Using ArcGIS, anyone can quickly make and share a map-but creating an effective map requires knowing a few design fundamentals. Enroll in this plan to learn techniques to appropriately symbolize and label map features, apply settings that enhance user interaction with your maps, and create impactful data visualizations that resonate with your intended audience.Goals Choose appropriate map symbols to represent your data. Create attractive labels to provide information about map features. Visualize data in 2D and 3D.

Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation techniques. Each project has been classified according to visualisation and interaction methods, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.
The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

Narrativity. It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:

• non_narrative (boolean)

• narrative (boolean)

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:

• domain (categorical):

• History and archaeology

• Art and art history

• Language and literature

• Music and musicology

• Multimedia and performing arts

• Philosophy and religion

• Other: both extra-list domains and cases of collections without a unique or specific thematic focus.

Visualisation of uncertainty and interpretation. Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:

• uncertainty_interpretation (categorical):

• Interactive distinction

• Visual distinction

• Ambiguation

• Interpretative metrics

Critical adaptation. We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:

• critical_adaptation (boolean)

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:

• plot (boolean): visual representations that map data points onto a two-dimensional coordinate system.

• cluster_or_set (boolean): sets or cluster-based visualisations used to unveil possible inter-object similarities.

• map (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.

• network (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.

• hierarchical_diagram (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.

• treemap (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.

• word_cloud (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context

• bars (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.

• line_chart (boolean): the display of information as sequential data points connected by straight-line segments.

• area_chart (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.

• pie_chart (boolean): circular graphs divided into slices which can also use multi-level solutions.

• plot_3d (boolean): plots that use a third dimension to encode an additional variable.

• proportional_area (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.

• other (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:

• timeline (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.

• temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.

• animation (boolean): temporality is perceived through an animation changing the visualisation according to time flow.

• visual_variable (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).

Interactions. A set of categories to assess affordable interactions based on the concept of user intent [8] and user-allowed perceptualisation data actions [9]. The following categories roughly match the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the aspect of the visualisation or the visual representation of its data points, symbols, and glyphs are taken into consideration. Columns:

• basic_selection (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.

• advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.

• navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.

• arrangement (boolean): the organisation of visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or

This map visualisation service allows access to the set of information layers published in the Spatial Data Infrastructure of Navarra and that correspond to the public data of the SITNA. The Web Map Service (WMS) defined by the OGC (Open Geospatial Consortium) produces spatially referenced data maps, dynamically based on geographic information.

3D Visualisation Map (2017)

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/).

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

The 3D Visualisation Map (Non-textured models) are a set of digital data of 3D models featuring geometry models to represent the geometrical shape and position of different types of ground objects, including building, infrastructure and terrain. 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).

Traditionally, zoning plans have been represented on a 2D map. However, visualizing a zoning plan in 2D has several limitations, such as visualizing heights of buildings. Furthermore, a zoning plan is abstract, which for citizens can be hard to interpret. Therefore, the goal of this research is to explore how a zoning plan can be visualized in 3D and how it can be visualized it is understandable for the public. The 3D visualization of a zoning plan is applied in a case study, presented in Google Earth, and a survey is executed to verify how the respondents perceive the zoning plan from the case study. An important factor of zoning plans is interpretation, since it determines if the public is able to understand what is visualized by the zoning plan. This is challenging, since a zoning plan is abstract and consists of many detailed information and difficult terms. In the case study several techniques are used to visualize the zoning plan in 3D. The survey shows that visualizing heights in 3D gives a good impression of the maximum heights and is considered as an important advantage in comparison to 2D. The survey also made clear including existing buildings is useful, which can help that the public can recognize the area easier. Another important factor is interactivity. Interactivity can range from letting people navigate through a zoning plan area and in the case study users can click on a certain area or object in the plan and subsequently a menu pops up showing more detailed information of a certain object. The survey made clear that using a popup menu is useful, but this technique did not optimally work. Navigating in Google Earth was also being positively judged. Information intensity is also an important factor Information intensity concerns the level of detail of a 3D representation of an object. Zoning plans are generally not meant to be visualized in a high level of detail, but should be represented abstract. The survey could not implicitly point out that the zoning plan shows too much or too less detail, but it could point out that the majority of the respondents answered that the zoning plan does not show too much information. The interface used for the case study, Google Earth, has a substantial influence on the interpretation of the zoning plan. The legend in Google Earth is unclear and an explanation of the zoning plan is lacking, which is required to make the zoning plan more understandable. This research has shown that 3D can stimulate the interpretation of zoning plans, because users can get a better impression of the plan and is clearer than a current 2D zoning plan. However, the interpretation of a zoning plan, even in 3D, still is complex.

ReconMap2 GMT. Gene sets composing ReconMap 2.0 subsystems (available at https://navicell.curie.fr/pages/maps_ReconMap 2.html ). (GMT 21 kb)

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)

Strategic noise maps are diagnostics of the noise exposure of populations in a territory. They make it possible to assess the exposure to noise of populations in the vicinity of major transport infrastructure and in large agglomerations. Departmental and national roads 4 index level: 50 to 75 decibels-55 to 75 decibels-65 to 75 decibels-70 to 75 decibels

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. These files can be used to mark the state boundaries on the map of INDIA using folium library and the CSV also contains the state data and how to use it in our notebooks. I have used it in one of my kernels which can be viewed.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. folium supports both Image, Video, GeoJSON, and TopoJSON overlays. Due to extensible functionalities I find folium the best map plotting library in python. Do give it a try and use it in your kernels.

This batch of data consists of the layer of the communal perimeter, the regulatory zoning, the hazard layers (block fall and landslide) of the Plan de Prévention des Risques de Mouvements de Terrain du Mont Canisy approved on December 20, 2002, supplemented by the "Plan de Prévention des Risques de Mouvements de Terrain du Slope Nord du Mont Canisy", which concerns only the commune of Bénerville sur Mer and which was approved on November 23, 2007.

Sensitivity maps made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is based on recent occurrence data (1999-2018 or 2009-2018 by species). These are the natural regions in which at least one observation of the species has been made in the recent period as well as natural regions where the species is highly suspected (i.e. experts) or benefits from older data. In each of the natural regions with recent non-marginal observations, this presence is represented by the calculation of the proportion of meshes 1 x 1 km in which the species was observed. For an explanation of the method of calculation, refer to the explanation sheet of the Natural Regions maps. Natural regions identify territories in which abiotic conditions (relief, geology, climate...) are relatively homogeneous. In fact, the observation of a species in a natural region (even in one location) makes it possible to strongly assume the existence of other favourable habitats elsewhere in the natural region. Any comments shall be taken into account: these may be implanted populations, but also erratic individuals.

This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.

Refer to the map reading instructions as well as PDF cards for more information.

Mapping of the stakes on the municipality of Sauvimont during the development of the PPR flood

One-off tables of issues corresponding to sensitive establishments and installations whose flooding may aggravate or complicate crisis management, produced for reporting purposes for the European Flood Directive.The European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of flood risk management plans aimed at reducing the negative consequences of flooding on human health, the environment, cultural heritage and economic activity.The objectives and requirements for implementation are given by the Law of 12 July 2010 on the National Commitment for the Environment (LENE) and the Decree of 2 March 2011. Within this framework, the primary objective of flood area and flood risk mapping for IRRs is to contribute, by homogenising and objecting knowledge of flood exposure, to the development of Flood Risk Management Plans (RISMPs).This dataset is used to produce maps of issues on an appropriate scale.

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.

Raw data supporting map visualisation of the 2016-17 State budget on the Victorian Government State Budget website. Maps are located on www.budget.vic.gov.au.

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.

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