GIS and Remote Sensing

The rapid generation of temporally enabled spatial point information is a key feature of nearly every discipline from epidemiology to criminology. These points can often become cumbersome to display in unison, providing the user little information aside from overall distribution and perhaps qualitative clustering. Even in a web-based environment, this type of display can be distracting for the user. In static graphic, these issues are easily ameliorated by combining the characterization of these points into attributed groupings such as pie charts and bar charts. In a regularly updated database, how can we dynamically provide customers with a qualitative and quantitative sense of magnitude and space? Using a case study derived from Armed Conflict location & Event Data (ACLED), this presentation features a novel approach to convey basic patterns in regularly updated temporal point data using scheduled Python scripts in a server environment. The presentation will review an application that automatically updates service-enabled web maps, depicting violent acts event instances in Africa in 2018. By automating task and summarizing information, this approach ensures the users are receiving the most up-to-date information, without relying on analysts to update the service. Additionally, this approach provides value-added, qualitative and quantitative summarized information without requiring the customer to manipulate the raw data manually.