Jeff Cohn

Ask yourself it the data could use a geospatial input and display. If yes, then you have narrowed the number of platforms to only a few that are good and display map data and complex data.

Crowdsource data and let users contribute to a map solving a problem. If you empower a group of people they will help contribute to solving the problem in creative ways. Filtering out the noise is key and having someone approve the data first is important.

Traditional data visualization tools have primarily relied on graphs, charts, and dashboards to convey insights. While effective, these methods often fall short when analyzing location-based data. Spatial visualization platforms fill this gap by incorporating maps as dynamic canvases for data representation. By overlaying data onto maps, these platforms enable users to visualize geographic trends, spatial relationships, and distribution patterns. Whether plotting customer locations, monitoring supply chains, or mapping disease outbreaks, spatial visualization transforms abstract data points into intuitive visual narratives.

Delegate to users of the database to manage specific sectors or geospatial regions. Empower your users to help you. Give them something in return.

Geospatial data allows researchers to perform spatial analysis, which involves examining the relationships, patterns, and trends between geographic entities or locations. This type of analysis can provide unique insights into how phenomena vary across space and can be used to identify spatial clusters, outliers, and hotspots. Geospatial data is highly visual, making it easier for researchers to communicate complex information and findings. Maps, charts, and other visualizations based on geographic data can effectively convey patterns, trends, and relationships that might be difficult to discern from traditional tabular data alone.

The convergence of geospatial mapping with machine learning and artificial intelligence has been pivotal in enhancing its capabilities. AI algorithms can now interpret vast datasets and extract meaningful insights, enabling applications such as autonomous navigation, precision agriculture, and disaster response. Machine learning models optimize route planning, predict environmental changes, and even assist in epidemiological studies. Geospatial mapping powered by AI is not only accurate but also adaptive, continuously improving its performance with more data inputs.