top of page
GeoWGS84AI_Logo_edited.jpg

Top Tools for Data Visualization Maps in GIS and Geospatial Analytics

Today, geospatial analytics is a critical part of all types of business activity. Organizations continue to rely heavily on geospatial data visualisation to support their decision processes, and data visualisation maps are the central component of this industry. Selecting the best tools for geospatial visualisation is critical for organisations that need to create visualisations of their geospatial data with accuracy, flexibility, and scalability. In this article, we will provide a detailed overview of the most popular tools for creating data visualisation maps for GIS/Geospatial Analysis, along with descriptions of the tools' capabilities, technical specifications, and examples of when to use each tool.


Data Visualization on Maps
Data Visualization on Maps


  1. Esri's ArcGIS


ArcGIS continues to be the premier GIS solution in the industry. It is well-known for its numerous features, such as advanced mapping, as well as its various spatial analytics tools.


Main Capabilities:


  • As a complete set of GIS tools (desktop, server, and web-based), ArcGIS can be utilized in comprehensive ways.

  • ArcGIS can generate both two-dimensional and three-dimensional views with high-resolution, multi-layered imagery.

  • Users can connect their real-time datasets to the ArcGIS GeoEvent Server and have immediate access to new information.

  • In addition to its many unique mapping capabilities, ArcGIS has the largest collection of tools available for performing spatial analysis and processing geospatial information.


Applications:


  • Urban Land Use Planning.

  • Environmental Impact Assessment.

  • Monitoring Infrastructure and Asset Management.


ArcGIS is the best choice for technical users looking for precision cartographic products when working with large volumes of geospatial information.


  1. QGIS (Quantum GIS)


QGIS is a robust, open-source GIS platform that is now more popular among geospatial professionals who want powerful, yet low-cost, GIS tools.


Main Capabilities:


  • Supports many vector and raster file formats, including GeoJSON, Shapefile, and GeoTIFF.

  • QGIS has built-in support for integration with GRASS GIS and SAGA, allowing users to perform very advanced spatial analyses.

  • QGIS can be highly customized through the use of Python plugins and by using the PyQGIS scripting language.

  • QGIS has features that allow users to generate a dynamic map view of their data in real-time, as well as the ability to create and/or apply sophisticated symbology.


Applications:


  • Academic and Research Projects in Geography and Geospatial Sciences.

  • Environmental / Geospatial Modeling for Conservation Planning and Mapping.

  • Community Mapping and Open Data.


QGIS is the ideal GIS software for technical users who want a flexible and scriptable custom workflow and also have a need for open source extensibility.


  1. GeoWGS84.ai


Introducing GeoWGS84.ai - a cloud-native (SAAS) geospatial data ecosystem that facilitates the aggregation of spatial data and enables organizations to manage large amounts of geospatial data through well-defined processes. This allows companies to utilize advanced GIS technology and geo-spatial analytics to perform business operations using (cloud-capable) services.

Key Features:


  • The use of a cloud-native architecture to provide scalable geospatial processing without the need for on-premise servers

  • Ability to support vector and raster datasets while rendering at high speed

  • Uses artificial intelligence and its ability to use machine learning as a basis for automating the extraction of features, classifying objects, and producing predictive spatial models.

  • Provides an environment to capture and work with real-time data from satellite, drones, and Internet of Things (IoT) devices

  • Provides APIs and SDKs for integrating into web, mobile, and various enterprise applications.


Use Cases:


  • Large-scale urban development/infrastructure monitoring

  • Environmental monitoring and detection of changes to land use or disaster recovery

  • Logistics/transportation/resource management via predictive modeling

  • Processing of large-scale remote sensing data from either research or commercial projects.


GeoWGS84.ai seeks to address "the gap" between traditional GIS platforms and cutting-edge GeoAI solutions. By providing organisations with tools that allow organisations to effectively analyze, visualize, and extract useful information from a vast amount of geospatial data quickly, organizations can remain competitive in today's marketplace.


  1. Spatial Analytics with Tableau


Tableau is best known as a powerful business intelligence tool, but it's also a powerful solution for geospatial visualization. Tableau's key features include:


  • The ability to create maps using drag and drop functionality

  • Mapping both latitude and longitude as well as polygons

  • The ability to connect to spatial data stored on databases such as PostGIS and SQL Server Spatial

  • The ability to create real-time interactive dashboards with filtering capabilities.


A few examples of how Tableau can be used are:


  • For market analysis and location intelligence

  • To visualize transportation networks

  • To identify sales territories geographically, as well as KPI's


Organisations that wish to develop interactive maps and place them within the framework of their business intelligence dashboard(s) will find Tableau to be their best solution for this requirement.


  1. Mapbox


Mapbox is a new generation of mapping platforms that deliver high-performance, web mapping, and geospatial visualization solutions. Mapbox offers a number of features that make it a powerful tool for building web-based geospatial visualization solutions. Some of the key features offered by Mapbox include:


  • A library of customizable,ble vector tiles and high-resolution basemaps

  • The ability to stream real-time geospatial data

  • A Software Development Kit (SDK) for building mobile and web applications

  • Highly advanced style capabilities using Mapbox GL JS for interactive maps


A few examples of how Mapbox can be used are:


  • For real-time fleet tracking and logistics dashboards

  • For creating mobile location-based applications

  • For creating geospatial storytelling for media and marketing


Mapbox's greatest advantages are its ability to deliver speed, customizability, and scalability for web and mobile environments.


  1. Google Earth Engine


The Google Earth Engine is an interoperable, cloud-based tool developed by Google and others to enable worldwide environmental monitoring and geospatial analysis on a planet-wide scale, specifically through remote sensing technology.


Key Features


  • Accesses a vast collection of satellite images, remote Sensing, and GIS Datasets.

  • Integrates Large-Scale Raster Analysis and Machine Learning.

  • Provides a Powerful JavaScript API, allowing users to write scripts.

  • Allows for Time-Series Analysis and Change-Detection Analysis on a global basis.


Use Cases


  • Deforestation and Land-Use Change Monitoring.

  • Climate and Natural Disaster Risk Modeling.

  • Agricultural and Water Resources Management.


Google Earth Engine is especially well-suited for organizations seeking high-resolution satellite image analysis and scalable geospatial computations.


  1. Kepler.gl


Kepler.gl is an open-source tool for Geo-Spatial Visualization and Interactive Exploration of Large Scale Spatial Data.


Key Features


  • Handles millions of features of Geo-Spatial Data very efficiently.

  • Supports Time-Series and Point Clustering Layered Visualization.

  • Powered by WebGL Rendering for fast performance.

  • Seamless Integration with JavaScript and React Apps.


Use Cases


  • Urban Growth and Mobility Analysis.

  • Visualization of Sensor Data from Internet of Things Devices (IoT).

  • Social Media Geospatial Analysis.


Kepler.gl is best for technical analysts looking for Real-Time and Interactive Visualizations with Minimized Setup Time.


As sources of geospatial data have grown exponentially in both size and sophisticated methods of digitisation, those who use these tools efficiently can turn raw spatial datasets into business value through actionable insight and therefore make use of GIS or Geospatial Analytics as part of their daily business process when making decisions based upon data.


For more information or any questions regarding the data visualization, please don't hesitate to contact us at


USA (HQ): (720) 702–4849


(A GeoWGS84 Corp Company)

 
 
 

Comments

bottom of page