Developing an Emergency Response Tool: A Case Study of NYC's Use of CARTO
Technology Category
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Equipment & Machinery
Use Cases
- Search & Rescue
About The Customer
The customer in this case study is New York City, specifically its disaster response and preparedness department. The city was seeking a solution to enhance its disaster response capabilities following the devastating impact of Hurricane Sandy. The city's goal was to scale its ability to perform analytics immediately after a disaster and in preparation for future disasters. This involved finding a tool that could be used by any analyst to quickly visualize data and inform response efforts. The city also wanted to move away from segmenting analysis by a specific type of tool, which limited the ability of analysts to fully utilize and visualize the data.
The Challenge
In the aftermath of Hurricane Sandy, New York City (NYC) was faced with the challenge of scaling its ability to perform analytics immediately after a disaster and in preparation for future disasters. The city had a significant need to enhance its geospatial analytics capabilities, particularly in terms of map-making. The primary challenge was finding a tool that was user-friendly enough to be used by any analyst, regardless of their level of expertise in geospatial analytics. The goal was to quickly visualize data and use it to inform response efforts. The city was also looking to move away from segmenting analysis by a specific type of tool, which limited the ability of analysts to fully utilize and visualize the data.
The Solution
NYC found its solution in CARTO, a modern software tool that is both easy to use and visually appealing. CARTO was identified as a self-service tool that analysts enjoyed using, which was an important factor in its adoption. The tool enabled the city to teach any analyst how to create maps and perform spatial analysis, thereby enhancing their ability to visualize data. CARTO's speed, flexibility, and ease of use stood out among other tools that were considered, which often required a long learning curve and were not as efficient in quickly assembling and sharing data. The tool's user-friendly nature also made it fun for analysts to use, which was a significant win for the city.
Operational Impact
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