Skip to content

Visualizations

Why create visuals

Geospatial visualization is a technique used to represent and analyze geographic data visually. It combines geospatial information with graphical elements to create maps, charts, and other visual representations that help understand patterns, relationships, and trends in the data. Many times maps convey information more effectively and intuitively than tables alone, providing more context, highlighting patterns and relationships, allowing consumer to engage interactively with the findings, and, communicate complex information for decision-makers.

These powerful tools are used to create and share geospatial content, ranging from interactive narratives and immersive web experiences to customizable web applications and dedicated sites. They contribute to effective communication, data dissemination, and engagement with geospatial information.

Licensed Tools

ArcGIS Maps

ArcGIS Mapping and Cartography: Mapping is a fundamental component of geospatial visualization. It involves the creation of maps that depict spatial data and their attributes. Cartography focuses on designing and presenting maps effectively, considering visual elements like colors, symbols, labels, and scales. Mapping techniques can include:

  • choropleth maps, which use color gradients to represent data values within predefined regions;
  • heat maps, which display data density using color intensity;
  • proportional symbols, where the size of symbols represents data magnitude;
  • flow maps, which show the movement of objects or phenomena between locations.

ArcGIS Apps

ArcGIS Applications: Intuitive, interactive web applications can be created on DAS without writing code! ArcGIS Portal (Enterprise) provides drag-and-drop interfaces for building custom web apps including Story Maps, Applications and Geo-Websites. Generally, users start with creating a web-map then design and develop customized applications by combining widgets, themes, and layouts.

Open-Source Tools

QGIS

QGIS Mapping and Cartography: Mapping and cartography in QGIS involve the creation and design of visually appealing and informative maps to represent spatial data. Here are some techniques commonly used in QGIS for mapping and cartography:

  • Choropleth Maps: Choropleth maps use different colors or shading to represent data values within predefined regions. This technique allows you to visualize patterns and variations in data across different areas.

  • Heat Maps: Heat maps display data density or concentration using color intensity. They are particularly useful for visualizing the distribution and intensity of phenomena like population density, crime rates, or environmental factors.

  • Proportional Symbols: Proportional symbols use different-sized symbols or markers to represent data magnitudes or quantities. This technique allows you to visually compare values or quantities across locations.

  • Flow Maps: Flow maps visualize the movement of objects, phenomena, or relationships between locations. They use lines or arrows to represent the flow or direction, with varying widths or intensities to indicate the magnitude or volume of the flow.

  • Labeling and Annotation: QGIS provides options to label features on the map, allowing you to display attribute information or customize the appearance of labels. You can adjust placement, font styles, sizes, and formatting to enhance readability and clarity.

  • Layout Design: QGIS offers a map composer feature that enables you to design the layout of your map. You can arrange map elements, add legends, scale bars, north arrows, and text boxes. This allows you to create visually appealing and well-organized map layouts for presentations or printing.

Open-Source Notebooks*

Creating geospatial visuals in Jupyter Notebooks is a popular approach among data scientists and geospatial analysts. Jupyter Notebooks provide an interactive environment that combines code execution, data exploration, and visualizations in a single document. Several common packages are used for geospatial data processing and visualization within Jupyter Notebooks.

Popular python packages for visualizations include (but not limited too):

  • Geopandas
  • Matplotlib
  • Folium
  • Cartopy
  • Seaborn
  • Plotly