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.

Discover how to display and symbolize both 2D and 3D data. Search, access, and create new map symbols. Learn to specify and configure text symbols for your map. Complete your map by creating an effective layout to display and distribute your work.

Context

COVID-19, India This tutorial help in understanding basics of data visualization and mapping using Python.

Content

Data sets contain State wise confirmed cases, death toll, and cured cases till date.

Acknowledgements

I owe my thanks to the data sets provider.

Inspiration

Data visualization helps in creating trends, patterns, interactive graphs and maps. This will help policy and decision makers to understand,discuss and visualize the data.

NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.

The DC Office of Zoning (OZ) proudly announces an expansion of its online mapping services with the release of the DCOZ 3D Zoning Map. This new mapping application builds off existing DC Open Datasets and new OZ Zoning data to visualize the District in 3D, providing greater context for proposed development projects and helping enhance Board of Zoning Adjustment and Zoning Commission decisions throughout the District. The 3D Zoning Map was developed to enhance District resident’s understanding, knowledge, and participation in Zoning matters, and help increase transparency in the Zoning process.

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

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.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

This study presents data products to visualize past, current and alternate scenarios for an ecologically sensitive and development prone area in a sub-tropical coastal spit. Data products are created using a diverse range of geodesign tools that include existing and archived high resolution active and passive remote sensing datasets, existing, derived, and digitized spatial layers together with procedural modelling. The final products include 3d and interactive Cityengine Webscene files and fly-throughs in a generic movie format. While the fly-through movies can be played on standard digital devices, the Cityengine Webscenes once uploaded on ArcGIS website requires an Internet ready device for visualization and interaction.

Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

The Vector Map API provides high-resolution vector-based maps to efficiently visualize data such as terrain, traffic, and buildings. It supports real-time rendering and advanced customization features, making it suitable for a variety of applications. It also provides excellent performance in mobile and web environments through responsive design and cross-platform compatibility. It is implemented with Openlayers.

Repository providing free access to marine geophysical data (e.g. bathymetry, seismic data, magnetics, gravity, images) and related land-based data from NSF-funded research conducted throughout the global oceans. Data Portals include GeoPRISMS, MARGINS, Ridge 2000, Antarctic and Southern Ocean Data Synthesis, the Global Multi-Resolution Topography Synthesis, and Seismic Reflection Field Data Portal. Primary data types served are multibeam bathymetric data from the ocean floor, seismic reflection data imaging below the seafloor, and multi-disciplinary ship based data from the Southern Ocean. Other holdings include deep-sea photographic transects, and ultra-high resolution bathymetry, temperature probe data, biological species compilations, MAPR and CTD data. Derived data products and sets include microseismicity catalogs, images, visualization scenes, magnetic and gravity compilations, grids of seismic layer thickness, velocity models, GIS project files, and 3D visualizations. Tools to discover, explore, and visualize data are available. They deliver catalogs, maps, and data through standard programmatic interfaces. GeoMapApp, a standalone data visualization and analysis tool, permits dynamic data exploration from a map interface and the capability to generate and download custom grids and maps and other data. Through GeoMapApp, users can access data hosted at the MGDS, at other data repositories, and import their own data sets. Global Multi-Resolution Topography (GMRT) is a continuously-updated compilation of seafloor bathymetry integrated with global land topography. It can be used to create maps and grids and it can be accessed through several standard programmatic interfaces including GeoMapApp and Google Earth. The GMRT compilation can also be explored in 3D using Virtual Ocean. The MGDS MediaBank contains high quality images, illustrations, animations and video clips that are organized into galleries. Media can be sorted by category, and keyword and map-based search options are provided. Each item in the MediaBank is accompanied by metadata that provides access to a cruise catalog and data repository.

Spatial Mapping of HITECH Grants

Visualizing Health and Community Grant Locations in the U.S

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides crucial geographic data related to two of the U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. As part of the American Recovery and Reinvestment Act (ARRA), these grants were made available to provide citizens with access to health IT infrastructure investments throughout diverse communities across the United States. This crosswalk is an essential resource for anyone looking to link regional, state, county and zip code level program financials with performance metrics for visualization or comparison. With detailed information about region counties, codes, states, FIPS codes and ZIP codes associated with local HITECH grantees, this data presentation helps shed light on a financially impactful initiative from our federal government that can drastically improve healthcare delivery in thousands of cities nationwide!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides geographic data for the service areas of two of the HITECH programs, the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This can be used to map and visualize data related to those programs. Here is a guide on how to use this dataset:

• Get familiar with key columns: Familiarize yourself with the columns included in this dataset, including column names and descriptions for each column such as region, region_code, county_name, state_fips, county_fips and zip.
• Review data formats: If there are any discrepancies between your current format of data presented in this dataset versus what you may have currently in your system or within other sources of information - make sure to review those discrepancies prior exploring more from here onwards.
• Understand regional coverage: Refine the analysis by filtering out different grantee located based on specific regions or states - use necessary filters such as Region code or County FIPs code that will give you an easier view on which region/county grantee has been provided funding through these HHS programs as part of Hitech Act program distribution.
• Map & Visualize grantees: We can visualise geographically where are REC-Program & Beacon Communities Grants distributed across US by making a heatmap while taking desired geolocation coordinates like zip codes; query all available details under columns we need like zip codes along their respective countyp location & state value so that grants can be highlighted after it renders practical Map visuals for us giving an ease if further status / details required about entities who had taken these grants within certain area / regions!

Research Ideas

• Creating an interactive map to visualize grant program performance by region and county.
• Using the data to create a color-coded scatterplot graphic to show active grant program sites in the US.
• Generating reports on HITECH Grantee performance over time, grouped by geographic area or region

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: healthit-dashboard-areatype-crosswalk-csv-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | region | The region of the grantee. (String) | | region_code | The code for the region of the grantee. (String) | | county_name | The name of county where the grantee is located. (String) | | state_fips | The Federal Information Processing Standard (FIPS) code for knowledge of which state it is located in. (String) | | county_fips | The Federa...

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.

According to Cognitive Market Research, the global electronic cartography market size is USD 26.94 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 9.49% from 2024 to 2031. Market Dynamics of Electronic Cartography Market

Key Drivers for Electronic Cartography Market

Rising use of Smartphones and IoT - The prominent factor that drives the market growth include the widespread use of smartphones, tablets, and electronic devices. In addition rise in the usage of Internet of Things (IoT) devices, heightened the demand for real-time mapping solutions, consequently driving the demand for the electronic cartography market. In addition, growing dependence on location-based services (LBS), Geographic Information Systems (GIS), and GPS applications for searching nearby theatre halls, gasoline stations, restaurants, urban planning, disaster management, is another factor that drives the demand for electronic cartography during the forecast period.
The increasing need for real-time data mapping to create precise and current digital representations, combined with the capability to analyze and visualize streaming data from sensors, devices, and social media feeds, is expected to propel market growth.

Key Restraints for Electronic Cartography Market

Integrating geographic,and geo-social data from different sources, such as social media and satellite imagery, can be challenging due to differences in data formats and scales.
Lack of expertise among users regarding the adoption of electronic cartography in marine industry may hampered the market growth

Introduction of the Electronic Cartography Market

Electronic cartography is a technology that allows to simulate the surrounding area with the help of special technical means and computer programs. Electronic cartography integrated with various processes such as data processing, data acquisitions, map distribution, and map creation. As the demand for topographical information systems grows, the deployment of digital mapping has grown in the government and public sectors. The Science & Technology Directorate (S&T), in May 2024,has launched a digital indoor map navigator Mappedin. This digital indoor map navigator transform floor plans into interactive and easily maintainable digitized maps, and is currently being used by both response agencies and corporate clients. Mappedin provides high-quality 3D map creation, easy-to-use mapping tools and data, map sharing, and data maintenance, to city executives, building owner operators and first responders to make and deliver maps for a variety of safety-related situations—from advance preparation and planning to assistance during emergency incidents. Additionally the rapid rise in the number of smartphone and internet users has fueled industry expansion. Additionally, the increasing number of connected and semi-autonomous vehicles along with anticipated advancements in self-driving and navigation technologies, are expected to boost the demand for electronic cartography market.

The financially constrained element of Visualize 2045 identifies all the regionally significant capital improvements to the region’s highway and transit systems that transportation agencies expect to make and to be able to afford through 2045.For more information on Visualize 2045, visit https://www.mwcog.org/visualize2045/.To view the web map, visit https://www.mwcog.org/maps/map-listing/visualize-2045-project-map/.Download the ZIP file that contains a File Geodatabase

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here.
Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

• Python tools to read, generate, and visualize the dataset,
• 3DHDNet deep learning pipeline (training, inference, evaluation) for
  map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866,
  author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and 
  Fingscheidt, Tim},
  booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, 
  year={2022},
  pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

• Benjamin Sertolli: Major contributions to our DevKit during his master thesis
• Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json"
with open(json_path) as jf:
  data = json.load(jf)
print(data)

2. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

• traffic signs,
• traffic lights,
• pole-like objects,
• construction site locations,
• construction site obstacles (point-like such as cones, and line-like such as fences),
• line-shaped markings (solid, dashed, etc.),
• polygon-shaped markings (arrows, stop lines, symbols, etc.),
• lanes (ordinary and temporary),
• relations between elements (only for construction sites, e.g., sign to lane association).

3. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

4. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

• x-coordinates: 4 byte integer
• y-coordinates: 4 byte integer
• z-coordinates: 4 byte integer
• intensity of reflected beams: 2 byte unsigned integer
• ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np
import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin"
pc_dict = {}
key_list = ['x', 'y', 'z', 'intensity', 'is_ground']
type_list = ['

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.

Real-Time Map Update Market Outlook

According to our latest research, the global real-time map update market size in 2024 stands at USD 5.6 billion, reflecting robust demand across industries that require up-to-the-minute geospatial data. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 16.1 billion by the end of 2033. The primary growth factor driving this expansion is the increasing integration of real-time mapping technologies in automotive navigation, fleet management, and the rapid evolution of autonomous vehicles, which require continuous and precise map updates for optimal performance and safety.

One of the most significant growth drivers for the real-time map update market is the explosive growth in connected vehicles and smart mobility solutions. As vehicles become more sophisticated and networked, the demand for accurate, real-time geospatial information has surged. Modern navigation systems no longer rely solely on static maps; instead, they require dynamic updates to reflect real-world changes such as traffic conditions, road closures, and new infrastructure developments. This demand is further fueled by government initiatives aimed at improving road safety and traffic efficiency, as well as the proliferation of ride-hailing and delivery services that depend on precise, up-to-date mapping data for route optimization and customer satisfaction.

Another crucial factor contributing to the market’s expansion is the rapid advancement in satellite and sensor technologies, which have significantly improved the collection and dissemination of geospatial data. The advent of high-resolution imaging, IoT-enabled sensors, and advanced data analytics has enabled map providers to offer more granular, real-time updates. These technological innovations are being leveraged by a wide range of industries, including logistics, urban planning, and emergency response, all of which require accurate mapping for operational efficiency. Moreover, the integration of artificial intelligence and machine learning into mapping platforms has enhanced the ability to process and analyze vast amounts of spatial data in real time, leading to more reliable and actionable insights.

The rise of autonomous vehicles represents a transformative opportunity for the real-time map update market. Autonomous driving systems depend heavily on high-definition (HD) maps that are updated continuously to reflect real-world conditions. These systems require not only static road information but also dynamic data such as lane closures, temporary obstacles, and changing traffic patterns. As automotive OEMs and technology companies race to commercialize autonomous vehicles, the need for real-time, high-accuracy mapping solutions is becoming increasingly critical. This trend is expected to accelerate market growth over the coming years as more pilot programs and commercial deployments come online.

From a regional perspective, North America currently leads the real-time map update market, driven by early adoption of connected vehicle technologies and significant investments in smart infrastructure. However, Asia Pacific is poised for the fastest growth, with increasing urbanization, expanding transportation networks, and a surge in digital transformation initiatives across emerging economies. Europe also remains a key market, supported by stringent regulatory requirements for road safety and a strong focus on sustainable mobility solutions. Collectively, these regional trends underscore the global nature of the market and highlight the diverse opportunities for stakeholders across different geographies.

Component Analysis

The real-time map update market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. Software forms the backbone of real-time mapping solutions, encompassing the algorithms, platforms, and applications that process, visualize, and distribute geospatial data. As the complexity of mapping requirements increases, software solutions are evolving to incorporate advanced analytics, machine learning, and cloud-based architectures, enabling faster and more accurate updates. The growing demand for user-friendly interfaces and customizable mapping features is driving innovation in this segment, with vendors focusing on seamless integration with existing enterprise systems and mobile platforms to enhance usability and accessibility.

The ckanext-montrosemaps extension for CKAN appears to provide mapping capabilities. Based on the minimal documentation, it seems intended to enhance CKAN datasets with geographical visualization features. While specific functionality is undocumented in this readme, the name suggests an integration with mapping libraries to display datasets on a map. Key Features (Inferred): Geospatial Data Visualization: Likely provides the ability to display datasets containing geographical data on a map. Mapping Integration: Integrates with a mapping library (unspecified) to render map views. Potential Customization: May offer some level of customization for map display, such as marker styles or data overlays. Technical Integration: The installation instructions indicate that this extension operates as a CKAN plugin. To enable it, the plugin name, montrosemaps, must be added to the ckan.plugins setting within the CKAN configuration file. A CKAN restart is then required to activate the extension. Benefits & Impact (Inferred): By adding mapping capabilities, this extension could allow users to visualize and explore data geographically, enabling easier discovery and understanding of location-based datasets. It could enhance CKAN's usefulness for geographical data management and analysis. Due to limited documentation, the full extent of benefits is unknown.