Interactive data map of COVID-19 cases around the world. Shows number of total cases and deaths by country over time, starting from December 31, 2019 to present time.

The 3D Visualisation Map (Individualised models) are a set of digital data of 3D models featuring geometry models and texture maps to represent the geometrical shape, appearance and position of different types of ground objects, including building, infrastructure, vegetation, site, waterbody, terrain and generic (others). The dataset covers the whole territory of Hong Kong. You can click the link below to access the 3D Visualisation Map (https://3d.map.gov.hk/).

This map is just one of the many data visualizations on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

The 3D Visualisation Map (Tile-based models) are based on the mesh model made from the oblique aerial images. The dataset covers the whole territory of Hong Kong. You can click the link below to access the 3D Visualisation Map (https://3d.map.gov.hk/).

This data set accompanies the text at doi 10.5281/zenodo.3732273. // Correspondence: JH: info@africarxiv.org, SK: sk111@soas.ac.uk

Visual Map: https://kumu.io/access2perspectives/african-digital-research-repositories
Dataset: https://tinyurl.com/African-Research-Repositories
Archived at https://info.africarxiv.org/african-digital-research-repositories/
Submission form: https://forms.gle/CnyGPmBxN59nWVB38

Licensing: Text and Visual Map – CC-BY-SA 4.0 // Dataset – CC0 (Public Domain) // The licensing of each database is determined by the database itself

Preprint doi: 10.5281/zenodo.3732273.
Data set doi: 10.5281/zenodo.3732172 // available in different formats (pdf, xls, ods, csv)

AfricarXiv in collaboration with the International African Institute (IAI) presents an interactive map of African digital research literature repositories. This drew from IAI’s earlier work from 2016 onwards to identify and list Africa-based institutional repositories that focused on identifying repositories based in African university libraries. Our earlier resources are available at https://www.internationalafricaninstitute.org/repositories.

The interactive map extends the work of the IAI to include organizational, governmental, and international repositories. It also maps the interactions between research repositories. In this dataset, we focus on institutional repositories for scholarly works, as defined by Wikipedia contributors (March 2020).

Objective

The map of African digital repositories was created as a resource to be used in activities addressing the following aims:

1. Improving the discoverability of African research and publications

2. Enhance the interoperability of existing and emerging African repositories

3. Identify ways through which digital scholarly search engines can enhance the discoverability of African research

We promote the dissemination of research-based knowledge from African repositories as part of a bigger landscape that also includes online journals, research data repositories, and scholarly publishers to enhance the interconnectivity and accessibility of such repositories across and beyond the African continent and to contribute to a more granular understanding of the continent’s scholarly resources.

Data archiving and maintenance

The map and corresponding dataset are hosted on the AfricArXiv website under ‘Resources’ at https://info.africarxiv.org/african-digital-research-repositories/. The listing is not exhaustive and therefore we encourage any repositories relevant for the African continent not listed here to the submission form at https://forms.gle/CnyGPmBxN59nWVB38, or to notify the International African Institute (email sk111@soas.ac.uk). Both AfricArXiv and IAI will continue to maintain the list of repositories as a resource for African researchers and other stakeholders including international African studies communities.

In general, the stakes are people, property, activities, elements of cultural or environmental heritage, threatened by hazards and likely to be affected or damaged by it. The sensitivity of an issue to a hazard is called “vulnerability”. This class of object includes all the issues that have been taken into account in the PPR study. An issue is a dated object whose consideration is based on the object of the PPR and its vulnerability to the hazards studied. A PPR issue can therefore be taken into account (or not) depending on the type or types of hazard being treated. These elements form the basis of knowledge of the land use required for the development of the RPP, in or near the study area, at the time of the analysis of the issues.

The issue data represent a (fixed and non-exhaustive) photograph of assets and people exposed to hazards at the time of the development of the risk prevention plan. This data is not updated after approval of the RPP. In practice they are no longer used: the stakes are recalculated as needed with up-to-date data sources.

centres communities community_centres facilities groups lifestyle17

Web map service for visualisation of spacing data from mapping two territories comprising or Plan de Ordinación do Litoral de Galicia

The Risk Prevention Plans (RPPs) were established by the Law of 2 February 1995 on the strengthening of environmental protection. They are the essential tool of the State in the field of risk prevention. Their objective is to control development in areas at major risk. PPRs are approved by the prefects and generally carried out by the Departmental Directorates of Territories (DDT). These plans regulate land use or land use through construction bans or requirements on existing or future buildings (constructive provisions, vulnerability reduction work, restrictions on agricultural use or practices, etc.). These plans may be under development (prescribed), applied in advance or approved. The PPR file contains a submission note, a regulatory zoning plan and a regulation. Other graphical documents that are useful for understanding the approach (alases, challenges, etc.) can be attached. Each PPR shall be identified by a polygon which corresponds to the set of municipalities concerned within the scope of prescription when it is in the prescribed state; and the envelope of restricted zones when it is in the approved state. This geographical table makes it possible to map existing PPRNs or PPRTs on the department.

A visualization plot of a data set of molecular data is a useful tool for gaining insight into a set of molecules. In chemoinformatics, most visualization plots are of molecular descriptors, and the statistical model most often used to produce a visualization is principal component analysis (PCA). This paper takes PCA, together with four other statistical models (NeuroScale, GTM, LTM, and LTM-LIN), and evaluates their ability to produce clustering in visualizations not of molecular descriptors but of molecular fingerprints. Two different tasks are addressed: understanding structural information (particularly combinatorial libraries) and relating structure to activity. The quality of the visualizations is compared both subjectively (by visual inspection) and objectively (with global distance comparisons and local k-nearest-neighbor predictors). On the data sets used to evaluate clustering by structure, LTM is found to perform significantly better than the other models. In particular, the clusters in LTM visualization space are consistent with the relationships between the core scaffolds that define the combinatorial sublibraries. On the data sets used to evaluate clustering by activity, LTM again gives the best performance but by a smaller margin. The results of this paper demonstrate the value of using both a nonlinear projection map and a Bernoulli noise model for modeling binary data.

Raw data supporting map visualisation of the 2017-18 State budget on the Victorian Government State Budget website. Maps are located on www.budget.vic.gov.au.

Area exposed to one or more hazards shown on the hazard map used for the RPP risk analysis. The hazard map is the result of the study of hazards whose objective is to assess the intensity of each hazard at any point in the study area. The assessment method is specific to each type of hazard. It leads to the delimitation of a set of zones on the study perimeter constituting a graduated zoning according to the level of the hazard. The assignment of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For PPRTs hazard levels are determined effect by effect on maps by type of effects and overall according to an aggregate level on a synthesis map.

All hazard areas shown on the hazard map are included. Areas protected by protective works must be represented (possibly in a specific way) because they are always considered subject to hazard (case of rupture or insufficiency of the structure). Hazard zones can be described as elaborated data to the extent that they result from a synthesis using several calculated, modelled or observed hazard data sources. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas of the study perimeter are considered “zero or insignificant hazard areas”. These are the areas where the hazard has been studied and is zero. These areas are not included in the object class and do not have to be represented as hazard zones.

Regulatory zoning of the Risk Prevention Plan Withdrawal of the Argiles in the municipality of Saint-Mont in the Gers department. The RPP Regulations describe the different requirements and recommendations to apply to each of the areas of the Regulatory Map. These requirements are essentially constructive provisions and are mainly aimed at the construction of new houses. Some, however, also apply to existing buildings. Depending on the type of construction (existing or future), some of these requirements are mandatory or simply recommended. The approved PPR is a public utility servitude and is enforceable against third parties.

3D Mapping Management Software Market Outlook

The global market size of 3D Mapping Management Software was valued at USD 4.2 billion in 2023 and is forecasted to reach USD 12.6 billion by 2032, growing at an impressive CAGR of 13.2% during the forecast period. This remarkable growth can be attributed to increased urbanization, technological advancements, and the rising adoption of 3D visualization in various industries.

The proliferation of smart city projects worldwide is a significant growth driver for the 3D Mapping Management Software market. Governments and urban planners are increasingly leveraging this technology to create accurate and detailed 3D maps for better planning and management of urban spaces. These maps assist in visualizing infrastructure, zoning, and landscape features, thus enabling more efficient and sustainable city planning. The technology's capability to integrate various data sources, such as satellite imagery, LiDAR data, and GIS, enhances its utility and application range, further fueling market growth.

Another major growth factor is the increasing need for disaster management and mitigation solutions. With climate change leading to more frequent and severe natural disasters, the demand for advanced tools to predict, simulate, and manage such events is on the rise. 3D Mapping Management Software offers robust solutions for simulating disaster scenarios, mapping vulnerable areas, and planning emergency responses. The ability to visualize and analyze complex geographical data in three dimensions provides a significant advantage in planning and executing disaster management strategies, thereby driving market demand.

Infrastructure development projects, particularly in emerging economies, are also propelling the 3D Mapping Management Software market. The construction sector is increasingly adopting 3D mapping for project planning, design, and management. These tools enable the creation of accurate and detailed 3D models of construction sites, which help in visualizing the project from different angles, identifying potential issues, and improving overall efficiency. Additionally, asset management within the infrastructure sector benefits greatly from 3D mapping, as it allows for precise tracking and maintenance planning of various assets.

The development and utilization of High-Precision 3D Map technology are becoming increasingly crucial in the realm of urban planning and infrastructure management. These maps provide an unparalleled level of detail and accuracy, which is essential for the meticulous planning and execution of large-scale projects. By offering a comprehensive view of the terrain and existing structures, high-precision 3D maps enable planners and engineers to make informed decisions that enhance the efficiency and sustainability of urban development. This technology is particularly beneficial in the context of smart city initiatives, where the integration of precise mapping data can significantly improve the management of resources and services.

In terms of regional outlook, North America holds a significant share in the 3D Mapping Management Software market. The presence of numerous leading technology companies and widespread adoption of advanced mapping solutions in various sectors drive the market in this region. Additionally, Europe and Asia Pacific are expected to witness substantial growth due to increasing investments in smart city projects, infrastructure development, and disaster management initiatives. The rapid urbanization in Asia Pacific, coupled with government initiatives promoting advanced mapping technologies, makes it a lucrative market for 3D mapping solutions.

Component Analysis

The 3D Mapping Management Software market can be segmented by component into Software and Services. The software segment dominates the market, driven by the increasing adoption of advanced 3D mapping software solutions across various industries. These software solutions offer a range of functionalities, including data integration, visualization, simulation, and analysis. Continuous advancements in software capabilities, such as real-time data processing and AI integration, further enhance their appeal, leading to higher adoption rates.

The services segment, although smaller than the software segment, is witnessing steady growth. This segment includes consulting, implementation, training, and support services. As organizations increasingly adopt 3D mapping softw

List of common proteins. List of the 252 proteins found in common between ACSN and ReconMap 2.0 maps (available at https://navicell.curie.fr/pages/maps_ReconMap 2.html ). (TXT 1 kb)

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

Intuitive visualization of data and results is very important in genomics, especially when many conditions are to be analyzed and compared. Heat-maps have proven very useful for the representation of biological data. Here we present Gitools (http://www.gitools.org), an open-source tool to perform analyses and visualize data and results as interactive heat-maps. Gitools contains data import systems from several sources (i.e. IntOGen, Biomart, KEGG, Gene Ontology), which facilitate the integration of novel data with previous knowledge.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This interactive map, part of the City of Atlanta Data Visualization Suite, displays about 400 indicators for City of Atlanta neighborhoods. To define the neighborhoods, “Neighborhood Statistical Areas” are used, which sometimes combine smaller neighborhoods into one. Visit the Neighborhood Table to find your Neighborhood Statistical Area, City Council District, and NPU. The map includes city-specific data such as code enforcement, housing conditions, and 911 calls. The map uses the Weave interactive platform, which allows the user to select data variables and customize related data visualizations (charts/graphs).

This interactive map, part of the City of Atlanta Data Visualization Suite, displays about 400 indicators for the City of Atlanta’s Council Districts. In addition to U.S. Census data, the map includes city-specific data such as code enforcement, housing conditions, and 911 calls. Visit the Neighborhood Table to find your City Council District, Neighborhood Statistical Area, and NPU. This map uses the Weave interactive platform, which allows the user to select data variables and customize related data visualizations (charts/graphs).