Crowdsourcing New Orleans community experiences on heat: an analysis of user posts on the ISeeChange platform

ISeeChange (https://www.iseechange.com/) is a global platform that allows users to document the effects of climate change observed and experienced in their communities. The platform combines user posts with sensors, meteorological data, and AI to generate a comprehensive understanding of both real-time and long-term climate change impacts in an area, including at highly localized levels.

Crowdsourcing is the process of generating “wisdom” by aggregating the observations or services of a networked group of individuals. In the environmental public health field, crowdsourced data has demonstrated utility in understanding community concerns about an issue, identifying community priorities, tracking exposures or disease, providing real-time information during disaster events, and managing natural resources. Crowdsourcing has been used to monitor heat and map temperatures at hyperlocal levels, but there has been no exploration of the use of crowdsourcing for also understanding community’s personal experiences and observations of heat impacts. ISeeChange posts are valuable in their potential to provide on the ground insights into community experiences of heat at local levels. These posts allow for examinations of local-level variability in experiences, identification of patterns in heat experiences, and identification and exploration of variables that might explain varying experiences in heat-related issues.

We will present an analysis of ISeeChange user posts related to heat experiences over the last five years in New Orleans (485 total posts). We assess major themes that arise in categories related to physical health, mental health, social or community health, environment, economic or financial health, and education. We also identify priority concerns raised by users as indicated by theme salience; whether the perception and experience of heat has changed over time as indicated by analyses of post content; and compare ISeeChange user postings to relevant posts about heat on other social media or crowdsourcing platforms, such as X or Facebook. We focus on New Orleans as a case study to understand the potential of crowdsourced observational and experiential data on heat, identify limitations to using crowdsourced data for understanding community heat experiences, and determine ways to improve such data for future applications.