Although powerful, approaches like remote sensing techniques (satellite imagery and aerial photography) and Geographic Information Systems (GIS)’s mapping and visualization help analyze socio-economic data (poverty rates and access to infrastructure), these approaches have fallen short supporting community-based efforts.  In many cases, climate hazards are forecasted and predicted, yet devastating and costly consequences wipe out cities increasing poverty, unemployment, homelessness, unreliable transportation, and equitable access to healthcare and food security. 

The absence of main street solutions empowering their own residents to prevent and mitigate climate change is broader because critical decisions are made through systemic inequality. From the front lines of unaffordable services to the government, decisions are often made by the people far from main street and actual local residents.  Data under the Social Vulnerability Index (SVI) is out of touch with the day-to-day realities of people experiencing lack of skills, wage-living paying jobs and access to more adequate economic opportunity.  Data is disjointed, inconsistently collected, outdated and challenged by geography.  This fundamental disconnect creates fragmented solutions that address part of the problem and interventions that do not effectively improve economic opportunity for people who need it the most.