to OSM since 2008.\n\nFor each urban center we calculated the OSM building completeness using the area ratio method which has been applied55 by several other researchers in the context of urban areas [9\u201311] . Several measures have been adopted to describe the temporal evolution of inequality in urban OSM building mapping on the global scale and per World Bank region. First, we analyzed the share of population living in urban centers with low completeness (<20%) and high completeness (>80%). Gini coefficient has been utilized to derive the degree of evenness of urban OSM building completeness following an approach proposed by Massey & Denton (1988) to study residential segregation 12 . Moran\u2019s I has been selected as a measure of global spatial autocorrelation of urban OSM building completeness. Spatial autocorrelation has been proposed as an explicitly spatial indicator of segregation covering the dimension of clustering [12, 13]. These analyses has been conducted for annual snapshots from 2008-01-01 up until 2022-01-01.\n\nOverall, urban OSM building completeness is estimated at 38% globally. Our results emphasize that although the well-examined Global North - Global South bias in OSM still exists, over the past years mapping has spread substantially across the globe and within regions. The analysis of the spatial clustering of high completeness values and low completeness values disclosed that global spatial inequality in OSM building completeness has sharply increased between 2008 and 2014. This shows that although overall OSM building completeness became more even in the same period, mapping activity in that time favoured cities which were located close to other cities which were mapped already. One might interprete this as a reinforcing effect. Ongoing mapping in one area triggered even more mapping in surrounded areas. At the same time this also indicates that up until 2014 the expansion of OSM mapping to distant and un-mapped regions (likely to be located in the Global South) didn\u2019t happen at a significant scale.\n\nNevertheless, since 2014 Moran\u2019s I global spatial autocorrelation declined and was measured at 0.55 as of 2022. Combined with the decrease of the Gini coefficient in the same time, this suggests that OSM building completeness has become more even because mapping activity has been expanded to regions which were previously mapped much less. In that regard, OSM building data as of today was much less segregated in terms of both dimensions (evenness and clustering) compared to the state-of-the-map in 2014. This process was to a limited extend positively influenced but humanitarian mapping activity organized through the HOT Tasking manager, but hardly influenced by corporate mapping activity.\n\nWe developed a typology of urban centers based on a methodology to quantify intra-urban completeness pattern by means of evenness and spatial clustering. For this we utilized a fine-scale 1x1 km resolution dataset. In total this analysis covered 4,722 urban centers each with a minimum area of 25 square kilometers. Urban centers have been classified into five different types utilizing an agglomerative clustering approach. Our proposed typology of urban centers incorporates the fact that OSM mapping is rarely distributed equal within cities. Similar findings have been reported for Haiti, where densely mapped zones of Port-au-Prince co-exist alongside zones that remain entirely unmapped [14]. Here we provided a method to quantify these pattern and compare across cities.\n\nThe results reveal the need to address the remaining stark data inequalities, which could not be turned around so far by humanitarian and corporate organized mapping activities. We conclude with recommendations directed at stakeholders working with OSM data: (1) Multi-scale building completeness measures should be applied before subsequent usage of OSM data to outline the potential negative effect of missing data. (2) Completeness maps should be used in combination with socio-demographic information to guide future mapping activities to ensure that \"nobody is left behind\" as encouraged by the SDGs.", "recording_license": "", "do_not_record": false, "persons": [{"code": "E8WPXD", "name": "Benjamin Herfort", "avatar": "https://pretalx.com/media/avatars/E8WPXD_goLPTHQ.webp", "biography": "Benjamin Herfort is researcher at HeiGIT and doctoral candidate in Geography at Heidelberg University. In his research and work he is dealing with the temporal evolution of OpenStreetMap data, MapSwipe and information from social media. He is developing open source tools and methods that incorporate geographic information systems for disaster management and humanitarian aid.", "public_name": "Benjamin Herfort", "guid": "3fb7b645-8189-5461-ba5e-78608f4e568f", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/E8WPXD/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/GPMSLW/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/GPMSLW/", "attachments": [{"title": "SOTM_2022_Herfort_lightning_talk", "url": "/media/state-of-the-map-2022-academic-track/submissions/GPMSLW/resources/SOTM_2022_Inequalities_xTVAics.pdf", "type": "related"}]}, {"guid": "86998c4b-2f4f-5fb3-ba2e-4a20e6588852", "code": "9HBH3X", "id": 19674, "logo": null, "date": "2022-08-21T10:35:00+02:00", "start": "10:35", "end": "2022-08-21T10:40:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19674-the-cell-size-issue-in-openstreetmap-data-quality-parameter-analyses-an-interpolation-based-approach", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/9HBH3X/", "title": "The cell size issue in OpenStreetMap data quality parameter analyses: an interpolation-based approach", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "The quality of OSM data is dependent on many different factors and is quite heterogeneous. Therefore, in both intrinsic and extrinsic quality parameter analyses, a common practice is subdividing the study areas into subareas. In this paper, we worked on a method for obtaining the optimal grid cell size for OSM data quality analysis. Furthermore, we proposed that if the quality is homogeneous in a region, it can be estimated using an IDW interpolation. . In this summary, we have done a preliminary analysis for a Brazilian city, Curitiba, with about 28,000 points of known accuracy.", "description": "Knowing the quality of a given geospatial data allows measuring how much its use can be viable in specific applications and assist in decision making. ISO 19157 [1] established that the geospatial data quality indicators are positional accuracy, temporal accuracy, thematic accuracy, logical consistency, and completeness. These measures are represented by values that summarize the condition of a product as a whole. These values tend to be homogeneous throughout the evaluated area in traditional mapping. In contrast, in VGI, data quality can be affected by several conditions related to editing history, contribution period, and contributor profiles [8,9]. Given the mentioned aspects, data quality in VGI platforms tends to be heterogeneous, i.e., the results may show significant discrepancies according to the area assessed or even within the same region.\n\nGiven the heterogeneity issues described, several researchers around the world have performed the quality assessment of these types of information based on the principle of subdividing the study area into cells [2,3,4,5,6,7]. Such a procedure has been used in extrinsic quality assessment processes based on ISO 19157 indicators or intrinsic parameters associated with the characteristics of the contributions and contributors. Given the results obtained, the representation of the quality of the data from sub-areas makes it possible to obtain analyses regarding the existence of patterns and establish relationships with other agents and their predominance. The discretization of space into rectangular or hexagonal grids is central to this type of analysis.\n\nThe subdivision can occur regularly or irregularly. The units with irregular dimension cells allow us to perform analyses accepting other features or spatial phenomena that define these dimensions (e.g., neighbourhood border, a river or a railway track, areas with different population densities, and the dichotomy between rural and urban areas). However, these methods make operations difficult because they demand that the area value weigh the values; and the spatial analysis considering the neighbourhood is more complex. Units with regular-sized cells solve these two limitations. However, the problem of the grid of cells not conforming to spatial phenomena or features reappears. In order to conform to them, it is necessary to determine the optimal size of the cells.\n\nHowever, one issue remains little discussed: how to determine the size of such cells. Using too large a cell would treat unequal areas equally. On the other hand, using too small a cell and the increased computational cost of the process, ultimately, the ability to generalize the results is lost. Therefore, in this work, we seek to develop an interactive approach for determining the grid cell size calculation, initially using points of known positional accuracy. The hypothesis here is that when the analyzed subarea is of optimal size, one can interpolate the error within the cell via an IDW and generate minimal residuals at the control points. Furthermore, by consecutively subdividing the grid, the mean squared error versus cell size curve will approach stability, thus revealing the optimal size for a given region.\n\nInverse distance weighted interpolation (IDW) calculates cell values using sample point sets. This method considers that the higher weights in the interpolation should be due to the proximity of the unknown value point. Thus, if we had a homogeneous behaviour of the quality parameter in an individual area, by interpolation, we could estimate the quality of the points where this value was unknown.\n\nThe methodological procedures developed using python in the QGIS environment are:\n\n1. For the study area, points of known positional accuracy are chosen (in our case, intersections of the road system), from which a random subset of 10% is separated as a control set.\n\n2. Definition of a first grid.\n\n3. The points are used for interpolation within each cell by the IDW method. The Root-mean-square deviation (RMSE) is calculated using the control points for each cell and the average of the RMSEs for the entire area;\n\n4. Definition of a second grid with half the resolution of the first grid;\n\n5. Repeat the process described in item 3 for the second grid;\n\n6. Calculate the differences between the average error values of the second grid and the first grid and check their significance ;\n\n7. Repetition of the process described in case there are still values indicated as significant.\n\nIn a first analysis, we did a preliminary study for a Brazilian city, Curitiba, with about 28 thousand points of known accuracy. We separated 2.8 thousand control points, and the city was divided into 8 km to 250 m cells. From the preliminary study performed, it was noted that the method show promise in obtaining the necessary analyses to identify the aspects proposed in this work. Furthermore, it was noticed that, as the cell size decreased, the results tended to be more constant, which corroborates the hypothesis of this relationship with data quality. The next steps are to continue the analyses, starting with the verifications and the representation of the magnitude of the differences between different cell sizes.\n\nAlthough it is a method that still has a relatively high computational cost to be realized, the results are exciting and can be optimized. It is assumed that if it is possible to identify the minimum cell size in which it is possible to estimate the quality of the features, this will help in decision making regarding the incorporation of procedures in different \u00e1reas. This method may need even smaller clippings in regions with very heterogeneous characteristics concerning their surroundings (e.g., slums). It is an initial approach to resolve with data a fundamental issue arising from the lack of knowledge of the granularity of discrepancies for each study area.", "recording_license": "", "do_not_record": false, "persons": [{"code": "YLANUK", "name": "Silvana Philippi Camboim", "avatar": null, "biography": null, "public_name": "Silvana Philippi Camboim", "guid": "3a716324-dbb5-53ce-9830-9500cf57be67", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/YLANUK/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/9HBH3X/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/9HBH3X/", "attachments": []}, {"guid": "0c025aaa-29c7-53fa-84c8-b1a4bd8597ca", "code": "FRJXCQ", "id": 19438, "logo": null, "date": "2022-08-21T10:40:00+02:00", "start": "10:40", "end": "2022-08-21T10:45:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19438-investigating-the-capability-of-uav-imagery-in-ai-assisted-mapping-of-refugee-camps-in-east-africa", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/FRJXCQ/", "title": "Investigating the capability of UAV imagery in AI-assisted mapping of Refugee Camps in East Africa", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This pilot project is connected to a larger initiative to open-source the assisted mapping platform for Humanitarian OpenStreetMap (HOTOSM) based on Very High Resolution (VHR) drone imagery. The study test and evaluate multiple U-Net based architectures on building segmentation of Refugee Camps in East Africa.", "description": "Introduction\n\nRefugee camps and informal settlements provide accomodation to some of\nthe most vulnerable population, the majority of which are located in Sub-\nSaharan East Africa (UNHCR, 2016). Many of these settlements often lack\nup-to-date maps of which we take for granted in developed settlements. Hav-\ning up-to-date maps are important for assisting administration tasks such as\npopulation estimates and infrastructure development in data impoverished\nenvironments, and thereby encourages economic productivity (Herfort et al.,\n2021). The data inequality between the developed and developing countries\nare often resulted from a lack of commercial interest, especially with the\nrecent trend of corporate OSM mappers (Anderson et al., 2019, Veselovsky\net al., 2021). Such disparity can be reduced using assisted mapping tech-\nnology. To extract geospatial and imagery characteristics of dense urban\nenviornments, a combination of VHR satellite imagery and Machine Learn-\ning (ML) are commonly used (Taubenb\u00f6ck et al., 2018). Classical ML based\nmethods that exploit the textual (e.g. GLCM), spectral, and morphological\ncharacteristics of VHR imagery are based on the principles of Computer\nVision (CV). Although many have shown promising results in satellite VHR\n(1m to 5m resolution) scenarios such as differentiating slum and non-slum\n(Kuffer et al., 2016 & Wurm et al., 2021), in VHR drone imagery (5cm to\n10cm resolution) however, results might suffer from noise caused by environ-\nment and drone-based specific problems such as motion artefacts and litter.\nRecent advances in CV based Deep Learning might be able to address these\nissues (Chen et al., 2021 & Carrivick et al., 2016).\n\nPurpose of the study\n\nThe study is connected to a larger initiative to open-source the assisted\nmapping platform in the current Humanitarian OpenStreetMap (HOTOSM)\necosystem. This study is a pilot-project to investigate the capabilities of\napplying semantic segmentation using community open-sourced VHR drone\nimagery collected by the partner organisation OpenAerialMap. The study\naims to rigourosly assess the various components and inputs that would\ncontribute to the ML based mapping system, and to produce a detailed\nevaluation on class-based accuracy assessment (Congalton & Green, 2019).\nThis pilot study focuses on 2 camps in East Africa, where data availability\nand the geography of the camps are within a similar savannah ecosystem.\nThis enables highly-detailed method testing and analysis of transferability\nof the results between the two camps.\n\nData and Methodology\n\nThe first camp is located in Dzaleka, Dowa, Malawi, which is sub-divided\ninto the Dzaleka North and Dzaleka main camp. The Dzaleka camps are\nhome to around 40,000 refugees mainly coming from the African Great Lakes\nregion. The Dzaleka North camp is characterised by a newer, spatially well-\nplanned metal-sheeted roofs, while the southern main camp is characterised\nby complex, dense mud-walled building with stone-lined thatched-roofs (UN-\nHCR, 2014). The second camp, the Kalobeyei settlement is part of the sub-\ncamp of Kakuma, located in the rural county of Turkana, North-West Kenya.\nThe Kalobeyei settlement was home to approximately 34,849 refugees as of\n2019. This camp is significantly more spacious and is characterised by spa-\ntiall well-planned metal-sheeted roofs (UNHCR & DANIDA, 2019). VHR\ndrone imageries were provided for both camps and vector labels produced\nby HOTOSM volunteers were provided for the Dzaleka and Dzaleka North\ncamp.\n\nSince CV based Deep Learning is very dependent on the quality of the\nlabelled referenced data, especially when performing pixel-based semantic\nsegmentaion, it is of crucial importance that care is taken when producing\nhighly accurate labels that ensure sucessful training (Ng A., 2018). A large\nquanitiy of available labels did not have such a task in mind, imperfection\nin labelling around existing drone artefacts could cause the trained model\nto misclassify such pixels. In order to train a model which performs well\non drone imagery, the motion artefact will be a signficant feature for the\nmodel to learn.he combination of data availability have allow a unique set of\nresearch questions concerning the input data quality and experiment setup\nto surface. Therefore, to test out U-Net and a few variation of the U-Net\nperformance, an additional set of label data was created in order to supple-\nment the imperfection in the labelled data of the Dzaleka camps. Initially,\nthe models will be trained on the pixel-perfect and less complex Kalobeyei\ndataset, this will be then be followed by introducing the Dzaleka datasets\nof higher complexity. A comparison of baseline performance between the U-\nNet variations (Ronneberger et al., 2015) and the Open-Cities-AI-Challenge\n(OCC) winning model is conducted. The baseline experiement aims to keep\nthe hyperparameters (e.g. optimiser, learning rate, weight decay etc.) con-\nstant to obtain an objective view of the architectual responses on the same\ndataset setup. This will provide a clear picture of the feasibility and how to\ntake this project further, so that further resources could be justified to scale\nfuture experiments.\n\nFindings and Discussion\n\nInitial baseline experiments on the Kalobeyei dataset and Kalobeyei with\nthe Dzaleka(s) seem to suggest limited transferability from the OCC model.\nThis suggests that the OCC model is perhaps over-generalised to the compe-\ntition test dataset. Despite achieving very high confidence on metal-sheeted\nrooftops, it does not detect any of the more complicated thatched roofs com-\nmon in the Dzaleka camp. The OCC model also struggle with\nsome of the more obscure drone motion artefacts occuring at the edge of the\nimagery in the Kalobeyei camp. Meanwhile, the Precision and Recall statis-\ntics favour other variations or further transfer training on the OCC model,\nand the EfficientNet B1 header U-Net pretrained with ImageNet weights.\nHowever validation loss suggests there might be little room for improvement\nin the further transfer training of the OCC model.\n\nPrecision and Recall have both reached above 0.7 in most experiments,\nwhich outline the general capability of the strategies used. However there\nare still significant variations among different architectures and setups. The\nnext step is to perform systemic fine-tuning to increase the confidence level\nof the appropriate architectures.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WHUEHK", "name": "Christopher Chan", "avatar": "https://pretalx.com/media/avatars/WHUEHK_U6ijSBf.webp", "biography": "I am currently a MSc. EAGLE student at the Julius Maximilian University of Wuerzburg. My main academic interest lies in urban remote sensing, AI application in remote sensing, and geostatistics. Before this, I finished my BSc. (hons) Geography at the University of Edinburgh, focusing on glaciology and geomorphology. When I'm distracted, I enjoy a board range of other topics from epistemology to anthropology to various languages. Outside academia, I am an avid climber and traveller.", "public_name": "Christopher Chan", "guid": "72677a5b-feca-5306-97c5-fc85472059cc", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/WHUEHK/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/FRJXCQ/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/FRJXCQ/", "attachments": [{"title": "Presentation upload", "url": "/media/state-of-the-map-2022-academic-track/submissions/FRJXCQ/resources/Chris_Lighting_talk_nNcbDx7.pptx", "type": "related"}]}, {"guid": "282251a7-9210-5c72-a811-7653a74ae985", "code": "EZPVPB", "id": 18795, "logo": null, "date": "2022-08-21T10:45:00+02:00", "start": "10:45", "end": "2022-08-21T10:50:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-18795-corporate-editing-and-its-impact-on-network-navigability-within-openstreetmap", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EZPVPB/", "title": "Corporate editing and its impact on network navigability within OpenStreetMap", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Using intrinsic quality indicators we explore how network quality, in terms of its suitability for navigation, varies across areas with relatively high and low corporate editing in OpenSteetMap. Our work shows areas with relatively high rates of corporate editing exhibit not only an overall increase in data quality, but also increased rates at which quality improves.", "description": "OSM (OSM) contributors have traditionally lacked explicit monetary incentives for contribution [1].  Since 2016, a handful of large corporations (including Apple, Facebook, Microsoft, and Uber) have increasingly contributed data to OSM. Corporate editors (CEs) represent a distinct community as their editors are compensated and thus their contributions cannot be labeled as \u2018volunteered\u2019. Additionally, corporations employ large editing teams and new state-of-the-art editing techniques aided by artificial intelligence, making them capable of editing large swaths of information in relatively short time [2]. Corporate teams are often led by long-time OSM community members themselves, emphasizing the multifaceted nature of a rapidly growing open mapping platform [3]. While there has been some contention about the quality of edits done by CEs, corporations argue their contributions improve existing data [7]. Our study provides a preliminary quantitative evaluation of data quality impacts of corporate edits  on OSM.\n\nWe assess intrinsic data quality across five regions that have high levels of corporate contributions: Dallas-Ft. Worth, Egypt, Jamaica, Thailand, and Singapore. The quality of these regions is compared to that of Denmark, a region which has witnessed relatively less corporate interest, yet possesses a well-mapped OSM presence due to a well developed local mapping community [4]. These evaluations were performed using measures of intrinsic map quality. While the most straightforward evaluation methods involve comparing against extrinsic sources, such as either ground reference information or authoritative data sources; lack of data availability, licensing terms, and costs often render this comparison untenable [5,6,7]. A transferrable, data driven way of assessing quality remains using Intrinsic Quality Indicators (IQIs), a sub-field of OSM analysis which provides a variety of approaches for evaluating intrinsic OSM data quality. We chose to focus on IQIs that apply to networks, and to evaluate IQIs for land-based transportation networks within OSM. We analyzed networks for our specified locations for every other year between 2014 and 2022.\n\nOSM editing archives were processed using R to extract maps of the relative activity of corporate editors [8]. Our list of corporate editors was sourced from OSM\u2019s publicly available list of corporate editors accounts. We extracted entire networks that represented the first day of each year of interest (2014, 2016, 2018, 2020, 2022) from OSM\u2019s historical archives. For the purposes of this study, we extracted all networks where \u201cOSM WAY = Highway\u201d.\n\nWe evaluated several IQIs for our areas of interest.  We focused on completeness of network, both in terms growth over time and in terms of its navigability. We operationalized \u201ccompleteness for navigability\u201d as an intrinsic measurement by exploring the percentages of networks that possessed attributes necessary for GPS navigation \u2013 street names and speed limits. Navigability was assessed and compared across time points using Origin-Destination matrices. By creating a regular matrix across the area and calculating the ratio between a direct route between points, and a route navigated within our network, we calculated a ratio that can be compared across time to evaluate the changing efficiency of the navigable network. Additionally, when building routing networks, we discovered an additional IQI : the presence and qualities of topological islands within our network. That is, areas which are disconnected from the main network due to mapping errors or incompleteness. \n\nAfter mapping these metrics, we analyzed how they correlate with each other and how they change over time. Overall, IQI trends for the road network reveal consistent patterns across all measures and locations. There is a trend towards increasing data quality in terms of gradual increase of network length, completeness in terms of attributes (name, speed limit, and pedestrian access), the increasing efficiency of ODM routing ratios, and the increasing amount of places that have \u201cnavigable\u201d attributes. Importantly, we found differences between our control location (Denmark) and our other areas of interest. The primary difference of note is not with regards to the quality of the data, but with respect to the rate at which data quality improves: Denmark\u2019s rate of quality improvement is slower than other locations. The faster rate of quality improvement in the test areas highlights that the data creation and editing activity by corporate editors and other organized editors in these locations are helping narrow gaps in data quality.\n\nWhile this presentation highlights the trends of data quality increase, it does not tease apart the quality assessment of contributions by corporate teams versus other mapping groups. As a crowdsourcing platform, data in OSM is co-produced by repeated editing of data objects by different members of the community [9]. The appearance of CEs in OSM represents the arrival of another community of \u2018produsers\u2019 in the OSM ecosystem, and thus a new evolution in its overall trajectory [2,10,11]. Consequently, there is significant interaction between CEs and non-CEs in data co-production in OSM, further reinforcing the idea that OSM is a \u2018community of communities\u2019 [11]. Each location has their own patterns regarding editing communities, what they edit, and the sociopolitical and economic ground truth in the real world.  Each of these factors impact the data, and may make comparing editing patterns difficult, especially given the diversity of motivations both with CE communities and within other OSM communities. Hence, we do not try to pry apart the differences in trends between individual countries. Instead, we focus on the overall trend between our test and control locations. With these caveats, we find that the quality of the network has increased in these areas across all tracked metrics at a faster rate than it has in areas with low rates of corporate edits, indicating that corporate editing may have a positive effect on the overall quality of the map.", "recording_license": "", "do_not_record": false, "persons": [{"code": "DZHREX", "name": "Corey Dickinson", "avatar": "https://pretalx.com/media/avatars/DZHREX_z9qWJOx.webp", "biography": "Corey Dickinson is a researcher, consultant, and educator currently based at McGill University.", "public_name": "Corey Dickinson", "guid": "7bbbb7d4-c89d-54bc-a0c5-2d2a09945634", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/DZHREX/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EZPVPB/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EZPVPB/", "attachments": []}, {"guid": "df213ca2-5675-5b51-b419-001ca869ec38", "code": "EKEZ7R", "id": 19407, "logo": null, "date": "2022-08-21T10:50:00+02:00", "start": "10:50", "end": "2022-08-21T10:55:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19407-returning-the-favor-leveraging-quality-insights-of-openstreetmap-based-land-use-land-cover-multi-label-modeling-to-the-community", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EKEZ7R/", "title": "Returning the favor - Leveraging quality insights of OpenStreetMap-based land-use/land-cover multi-label modeling to the community", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "The fitness of OSM for multi-label classification is proven. A workflow to enhance OSM-based multi-labels using machine learning is established. The results are provided to the OSM community via the HOT Tasking Manager.", "description": "# Introduction\n\nLand-use and land-cover (LULC) information in OSM is a challenging topic. On the one hand, this information provides the background for all other data rendered on the central map and is used by applications like https://osmlanduse.org. On the other hand, this information has a difficult position within the OSM ecosystem. LULC information can be quite cumbersome or even difficult to map e.g. due to natural ambiguity. The growing tagging scheme provides a collection of sometimes ambiguous or overlapping tag definitions that are not fully compatible with any official LULC legend definition [1]. Furthermore, the data is highly shaped by national preferences and imports.\n\nThis diversity of the LULC data in OSM is a fundamental principle of OSM that enabled the success of the project. Yet, this can create considerable usage barriers or at least caveats for data users unfamiliar with the projects' ecosystem. The remote sensing community for instance has started to use OSM LULC information as labels in their classification models. Frequently, OSM LULC data has thereby been taken at face value without critical reflection. And, while the quality and fitness for purpose of OSM data has been proven in many cases (e.g. [2,3]) these analyses have also unveiled quality variations e.g. between rural and urban regions. The quality of OSM therefore can be assumed to be generally high, but remains unknown for a specific use-case.\n\nThe proposed work first assesses the impact of these challenges on a use-case of multi-label remote sensing (RS) image classification and then provides a machine learning (ML) based workflow to overcome and finally mitigate them. Multi-labels are a type of image classification where a satellite image is labeled with multiple containing LULC classes. In the presented study these labels are extracted from OSM and used to train the ML algorithm.\n\n\n# Methods and Results\n\nThe fitness for purpose of OSM for multi-label RS image classification was tested on a Sentinel 2 scene with a resolution of 10m and four bands in south west Germany recorded in June 2021. The area was chosen for its estimated high completeness and low amount of imported data. OSM data was grouped by its tags into the four LULC classes 'forests', 'agricultural areas', 'build-up area' and 'water bodies'. 18 tags that could unequivocally be mapped to these classes were used and small elements below the image resolution or the classes minimal mapping unit were filtered. The chosen scene was then tiled into a 1.22 x 1.22 km grid of 8100 image patches. Zero to four labels were assigned to each patch, based on the OSM LULC elements therein. Evaluation was performed manually on 910 random patches, of which 80% had a correct OSM-based multi-label, thereby proving the assumed high completeness and quality in the region.\n\nThe proposed workflow provides a method to enhance this OSM-based RS image multi-label classification and extend it to areas of lower OSM quality and completeness using ML (specifically deep learning (DL)). The main obstacle for ML and especially DL is the required amount of labeled training data. Volunteered geographic information (VGI) like OSM offers a potential solution to this challenge by providing an overabundance of LULC information that is suitable for this purpose if data quality is sufficiently high. The workflow uses the multi-label information extracted from OSM for training and then detects discrepancies between its predictions and OSM.\n\nUsing this information and pinpointing the exact location of error within the patches provides valuable OSM data quality information. Apart from facilitating a fast quality estimation for large areas, the workflow can make its findings automatically available to the OSM community in a feedback loop using the HOT Tasking Manager framework. Thereby the valuable service by the OSM community of providing large amounts of free and generally high quality training data is recognised in the form of quality feedback including mapping hints to the OSM community. \n\nThe five workflow stages are: 1) RS data collection and preprocessing, 2) OSM data collection and preprocessing, 3) LULC multi-label modeling, 4) OSM data issue flagging and 5) the community feedback loop. While each step is an atomic use case and application, the combination of all four steps creates a tool that is useful for the RS and the OSM community likewise. The tool is openly available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-multitag under the GNU Affero General Public License v3 including example datasets. Manual input was kept as low as possible while enabling the 'human in the loop' to take full control over all input and output.\n\nThe workflow extracts multi-label training data in stages 1) and 2) as described. Stage 3) then trains a DL model to predict multi-labels using solely RS imagery. For demonstration, the model was trained on the described Sentinel 2 scene in Germany. The models' performance was validated on the manually labeled 910 patches where it outperformed OSM in terms of multi-label accuracy by 7%. When additional errors were manually introduced to the training labels to simulate areas of lower OSM quality or completeness, the model maintained an overall prediction accuracy above the noisy training labels. Alternatively, in cases where OSM LULC multi-label accuracy is suspected to be low, pretrained models from comparable regions with higher OSM data quality can be applied, making the workflow widely applicable.\n\nAny patches where the models' multi-label prediction contradicts the OSM-based multi-label are then detected in stage 4). Multi-labels can be incorrect if either a label is missing (omission), meaning data is missing in OSM, or if a label is wrongly assigned, meaning OSM data is falsely mapped within the tile. The data error type and location within the patch is then extracted using explainable AI [4].\n\nThe final stage 5) uses these localised potential OSM data errors to create HOT Tasking Manager projects via the public API. These projects provide additional correction hints. Yet, no automatic editing takes place. The community is kept in full control of all mapping actions as a 'human in the loop'.\n\n# Discussion\n\nThe high quality but diverse nature of OSM has been proven for the use-case of multi-label RS image classification. The proposed tool provides an automated OSM multi-label extraction, modeling and verification procedure including a return of results to the OSM community. A major challenge of the approach is the tiled view on the data. If OSM assigns correct multi-labels to a patch, more fine grained data issues will not be detected. Yet, this approach allows large scale data assessments, before the topic of more detailed data improvement is tackled. It also allows to run repeated OSM LULC quality and completeness estimations for large areas over time.\n\nAnother major benefit is the usage of local OSM data for modeling, thus making regionalised models the standard procedure. This is required for OSM LULC information as regional data structures and communities exist, that need to be preserved. The model can lead to regional homogenisation and data cohesion within these regional communities.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3JDZE9", "name": "Moritz Schott", "avatar": "https://pretalx.com/media/avatars/3JDZE9_0p6k6pE.webp", "biography": "- 2020 - present: Research Assistant at the GIScience Research Group of the Institute of Geography in Heidelberg\n Project: IDEAL-VGI - Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information\n - 2015 - 2019: Master of Science (MSc) in Geography at Heidelberg University\n Thesis: Under the Spell of Community Happenings \u2013 Analysing the Effects of Mapping Events on OpenStreetMap Contributors\n - 2012-2015: Bachelor of Science (BSc) in Geography at Freie Universit\u00e4t Berlin", "public_name": "Moritz Schott", "guid": "647bb5de-2db7-5bab-84c4-01445d832de9", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/3JDZE9/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EKEZ7R/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/EKEZ7R/", "attachments": [{"title": "presentation_slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/EKEZ7R/resources/2022_08_21_schott_ligh_Bl93pVn.pdf", "type": "related"}]}, {"guid": "d46319a7-2e37-5f5e-804b-109fa153a120", "code": "ASADTB", "id": 19554, "logo": null, "date": "2022-08-21T11:30:00+02:00", "start": "11:30", "end": "2022-08-21T12:00:00+02:00", "duration": "00:30", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19554-automated-derivation-of-public-urban-green-spaces-via-activity-related-barriers-using-openstreetmap", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ASADTB/", "title": "Automated derivation of public urban green spaces via activity-related barriers using OpenStreetMap.", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Urban green spaces serve people for active and passive recreation. On the basis of OpenStreetMap data, suitable green spaces are to be derived in order to incorporate them as recreation destinations in a location-based service (the \u201cmeinGruen\u201d app) as polygons. The modelling approach focuses on activity-related barriers in the context of urban green, transitions between different land use classes, and public accessibility. The case study was implemented for the city of Dresden in Germany.", "description": "In addition to important ecosystem services such as clean air or local climate regulation, green spaces provide peace and recreation, contributing to a good quality of life for the population. In high-density urban areas, publicly accessible green spaces are used for a variety of recreational activities, which has become even more important, not least because of the COVID-19 pandemic [1]\u2013[4]. In this context, the research project \"Information and Navigation on Urban Green Spaces in Cities - meinGruen\" examined publicly accessible green spaces with regard to a variety of criteria in order to assess their suitability for the pursuit of leisure activities, such as going for a walk or playing soccer [3], [5], [6]. The aim of this study is to derive a suitable polygon dataset to describe the spatial distribution of publicly accessible urban green spaces. The presented approach favors the use of OpenStreetMap data and intrinsic knowledge. Advantages of the use of OpenStreetMap data are the global availability, the often high completeness in urban areas as well as the unified open data license ODbL 1.0. In this way, problems with data availability and heterogeneity due to different responsible authorities can be avoided. Ludwig et al. [7] describe an approach to mapping public green spaces based on OpenStreetMap and Sentinel-2 satellite imagery in which barriers and land use changes are considered based on a priori (expert knowledge) assumptions for polygon generation. In the approach presented here, spatial delimitation is to be refined by describing barriers by probability values. The term \"barrier\" is first analyzed in an interdisciplinary way in order to then work out its meaning for the spatial delimitation of a green space. Here, barriers describe the action space of a recreational activity. While there are a number of object types (such as walls, fences, rivers, roads or railroad lines) can be assumed to be barriers with certainty, there are others (such as paths or the change of land use) for which knowledge is still lacking. The study area includes the city of Dresden in Germany, plus a buffer of five kilometers. OpenStreetMap represents the main data source. For training and validation, official cadastral data (ALKIS) as well as a dataset on cadastral parcels owned by the city of Dresden were used.\nThe methodology consists of six steps: First, according to defined rules, types of barriers were extracted from OpenStreetMap data. Second, we derived a land use layer without overlaps and holes from OpenStreetMap. Here, two options were compared regarding different target schemes for land use classification. Third, a mapping in terms of a \u201cground-truth\u201c in selected areas in Dresden followed in order to be able to evaluate the existence of a barrier on site for the extracted paths and changes of land use. Fourth, generic probabilities for the existence of a barrier were determined based on path type or land use change type. Fifth, a polygon mesh was created by applying thresholds to the determined barrier probabilities. Sixth, the generated polygons were enriched with attributes on the number of green space-related POI, such as benches, trash cans, or trees. Models for \"greenness\" and \"accessibility\" are thereby trained.\nFor the technical implementation mainly Docker, PostgreSQL/ PostGIS, Python (Geopandas, Scikit-Learn) and Jupyter Notebook were used. Data import was performed by osm2pgsql and ogr2ogr. For mapping we used the app QField.\nLand use layers were successfully generated for the study area using a residual class. The results indicated that the land use classification according to the area scheme of the IOER-Monitor (option B) has a higher thematic accuracy with a maximum of 33 classes (433 original OSM tags were assigned) than the option A based on a classification according to osmlanduse.org/ Schultz et al. (up to 13 classes, based on 61 OSM tags) [8], [9]. The classes of arable land (A: 28.40% / B: 28.06% share of area) as well as forest (A: 21.81% / B: 23.33%) are dominant in both variants. While the residual class takes up 6.29% of the area in option A, it is only 4.88% in option B. For the \u201cground-truth\u201d, a total of approximately 82.3 km of paths (with 408 line objects) and approximately 64.2 km of land use changes (1720 line objects) were evaluated for the presence of a barrier in two selected areas in Dresden. The land use changes are based on variant B. Data were collected on 61 different land use transitions and four different trail types. While bike lanes can be safely assumed to be a barrier, the \"track\" (96.8%), \"footway\" (92.7%), and \"path\" (86.0%) trail types have a slightly lower barrier probability. Among land use transitions, the forest-meadow (12.6%), meadow-sports facility (22.8%), meadow-park (24.6%), and forest-grassland (26.7%) transitions have the lowest barrier probabilities. Together with the barriers assumed to be safe at the beginning, a line pool is formed, from which different polygon meshes are generated based on different intervals for the barrier probability (p \u2265 0%; p \u2265 20%; p \u2265 40%; p \u2265 60%; p \u2265 80%; p = 100%). The lower the probability threshold, the higher the number of polygons created (whose area decreases). For the \"accessibility\" model, the number of benches, trash cans, public toilets and public internet were considered per polygon. The logistic regression achieved 76.7% accuracy here, similar to a Support Vector Classifier (SVC). The \"greenness\" model is based on number of benches, picnic tables, trees, and trash cans per polygon. The accuracy is about 92.3% (for logistic regression and also Support Vector Classifier). \nThis work successfully demonstrates a new approach to derive publicly accessible green spaces based on OpenStreetMap data considering different qualities of barriers in contact of green spaces.  Based on the examined barrier probability of path types and land use transitions, more realistic spatial delineations of green spaces were made possible. The chosen approach is globally applicable due to the use of OpenStreetMap. In each case, locally prevailing climatic and cultural influences must be taken into account. The knowledge collected here can be applied in the Central European region. For other areas, a renewed \u201cground-truth\u201d may have to be carried out on site. The schematic transformation of the land use into the area scheme of the IOER-Monitor leads to a reduction of classes compared to the original data. In addition to benefits in capturing barrier probability, it also simplifies comparability and transferability. Thus, other data could also be migrated into this scheme. The determined barrier probabilities correspond to the expectations. The polygon generation based on different barrier probabilities allows here a differentiated setting of the desired action space for the relevant leisure activities. The quality of the trained models is good, but can be improved. A variety of additional features can be calculated for each potential green space (polygon), such as path network density or density of path network intersections (see also Ludwig et al. [7]). Questions about the perception and use of green spaces can also be part of interdisciplinary research in the future.", "recording_license": "", "do_not_record": false, "persons": [{"code": "XM3JTL", "name": "Theodor Rieche", "avatar": "https://pretalx.com/media/avatars/XM3JTL_kfoJflx.webp", "biography": "Since 12/2021 \t\tResearch associate at Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden\n\n10/2018 \u2013 11/2021\tMaster programme: geoinformatics and management  at University of Applied Sciences (HTW) in Dresden\n\n09/2014 \u2013 04/2018\tBachelor programme: cartography and geoinformatics at University of Applied Sciences (HTW) in Dresden", "public_name": "Theodor Rieche", "guid": "7f9f379a-afdc-5e21-85d3-f33fb9ef8d99", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/XM3JTL/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ASADTB/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ASADTB/", "attachments": [{"title": "slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/ASADTB/resources/slides_TheodorRieche_S_C9M9X9t.pdf", "type": "related"}]}, {"guid": "d13397f0-860f-56a7-82f1-f99fcf0ff2d4", "code": "LTA77E", "id": 19417, "logo": null, "date": "2022-08-21T12:00:00+02:00", "start": "12:00", "end": "2022-08-21T12:20:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19417-null-island-a-node-of-contention-in-openstreetmap", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/LTA77E/", "title": "Null Island - a node of contention in OpenStreetMap", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Null Island is where the prime meridian meets the equator at (0,0) longitude and latitude. While Null Island is a fictitious, dimensionless, point object, its existence stimulates vigorous debate making it worthy of serious consideration. Many examples exists illustrating how Null Island impacts OSM discourse. Our study considers what the geographic oddity of Null Island means for OSM. The main contribution is a structured knowledge-based resource facilitating understanding of Null Island\u2019s impact on OSM. This socio-technical and philosophical investigation of Null Island can become a catalyst for deeper discussions and debates in OSM around mapping practices.", "description": "Null Island refers to the location where the prime meridian meets the equator at 0o longitude and 0o latitude. With coordinates (0, 0), it is the origin of the WGS84 geographic coordinate system. It has been argued that Null Island can be considered a real place that is a product of our digital age [1]. Null Island\u2019s significance comes from the fact that it is erroneously associated with large amounts of geographic data that spans across geo-social media, location-based services and map databases. Even though Null Island is a fictitious, dimensionless, point object, its existence stimulates debate that elevates Null Island into a global issue worthy of serious consideration (a detailed description of associated issues is given in [1]). Members of the OpenStreetMap (OSM) project often interact with this location in various ways, and therefore understanding what Null Island means for OSM is relevant. We can find several examples of Null Island affecting OSM, such as a recent debate that arose in the talk mailing list in January 2022 with the title \u201cWas the deletion of Null Island reasonable?\u201d [2], where contributors argued for or against the deletion of Null Island. In addition, a web search for the term \u201cNull Island\u201d on the openstreetmap.org domain [3] reveals that Null Island was mentioned across the entire OSM ecosystem, including mailing lists, forums, user diaries, notes, features, changesets, wiki pages, help articles, blogs and even the Ruby on Rails codebase of the OSM website uses Null Island for testing (https://tinyurl.com/OSM-Ruby-Null). These suggest that Null Island already has a widespread reach within the OSM project. \nThe purpose of this study is to consider both qualitatively and quantitatively what the geographic oddity of Null Island means for OSM. No research works exist which tackle this issue in depth. Previous studies mentioning Null Island do so in a simplistic way and use the term to refer to the (0, 0) location (see e.g. [4]\u2013[6]). Only a few studies recognize it as a special location and unique phenomenon ([7], [8]), and to our knowledge, only one study tackles the issue in depth [1]. In addition to contributing a robust academic study of Null Island, this work will produce a structured knowledge-based resource for the community to understand Null Island\u2019s impact on OSM. \n\tBuilding on [1] we investigate the various ways Null Island is represented in the OSM project subsequently contributing an evidence-based narrative history on the evolution of Null Island. This includes the qualitative review of various OSM communications channels (e.g. mailing lists, discussion boards and wikis) for mentions and references to Null Island. We believe these channels help provide insights about how the OSM community contextualizes, describes  and deals with Null Island. The history of special map features related to Null Island, such as node #1 (https://tinyurl.com/osm-first-node) and the node located at (0, 0) (https://tinyurl.com/OSM-Center) will also be reviewed to illustrate what actions the OSM community took in terms of adding and removing Null Island to the database. In addition to these qualitative approaches, we utilize the ohsome API [9] to extract and analyze map edits made on or near Null Island, which provides a quantitative way to assess the frequency of erroneous data added to OSM near (0, 0) as well as the semantics of such data.\nInteresting patterns have already emerged from the preliminary analysis of data. The most recent mailing list debate mentioned above [2] can be summarized as follows. 17 individuals contributed 45 e-mails to the discussion between January 3 and January 10, 2022. One of the (very few) rules of OSM is that data should be verifiable, meaning that others can visit the real location of a map object and see for themselves if the data is correct. This is also known as the \u201cground-truth rule\u201d [10]. Null Island as a fictional place violates this rule, therefore a popular stand in the debate is that it should not be part of OSM. This was explicitly expressed by five individuals, including a member of the authoritative Data Working Group. A counter argument is that Null Island is fundamentally similar to localities and neighborhoods, that might not exist as political or physical entities, but are known only informally to a group of people inhabiting that area. In this sense, Null Island is a place that exists in the collective consciousness of people and the name refers to the same geographic area. This justifies tagging the (0, 0) location as place=locality and name=\u201dNull Island\u201d in OSM. This view was explicitly supported by seven members on the mailing list. The remaining five individuals that contributed to the discussion did not take a clear stand on whether to remove or keep Null Island, but have provided arguments both for and against the deletion of it.\nThe full history of OSM data was extracted from the elementsFullHistory endpoint of the ohsome API [9] within the geographic bounding box defined by the southwest point of (-0.001, -0.001) and the northeast point of (0.001, 0.001) between January 1, 2012 and January 1, 2022. During this 10-year-long period, a feature was added, deleted or modified every three days on average within this bounding box, resulting in 1323 unique features (nodes, ways or relations). In addition, map Notes as well as GPS traces are also constantly being created, which makes Null Island and its surrounding a busy area in terms of OSM data activity.\nNull Island is a socio-technological concept that has only been sparsely present in the GIScience literature so far. Our novel approach highlights how a seemingly lighthearted topic like Null Island can generate serious debates that are technological, social and even philosophical in nature. OSM and Null Island have a long tradition together with sometimes heated mapping debates resurfacing from time to time with no apparent resolution in sight. While resolving these debates is entirely in the hands of the OSM community, our research contributes to the potential resolution of them in a meaningful way by providing a factual, detailed, and accurate account of Null Island in OSM. Furthermore, while Null Island is potentially the most prominent example of a fictional place affecting maps and mapping practices, other examples also exist. For example, the most remote location on Earth, Point Nemo (which is the point in the ocean that is farthest from land) [11] is also present in OSM (https://tinyurl.com/OSM-PointNemo). Our OSM specific investigations together with a more general introduction of Null Island from both technological and social perspectives presented in [1] will help demystify the abstract concept of a fictional place that is present in real databases. Increased understanding will potentially help OSM members resolve mapping debates about \u201creal fictional places\u201d. Discussion around Null Island and other fictional places is unlikely to end with this work. Our work will contribute in a technical, socio-technical and philosophical way to the Null Island story in OSM with the potential to become a catalyst for further discussions related to wider debates in OSM around mapping practices.", "recording_license": "", "do_not_record": false, "persons": [{"code": "UNDQDX", "name": "Peter Mooney", "avatar": "https://pretalx.com/media/avatars/UNDQDX_XF8WLhk.webp", "biography": "Peter is an Assistant Professor at the Department of Computer Science in Maynooth University and has been working in the domain of geospatial data research for over a decade. He is particularly interested in understanding the processes behind the collection of volunteered geographic information data and how computing techniques such as machine learning can be applied to these datasets and sources. He has been involved for over a decade in research and scholarship related to OpenStreeMap. He is heavily involved in OSGeo (Open Source Geospatial Foundation) activities in Ireland. His teaching philosophy sees the exclusive use of FOSS4G (Free and Open Source Software for Geomatics) and Open Data for all student teaching and learning activities. He is currently an editor of the Transactions in GIS journal.", "public_name": "Peter Mooney", "guid": "6d5f35b4-0305-5b6b-836d-76e985025612", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/UNDQDX/"}, {"code": "KW8XBC", "name": "Levente Juh\u00e1sz", "avatar": "https://pretalx.com/media/avatars/KW8XBC_UB32H3a.webp", "biography": null, "public_name": "Levente Juh\u00e1sz", "guid": "2e75e216-a21a-55e7-898a-abbcc69bce9c", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/KW8XBC/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/LTA77E/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/LTA77E/", "attachments": [{"title": "Null Island - a node of contention in OpenStreetMap SLIDES", "url": "/media/state-of-the-map-2022-academic-track/submissions/LTA77E/resources/Null_Island_-_a_node_o_lteSKYq.pdf", "type": "related"}]}, {"guid": "48d7305f-11f4-5ac8-90e3-4395a005201f", "code": "CEMMTQ", "id": 19448, "logo": null, "date": "2022-08-21T12:30:00+02:00", "start": "12:30", "end": "2022-08-21T12:50:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19448-osm-for-sustainable-transport-planning", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/CEMMTQ/", "title": "OSM for sustainable transport planning", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "OpenStreetMap (OSM) data has the potential to facilitate bottom-up approach to transport planning which is essential for localized data-driven policy interventions. Given this, OpenInfra project is exploring the potential of OSM data in transport research with a focus on active travel. The exploration showed that currently missing data limits the applicability of OSM data. Nevertheless, we argue that the potential and relevance of OSM data can be demonstrated by recategorizing OSM data to provide more actionable insights to policy-makers. This, therefore, could encourage the uptake of open data leading to more transparent, reproducible, and participatory transport planning.", "description": "One of the key domains in which OpenSteetMap (OSM) data has been utilized is transport research [1]. OSM has been used in agent-based transport simulation [2] and routing [3], including cycling [4], walking [5], wheeling [6], and blind pedestrian routing [7]. Another application of OSM data is in transport infrastructure planning. Nelson et al. [8] argue that OSM has the potential to become a primary source of data on infrastructure across the globe. \n\nRegardless of OSM\u2019s potential to become a primary source of data on infrastructure, its potential in active travel infrastructure planning is yet to be realized. One of the potential reasons behind this lag might be linked to the perceived unreliability of open-access crowdsourced data [9]. The quality of OSM has received extensive examination [1] in which the question concerning data completeness plays a significant role because, it is argued, the mappers are not coordinated to guarantee systematic coverage [10]. To address this issue, Barrington-Leigh and Millard-Ball [11] assessed OSM road completeness and found that globally over 80% of roads are mapped. Problematically, however, their assessment focused on roads designed for motor traffic, thus excluding other modes of transport. This gap has been partially addressed by Ferster et al. [12]who examined and compared OSM cycling infrastructure in Canada. They have not, however, considered the infrastructure from the perspective of accessibility. Moreover, there seems to exist no equivalent study using OSM data in the context of pedestrian infrastructure planning. \n\nNevertheless, open-access crowdsourced data, such as OSM, can support an increasing need for local evidence to inform transport policies. This is important in the context of the UK in which a shift from provision for motorised modes towards more sustainable active modes of travel, such as walking, wheeling, and cycling, takes place [13]. The importance of localizing interventions to meet the needs of local communities has been outlined in both policy [15] and academic [16] papers. A potential way to engage citizens in the decision-making is to encourage \u201cprodusage\u201d \u2013 a model in which citizens both produce and use data [17]. \n\nAcknowledging the potential of OSM to boost citizen participation, OpenInfra project, run at the University of Leeds (UK), aims to address the gap of literature regarding the potential OpenStreetMap in transport research. The project started by examining the existing OSM tags relevant to active travel infrastructure in England with a focus on West Yorkshire, Greater Manchester, Greater London, and Merseyside. The data has been queried using osmextract [18], a package in R, and explored using exploratory data analysis (EDA) approach. A reproducible code containing all the figures discussed here can be found on GitHub: https://github.com/udsleeds/openinfra/tree/main/sotm2022 \n\nGiven the extensive use of OSM data in transport research, it is not surprising that OSM provides a comprehensive active travel network, yet there is a lack of specification concerning the type of infrastructure that is present (e.g. is it a cycle lane or a cycle track?). For instance, cycleways and footways constitute about 1/3 of all the mapped highways on which one can legally walk, wheel or cycle but only a few percent of the cycleways and footways have tags detailing their type. The data gets even scarcer in the context of accessible infrastructure planning. For example, there is a lot of missing information on the presence and type of kerbs \u2013 a street element that might make the movement of a wheelchair user more challenging [19]. \n\nThe missing data currently limits the use of OSM data in active travel planning, however this does mean that the use of OSM data should be dismissed. Following Nelson et al.\u2019s [8] argument that it is important to make crowdsourced data more actionable, we decided to recategorize OSM data based on Inclusive Mobility (IM) [15], a guide that outlines the best practices in creating inclusive pedestrian infrastructure in the UK. For this, a function has been written (documentation can be found here: https://udsleeds.github.io/openinfra/articles/im_get.html). It takes an OSM dataframe, recategorizes its tags based on the definitions outlined in the guide, and returns an OSM dataframe with new columns to use in further analysis. However, the function provides a simplification of the IM guide for a couple of reasons. The first one could be considered in terms of definitional discrepancies. For instance, the guide defines footways as \u201cpavements adjacent to roads\u201d, yet this is not easily extracted from the OSM in which highway=footway is a generic tag and often there is no further refinement (e.g., sidewalk=*) to determine if it is a pavement adjacent to a road. Another reason is linked to assigned values. For example, the guide identifies six tactile paving surfaces but OSM focuses on the presence/absence of tactile paving, thus limiting how much information can be extracted from the data. \n\nOne potential application of the IM function could be to explore the existence and geographic distribution of accessibility indicators, such as the presence of a flush kerb. Yet, more interesting results can be produced by using recategorised OSM data in conjunction with other datasets that would help to improve the understanding of the accessibility of streets. As an illustration for this, an open-access Leeds Central Council Footfall data was used [20]. We reasoned that the locations at which footfall data were collected are heavily used by pedestrians, thus demonstrating the need to ensure inclusive spaces. 5 unique streets were identified, which resulted in 35 linestrings in OSM. Then, a basic index of accessibility, ranging from 0 to 5, was created. For example, if a linestring is classified as a footway, footpath, or implied footway based on the IM guide, then it received 1, otherwise 0. If a flush kerb is mapped, it received 1, otherwise (e.g., not flush or NA), 0 is given. Finally, the values were added and a final index produced. Following this, the highest index score is 2 (19 linestrings), while the rest scored 1. This example does not necessarily show that the streets are inaccessible because the missing data make it hard to make a fair judgement (e.g., in this case not a single linestring has data on kerbs). However, we would argue that this is a space for OSM to produce more readily actionable insights regarding transport infrastructure, especially if joined with other (open) datasets that would help to overcome some of its current data limitations. \n\nThe following steps of the OpenInfra project are focused on scaling up. The goal is to produce \u2018OSM transport infrastructure data packs\u2019 for transport authorities in England to support the uptake of open-access data, such as OSM, in transport planning. We believe that the utilization of open-access data could make transport planning more transparent, reproducible, and participatory which, consequently, would support an uptake of sustainable modes of travel. OSM specifically has the potential to provide localized insights on the existing transport infrastructure and facilitate more inclusive and accessible transport planning.", "recording_license": "", "do_not_record": false, "persons": [{"code": "WCZYCM", "name": "Robin Lovelace", "avatar": "https://pretalx.com/media/avatars/WCZYCM_luwW6sB.webp", "biography": "Robin Lovelace\u00a0is\u00a0Associate Professor of Transport Data Science\u00a0at the Leeds Institute for Transport Studies (ITS)\u00a0specialising in the analysis of regional transport systems and modelling scenarios of change.\u00a0Robin is\u00a0Lead Developer of the\u00a0Propensity to Cycle Tool\u00a0(see\u00a0www.pct.bike), and led the ActDev project. These show the feasibility of nationally scalable tools based on OSM data to support active travel and the transition away from fossil fuels that is needed to save the world from the worst impacts of climate change.", "public_name": "Robin Lovelace", "guid": "dd6557ba-65a9-5bba-8ee8-7763ced13750", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/WCZYCM/"}, {"code": "QLGPWP", "name": "Greta Timaite", "avatar": null, "biography": "Greta is an early career researcher at Leeds Institute for Data Analytics, University of Leeds. She holds a BA in Sociology and MSc in Big Data and Digital Futures. Greta is passionate about open, interdisciplinary research and R. For the last 6 months, she has been busy learning as much about geocomputation as possible.", "public_name": "Greta Timaite", "guid": "622949d7-ef61-5c5f-adf9-798ae383452e", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/QLGPWP/"}, {"code": "JKFJUJ", "name": "James Hulse", "avatar": null, "biography": "Data Scientist working for the Leeds Institute of Data Analytics (LIDA), currently working on the Open Infrastructure (OpenInfra) project, looking to explore the utility of OSM data for use in transport planning tools, methods, and research.", "public_name": "James Hulse", "guid": "5153829b-3d9a-5376-9ae2-30df61ddfe51", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/JKFJUJ/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/CEMMTQ/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/CEMMTQ/", "attachments": [{"title": "Academic Track Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/CEMMTQ/resources/sotm2022_slides_update_HxoiqCX.pdf", "type": "related"}]}, {"guid": "3ae650d8-8c3b-5db0-a3c0-6545518271c4", "code": "JNCVKY", "id": 19561, "logo": null, "date": "2022-08-21T14:00:00+02:00", "start": "14:00", "end": "2022-08-21T14:05:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19561-combining-volunteered-geographic-information-and-wpdx-standards-to-improve-mapping-of-rural-water-infrastructure-in-uganda", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNCVKY/", "title": "Combining Volunteered Geographic Information and WPdx standards to Improve Mapping of Rural Water Infrastructure in Uganda.", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "The lack of data on the distribution of the water resources, possess a great challenge for the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. This paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making.", "description": "Access to clean and safe drinking water is critical to public health and socioeconomic prosperity, yet an estimated quarter of the world\u2019s population lacks such. This was evidenced by the unprecedented outbreak of the COVID-19 pandemic, which left communities extremely vulnerable to fatal illnesses due to the limited access to water for handwashing or lack of knowledge of the existence of the utility. Subsequently, the lack of data on the distribution of the water resources poses a great challenge to the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. Influencing the frequency of water point data collection through crowdsourcing and volunteered geographic information, would greatly improve the availability of water point data, and contribute to the extended roles of water resource distribution, monitoring, and management especially in rural communities.  Therefore, this paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making. \nThe recent technological advances including the web 2.0, cameras, smartphones and sensor networks continue to empower the development of empirical methods as well as the generation of big data and analytical platforms that provide predictive performance on the various socioeconomic needs for sustainable development. OpenStreetMap (OSM) is a crowdsourcing platform which offers a collaborative experience through its database, community, and wiki platforms, to create and update data relevant to support or transform various data deficiencies whether humanitarian or planning. However, the project\u2019s data quality shortcomings often hinder simultaneous data integration with other analytical platforms such as the Water Point Data Exchange (WPdx) that would explicitly maximize the usage and application of these crowdsourced data. Through a project dubbed \u2018Water Infrastructure Mapping Uganda\u2019, a data model based upon open mapping methods and survey tools was developed to facilitate the mapping of water infrastructure data points and simultaneous updates of both the WPdx and OSM databases. \nThe project engaged a comprehensive review of the OSM water tag, rural water infrastructure data standards and the WPdx database to generate a survey data form that supported one-time collection of a water point for both OSM and WPdx databases. Underlying the development of the data model/schema in the overall project, a design criterion was established which guided and justified the overall selection of the most relevant factors to include in the process that would eventually become detailed to communicate water infrastructure and functionality. The criteria were followed by an assessment of the; compliance [agreement of the tag], consistency [temporal and spatial representation of the tag], completeness [attribute description of the tag], and granularity [quality of the event information] of the OSM tag to support the development of the. osm language in the Kobo toolbox.\nGulu district, located in the North of Uganda, was identified as a potential pilot area for improving the approach created by the project based on its rich WPdx footprint as well as a well-established OSM community of YouthMappers. Up to date satellite imagery of up to 50cm spatial resolution was acquired through the USAID GeoCentre to facilitate any visual detection of water points, and the digitization of base map data including, buildings, roads and waterways, to be employed in the field mapping exercise. A field mapping workflow was designed to facilitate the field-data collection employing the developed water infrastructure data model and Kobo toolbox. An API link was developed that simultaneously tapped the open-source field collected data into the WPdx database. \nThrough the project, more than 15000 buildings, 1400square kilometres of roads and over 500 water data points were added to OSM as well as the WPdx database for the later data. Also, from the project, several observations were made regarding the improvement of such processes and the extension of the data model beyond one geographical area. The developed workflows characterized and provided a general improvement in the water infrastructure data quality especially for OSM   based on WASH indicators used to officially report on the sustainable development agenda. The workflow development waivered the interoperability gap in geospatial data sharing platforms which often results from unharmonized data structures. It was established that the designed methodology cannot be applied to water data updates but rather to freshwater point data collection. This would lead to exponential water point data increase, however, the workflow may be revised to include the framework for data updates without having to engage the full field mapping process. As well, the data model design was mainly based on the African water infrastructure and open mapping reviews, hence, the transfer of the data model from one continent to another may require a review of some data factors to create better insights of the water indicators in a place of that given continent.", "recording_license": "", "do_not_record": false, "persons": [{"code": "PCBWXG", "name": "Stellamaris Nakacwa", "avatar": "https://pretalx.com/media/avatars/PCBWXG_ldarD7z.webp", "biography": "Stellamaris Nakacwa is a master\u2019s Geography candidate at the University of West Virginia. Her passion for open-source geospatial engagement has been developed and enriched by the continuous engagement through YouthMappers and her desire to apply geospatial data to solve policy challenges, locally and globally.", "public_name": "Stellamaris Nakacwa", "guid": "186887a0-f9f2-5480-8c23-79c710b2dba9", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/PCBWXG/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNCVKY/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/JNCVKY/", "attachments": [{"title": "Presentation Slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/JNCVKY/resources/SOTM_2022_Stellamaris_S8xbiW2.pdf", "type": "related"}]}, {"guid": "b55fbb53-3993-525c-8296-d1984573c4b5", "code": "ZUXTN8", "id": 19570, "logo": null, "date": "2022-08-21T14:05:00+02:00", "start": "14:05", "end": "2022-08-21T14:10:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19570-floor-plan-extraction-from-digital-building-models", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ZUXTN8/", "title": "Floor plan extraction from digital building models", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "As part of a larger endeavour to make floor plan representations from building models available for indoor map and navigation services, we study the integration of  IFC and OSM.", "description": "# Introduction, background, motivation\n\nOfficial geo data is increasingly published not only in the form of 2D maps,\nbut also in 3D, mainly as city models in CityGML. Usually the outer shell of\nbuildings is captured in such models, but they may also involve more intricate\ndetail. Even more detailed building models are generated during the planning\nprocess for new buildings and renovations. These are nowadays produced in\ndigital form, archived in as-built phase by owners and operators for the life\ntime of a building and, in the future, may even be required to be submitted for\nbuilding permits.\n\nAt the same time there is an increasing public interest in detailed information\nabout public and semi-public interior spaces, for example about their\naccessibility, localization of barriers or targets (e.g. contact persons in\npublic administration, shops in malls, booths on fairs, markets or larger info\nevents, departments or hospital wards) or resources (e.g. books in libraries,\ncharging stations, fire equipment or defibrillators) or to get a first\nimpression in advance (e.g. virtual open day). The interest and the points of\ninterest may be temporary or permanent.\n\nSince the context of creating and capturing geo data and building data is\nfundamentally different, there is hardly any integration. Indoor data for maps\nand navigation models is manually captured or at best derived in undocumented\nmulti-step semi-automatic workflows.\n\n\n# Aim and purpose of the study\n\nThe project \"Level Out\" sets out to develop automated methods and services to\nmake detailed indoor data from digital building models selectively available\nfor the population of city models, map and navigation services  (in the form of\n2,5 D floorplans).\n\nTowards this end, we are developing a platform to check building models whether\nthey are suitable and contain required data, extract selected and simplified\nindoor data and convert it into various formats: CityGML LOD0 (Indoor),\nIndoorGML and OSM Indoor.  As input we rely on data in the format IFC (Industry\nFoundation Classes), the most widespread standard format for digital building\nmodels.  Indoor OSM, in particular geometry with Simple Indoor Tagging, is one\nof the various extraction targets. The data created may not be directly fed\ninto OpenStreetMap, but serve as a viable base for further mapping.\n\nThere are already older solutions, e.g. BIMServerOsmSerializer\n(<https://github.com/BIMDataHub/BIMServerOsmSerializer>), which are only built\nfor a version of IFC, which has been a long time standard version, but\ncurrently approaches towards its end of life: IFC2x3.  There are also solutions\nunder active development, e.g. the JOSM plugin \"Indoor Helper\"\n(<https://wiki.openstreetmap.org/wiki/JOSM/Plugins/indoorhelper>), which,\nhowever, lack some general approach on the IFC side and coverage of the\nheterogeneous options to represent geometry in the IFC schema.  With this\nresearch and development we aim to provide a workflow and software to\nsystematically access floorplan data in IFC.\n\n\n# Methodology\n\nWe start from both ends of integration by looking at the detailed structures of\nthe source and target models in parallel.\n\nFrom the group of target models, we derive a common model, which will have, at\nbest, near-trivial mappings to OSM Indoor, CityGML, IndoorGML. Although not\nstrictly necessary for the IFC-to-OSM conversion case or any other bilateral\nintegration, the intermediate model will not only allow to tackle integration\nof IFC with multiple targets besides OSM, but also integration of OSM with\nmultiple sources besides IFC.\n\nNext, we identify relevant information in the source model. IFC exposes a wide\nvariety of geometry modelling constructions from CAD software, mainly following\nthe modelling paradigm of constructive solid geometry (CSG). So far, we found\nthe following principle representation options:\n\na) Direct floorplan representation in 2.5D: Here we have 2D representations\n   located in 3D space, usually located at the level of the floor finish for a\n   particular storey. There are two versions to be distinguished: space\n   boundaries versus abstract representations of space-defining elements.\n\nb) Extraction from CSG: Spaces (as well as constructive elements) are often\n   represented as solids resulting from extrusion of a planar shape. If extruded\n   in z-direction, the base shape can be extracted and used as 2.5-D\n   representation.\n\nc) Projection onto floor level: If the geometry is not in CSG-form with\n   extrusions, but in BREP (boundary representation), then projection followed by\n   a simplification of the projection result is a possible way to extract.\n\nIn addition to the geometric elements, there are semantic elements connected to\nthe geometry that are connected themselves and can be used to charge the\ngeometric model elements with meaning. Depending on the geometry extraction\nmethod, correlation and consideration of semantic elements is more evident or\ncomplicated - hence possible to different degrees. The paper will discuss these\nimplications.\n\nAfter identification of the relevant entities, we are developing a three stage\nprocess for the actual population of target models from IFC.\n\n1. Building model enrichment: Information that can be represented in IFC will\n   be played back to the building model instead of being promoted to the generic\n   model only.\n2. Building to intermedite model: This essential step is coved with a flexible\n   rule-based mapping.\n3. Intermediate model to target models: Following a careful design of the\n   generic model, this step should be simple.\n\nWe are testing the processes with data from public buildings, two sets of\nuniversity campus buildings as well as one newly built municipal administration\ncentre. From assessment of the original building data, we will also develop\nmodelling and export guidelines for BIM software. As far as possible, the demo\ndata will be made available publicly as open data. More important, the\nconversion procedures will be published open source and a respective conversion\nservice will be offered online.\n\n\n# Discussion\n\nIn summary, our work provides practical benefit in terms of tools to support\nthe mapping process as well as a scientific contribution in terms of spatial\ndata integration and expert involvement via domain-specific languages.\n\nThe practical benefit of the conversion seems obvious: Building owners can\npublish data of their publicly accessible spaces to help with volunteer mapping\nwork. In the future we will also tackle update, checking and comparison with\nexisting OSM indoor data.\n\nScientific contributions are also made in different ways: First, an application\nscenario for the OGC Indoor Feature Model is provided and - interesting for the\naudience of this conference - evaluation of how OSM data fits with the\ngeneralized model. Further, we explore methods for flexible data integration\nwith domain specialist and expert community involvement. Finally, but beyond\nthe scope of this conference, the applicability of integration methods for\nbidirectional integration with  multiple sources and targets via intermediary\nformats is evaluated.", "recording_license": "", "do_not_record": false, "persons": [{"code": "EBQVT3", "name": "Helga Tauscher", "avatar": "https://pretalx.com/media/avatars/EBQVT3_sDdi54B.webp", "biography": "Researcher and developer for computer science in architecture, engineering and construction", "public_name": "Helga Tauscher", "guid": "d2057da6-b8e8-5b4d-9b63-12e6902a986c", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/EBQVT3/"}, {"code": "CYPKDZ", "name": "Subhashini Krishnakumar", "avatar": null, "biography": "I am a research associate working at the Bauhaus-Universit\u00e4t Weimar, Germany.", "public_name": "Subhashini Krishnakumar", "guid": "501581fc-d83e-5f54-9683-a3072f10c493", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/CYPKDZ/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ZUXTN8/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/ZUXTN8/", "attachments": [{"title": "Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/ZUXTN8/resources/SOTM2022-Floorplans_WU5xINf.pdf", "type": "related"}]}, {"guid": "68d59b0e-d536-5bef-88a0-fab85c32d3b0", "code": "MXS9R8", "id": 18818, "logo": null, "date": "2022-08-21T14:10:00+02:00", "start": "14:10", "end": "2022-08-21T14:15:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-18818-leveraging-openstreetmap-to-investigate-urban-accessibility-and-safety-of-visually-impaired-pedestrians", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/MXS9R8/", "title": "Leveraging OpenStreetMap to investigate urban accessibility and safety of visually impaired pedestrians", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "Cities worldwide encourage urban active mobility by advocating policy and planning. Although contribution is evident, in practice, these actions disregard population parts that have mobility impairments. This research suggests using OpenStreetMap data in customized analytical models to assess the accessibility level of the urban environment for visually impaired pedestrians. Models results show the existence and spatial distribution of existing accessibility problems, including challenging street network connectivity and dangerous walking areas. These models can be used to enable decision makers, city stakeholders and practitioners to enrich management, monitoring and development of their cities, and support sustainable, livable lifestyles and walkability equality.", "description": "Many efforts that include city policy and planning strategies are implemented to encourage urban active mobility. The outcome of these actions is measured by how transportable and accessible the city is. Although contribution is evident, in practice, the commonly used measures mostly disregard a huge part of the population that have mobility impairments, which require specific accessibility needs, preventing them to be an equal part of the sustainable city vision.\n\nThis research suggests using OpenStreetMap (OSM) data in customized analytical models to assess the accessibility level of the urban environment for visually impaired pedestrians. In principle, the models analyze the city on two levels: routing and accessibility. These are evaluated, correspondingly, based on possible routes, e.g., how long the optimal route is for visually impaired pedestrians compared to the shortest one, and on area, e.g., what is the overall accessibility and safety of a predefined urban extent. The play of both measures enables us to quantify the level of mobility and accessibility of the analyzed city. To do so, we implement the following steps:\n1.\tWe examine the navigation preferences of visually impaired pedestrians in the urban space. This allows a better understanding of the various environmental and morphological factors and characteristics of the urban form that promote safe and accessible navigation. These are translated into spatial and temporal criterion: a) Way Type, which quantifies how suitable the path is in terms of usage and safety; b) the existence of Vision Impairment Assistive Landmarks that support safe wayfinding and navigation; c) Way Complexity, which measures the level of linearity of the path; and d) Crowdedness, which measures the overall pedestrian traffic volume.\n2.\tWe transform OSM\u2019s street network into a weighted graph, where for each graph edge we calculate the cost according to the above criteria. Cost is derived from segments that facilitate safe and accessible walking for visually impaired pedestrians (e.g., separated sidewalks and straight paths), and segments that hinder safe and accessible walking for visually impaired pedestrians (e.g., shared and overcrowded streets).\n3.\tWe develop three analytical models that measure the accessibility level of the urban environment for visually impaired pedestrians: a) street-based, which relies on averaging the costs of all graph edges for a given area, hence it can be implemented for different urban levels (spatial extents); b) centrality-based, which adds on the street-based the centrality indices betweenness and closeness that consider the significance of each graph edge in the street network in respect to all other edges (high centrality values mostly signify streets that attract large pedestrian traffic flow); c) route-based, a navigational method, in which numerous routes are generated on the graph for location tuples, and then the weight ratio of the optimal route for visually impaired pedestrians and the shortest route (commonly used for seeing pedestrians) is evaluated. The smaller the weight value, the more accessible the route.\n\nThe developed models are evaluated for Greater London, the UK. 33 boroughs with their wards are analyzed, resulting in processing 421,107 streets, 377,164 OSM nodes and 634, 871 OSM ways. Results show the existence and spatial distribution of accessibility problems for visually impaired pedestrians. The street-based model highlights the fact that urban nature and green spaces, which are typically considered as contributing to wellbeing and encourage walking, are less accessible for visually impaired people, mostly due to the existing road types, e.g., gravel and dirt roads or shared spaces (bikes and pedestrians that share the same path), which are less accessible for this population. The centrality-based model shows that central streets are mostly more accessible, meaning that borough centers are considered in general as accessible, but as distance from city centers grows, the urban environment becomes less accessible. The route-based model, where more than 1,500,000 routes (with length shorter than 1,000 meters) were calculated, showed that on average the optimized routes are 11% longer and 17.5% more accessible than the shortest ones. Some optimal walking routes are twice as long as the shortest ones, where some impose safety issues that critically endanger visually impaired pedestrians. Wards that have a large proportion of street segments with poor accessibility evenly distributed throughout the ward tend to show less efficient route planning in terms of optimal routes that are considerably longer. In general, the route-based model produces clearer results to understanding the city\u2019s morphology in terms of accessibility for visually impaired pedestrians.\n\nTo a large extent, these models depend on the quality of OSM data, such that feature completeness and tag correctness should be investigated. In terms of completeness, we found that sidewalks and crossings, which are two important model features, are not always mapped in OSM, mostly in the outskirts of London. One solution is to use learning methods and prediction models to complete missing data. In terms of tag correctness, we found that some inconsistencies exist with certain tags. One solution can be to make tag definitions in, e.g., OSM Wiki, more inclusive and clear, with a focus on accessibility aspects.\n\nResults show how various accessibility levels for visually impaired pedestrians might be assessed and where they are found in the city, pointing to the existing problems this community faces today when navigating. These include challenging street network connectivity and dangerous walking areas. The results also demonstrate that the current practice of urban planning and design worldwide still suffers from lack of democratization, limiting the mobility and navigation of certain groups. The accessibility models developed in this research can be used for better city planning and design, enhancing the city mobility and walkability equality and improving quality of life for these vulnerable road users. Our findings provide analytical tools to enable decision makers, city stakeholders and practitioners to enrich management, monitoring and development of their cities, and support sustainable, livable lifestyles and walkability equality. These, in turn, will ease navigation and mobility of visually impaired pedestrians, overall improving health outcomes and their integration into society.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HXJM9Z", "name": "Sagi Dalyot", "avatar": "https://pretalx.com/media/avatars/HXJM9Z_wDMiH7i.webp", "biography": "A faculty member at the Mapping and Geoinformation Engineering, Civil and Environmental Engineering Faculty, The Technion. A geodata scientist developing methods of interpretation, mining, and integration of crowdsourced user-generated content to augment and develop location-based services and smart mapping infrastructures, focusing on routing and navigation solutions for people with mobility disabilities. Chair of FIG WG III/3 on User-Generated Spatial Content Empowering Communities, and he serves as secretary of ISPRS WG IV/5 on Indoor/Outdoor Seamless Modelling, LBS, and Mobility.", "public_name": "Sagi Dalyot", "guid": "f23eca8c-b10a-51a4-a8ed-04ea2046297b", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/HXJM9Z/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/MXS9R8/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/MXS9R8/", "attachments": [{"title": "Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/MXS9R8/resources/osm_rU5nTwd.pptx", "type": "related"}]}, {"guid": "bf7dee86-e2cb-55d4-897a-2050aa9aa7ad", "code": "TA9VAF", "id": 19543, "logo": null, "date": "2022-08-21T14:15:00+02:00", "start": "14:15", "end": "2022-08-21T14:20:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19543-understanding-and-modelling-accessibility-to-public-green-in-large-urban-centers-using-openstreetmap-data", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/TA9VAF/", "title": "Understanding and modelling accessibility to public green in large urban centers using OpenStreetMap data", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "OpenStreetMap data represents a valuable source of information for public green areas in large urban centers and effectively measures the United Nations' Sustainable Development Goal 11.7. Our study provides a threefold contribution in this direction. First, we validate land-use-related tags in OpenStreetMap, through a comparison with satellite data from the European Urban Atlas. We then propose a framework and an interactive tool to measure access to public green areas through several established indices. Finally, we show how the framework can be used to simulate the impact of new green areas and help policymakers design effective interventions.", "description": "As of 2020, around 55% of the worldwide population lives in urban areas and the World Bank estimates forecast an increase of around 1.5 times in the urban population by 2045. Cities are also major contributors to the climate-change, with a consumption of about 78% of the worldwide energy and a production of 60% of greenhouse gas emissions. A transition toward greener cities is often called as one of the solutions to reduce the environmental impact of cities, but also to make the urban environments more liveable, with positive spillovers on the mental and physical health of their population. In this context, the United Nations' Sustainable Development Goals 11.7 [1] indicates the need to make cities more inclusive and safe, but also environmentally sustainable, calling for the universal provision of safe, inclusive, and accessible, green and public spaces.  A proper evaluation of this target requires complementing standard average metrics, looking for instance at the surface of green areas per capita within an urban area, with more sophisticated metrics, that are able to capture the interplay between the spatial distribution of both the population and green areas within a city. \nA few studies on selected cities worldwide highlighted the importance of considering this interplay [2-7].\nA recent study on the city of Seoul [3] shows that vast portions of the parks in the city are located in outer areas so that frequent opportunities to visit them are relatively minimal. In general, urban green areas in Seoul are inadequately distributed in relation to population, land use, and development density. By contrast, in the case of Shanghai [6], the degree of accessibility to green areas appears to decrease as we move from the city core to the urban periphery. The authors also found a negative association between the degree of accessibility to green areas and the housing prices, which translates directly into a large environmental inequality, wherein wealthier communities benefit more from green space accessibility than disadvantaged communities. A similar socio-economic, but also ethnic, stratification is observed in the city of Chicago, where white-majority census tracts generally enjoy a significantly higher degree of accessibility to green areas than minority-dominated census tracts [7]. The former ethnic group also presents a lower income-based green-areas accessibility inequity compared to the other racial-ethnic groups.  \nEfforts to move beyond case studies and provide more accurate cross-country indicators have led to the construction of the 'generalised potential access to green areas\u2019 from the European Commission, which is provided as one of the city-level indicators of the Global Human Settlement - Urban Centers Database [8]. The metric measures the proportion of the urban population for urban centers included in the atlas living in high green areas. Based on satellite data on the Normalized Difference Vegetation Index, the metric is however agnostic with respect to the characteristics of these high green areas - for instance, whether these are public or private green areas - and any accessibility notion, since the metric does not consider that people can move from their residential location. These limitations are accounted for in a recent study for the European Environmental Agency [9], whose geographical coverage is however limited to specific urban hotspots in Europe, for which high-resolution land use data from the Urban Atlas (https://land.copernicus.eu/local/urban-atlas) is available. \nWith its worldwide coverage and detailed mapping, the use of land use and street network data from OpenStreetMap [10] allows to expand the analysis beyond the European boundary. Our study provides a threefold contribution in this direction. First, we compare detailed high-resolution land use data on green uses for European hotspots included in the Urban Atlas with land use-related tags in OpenStreetMap for similar geographical areas. We use similarity indices to assess the degree of completeness of the OSM tags of natural land uses in urban environments and show how the quality varies according to the type of natural use and the size as well as the geographical area of the urban center under consideration. Second, we propose a framework for the monitoring of the target for large urban centers worldwide. In particular, by leveraging data from OpenStreetMap and population estimates from the Global Human Settlement [11], we develop a framework to measure accessibility to public green in large urban centers worldwide at a high resolution. For each urban center, we identify natural green areas using OSM tags on \u2018land use\u2019, \u2019natural\u2019 and \u2018leisure\u2019 (e.g.: \u2018leisure\u2019:\u2019park\u2019) and extract the walkable street network to measure walking distances. Accessibility indices are then constructed for each populated cell of the population grid. The framework is also used to build an interactive tool to navigate our results, which can be customized to select the type of green of interest, as well as the size of the green area. Following the academic literature on urban accessibility, we build several accessibility indices, from a minimum distance index to exposure metrics. The resulting database represents a valuable source of information for policymakers to identify cities that are missing out and direct attention to those subareas within otherwise well-performing cities where the degree of accessibility is still insufficient. The constructed indices are then used to study the relationship between the measured level of accessibility and the structural characteristics of the cities and unveil the role of small green areas as accessibility enhancers, particularly in densely inhabited urban centers. Thirdly, we show how the framework can be used to simulate the impact of different urban interventions, from the addition of a new public green area to infrastructural interventions to the street network, to help policymakers to shape transitions toward more sustainable and accessible urban environments.", "recording_license": "", "do_not_record": false, "persons": [{"code": "TBJRGL", "name": "Alice Battiston", "avatar": "https://pretalx.com/media/avatars/TBJRGL_SwR77Pa.webp", "biography": "My name is Alice Battiston and I am a second-year PhD candidate in Modeling and Data Science at the University of Turin, working with Prof. Rossano Schifanella.\nMy research is at the intersection of computational social science, spatial analysis and the modeling of urban systems. I am interested in data-driven approaches to policy-making in urban environments, with a focus on enhancing the liveability and sustainability of our cities.\nMy background is in Economics and Statistics, which I studied in Turin @UniversityOfTurin and @CollegioCarloAlberto and in London @UniversityCollegeLondon.\nBefore joining the academia again in 2020, I worked for three years as data analyst and economic consultant (@LondonEconomics, London, UK), specializing in applied micro-econometrics.", "public_name": "Alice Battiston", "guid": "c8b6e109-5a45-5875-b3e2-3296989e02dd", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/TBJRGL/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/TA9VAF/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/TA9VAF/", "attachments": [{"title": "Slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/TA9VAF/resources/Battiston_SotM2022_FIN_0jhE1wH.pdf", "type": "related"}]}, {"guid": "673b42ce-e1c8-59fb-9060-c245a143bec5", "code": "HSSWBD", "id": 19544, "logo": null, "date": "2022-08-21T14:20:00+02:00", "start": "14:20", "end": "2022-08-21T14:25:00+02:00", "duration": "00:05", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19544-openstreetmap-as-a-tool-for-skill-building", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/HSSWBD/", "title": "OpenStreetMap as a tool for skill building", "subtitle": "", "track": null, "type": "Academic Lightning Talk", "language": "en", "abstract": "This talk explores the effects of OpenStreetMapping on the mappers. These effects also infer that OSM mapping can be used as a tool for skill-building.", "description": "OpenStreetMap, the crowdsourced geospatial database, currently has over eight million registered members [1]. This makes it one of the largest VGI projects with proven multifaceted use cases e.g. post-disaster response, combating female genital mutilation, app development, and navigation. The database is wholly made and maintained by its contributors, making all decisions without a top-down governing authority.  People in OSM contribute in multiple ways, extending databases, onboarding newcomers, building community, exchanging information, and providing public benefit. Within the OSM community, OSM mapping is regarded as a form of volunteering to create freely accessible geodata. However, recent studies suggest that the experience a mapper gains through the mapping process could be equally important as well [2-4]. Building on the existing body of knowledge, in this talk, we will share the findings our research on how mapping in OSM affects the mapper. \n\nBeing a quality OSM mapper requires training and practice. The act of OSM mapping requires transitioning from having an interest in mapping to creating an OSM account, learning how to use the application, developing an understanding of the technical and theoretical dimensions of mapping, and then applying these skills and knowledge to accurately convert satellite imagery into map data. Such a process engages mappers in multiple decision-making processes and continuously exposes them to buildings, topographies, and features of satellite imagery. We suggest that such experiences affect the mapper in multiple ways. \n\nWe studied a youth mapping internship called Digital Internship and Leadership (DIAL) Program conducted in three cohorts. We chose this internship program for its inclusiveness in terms of academia, gender, and the geographical locations that the participants came from. Participant mappers were called through an open invitation on social media. Recent high school graduates and undergraduate students participated in the mapping internship. They were from diverse academic backgrounds (geomatics engineering, architecture, crisis management, management, forestry, geomatics engineering, computer science and engineering, electronics engineering, management, public health, mechanical engineering). The internship aimed to reduce OSM data gaps in rural Nepal through the involvement of Nepali high school graduates. The program was designed and executed by Kathmandu Living Labs (KLL). We studied the self-assessed experiences of the participant mappers at two different points of time: (i) during the mapping program (ii) after two years for Cohorts II and III, and three years for Cohort I. Short-term effects were studied through grounded theory coding of reports and blogs documented during the internship period. For long-term impacts, an online survey administered to identify if the effects persisted. \n \nResults show OSM mapping helps the mappers develop a number of vital skill sets and expand their knowledge in a variety of areas. Some of them are: deepening of civic engagement, development of social identity, expansion of geographic knowledge, spatial awareness, increase in happiness and satisfaction. They retain most of these skills even in the long run, irrespective of differences in gender, academic, or professional backgrounds. Surprisingly, 44.8% of the participants cited \u200cthey considered being a professional mapper or cartographer at some point in time because of their experience in DIAL. The same people report that OSM mapping increased their belief in their ability to help society.\n\nApart from these individual benefits, we also sense collective benefits. Collective benefits such as network development and an increased sense of civic responsibility hold potential to facilitate broader public good. These might also be applicable for youth mobilization, team building, and collective work.\n\nIt is difficult to pinpoint the exact root cause of this development, however, the benefits of OSM mapping may be in part related to the continuous exposure to satellite imagery, continuous use of technology, the requirement of multiple layers of decision-making, humanitarian aspects of OSM, and the growing global OSM community encouraging conversations around it. \n\nOur findings build upon the studies of the use of OSM in high schools, which was noted to increase creativity and spatial awareness among the students [5, 3, 2]. When compared to Minghini et al.\u2019s (2016) study with ten-year-olds, the similarity in findings suggests \u200cthese developments might be similar across ages. These developments suggest new directions toward the use of OSM as a tool for youth skill building and youth community engagement and design newer incentive mechanisms for people to join and retain in OSM.\n\nThere is still a huge scope of investigation left in this area, ideally through a longitudinal study with a bigger and more diverse sample and comparisons between different program designs, to fully understand the wide array of effects of OSM mapping on the mappers, as well as the potential to deepen positive outcomes via associated youth learning and leadership programs. There are undoubtedly other categories of benefits of OSM mapping that are yet to be identified. Hence, it is worthwhile to reconsider the idea of participatory mapping and related programs, and their effects on the contributing mappers.", "recording_license": "", "do_not_record": false, "persons": [{"code": "3MZ9U7", "name": "Aishworya Shrestha", "avatar": "https://pretalx.com/media/avatars/3MZ9U7_XmBweOU.webp", "biography": "Aishworya is a research assistant under the PEER (Partnership for Enhanced Engagement in Research) Science Project at Kathmandu Living Labs - a civic tech company working at the intersection of society and technology. She has been working in the field of social science for the last six years and is passionate about using technology for better human life. She believes societal needs should be at the center of any technological advancement and the constant effects on the people due to exposure to technology is as important to consider as its benefits.", "public_name": "Aishworya Shrestha", "guid": "af301b27-efa7-5272-907d-fdbed2dd6b94", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/3MZ9U7/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/HSSWBD/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/HSSWBD/", "attachments": [{"title": "Presentation Slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/HSSWBD/resources/SOTM_2022_Presentation_3MNIEHk.pdf", "type": "related"}]}, {"guid": "96eed09e-0832-5cde-9777-c54fa1f461e6", "code": "THYCMA", "id": 19305, "logo": null, "date": "2022-08-21T14:30:00+02:00", "start": "14:30", "end": "2022-08-21T14:50:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19305-youthmappers-a-hybrid-movement-design-for-the-openstreetmap-community-of-communities", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/THYCMA/", "title": "YouthMappers: A Hybrid Movement Design for the OpenStreetMap Community of Communities", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "The YouthMappers experience lends itself to explore interesting questions about the cultural and organizational aspects of data production and usage practices in OpenStreetMap, in order to improve them. First, this study aims to identify what are some of the qualitative and quantitative characteristics distinguishing the performance of YouthMappers as an academic-based community within OSM. Second, this study aims to better understand how the design approach taken by and on behalf of YouthMappers reinforces an identity as unique contributors.", "description": "Increasingly ubiquitous open spatial technologies offer the opportunity for new actors to participate in creating knowledge about the places where they live and work, and where they connect to others around the world. University students are one set of actors who have grown significantly in their visibility and contributions to OpenStreetMap, in part through the establishment of YouthMappers in 2015. This inclusive international network of university-based, youth-led, faculty-mentored chapters on more than 320 campuses in 66+ countries works to mobilize and support university student mapping action that responds to humanitarian and development needs by creating and using an ecosystem of data and tools centered on OpenStreetMap. \n\nThe YouthMappers experience lends itself to explore interesting questions about the cultural and organizational aspects of data production and usage practices in OpenStreetMap, in order to improve them. In this case, we explore these aspects as they occur within and through the academic sector, particularly through the hands and eyes of student youth. As a consortium design, this networked set of local groups works on the one hand, to create and use data on their local campuses and home communities, and on the other, to remotely contribute data on imagery-visible features in response to humanitarian, development, and knowledge needs wherever they may occur around the globe. Furthermore, they act not only within the OSM \u201ccommunity of communities\u201d framework (Sol\u00eds 2016), but also within an existing global infrastructure of academic institutions with its own set of shared educational aims, knowledge generating practices, and cultural norms. Meanwhile, students are motivated both by learning and using new skills and workforce competencies as well as by the opportunity to participate in the world\u2019s largest volunteered geographic information project and the activities that make common good use of the data. So how do YouthMappers navigate these different aims within these different spaces of action?\n\nTo address this question, two aspects of this experience are the focus of attention in this study. First, this study aims to identify what are some of the qualitative and quantitative characteristics distinguishing the performance of YouthMappers as an academic-based community within OSM. Second, this study aims to better understand how the design approach taken by and on behalf of YouthMappers reinforces an identity as unique contributors.\n\nThe presentation first will provide a description and justification for the purposeful design of the YouthMappers consortium (Sol\u00eds et al. 2018) within the context of OpenStreetMap (Brovelli et al. 2019). The study will be contextualized with a review of literature on the current state of higher education, particularly with respect to a present tension around higher ed institutions\u2019 purpose as sites for both workforce preparation and global citizenship. The latter point will be situated with reference to scholarship on the global targets of the Sustainable Development Goals (SDGs), as perhaps the predominant discourse for international action across humanitarian domains. This review sets up three interlocking hypotheses that the evidence is anticipated to reject:\nH1: (Action-of-Performance) Participating youth either map only locally or remotely, but not both; \nH2: (Hybrid-Roles) Participating youth cannot simultaneously pursue personal aims to prepare themselves for the workforce and to express their identities as global citizens; and\nH3: (Movement-Minded) Participating youth cannot articulate the impacts/benefits of actions undertaken for broader communities or society through their work with OSM, nor identify the roles/contributions of youth action in this work for the common good.\n\nData to test the first hypothesis relate to performance and include a range of metrics of participation (Andal et al. 2022; Boateng et al. 2022; Walachosky et al., 2022); statistics of known users (Anderson 2022), and a review of data from other studies of YouthMappers\u2019 editing contributions (e.g., Mahmud et al. 2022). Data to test the second hypotheses relate to identity and come from the long-running student-authored blogs (Hite et al., 2018), as well as a global survey of YouthMappers collected in 2019 accompanied by a qualitative set of member queries to iterate interpretation of the survey results (Sol\u00eds, Anderson & Rajagopalan 2020; Sol\u00eds et al. 2022). Data to test the third hypotheses come from a set of case studies that are considered with respect to the SDGs (Sol\u00eds & Zeballos, forthcoming). Collectively, these data are analyzed with respect to the above hypotheses. \n\nResults reveal a spectrum of interests balancing local and global mapping across the consortium, and across regions, and other axis of participation. They also indicate the extent to which youth reflect on local benefits, including personal skill development, versus global citizenship, including how they understand the meanings of their actions for SDGs, locally and globally. Detected differences by gender, world region, and duration of participation are interpreted and validated with additional qualitative data. The results are presented with respect to rejecting all three hypotheses, which validate the model.\n\nThese findings help to begin to build a case for understanding the potential of the YouthMappers design to advance the goals of OSM, of the academic community, and potentially the SDGs. In particular, we discuss the possible role of university systems as third space sites for enabling performance and identities for youth action (Bawakyillenuo et al., 2013; Soja, 1996). This gives consideration to the possibility for universities to serve as sites that Heaney and Rojas (2014) characterize as hybrid organizations that, when linked to global discourses on issues like sustainability (SDGs) and open data (OSM), can mobilize youth to create and participate in what we term digital humanitarian \u201chybrid movements\u201d (Sol\u00eds, et al. 2022). In turn, this hybrid movement puts into place both a framework of performance and a space of identity which can serve to advance OSM communities.\n\nThese findings offer insights for how other types of communities could leverage their existing milieu in ways that strengthens OSM broadly. Ultimately, the idea of hybrid movements encourages OSM to embrace a pluralistic, inclusive and diverse set of communities that not only bring individual contributions but leverages other systems like the landscape of academia was systematically enlisted via the YouthMappers design.", "recording_license": "", "do_not_record": false, "persons": [{"code": "ZNEQGS", "name": "Patricia Solis", "avatar": "https://pretalx.com/media/avatars/ZNEQGS_yD1IDz7.webp", "biography": "Patricia Sol\u00eds, PhD, is Executive Director of the Knowledge Exchange for Resilience at Arizona State University, and an Associate Research Professor in the School of Geographical Sciences and Urban Planning. Her expertise centers on the application of participatory geospatial technologies for humanitarian, environment and development needs. She is designer, co-founder and director of YouthMappers, a consortium of student-led mapping chapters on 300+ university campuses in 65+ countries, creating open, volunteered spatial data in collaboration with USAID. Sol\u00eds serves a diplomatic appointment as the first woman President of the PanAmerican Institute of Geography and History for the Organization of American States.", "public_name": "Patricia Solis", "guid": "4a8c8f49-8f70-5265-9df1-497a8d8bad56", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/ZNEQGS/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/THYCMA/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/THYCMA/", "attachments": [{"title": "Presentation SOLIS", "url": "/media/state-of-the-map-2022-academic-track/submissions/THYCMA/resources/SOTM2022-SOLIS-PDFv_y2HmZ09.pdf", "type": "related"}]}, {"guid": "84782692-c90c-5c19-a971-803e06bf80c7", "code": "3YQRDX", "id": 19251, "logo": null, "date": "2022-08-21T14:55:00+02:00", "start": "14:55", "end": "2022-08-21T15:15:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19251-landmarks-for-accessible-space-promoting-geo-literacy-through-geospatial-citizen-science", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/3YQRDX/", "title": "Landmarks for accessible space \u2013 promoting geo-literacy through geospatial citizen science", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "Geo-literacy provides skills to read, interpret and use geospatial information, where little evidence exists regarding the potential and capacity of new education programs in advancing these skills. We present a citizen science project held in 13 high schools in Israel, where the students practice participatory mapping with OpenStreetMap to map features relevant to the navigation of visually impaired pedestrians. We show that students improve their geospatial thinking and reasoning skills, including their self-esteem. We believe that this research contributes to various pedagogic and education levels, in terms of theoretical knowledge about the integration of innovative geo-literacy programs.", "description": "The 21st Century dictates that people have a good spatial and geographic understanding and knowledge. Geo-literacy is aimed to provide skills to read, interpret and use geospatial information. This is achieved by acquiring critical spatial thinking, reasoning, and analysis, and presenting understanding of the world using geographical terms and spatial language. Recent years have led to the development of new geo-literacy education programs specifically designed to nurture and promote these skills. These education programs build on geographical education that promotes spatial thinking and active citizenship. Still, little evidence exists regarding the potential and capacity of these programs in advancing civic and geographic skills and knowledge in the 21st Century, and on its contribution to - and advancing of - the individual and the society.\n\nThe aim of this research is to gain a better understanding of the development of geo-literacy in the framework of a citizen science project in high schools. The citizen science project implemented in several schools in Israel \u2013 landmarks for accessible space, advances scientific research that aims to make the urban environment more accessible for visually impaired pedestrians. The participating high school students practice participatory mapping with OpenStreetMap (OSM) to map features relevant to the navigation of visually impaired pedestrians. These map features are used for the automatic calculation of optimal walking routes. The project combines social involvement, learning through geographic information systems, and familiarity with the field of urban accessibility for visually impaired people. The project includes the following stage:\n1.\tPre-stage that includes a) the design of the modular learning environment, b) the organizational and pedagogical preparation of the project integration in schools, and c) questionnaires examining the current level of geographic literacy of the participating students and their perspective regarding the integration of citizen science in schools.\n2.\tIntervention program that includes guest seminars (including YouTube videos), lectures and learning activities, exposure to the world of visually impaired people, and the need for accessible environments and learning activities in the field of geoinformation with emphasis on OSM, crowdsourcing, and participatory mapping.\n3.\tMapping missing data into OSM. This stage is carried out in the field with a designated app developed for this project. The app - \u201cMundi\u201d - allows the mapping of specific geographic features (mapping elements) used for the calculation of accessible routes designed specifically for visually impaired pedestrians. The features include, among others, sidewalks, crossings, accessibility aids, and handrails. The app includes gamification and tasks to encourage the students to map the missing features in their area of residence.\n4.\tPost-stage questionnaires aimed to investigate and analyze the development of spatial skills in the context of participation in this program, examining whether students\u2019 level of geographic literacy improved and whether they gained new knowledge on urban accessibility and the navigation proficiencies of visually impaired pedestrians. This stage also included a quantitative analysis of the students\u2019 contributions in terms of OSM mapping, among others, the number of map edits, type of mapped features, the spatial coverage and temporal extent of their mapping activity.\n\nThe study was conducted in the last two years in 13 high schools, including 25 classes and 460 students. The intervention model was implemented for three months in each class. The participating students implemented this project within their Cyber Geography studies, enabling them to learn through various geographic information systems. In total, close to 10,000 OSM edits were made by the students, which included more than 3,000 new crossings (and attributed tags), 400 new sidewalks, and 7,000 new street objects and obstacles (e.g., bus stations, light poles, trees, gates, bicycle parking).\n\nPreliminary analysis showed that participation in the citizen science project increased the students\u2019 geospatial thinking and reasoning. For example, according to the questionnaire variables, on a score scale of 0-100, the geospatial thinking score has increased from 31 to 56, while the spatial awareness score has increased from 34 to 73 (p < .001). The geographic skills knowledge has increased from 3 to 3.9 (scale of 1-5). Moreover, the students' self-esteem with respect to their knowledge and use of geographic skills has improved considerably. In addition, results show that the broad and in-depth intervention model increased the students' appreciation of the scientists' contribution to the project, the contribution of the program in general, and the satisfaction with their participation in the project.\n\nThis is the first project to introduce the use of OSM-based learning to the study of geography in Israel. Based on its outcome and analysis, we believe that this research contributes to various pedagogic and education levels, in terms of theoretical knowledge about the integration of innovative geo-literacy programs. These promote the drawing of operational and applicable actions regarding the planning of future projects and serving stakeholders in academia and the education system in terms of integrating scientific projects that increase students\u2019 involvement in science and society and promote geo-literacy.", "recording_license": "", "do_not_record": false, "persons": [{"code": "HXJM9Z", "name": "Sagi Dalyot", "avatar": "https://pretalx.com/media/avatars/HXJM9Z_wDMiH7i.webp", "biography": "A faculty member at the Mapping and Geoinformation Engineering, Civil and Environmental Engineering Faculty, The Technion. A geodata scientist developing methods of interpretation, mining, and integration of crowdsourced user-generated content to augment and develop location-based services and smart mapping infrastructures, focusing on routing and navigation solutions for people with mobility disabilities. Chair of FIG WG III/3 on User-Generated Spatial Content Empowering Communities, and he serves as secretary of ISPRS WG IV/5 on Indoor/Outdoor Seamless Modelling, LBS, and Mobility.", "public_name": "Sagi Dalyot", "guid": "f23eca8c-b10a-51a4-a8ed-04ea2046297b", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/HXJM9Z/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/3YQRDX/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/3YQRDX/", "attachments": [{"title": "Presentation", "url": "/media/state-of-the-map-2022-academic-track/submissions/3YQRDX/resources/SotM2022_FoD5ODt.pdf", "type": "related"}]}, {"guid": "1e03a43d-5a75-50f7-b4db-3d4ca0d34a91", "code": "NWB9QF", "id": 19572, "logo": null, "date": "2022-08-21T15:30:00+02:00", "start": "15:30", "end": "2022-08-21T15:50:00+02:00", "duration": "00:20", "room": "Auditorium B", "slug": "state-of-the-map-2022-academic-track-19572-mapping-crises-communities-and-capitalism-on-openstreetmap-situating-humanitarian-mapping-in-the-open-source-mapping-supply-chain", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/NWB9QF/", "title": "Mapping crises, communities and capitalism on OpenStreetMap: situating humanitarian mapping in the (open source) mapping supply chain", "subtitle": "", "track": null, "type": "Academic Talk", "language": "en", "abstract": "This proposal expands an understanding of humanitarian mapping from an ethnographic perspective, seeking to understand the complex mechanics behind this confluence of humanitarianism, technology, and crowdsourced labor. It seeks to scaffold a notion of the \u201copen source mapping supply chain\u201d, situating both humanitarian mapping and OpenStreetMap itself within a larger ecosystem of commercial, humanitarian, open source, government, and other actors in developing geospatial-related technologies.", "description": "This presentation presents a selection of a MA dissertation project, pursued over the course of more than 1.5 years of immersive fieldwork on OpenStreetMap. This presentation will focus on humanitarian mapping through qualitative study, seeking to expand an understanding of humanitarian mapping (particularly that which has emerged from mappers associated with the Humanitarian OpenStreetMap Team - also known as HOT) through the use of ethnographic tools, seeking to understand the complex mechanics behind this confluence of humanitarianism, technology, and crowdsourced labor, asking how and why people contribute to open-source platforms like OSM, and what role humanitarian mapping plays within the wider ecosystem of geospatial and mapping technologies. Ultimately however, it seeks to scaffold a notion of the \u201copen source mapping supply chain\u201d, situating both humanitarian mapping and OpenStreetMap itself within a larger ecosystem of commercial, humanitarian, open source, government, and other actors in developing geospatial-related technologies.\n\nFounded in the aftermath of the 2010 earthquake in Haiti, the Humanitarian OpenStreetMap Team (HOT) helps both globally remote and local in-person volunteers to identify roads, buildings, and other features on the OpenStreetMap (OSM) platform. Created as a \u201cfree, editable map of the world,\u201d OSM has enabled the mass-creation of volunteered geographical information (VGI) on a scale that is now more accurate than proprietary maps in many places, particularly as \u201ccrisis-mapping\u201d has emerged as a means to gather real-time data on areas that have been affected by natural disasters or socio-political conflicts. OSM has also become also a site of resistance, where local and indigenous communities have engaged in mapping projects to reclaim autonomy, agency, and space through the historically contested practice of (digital) mapping. For these reasons, such crowdsourced maps have increasingly been used by humanitarian organisations to facilitate aid and disaster relief, and as open training data for algorithms learning how to automatically detect features through Artificial Intelligence (AI). As a key partner of humanitarian, corporate, and local actors, and having mobilised over 200,000 volunteers since 2010, HOT lies at the crux of these ongoing entanglements and contestations, both within and around the field of OSM. \n\nPrevious studies of crowdsourced geographical information and crisis-mapping have generally revolved around quantitative analyses of OSM\u2019s data, focusing on the credibility of the data itself, the makeup of the communities that contribute to it, the effects of \u201cevent-centric\u201d crowdsourcing, or \u201cnewcomer retention\u201d in humanitarian mapping (Dittus et al., 2016a, 2016b, 2017; Haklay, 2010; Haworth et al., 2018; Sui et al., 2013). Alternatively, they have also focused on the \u201cspatial knowledge\u201d, \u201chacker political imaginary\u201d, and gender composition of mappers themselves (Brandusescu & Sieber, 2018; McConchie, 2015; Stephens, 2013).\n \nParallel studies of other volunteer-driven communities like \u201cWikipedians\u201d have taken similar approaches, analysing \u201cuser-generated content\u201d and the motivations behind them (Nov, 2007; Yang & Lai, 2010). Both hacking and free and open source software (F/OSS) have also been explored ethnographically (Coleman, 2012; Kelty, 2008). While automated detection of features on OpenStreetMap has only recently become an important topic of research, ongoing studies have primarily focused on the accuracy or credibility of this endeavour (Brovelli et al., 2017; Resor, 2016).\n \nWhile existing studies of digital communities have focused on the socialities they engender or labor they require, they tend to forget the bureaucratic apparatuses that have emerged to govern them, both implicitly and explicitly (Coleman, 2012; Kelty, 2008). Similarly, studies of humanitarianism have focused on the ethics they operationalize, or the technologies that are mobilized in turn, but often at the expense of engaging in the wider spectrum of social and economic life that they enable (Cross, 2013; Redfield, 2012, 2016a; Scott-Smith, 2013, 2016a, 2019; Ticktin, 2014a). While this project draws upon these overlapping strains of research, it seeks to push the debate in an ethnographic direction, scaffolded by theories of bureaucratic technology, political economy, and humanitarianism.\n \nThis research draws from participation in over 40 online events over 1.5 years, including mapathons, conferences and online lectures with OSM mappers, as well as semi-structured interviews conducted with 27 key-informants, alongside watching more conference videos, and reading blogs, mailing list emails, Twitter exchanges, and other internet archives. While empirically influenced by studies of hacking and open source software, this work ultimately focuses on the mechanisms and means through which this \u201cfree and open map\u201d is created, and ultimately the ways of seeing and doing that it enables (Coleman, 2012; Kelty, 2008). Ultimately, it was the \u201csupply chains\u201d heuristic that emerged as a means to understand and illustrate this process.\n\nSimilar to how supply chains \u201clink ostensibly independent entrepreneurs, making it possible for commodity processes to span the globe\u201d, the OSM project relies upon a series interconnected processes that enable the creation of the world\u2019s crowdsourced map in a process that is far more precarious, and much less secure than promotional material might have one think (Tsing, 2009). Similar to how the satellite, computer, and software industries converged to create the conditions that allowed for OSM\u2019s creation, so do people \u2013 and their associated institutions create map data through an almost miraculous collision of circumstances, assured precedent, and training. The Humanitarian OpenStreetMap Team, which was the initial entry point into open source mapping through, made its name by optimizing the mapping value chain: that is, by making it easier to contribute to OSM. But it also extended outwards: contributing to OSM was enabled not only by the wider socio-economic forces that coalesced to produce the project in the first place, but also by a series of digital value chains \u2013 both past and present.\n\nBy delineating this supply-chains approach, this study hopes to scaffold a mental model of humanitarian mapping and the OpenStreetMap more broadly, to be employed in future studies \u2013 both quantitative and qualitative. Practically, it hopes to provide a heuristic and application of ethnographic tools, and present questions and queries directly to the community more broadly.", "recording_license": "", "do_not_record": false, "persons": [{"code": "CBRUZQ", "name": "Anne Lee Steele", "avatar": "https://pretalx.com/media/avatars/CBRUZQ_HqSpR5G.webp", "biography": "Anne is a Community Manager based at the Alan Turing Institute. Previously, she completed her graduate studies in Anthropology and Sociology at The Graduate Institute of International and Development Studies, Geneva, where she focused on the OSM ecosystem and open source labor. She is an early career fellow at the Internet Society.", "public_name": "Anne Lee Steele", "guid": "b89a719b-2c6f-540f-ae14-3a286f2dce63", "url": "https://pretalx.com/state-of-the-map-2022-academic-track/speaker/CBRUZQ/"}], "links": [], "feedback_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/NWB9QF/feedback/", "origin_url": "https://pretalx.com/state-of-the-map-2022-academic-track/talk/NWB9QF/", "attachments": [{"title": "slides", "url": "/media/state-of-the-map-2022-academic-track/submissions/NWB9QF/resources/SOTM2022-presentation-_DfoVTcO.pdf", "type": "related"}]}]}}]}}}