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Maps Visualization in Workspace to Create a Heat Map/ Bubble Map




When a map has many points that are close together, overlap, or stack on top of each other, it is difficult to get a realistic view of the data that is needed to draw meaningful conclusions. Often, a visual effect, such as a heat map or clustering, is needed to achieve this data view. In many cases, the visual patterns revealed in these methods lead to better questions being asked of the data, which in turn require the aggregation of data by areas. 

Heat Maps

Heat maps allow you to visualize areas with the most point features as the "hottest." Heat maps can help you understand the distribution of data points, identify areas of a graph with a higher relative density of points, weigh the density of points based on a numeric data value, and view the density of features based on their location. In Workspace, consider adjusting the "Max Value" bar depending on the story or message you want to convey. Adjusting the Max Value bar can reveal a different pattern than the density calculated using location only. Furthermore, heat maps are more of a visual aid as opposed to an accurate manner of showing point density. As a result, heat maps prove most valuable when used in conjunction with another visualization type. Note: Adjusting the cluster radius for heat maps is not possible in Workspace. 

Bubble Maps

Bubble maps are valuable in helping you visualize the magnitude of values contained within a location's data. Bubble maps help you to identify which areas are under-served and which areas are over-served. The bubbles are represented by markers that vary in size, indicating relative importance, meaning the bigger the bubble, the greater the value of the data point.

If the map contains a large number of points, clustering will enable you to identify patterns in the data that are difficult to visualize. Enabling clustering will group points that are within a certain distance of one another on the map. The distance of the data points can be adjusted using the "Cluster Radius" bar, as this feature groups data points together by proximity. Clusters are represented by proportionally sized symbols that are dependent upon the number of point features in each cluster, meaning smaller cluster symbols have fewer points, while larger cluster symbols have more points. The number of point features in each cluster can be adjusted using the "Max Value" bar, which sets the value for the largest circles and most intense colors.