Aryandoust, A., van Vliet, O. & Patt, A. City-scale car traffic and parking density maps from Uber Movement travel time data. Scientific Data 6, 158 (2019). https://doi.org/10.1038/s41597-019-0159-6

Data to reproduce figures from the paper:>Cats, O., Kucharski, R., Danda, S. R., & Yap, M. (2021). Beyond the Dichotomy: How Ride-hailing Competes with and Complements Public Transport. arXiv preprint arXiv:2104.04208.### fig_1_data.csv* each row denotes a single histogram line with 7 percentile values (in columns), each row is named after city and attribute name### fig_2_data.csv* each row denotes a single histogram line with 7 percentile values (in columns), each row is named after city and attribute name### maps.zip* each map has its own csv file named hexdata_city_attribute_level.csv.* each row in .csv file denotes single hex and contains: hex_id (https://github.com/uber/h3-py), hex geometry (in WKT format) and attribute value (used as a coloring scale in the maps)### fig. 6* each dot in the file is stored in the .csv as the x,y,value in respective lines in rows

Deze kaart laat zien waar Uber in Europa wel en niet verboden is.

About of Uber dataset

This dataset coming from mobility startup that lets any user to book a ride to from any point A to any point B within the city using a smartphone. Ride value is calculated at the time of request automatically by the app, considering distance, estimated travel time, and current car availability (demand / offer balance).

Once the ride ends, we charge passenger's credit card, and transfer X% of this value to the driver's bank account. Finally, before the passengers gets picked up, the ride can be cancelled by either the driver or the passenger.

A descriptive data analysis: ○ how many? (e.g: vehicles, riders, drivers)
○ when? (e.g: journeys/price/cost per time period, are the journeys quick?
○ what? (e.g: reservations/asap, vehicle type)
○ where? (e.g: origin map, best origins)
○ who? (e.g: worst riders, best drivers)
○ any question you consider interesting

Disclaimer: The dataset come from HR technical interview, so the company is real but I don't know if values are real or not. But is good start point to understand how collect some values the carsharing companies as well.

This item is the root of the tree that represents the navigable items or 'enabled' items. Adding aggregations or data items to this will display them as top-level items on the portal home. This item is not displayed on the portal, so none of these fields need ever be edited.

Research Background International developments in video art include questioning the world through the social device of the language of memory and time, from Vasulka and Tarkovsky's theory to new works exploring memory and metamodernism. This series interrogates how memory can be used to first, represent memory as a device for social narrative; and second, situate this narrative as an advancement of both slow cinema and meta-modernism. Wilson addresses this research question in different ways in order to find new knowledge and evolve research in this area further. Research Contribution Uber Memoria XIX. Part II differs from Part I by focusing on metamodernism. The core of this work is based on the Metamodernist Manifesto (Turner, 2011) which opens with the proclamation for 'oscillation to be the natural order of the world' and concluding that metamodernism is "the mercurial condition between and beyond irony and sincerity, naivety and knowingness, relativism and truth, optimism and doubt, in pursuit of a plurality of disparate and elusive horizons". Wilson uses these principles as a framework to test how memory can be utilised through video as an oscillation between time and subject, thus providing a new way for the discipline to understand how metamodernism can be positioned as a cognitive mapping and narrative device to overcome barriers for visually experiencing the implications and limitations of memory in art and develops the work of Holden (2011) and his exploration of oscillation. Research Significance Shown in Venice (curator Luca Curci), in Brazil (Laura Baber Gallery, Buenos Aries 2014 ) and the Electronic Language International Festival, San Paulo (2014); Bideodromo Experimental Film and Video Festival, Bilbao, Spain (2014); and the Hamilton Music and Film Festival, Canada (2014).

High-Precision 3D Map Market Outlook

The global market size for High-Precision 3D Maps was valued at approximately USD 6.3 billion in 2023 and is projected to reach around USD 21.8 billion by 2032, growing at a robust CAGR of 15.2% during the forecast period. One of the remarkable growth factors driving this market is the increasing demand for autonomous vehicles, which rely heavily on high-precision 3D maps for navigation and safety.

The burgeoning adoption of autonomous vehicles stands as a cornerstone of growth in the High-Precision 3D Map Market. Companies like Tesla, Waymo, and Uber are investing significantly in the development of self-driving technologies, which necessitate the use of high-accuracy 3D maps to operate effectively. These maps provide detailed spatial data, crucial for safe and efficient navigation, thereby driving their demand. Additionally, advancements in sensor technologies, including LiDAR and radar, have made the creation of high-precision 3D maps more accurate and cost-effective, further propelling market growth.

Another notable growth driver is the rapid urbanization and infrastructure development across the globe. Smart city initiatives, which involve the deployment of advanced technologies for efficient urban management, rely heavily on high-precision 3D maps. These maps are used for planning, construction, and maintenance of urban infrastructure, ensuring seamless integration of various city components. Governments and private sectors are increasingly investing in these technologies to enhance urban living conditions, thereby fueling the demand for high-precision 3D maps.

Additionally, the growing application of high-precision 3D maps in sectors such as healthcare and logistics is opening new avenues for market expansion. In healthcare, for instance, 3D maps are used for precise surgical planning and navigation, improving patient outcomes. In logistics, high-precision 3D maps enable optimized route planning and real-time tracking, enhancing efficiency and reducing costs. The increasing integration of these maps in various industries underscores their versatile utility and potential for growth.

The concept of 3D Mapping has revolutionized the way spatial data is utilized across various sectors. In the realm of urban development, 3D Mapping provides a comprehensive view of city landscapes, enabling planners to visualize and simulate urban growth effectively. This technology aids in identifying potential areas for development, assessing environmental impact, and optimizing land use. By integrating 3D Mapping with Geographic Information Systems (GIS), urban planners can create dynamic models that reflect real-world scenarios, facilitating informed decision-making processes. The ability to visualize complex data in three dimensions enhances the accuracy and efficiency of urban planning, contributing to the sustainable development of smart cities.

On a regional level, North America currently holds a significant share in the High-Precision 3D Map Market, driven by technological advancements and substantial investments in autonomous vehicles and smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate, attributed to rapid urbanization, increasing adoption of advanced technologies, and supportive government initiatives. Countries like China, Japan, and South Korea are at the forefront of this growth, making significant strides in smart infrastructure and autonomous vehicle development.

Component Analysis

The High-Precision 3D Map Market is segmented into three main components: Hardware, Software, and Services. Each of these segments plays a crucial role in the overall functionality and adoption of high-precision 3D mapping technologies.

The Hardware segment encompasses various physical devices required for capturing, storing, and processing spatial data. This includes advanced sensors like LiDAR, cameras, and GPS systems. The increasing demand for high-accuracy and high-resolution data capture has driven significant advancements in hardware technologies. For instance, modern LiDAR systems offer greater accuracy and range, enabling the creation of more detailed and precise 3D maps. With continuous innovations in sensor technology, the Hardware segment is expected to maintain a steady growth trajectory.

On the other hand, the Software segment involves applications and platforms used to process, analyze,

The rapid growth of digital transformation in transportation services has highlighted significant concerns in user safety and usability, particularly in applications like Uber. Despite various safety features, such as real-time tracking and emergency buttons, many users find these features ineffective during urgent situations. This research aims to evaluate the usability issues within Uber’s safety features by using the TOGAF (The Open Group Architecture Framework) approach. A qualitative descriptive method was applied, involving interviews with six Uber users from different backgrounds who had experienced safety-related challenges. Thematic analysis was conducted to map their experiences onto TOGAF’s four architecture domains: Business, Application, Data, and Technology. Findings reveal weaknesses in learnability, efficiency, memorability, error prevention, and user satisfaction, often linked to poor system design and lack of cross-domain integration. Recommendations include improvements in interface visibility, real-time data accuracy, and proactive safety monitoring. This study offers a novel approach by merging usability dimensions with enterprise architecture, enabling a holistic and scalable redesign for safety systems in ride-hailing apps.

India Location-based Services Market size was valued at USD 460 Million in 2024 and is projected to reach USD 1563 Million by 2032, growing at a CAGR of 16.7% from 2026 to 2032.India Location-based Services Market: Definition/ OverviewLocation-based services (LBS) are applications or services that use a user's geographic location to provide personalized content, services, or information. These services typically rely on technologies such as GPS, Wi-Fi, or cellular data to determine the user's position and tailor experiences based on that location. LBS can be offered through mobile apps, websites, or IoT devices, providing users with relevant information or guidance wherever they are.The application of location-based services spans across various industries, from navigation and travel to retail and marketing. For instance, apps like Google Maps or Uber use LBS to offer real-time route guidance, ride-hailing services, and traffic updates. Retailers use LBS for targeted advertising, sending promotional offers to customers when they are near a store. Additionally, LBS are used in healthcare for monitoring patient movement, in logistics for fleet management, and even in social networking apps where users can share their locations with friends.

Vermont extract of Uber's H3 Hexagonal Hierarchical Spatial Index, at resolution level 8, approximately 0.5 km per edge, for indexing locations. Extracted from Uber's dataset at https://eng.uber.com/h3/ via FME's H3HexagonalIndexer transformer, for all filling indexes within and intersecting with the Vermont State Boundary in July 2022.Field Descriptions:_h3index / H3 INDEX: Uber-assigned unique identifier per each individual hexagon at any resolution level._h3res / H3 RESOLUTION LEVEL: Uber-assigned index resolution level between 0 and 15, with 0 being coarsest and 15 being finest.

Die Base Map ist eine dynamische Webkarte auf Basis der Vectortiles-Technologie mit Fokus auf mobile Nutzung, bei der die Kartenelemente vollständig in vektorieller Form (einschl. Geländedarstellungen) vorliegen. Integrierter Bestandteil der Base Map sind Point-of-Interest zu verschiedenen Themenbereichen. Neben amtlichen Daten (u.a. topografisches Landschaftsmodell, digitales Höhenmodell, digitales kartografisches Modell, Haltestellen des öffentlichen Verkehrs und amtliches Verzeichnis der Strassen) sind Daten Dritter integriert. Der Inhalt variiert je nach Zoomstufe. Die Vektordarstellung basiert auf der Maplibre Style Spezifikation. Vektorkacheln (Base Vector Tileset, Relief Vector Tileset) bilden die Datengrundlage. Im Ausland werden von openmaptiles.org zur Verfügung gestellte Vector Tiles aus Open Street Map verwendet.

OMOP2OBO Measurement Mappings V1.0

The mappings in this repository were created between OMOP standard measurement concepts (i.e., LOINC) to the Human Phenotype Ontology (HPO), Chemical Entities of Biological Interest (CheBI), Vaccine Ontology (VO), National Center for Biotechnology Information Taxon Ontology (NCBITaxon), Protein Ontology (PRO), Cell Ontology (CL), and the Uber-anatomy Ontology (UBERON).

For each measurement, all levels of the test result (results above, below, and within a reference range) were mapped, not only those deemed clinically relevant. Results outside of a reference range, but not currently deemed clinically relevant (as advised by the literature or consultation via domain expert), were annotated to the nearest relevant ontology concept ancestor. For example, when annotating the results of a test for Asparagus IgE Ab RAST class [Presence] in Serum (LOINC:15547-3), a result above a reference range would be annotated with an increased anti-plant-based food allergen IgE antibody level (HP:0410228). While a low level of this antibody may not be deemed clinically relevant, it is still outside of the provided reference range and thus was annotated to the nearest applicable concept ancestor, abnormal immunoglobulin level (HP:0010701). There is one exception to this rule: all measured drugs and toxins (entities not normally found in the human body) with normal results (results that were not outside of a given reference range) were annotated to the same HP concept as the clinically relevant result and logically negated. For example, Amphetamine [Presence] in Urine by Screen (LOINC:19343-3), a positive finding was mapped to a positive urine amphetamine test (HP:0500112) and a negative finding was mapped to a positive urine amphetamine test and logically negated (NOT HP:0500112).

LOINC2HPO currently aligns LOINC to HP. The current work extends existing LOINC2HPO annotations to match the OMOP2OBO mappings in the following two ways: (1) annotations were updated if new and/or more specific concepts had been added to the HP; and (2) existing mappings were expanded to include the measurement substance (body fluids, tissues, and organs via Uberon), the entity being measured (chemicals, metabolites, or hormones via ChEBI; cell types via CL; and proteins and protein complexes via PR), and the species of the measured entities (organism taxonomy via NCBITaxon). Consistent with LOINC2HPO, all measurements lacking sufficient specimen detail (those measured in non-specific body substances) were annotated as “Unspecified Sample” and all measurements without a valid result type were annotated as “Not Mapped test Type”. All modifications to the original LOINC2HPO annotations were meticulously recorded in the mapping evidence field enabling users to easily identify when an original LOINC2HPO annotation had been updated.

For this OMOP domain, the owl:complementOf (“not” and was used to model normal test results), owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.

Mapping Details
Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.

Mapping Categories

• Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1
• Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1
• Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many
• Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many
• Manual One-to-One: Hand mapping created using expert suggested resources; 1:1
• Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many
• Cosine Similarity: score suggested mapping -- manually verified
• UnMapped: No suitable mapping or not mapped type

Mapping Statistics
Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:

Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.

Caveats: Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.

Important Resources and Documentation

• GitHub: OMOP2OBO
• Project Wiki: OMOP2OBO - wiki
• Zenodo Community: OMOP2OBO
• Preprint Manuscript: 10.5281/zenodo.5716421

These data provide geologic information, including generalized lithology, geologic age, and paleo-latitude and -longitude of geologic units, for the United States, Canada, and Australia, in an H3 Discrete Global Grid System (DGGS) hexagonal format (Uber Technologies Inc., 2020) with an average hexagon area of 5.16 square kilometers. The data are presented as the shapefile version of ASCII data developed by Lawley and others (2021) for prospectivity modeling of basin-hosted Pb-Zn mineralization in the United States, Canada, and Australia (Lawley and others, 2022). References Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Gadd, M.G., Huston, D.L., Kelley, K.D., Paradis, S., Peter, J.M., and Czarnota, K., 2021, Datasets to support prospectivity modelling for sediment-hosted Zn-Pb mineral systems: Natural Resources Canada Open File 8836, https://doi.org/10.4095/329203. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Uber Technologies Inc., 2020, H3: A hexagonal hierarchical geospatial indexing system: GitHub Repository, https://github.com/uber/h3.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

Les cartes mécaniques des sols ont été élaborées par le Centre de cartographie mécanique des sols de l'Université de Gand et le Groupe de travail ou la Commission de cartographie mécanique des sols (plusieurs auteurs) et publiées sous les auspices de l'Institut national de mécanique des sols. Citation des textes explicatifs aux cartes mécaniques du sol: «Les cartes mécaniques des sols répondent au besoin d’un résumé des composantes de l’environnement géologique qui jouent un rôle dans l’utilisation des sols et influencent la conception, la construction et l’entretien des bâtiments. Toutefois, les données fournies ne doivent pas être absolument exactes en raison des interpolations effectuées lors de leur compilation. Les cartes fournissent des informations sur l'état géologique et mécanique général du sous-sol tel qu'il peut être déduit des essais disponibles au moment de la cartographie. Ils ne sont donc que des documents d'orientation et les auteurs ne peuvent être tenus responsables de leurs éventuelles applications. Les cartes géomécaniques ne peuvent en aucun cas dispenser l'utilisateur d'effectuer des essais supplémentaires en fonction de projets bien définis. » Pour illustrer la structure générale de la zone cartographiée, une section géologique a été dessinée. Son emplacement est indiqué sur toutes les plaques des cartes géomécaniques. Les zones mécaniques au sol sont également indiquées sur les sections transversales.

The Google Maps mobile app reported the highest number of downloads in the United States among the selected travel apps in 2023. That year, this app recorded nearly ** million aggregated downloads on iOS and Google Play. The Uber app was the second most downloaded app in the ranking, with around **** million downloads.

Die «Imagery Base Map» stellt dank einer Kombination aus Orthobildern (entzerrte Luftbilder) und kartografischen Elementen einen guten Überblick der gegenwärtigen Landschaft dar. Sie baut auf amtlichen Daten auf: dem topografischen Landschaftsmodell, dem digitalen Höhenmodell, den digitalen kartografischen Modellen und dem amtlichen Verzeichnis der Strassen. Der Inhalt variiert je nach Zoomstufe. Die Vektordarstellung basiert auf der Maplibre Style Spezifikation. Vektorkacheln (Base Vector Tileset) bilden die Datengrundlage. Die Orthobilder (SWISSIMAGE) werden als Rasterkacheln dargestellt. Im Ausland werden von openmaptiles.org zur Verfügung gestellte Vector Tiles aus OpenStreetMap verwendet.

This dataset shows the afforestation on the Ferrari maps (1771-1778). Very suitable as an addition to the forest age map or for specific research such as locating deforestation. The use for detailed studies is strongly discouraged, except for terrain exploration. After all, the Ferrari maps show important geographical errors. It should be remembered that the Ferrari maps are a snapshot like all other maps and that the land use in Flanders already had a great dynamic before 1800. Locations that are marked as forest on the Ferrari maps are therefore not necessarily 'never mined'. A small part of present-day Flanders (Lommel, a piece of Voeren, Westouter) did not belong to the Austrian Netherlands and is therefore not shown on the maps of Ferraris.

Les cartes mécaniques des sols ont été élaborées par le Centre de cartographie mécanique des sols de l'Université de Gand et le Groupe de travail ou la Commission de cartographie mécanique des sols (plusieurs auteurs) et publiées sous les auspices de l'Institut national de mécanique des sols. Citation des textes explicatifs aux cartes mécaniques du sol: «Les cartes mécaniques des sols répondent au besoin d’un résumé des composantes de l’environnement géologique qui jouent un rôle dans l’utilisation des sols et influencent la conception, la construction et l’entretien des bâtiments. Toutefois, les données fournies ne doivent pas être absolument exactes en raison des interpolations effectuées lors de leur compilation. Les cartes fournissent des informations sur l'état géologique et mécanique général du sous-sol tel qu'il peut être déduit des essais disponibles au moment de la cartographie. Ils ne sont donc que des documents d'orientation et les auteurs ne peuvent être tenus responsables de leurs éventuelles applications. Les cartes géomécaniques ne peuvent en aucun cas dispenser l'utilisateur d'effectuer des essais supplémentaires sur la base de projets bien définis. » Pour illustrer la structure générale de la zone cartographiée, une analyse géologique a été réalisée. Son emplacement est indiqué sur toutes les plaques des cartes géomécaniques. Les zones mécaniques au sol sont également indiquées sur les sections transversales.